modelId
stringlengths 5
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stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| library_name
stringclasses 533
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SuperAI2-Machima/mt5-small-thai-qg
|
SuperAI2-Machima
| 2022-02-23T06:20:38Z | 14 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"dataset:NSC2018",
"dataset:wiki-documents-nsc",
"dataset:ThaiQACorpus-DevelopmentDataset",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- question-generation
language:
- thai
- th
datasets:
- NSC2018
- wiki-documents-nsc
- ThaiQACorpus-DevelopmentDataset
widget:
- text: "โรงเรียนบ้านขุนด่าน ตั้งอยู่ที่ขุนด่าน จ.นครนายก"
example_title: "Example 01"
- text: "พลเอก ประยุทธ์ จันทร์โอชา (เกิด 21 มีนาคม พ.ศ. 2497) ชื่อเล่น ตู่ เป็นนักการเมืองและอดีตนายทหารบกชาวไทย"
example_title: "Example 02"
- text: "วันที่ 1 กันยายน 2550 12:00 น. ตำรวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่า 80 แผ่น"
example_title: "Example 03"
license: mit
---
[SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
[Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
model = T5ForConditionalGeneration.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg')
tokenizer = T5Tokenizer.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg')
source_text = 'บุกยึดไม้เถื่อน อดีต ส.ส.บุรีรัมย์ เตรียมสร้างคฤหาสน์ทรงไทย 1 กันยายน 2550 12:00 น. ตำรวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่า 80 แผ่น'
print('Predicted Summary Text : ')
tokenized_text = tokenizer.encode(source_text, return_tensors="pt").to(device)
summary_ids = model.generate(tokenized_text,
num_beams=4,
no_repeat_ngram_size=2,
max_length=50,
early_stopping=True)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
#Predicted Summary Text :
#answer: 80 แผ่น question: ตํารวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่ากี่แผ่น
```
|
FOFer/distilbert-base-uncased-finetuned-squad
|
FOFer
| 2022-02-23T04:37:46Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2169 | 1.0 | 8235 | 1.1950 |
| 0.9396 | 2.0 | 16470 | 1.2540 |
| 0.7567 | 3.0 | 24705 | 1.4306 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
jkang/espnet2_librispeech_100_conformer_word
|
jkang
| 2022-02-23T00:23:45Z | 1 | 1 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `jkang/espnet2_librispeech_100_conformer_word`
This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504
pip install -e .
cd egs2/librispeech_100/asr1
./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer_word
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Feb 22 17:38:22 KST 2022`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `e79e7185780b90e56618859855a038b4369b002c`
- Commit date: `Tue Feb 22 15:34:12 2022 +0900`
## asr_conformer_lr2e-3_warmup15k_amp_nondeterministic
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|91.0|8.4|0.6|1.0|10.0|70.1|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|82.9|15.6|1.5|2.5|19.6|83.3|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|90.7|8.7|0.6|1.0|10.3|71.4|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|82.1|16.1|1.7|2.3|20.2|85.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|95.7|2.6|1.7|1.3|5.6|70.1|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|91.0|5.6|3.4|2.5|11.5|83.3|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|95.7|2.7|1.7|1.2|5.5|71.4|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|90.9|5.6|3.6|2.5|11.6|85.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_conformer_lr2e-3_warmup15k_amp_nondeterministic
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 70
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 400
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word_sp/train/speech_shape
- exp/asr_stats_raw_en_word_sp/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word_sp/valid/speech_shape
- exp/asr_stats_raw_en_word_sp/valid/text_shape.word
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_clean_100_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_clean_100_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
- THE
- AND
- OF
- TO
- A
- IN
- I
- WAS
- HE
- THAT
- IT
- HIS
- HAD
- AS
- WITH
- YOU
- FOR
- HER
- BUT
- IS
- NOT
- SHE
- AT
- 'ON'
- BE
- HIM
- THEY
- BY
- HAVE
- THIS
- MY
- WERE
- WHICH
- ALL
- FROM
- SO
- SAID
- ONE
- ME
- WE
- THERE
- THEIR
- 'NO'
- WHEN
- AN
- OR
- THEM
- WOULD
- IF
- WHO
- ARE
- BEEN
- WHAT
- UP
- THEN
- OUT
- COULD
- WILL
- INTO
- MORE
- SOME
- VERY
- MAN
- DO
- NOW
- LITTLE
- ABOUT
- YOUR
- DID
- THAN
- TIME
- LIKE
- UPON
- WELL
- HAS
- ONLY
- TWO
- OTHER
- ANY
- OUR
- MADE
- AFTER
- BEFORE
- ITS
- DOWN
- OVER
- SUCH
- OLD
- SEE
- THESE
- KNOW
- CAME
- DAY
- GREAT
- US
- MISTER
- GOOD
- SHOULD
- MUCH
- CAN
- HOW
- WAY
- NEVER
- MUST
- COME
- AGAIN
- BACK
- FIRST
- WHERE
- GO
- HIMSELF
- OWN
- LONG
- MAY
- MEN
- EVEN
- WENT
- SAY
- JUST
- MIGHT
- HERE
- THROUGH
- EYES
- MAKE
- TOO
- WITHOUT
- HOUSE
- THINK
- THOSE
- THOUGHT
- MANY
- MOST
- EVERY
- LIFE
- AWAY
- BEING
- STILL
- AM
- WHILE
- NOTHING
- DON'T
- LAST
- THOUGH
- YOUNG
- YET
- FOUND
- PEOPLE
- THREE
- 'OFF'
- HAND
- GET
- TAKE
- ASKED
- SAW
- SAME
- NIGHT
- MISSUS
- HEAD
- RIGHT
- LEFT
- ANOTHER
- TELL
- ONCE
- SHALL
- PLACE
- EVER
- TOOK
- FACE
- SEEMED
- ALWAYS
- ROOM
- NEW
- UNDER
- WHY
- TOLD
- LOOKED
- HEARD
- PUT
- BECAUSE
- THINGS
- SOMETHING
- LET
- GOING
- GIVE
- LOOK
- SOON
- THING
- MIND
- FATHER
- LOVE
- KNEW
- EACH
- FAR
- AGAINST
- HAVING
- HEART
- MOTHER
- WORLD
- FEW
- BEGAN
- 'YES'
- MISS
- DOOR
- BETTER
- WORK
- HOME
- MOMENT
- YEARS
- ENOUGH
- SIR
- DONE
- GOT
- SIDE
- SEEN
- WOMAN
- CALLED
- IT'S
- WHOLE
- BETWEEN
- FELT
- KING
- MORNING
- HERSELF
- FIND
- TURNED
- HOWEVER
- WHITE
- ALSO
- HALF
- PERHAPS
- GIRL
- REPLIED
- HUNDRED
- QUITE
- OH
- MYSELF
- PART
- WATER
- COURSE
- VOICE
- POOR
- BOTH
- NAME
- GAVE
- HANDS
- WHOM
- DAYS
- ALMOST
- AMONG
- SET
- TOGETHER
- WORDS
- UNTIL
- ANYTHING
- FEET
- NEXT
- WANT
- STOOD
- FOUR
- I'M
- BROUGHT
- BEST
- LIGHT
- OTHERS
- FIVE
- LOOKING
- SMALL
- ALONG
- NOR
- NEAR
- RATHER
- SINCE
- BELIEVE
- PASSED
- DOES
- MONEY
- OPEN
- LAY
- END
- INDEED
- ROUND
- KIND
- FULL
- TWENTY
- CRIED
- TAKEN
- SURE
- MATTER
- WORD
- DEAR
- GONE
- COUNTRY
- WHOSE
- ANSWERED
- LESS
- HIGH
- THEMSELVES
- SAT
- AIR
- BLACK
- BEHIND
- POWER
- 'TRUE'
- UNCLE
- AROUND
- NATURE
- CHILD
- DEATH
- DURING
- CERTAIN
- REST
- KEEP
- JOHN
- OFTEN
- TILL
- WOMEN
- ALREADY
- CHILDREN
- THUS
- PRESENT
- HOPE
- LARGE
- LADY
- BECAME
- RETURNED
- WIFE
- CANNOT
- WISH
- DIDN'T
- GOD
- BOY
- SENT
- GIVEN
- LEAVE
- ALONE
- CASE
- SHORT
- BODY
- LAND
- EVERYTHING
- COMING
- GENERAL
- SAYS
- REALLY
- HELD
- DOCTOR
- ABOVE
- GROUND
- FELL
- FIRE
- HELP
- THOUSAND
- SPEAK
- EVENING
- FACT
- CITY
- SOMETIMES
- HEAR
- ORDER
- STATE
- FRIEND
- KEPT
- WITHIN
- POINT
- FRIENDS
- LEAST
- MASTER
- HOUR
- THAT'S
- USE
- FAMILY
- CARE
- MAKING
- WHETHER
- BEAUTIFUL
- SIGHT
- TIMES
- SUDDENLY
- BED
- SIX
- I'LL
- DEAD
- EITHER
- CALL
- ITSELF
- USED
- ABLE
- TOWARDS
- DARK
- MANNER
- MEAN
- SEVERAL
- CAPTAIN
- LOST
- APPEARED
- STORY
- TOWN
- KNOWN
- BIG
- POSSIBLE
- THOU
- FINE
- MEANS
- SEA
- SECOND
- CONTINUED
- STRANGE
- SON
- RED
- HUMAN
- LORD
- HARD
- PERSON
- STREET
- REACHED
- FEEL
- CLOSE
- HAIR
- QUESTION
- ARMS
- ROSE
- THEREFORE
- BECOME
- LONGER
- FOLLOWED
- BUSINESS
- UNDERSTAND
- YEAR
- TABLE
- SORT
- HAPPY
- DIFFERENT
- SOUND
- ACROSS
- LIVE
- CERTAINLY
- WINDOW
- MET
- TREE
- BLUE
- NEED
- ELSE
- WAR
- TURN
- WANTED
- FELLOW
- READ
- TOWARD
- REASON
- READY
- OUGHT
- EARTH
- ASK
- CARRIED
- LIVED
- GREEN
- TEN
- FEELING
- IDEA
- ANSWER
- RUN
- PRINCE
- BROTHER
- COLD
- LATER
- EIGHTEEN
- CHURCH
- FEAR
- ALTHOUGH
- ADDED
- STRONG
- PARTY
- SHOW
- EYE
- PETER
- RIVER
- CAN'T
- TAKING
- SUPPOSE
- GIRLS
- PRINCESS
- FOOT
- TREES
- BOOK
- PRETTY
- ENTERED
- ROAD
- HOURS
- SLEEP
- FALL
- RECEIVED
- CLEAR
- LOW
- FREE
- LETTER
- TRIED
- SPOKE
- PAST
- FORM
- DOUBT
- TALK
- BEYOND
- DAUGHTER
- OPENED
- LIVING
- SAYING
- HOLD
- NUMBER
- DOING
- HORSE
- SCHOOL
- BOYS
- ENGLAND
- O
- LED
- DEEP
- I'VE
- GLAD
- ENGLISH
- THY
- THEE
- RETURN
- HUSBAND
- EIGHT
- RAN
- STRUCK
- ILL
- SEVEN
- SNOW
- SOUL
- AGE
- MILES
- TRUTH
- FORWARD
- SUN
- WALKED
- AH
- POSITION
- BEAUTY
- MEET
- NEARLY
- WON'T
- SPIRIT
- SEEM
- NONE
- LATE
- BAD
- STANDING
- WONDER
- CUT
- SILENCE
- EARLY
- IMMEDIATELY
- WIND
- SENSE
- CHANCE
- HAPPENED
- REMEMBER
- GREW
- FRONT
- CAUGHT
- BRING
- NEITHER
- YOURSELF
- WILD
- GARDEN
- BLOOD
- MINUTES
- LINE
- FURTHER
- COMPANY
- THIRTY
- FORCE
- TROUBLE
- SEEMS
- FILLED
- ARM
- AFRAID
- ATTENTION
- PLEASURE
- FORTH
- LAW
- CHANGE
- PURPOSE
- WOOD
- SISTER
- STOPPED
- SUBJECT
- INTEREST
- PUBLIC
- STARTED
- FIFTY
- LOVED
- EXCEPT
- EXCLAIMED
- PAY
- TONE
- REAL
- YOUTH
- INSTEAD
- WALK
- HARDLY
- CHARACTER
- WAIT
- THIRD
- LENGTH
- DIED
- MOVED
- SITTING
- HE'S
- AGO
- GOLD
- CAUSE
- GETTING
- THERE'S
- FLOOR
- VIEW
- IMPOSSIBLE
- THOUGHTS
- TEARS
- STAND
- HILL
- PLAY
- PLACED
- SERVICE
- BROKEN
- PROPERTY
- FOREST
- LAUGHED
- TALKING
- OBJECT
- REMAINED
- COVERED
- DEAL
- TRY
- TOP
- LAID
- LONDON
- APPEARANCE
- WEEK
- MADAME
- HAPPINESS
- SMILE
- MARRIED
- WHATEVER
- BEAR
- ACCOUNT
- COMES
- OUTSIDE
- WONDERFUL
- NATURAL
- SAINT
- QUEEN
- ARMY
- SEEING
- BELOW
- THINKING
- FIGURE
- COMMON
- SOCIETY
- SWEET
- FAIR
- PLEASE
- SHOWED
- DESIRE
- MAN'S
- RICH
- GOVERNMENT
- QUICKLY
- BOAT
- NECESSARY
- ENTIRELY
- MINE
- FRESH
- AFTERNOON
- MOUTH
- GIVING
- DREW
- OPINION
- YOU'RE
- EXPRESSION
- COURT
- EASY
- FOOD
- SIT
- STEP
- EASILY
- DE
- SHIP
- GENTLEMAN
- PASS
- DISTANCE
- TURNING
- TERRIBLE
- WAITING
- WIDE
- SKY
- COULDN'T
- HEAVY
- WISHED
- ACT
- ESPECIALLY
- VALLEY
- HOUSES
- PAPER
- STAY
- KILLED
- OCCASION
- BESIDE
- STONE
- EXPECTED
- LIPS
- USUAL
- WINTER
- OFFICE
- SECRET
- HORSES
- DANGER
- SAVE
- MOUNTAIN
- CHAPTER
- PROBABLY
- BROKE
- SIMPLY
- ART
- STEPS
- JOY
- FOLLOWING
- CHIEF
- SLOWLY
- HALL
- DINNER
- BESIDES
- KNOWS
- SPRING
- SPEAKING
- BEGINNING
- CHANGED
- NORTH
- HISTORY
- STRENGTH
- CLOSED
- PLACES
- SMILED
- CHAIR
- ANNE
- MEANT
- TRYING
- FORTY
- DUTY
- BROWN
- STOP
- CORNER
- PRESENCE
- DIE
- QUIET
- SILENT
- SINGLE
- VISIT
- SCARCELY
- EFFECT
- MAKES
- ARRIVED
- PARTICULAR
- BORN
- CONVERSATION
- FORTUNE
- ALLOWED
- RACE
- PALACE
- LEGS
- WALL
- CARRY
- UNDERSTOOD
- GREATER
- VILLAGE
- NINE
- JANE
- CRY
- SELF
- FIGHT
- SPENT
- RAISED
- WOODS
- FIELD
- FRENCH
- WRONG
- REGARD
- DREAM
- BIT
- LIE
- SUDDEN
- LAKE
- MONTHS
- PALE
- MARRIAGE
- BELIEVED
- LETTERS
- CAMP
- SOUTH
- ISN'T
- OBSERVED
- LEARNED
- STRAIGHT
- PLEASED
- LADIES
- SOFT
- SURPRISE
- SEAT
- PLEASANT
- BREAD
- BRIGHT
- WEST
- EXPERIENCE
- NEWS
- MOVE
- CONDITION
- WALLS
- EAT
- FOLLOW
- O'CLOCK
- POCKET
- DECLARED
- MUSIC
- PATH
- EVIL
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- MARY
- WARM
- FINALLY
- LATTER
- INFLUENCE
- WATCH
- LEAVING
- KNOWLEDGE
- BATTLE
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- WASN'T
- PERSONAL
- PERSONS
- HANDSOME
- ACTION
- SHORE
- WALKING
- GOLDEN
- TWELVE
- HEAVEN
- FORGET
- SHOOK
- AMERICAN
- THANK
- VARIOUS
- JOURNEY
- MOON
- MARRY
- MERELY
- DIRECTION
- CROWD
- MAJOR
- I'D
- SUMMER
- UNLESS
- SHUT
- REMAIN
- ANXIOUS
- SHOT
- DRESSED
- WOULDN'T
- DRESS
- EAST
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- BENEATH
- THICK
- WORSE
- WORTH
- MOUNTAINS
- EVIDENTLY
- INSTANT
- ESCAPE
- WE'LL
- GRACE
- FATHER'S
- TALL
- SOMEWHAT
- DROPPED
- EXACTLY
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- KNOWING
- FALLEN
- DARKNESS
- GRAY
- EVERYBODY
- SIMPLE
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- MINUTE
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- PICTURE
- FAST
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- GROWN
- BREATH
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- BANK
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- ISABEL
- REACH
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- SITUATION
- DIFFICULT
- BOX
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- PERFECTLY
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- AUNT
- ANCIENT
- FRANK
- PIECE
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- MORROW
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- LYING
- FISH
- CLASS
- BILLY
- PLAIN
- PEACE
- LIKED
- HAT
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- CARRIAGE
- REPEATED
- LAUGH
- STRANGER
- SILVER
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- GLANCE
- FORGOTTEN
- IDEAS
- ENEMY
- WRITTEN
- LOWER
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- TAIL
- HONOUR
- PRESIDENT
- BUILT
- DISCOVERED
- PREPARED
- OBLIGED
- PAID
- BOUND
- GENTLEMEN
- MERE
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- PASSING
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- CONSIDERABLE
- BROAD
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- BEGIN
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- REMEMBERED
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- SHOWN
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- SERVED
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- ACCEPTED
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- REGULAR
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- ABSOLUTELY
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- PRICE
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- EXPLAIN
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- AWFUL
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- MAJESTY
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- PLENTY
- WORTHY
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- PATIENCE
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- REMOVED
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- CONCLUDED
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- SORROW
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- COMPLETELY
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- DRIVEN
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- SHARE
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- APPROACHING
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- INTENTION
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- STARED
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- TEMPLE
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- ALICE
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- BEARING
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- SLIM
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- TREATMENT
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- AGREEABLE
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- DRIVING
- UNUSUAL
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- ASTONISHED
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- PICK
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- IMAGINED
- MERCY
- FUNNY
- TYPE
- COVER
- CHEEKS
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- LITERATURE
- APART
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- ENERGY
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- ACCOMPLISHED
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- OWNER
- SOUNDED
- INVITED
- ACCIDENT
- DISCOVER
- DISTINGUISHED
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- CHARM
- TREMBLING
- FAMILIES
- MILLIONS
- RUSH
- BARON
- ATMOSPHERE
- SENSIBLE
- EDITH
- NEEDS
- DAVID
- OLDER
- WASH
- NED
- ANNIE
- SEVERE
- PURPLE
- MARBLE
- WORST
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- LEANING
- MERCHANT
- WEIGHT
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- HASTE
- SUSPECTED
- PRISONERS
- ABROAD
- TRIAL
- CONSENT
- CONVINCED
- ARGUMENT
- WESTERN
- BADE
- EVENT
- LIBRARY
- ABSOLUTE
- DISCOVERY
- FAILURE
- SUPERIOR
- FIFTH
- BELOVED
- DESTRUCTION
- GAIN
- VAGUE
- DAWN
- LILY
- HASTENED
- MACHINE
- FOREVER
- WOMAN'S
- CHANGES
- LIKEWISE
- DISTRICT
- DEPTHS
- STOUT
- PICTURES
- HIDE
- SUCCESSFUL
- GOODNESS
- IMMENSE
- BOBO
- APARTMENT
- SHOES
- SANG
- RETIRED
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- REGRET
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- PRACTICE
- CROWDED
- DESCENDED
- MISERABLE
- HATE
- OBSERVATION
- FAMILIAR
- MEASURES
- DISPOSED
- REFUSE
- DESCRIBE
- SHOCK
- MILK
- REPUTATION
- DEVELOPMENT
- FEARED
- LIGHTNING
- BROWNIE
- BEGGED
- STIFF
- LEVEL
- ADAM
- SETTING
- NEWSPAPERS
- HUNT
- CLASSES
- ROOTS
- MINGLED
- CONSEQUENCE
- APPROACH
- DIANA
- COLORED
- STAYED
- GRATITUDE
- BOW
- SPEED
- HOST
- PASSENGERS
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- MOSS
- AMUSEMENT
- REGARDING
- FANCIES
- APT
- GRANITE
- BOHEMIA
- PROTECT
- ANGRILY
- WHEREAS
- COMPARED
- VIGOROUS
- CLAIMED
- DELIVER
- BEATEN
- ROOT
- HEROIC
- PLEASURES
- WAVING
- BEDROOM
- CHECK
- ASSIST
- AMUSED
- ROAR
- REPROACH
- INDIFFERENT
- PERPETUAL
- ENABLED
- DEEPER
- INCIDENT
- GAMES
- LOTS
- PINK
- PATIENTLY
- BEGINS
- TRAINING
- HEALTHY
- CORRECT
- BARS
- TRACE
- CORONER
- PLANNED
- GLANCING
- OBJECTION
- ANSWERS
- CUTTING
- HIND
- CALF
- SCALE
- UNIFORM
- CAPTURE
- INQUIRY
- CENTER
- GOSSIP
- CORPSE
- FUNERAL
- OWE
- SCIENTIFIC
- B
- DISGUISE
- CROOK
- FLASHED
- COMMENCED
- SENSATIONS
- HESITATE
- TRICK
- GRIN
- TONES
- SAILING
- TREMBLE
- PREPARING
- GLEAM
- LE
- ALLIES
- PRINT
- PORCH
- COMPOSITION
- SATISFACTORY
- CONCEIVE
- REPOSE
- TIDE
- RESIDENCE
- SEIZE
- PROMPTLY
- COMRADES
- DOONE
- SHAKEN
- YOURSELVES
- GRANDMOTHER
- ANXIOUSLY
- LEISURE
- BOUGHS
- CLOCK
- COUNTY
- MILTON
- HEROES
- MACHINERY
- ENGLISHMAN
- MARS
- HALE
- HOPKINS
- PARKER
- ROBARTS
- COTTON
- RARELY
- EXPECTING
- WE'D
- TRAINED
- BEDS
- PREFERRED
- CARPET
- QUESTIONED
- TUMULT
- ANGUISH
- CLASPED
- OFFENCE
- DANCED
- REMINDED
- CARELESS
- DARING
- LIFT
- FLORENCE
- SAN
- FORTUNATELY
- GIFTS
- RECOGNISED
- COLLECT
- SHEER
- INFANT
- HOPELESS
- PHILOSOPHY
- FLAMES
- COARSE
- DEED
- KARA
- PASSES
- VALET
- DESCEND
- COMPLETED
- AGED
- BREATHED
- ADDRESSING
- HUSBAND'S
- LUNGS
- SUCCEED
- RESISTANCE
- INCLINATION
- GROOM
- COUSINS
- LAZY
- SCARCE
- RISEN
- CROWDS
- VIOLENTLY
- STRUGGLED
- HOLIDAY
- FURIOUS
- DESIRABLE
- REALIZE
- SIGHTED
- ROMANTIC
- RESPONSE
- SYMPTOMS
- FARMERS
- UNCONSCIOUSLY
- ADVISED
- REMOTE
- EMERGED
- SUBMIT
- CLAD
- GERMANY
- RAY
- RECENTLY
- PRINTED
- FAME
- CONFINED
- JOHNNY
- GAS
- EMBRACE
- SUPPLIED
- RYNCH
- LEAN
- ORGANS
- FAVORABLE
- ELEGANT
- GUIDED
- INFORM
- SINISTER
- PASSIONS
- MEDICAL
- NAMELY
- HESITATION
- PAGES
- SWORE
- BREATHE
- CAVE
- NATIVES
- CONSISTED
- MANIFEST
- EMBARRASSMENT
- HEAPS
- HURRYING
- STRING
- LOCK
- ETERNAL
- DETAIL
- ABSENT
- HOARSE
- SPECTATORS
- DISTINGUISH
- FROST
- SNOWY
- THEY'VE
- BACKWARD
- FIERY
- ILLNESS
- PRIESTS
- BALLOON
- QUIXOTE
- JAWS
- MISSION
- REFERENCE
- SHAW
- BARREL
- TERM
- BIBBS
- THEO
- FALK
- CRISTEL
- GENZABURO
- RAWDON
- LYNDE
- SLOPE
- GABLES
- SHY
- ENCOUNTERED
- EARTHLY
- BRED
- MAINTAIN
- APARTMENTS
- DAUGHTER'S
- APPLY
- RINGING
- COMMANDS
- ARRESTED
- ADVENTURES
- AMAZED
- GASPED
- STOOPED
- COUNTER
- JUDGED
- MINDED
- PROTEST
- DISAGREEABLE
- FAITHFULLY
- RESPONSIBILITY
- PEACEFUL
- PHRASE
- DESERVE
- CONSENTED
- OCTOBER
- PRESSURE
- RESPECTS
- LASTED
- INEVITABLE
- RESPONSIBLE
- BID
- YIELD
- EXCLUSION
- MAINTAINED
- SAUCE
- FORMIDABLE
- OLDEST
- WEAPON
- QUEST
- PARLOUR
- AFRICA
- DRAWER
- PANIC
- PLEASING
- DAMAGE
- WIT
- UNDERTAKE
- ENTERTAINMENT
- WINDING
- DWELT
- CEREMONY
- NET
- SUITS
- PRODUCT
- TENDENCY
- CEASE
- AVOIDED
- IMPROVEMENT
- BONE
- STOMACH
- ARRANGEMENT
- SEARCHED
- INQUIRIES
- FIX
- TRACES
- GRASP
- SPEAKER
- FACING
- CONVENIENT
- PRAYED
- TENDERNESS
- SUSPENDED
- LEARNT
- RESERVED
- SHOPS
- RULED
- UNCERTAIN
- SINK
- MARKS
- RELATIVES
- SENSITIVE
- SPAIN
- SINCERE
- DIGNIFIED
- SIGNIFICANT
- VEHICLE
- AVERAGE
- FIRES
- SUPPLIES
- ARRANGEMENTS
- TRIFLE
- REPEATING
- ADDING
- PHENOMENA
- AIM
- LIMITS
- LIP
- BOY'S
- MURMUR
- PILLARS
- BRIGHTLY
- SWIFTLY
- JOYOUS
- JEALOUSY
- WARRIOR
- CONTRAST
- EXTRA
- AWFULLY
- DEFEAT
- ENTHUSIASTIC
- INCHES
- DROPPING
- REDCOAT
- NERVES
- BITE
- CRACK
- SERGEANT
- DOCTRINE
- C
- MIXTURE
- INTERVALS
- FEATHERS
- BUFFALO
- FOLK
- OFFERING
- COMRADE
- BELLS
- STOLE
- SIGNAL
- SWINGING
- AUTHOR
- DISMISSED
- THORPE
- RELATE
- WILDERNESS
- TREASURES
- PROPHET
- FELIX
- COMPREHEND
- DARCY
- ASSUME
- FRANCES
- WEEP
- JACKET
- HERD
- ACCENT
- OPERATOR
- KNIGHTS
- LANTERN
- SIN
- METERS
- GREENLAND
- THRESHOLD
- TWAS
- GLACIER
- MACHINES
- KWAIRYO
- ASSISTANT
- BULLS
- REX
- ELK
- SHERIFF
- SPILETT
- CRAGGS
- STRONGEST
- WONT
- WIRE
- BRAND
- CHIN
- UNFORTUNATELY
- CONFESSED
- MUTUAL
- CARD
- FIRMNESS
- BLUSH
- CORNERS
- BABIES
- HELPLESS
- FRANKLY
- SURROUNDINGS
- HARSH
- INTERFERE
- RESTLESS
- BENCH
- PROPOSAL
- ORGAN
- AGITATED
- SUBLIME
- GREETED
- FEBRUARY
- PROCEEDING
- VAN
- ANGLE
- FAIRER
- PASSAGES
- PARCEL
- WASTED
- CORRIDOR
- ARTIFICIAL
- THOUGHTFULLY
- DEPARTMENT
- SPECTACLE
- AGENT
- BEHALF
- STAMPED
- OCCUPATION
- ELEMENT
- ROMANCE
- TEST
- PIG
- DEER
- FROG
- COMPLEXION
- LINEN
- RADIANCE
- CONTEST
- PARTNER
- LIABLE
- CALCULATED
- LATIN
- BALLS
- ADMIRABLE
- FOOTSTEPS
- REGULARLY
- INCLUDED
- UPWARD
- DISLIKE
- TEACHING
- COLLECTED
- SWALLOWED
- WONDERS
- FINISH
- GENIE
- EXPRESSIONS
- DESTINY
- RICHES
- CIGAR
- AMIABLE
- TRIBUTE
- BONDS
- FORMING
- HOSTILE
- BELT
- WARS
- QUIT
- FREQUENT
- IMPULSES
- INFLUENCES
- DISCUSSED
- CONSEQUENCES
- THEREBY
- BELIEVING
- SCHEME
- COMPLEX
- OUTWARD
- CLOAK
- TERRIFIC
- AMBITIOUS
- VANITY
- IMPROVED
- STROKE
- WHITHER
- LOCKS
- STRICTLY
- CHILD'S
- FRIDAY
- CHARGED
- MONDAY
- SHINE
- SONGS
- ENDURED
- EMBRACED
- BOWING
- POLE
- CART
- POPULACE
- VISITORS
- HERE'S
- CIRCUS
- SISTER'S
- STOVE
- SWOLLEN
- JAPAN
- ABOARD
- LADDER
- MILD
- BOILING
- ATTEMPTS
- AFFECT
- MURDERED
- SNAKES
- LACE
- APPETITE
- GENERATIONS
- GALLERY
- JOSEPH
- HURSTWOOD
- DANDY
- WHEREUPON
- ENTERTAINED
- PULLING
- MOSCOW
- POLITICS
- TOOLS
- MONSTROUS
- WOUNDS
- DOTH
- ANTS
- NICHOLAS
- DORA
- ACADEMY
- AIRSHIP
- CYRUS
- SEXUAL
- JOSIANA
- AVONLEA
- BARELY
- SITUATED
- PARLOR
- RIGID
- HUMOR
- HIRED
- BURNS
- STOLEN
- HORRID
- GLOVES
- REGRETTED
- SEEMING
- BETRAYED
- MOURNING
- SWEAR
- FEVERISH
- MURDERER
- LIKES
- INVENTION
- RECOMMEND
- PROTESTED
- TUNE
- DESTINED
- REMEMBERING
- NINTH
- OVERWHELMED
- CONSIDERABLY
- TENTH
- INDUCED
- INSIST
- ASSENT
- BUNCH
- DELICIOUS
- UNNECESSARY
- GROAN
- VERSES
- COWARD
- RECOGNITION
- ADJOINING
- ENCOURAGEMENT
- RIDICULOUS
- INTEND
- GREEK
- ATTRACTED
- OBVIOUSLY
- VOLUMES
- GRASPED
- NEIGHBOURS
- CARDS
- ADMIRE
- EXCHANGED
- ROWS
- REMARKS
- STRINGS
- LADEN
- DETERMINATION
- OCCUR
- LIVER
- WHALE
- BLOCK
- COMPLICATED
- DISTINCTLY
- UPRIGHT
- OPENLY
- PROMINENT
- GUARDED
- UPSTAIRS
- P
- VICTIMS
- PURCHASE
- CHERISHED
- COMPASSION
- MORALITY
- MERCHANTS
- WARMLY
- WELCOMED
- AMUSING
- FLOWED
- AVENUE
- ORGANIZATION
- LEAGUES
- UNEASINESS
- SNAPPING
- ROARING
- SMELL
- RIVERS
- ROUNDED
- EXAMINE
- AMERICANS
- COUNTING
- PLANTED
- REPORTS
- GRAVITY
- CITIZEN
- PANTING
- STRETCHING
- PROMISES
- ARMIES
- OBTAINING
- SUGGESTIVE
- SUGGESTIONS
- CRITICISM
- STRIVING
- WINNING
- STUDENTS
- GIGANTIC
- SILVERY
- BENDING
- FORGETTING
- HAIRED
- EXQUISITE
- EXCESS
- TORRENT
- POLICY
- NIECES
- THOUGHTFUL
- STABLE
- FLOATING
- HIGHNESS
- PROVIDENCE
- HASTY
- CANADA
- ROCKY
- SEEMINGLY
- MASSIVE
- RUBBING
- MIRANDA
- BRONZE
- UNDERNEATH
- PACK
- BURN
- ONLOOKER
- HORSEBACK
- KEEPER
- EUROPEAN
- CHAINS
- HAIL
- PLAYS
- STORMS
- DASHED
- MINES
- DRAG
- DARTED
- STICKS
- SIMON
- SLOPES
- DESCENT
- LILIES
- TEACHERS
- LAYING
- DETECTIVE
- LADY'S
- TRACK
- PRECEDING
- JEW
- BEWILDERED
- BUNDLE
- ALBERT
- BRIEFLY
- HYPNOSIS
- NOVEL
- BOLDLY
- CHARACTERISTIC
- PRIMITIVE
- ABANDON
- H
- MUSCLES
- PROVIDE
- NAPOLEON
- LAIN
- BORODINO
- SUPPOSING
- DURHAM
- DEMOCRACY
- HEROD
- BATES
- PEER
- STEPHEN
- ANTHONY
- PYE
- CHARLEY
- KOYO
- CONSTANCE
- CONNISTON
- BARGAIN
- PRESSING
- VISITS
- PRECISE
- DOCTOR'S
- ORPHAN
- DREADED
- SHE'LL
- FADED
- SPARED
- PHANTOM
- BLESSING
- CONDEMNED
- TWIN
- GAILY
- PRETEND
- HULLO
- QUICKER
- MOSTLY
- TRAGEDY
- OPPRESSED
- WANTING
- DECENT
- NEIGHBOUR
- INFERIOR
- EXISTING
- STROLLED
- PUNISH
- COMMERCE
- PROVINCES
- TROMP
- TERRIBLY
- MONK
- FIERCELY
- CONSULTED
- THREATENING
- STRAIN
- STIRRING
- MELTED
- INWARD
- DWELL
- RUNS
- ATTRACTION
- ESTEEM
- REPLACED
- ANSWERING
- YIELDED
- LIFTING
- CONFIRMED
- ELBOW
- SORE
- WHO'S
- SNEER
- STAINED
- STRUGGLING
- SWEEP
- COLUMBIA
- BANNER
- DOCTORS
- FINER
- NEEDN'T
- SWALLOW
- SUITED
- BIDDING
- PROBLEMS
- RESTORATION
- PROFIT
- WIVES
- PRODUCING
- ASSISTED
- INJURED
- HARVEST
- BEHAVIOUR
- OBSCURE
- JAIL
- SUITABLE
- ROOFS
- FORBIDDEN
- SALVATION
- WITS
- GHOSTS
- DOWNWARD
- DUG
- W
- AFFECTIONS
- RESTORE
- CONTAIN
- PIERCED
- EXCITE
- ENDEAVOURED
- SIRE
- TOBACCO
- GENERATION
- INSTITUTIONS
- SOUP
- SCHOOLS
- COURTEOUS
- WHEELS
- GRACIOUS
- ASSERTED
- DIFFERENTLY
- COLORS
- LUXURY
- RECEPTION
- MONTE
- CONSOLATION
- PAVEMENT
- ROTTEN
- HAILED
- ARDAN
- TOMB
- TRAVELING
- FOLLOWERS
- DRIFTED
- HEATHERSTONE
- FORTUNES
- HUMPHREY
- ATTENDANT
- SURRENDER
- LOVERS
- PARTICULARS
- CONFLICT
- DANGERS
- CLIMBING
- CRUELTY
- INJUSTICE
- BLANK
- INCAPABLE
- CONTINUAL
- AWKWARD
- TIMID
- TRADITION
- SWIMMING
- SWAM
- CONSTANTINOPLE
- TURKEY
- APPLES
- ACRES
- CAESAR
- PRACTISED
- CREEP
- PIPES
- SLAIN
- MEETINGS
- SEAL
- IRRESISTIBLE
- CROP
- ACCORD
- KILLING
- SYNDIC
- RESEMBLANCE
- DEAF
- BLOSSOMS
- DRINKING
- EDUCATED
- DETERMINE
- REVENGE
- MASK
- TWILIGHT
- AMIDST
- BLOWN
- DRAKE
- CHARLOTTE
- UNDOUBTEDLY
- LOGS
- OWL
- EXERTION
- DERIVED
- CIGARETTE
- LEADS
- ENABLE
- THIRST
- PERFORMANCE
- INTERVAL
- CONFIDENT
- DAT
- PROCURED
- APOLOGY
- ADMISSION
- ATLANTIC
- PERSONALLY
- FOUL
- THREAD
- MUSKETEERS
- DISTURBANCE
- RUINS
- HUNTER
- MOTOR
- PULSE
- V
- ROUTE
- EARLIEST
- BLOT
- GRANDCOURT
- GLEAMING
- COACHMAN
- ONWARD
- REVIEW
- WAGES
- CUPID
- GREATNESS
- BRIG
- FRERE
- WIRELESS
- MERRIWIG
- WHISTLER
- FERRIS
- CUTHBERT
- KNITTING
- MARE
- NOTION
- MAIL
- THEY'LL
- HALLS
- GLANCES
- PERFECTION
- CONTRACT
- WRETCH
- HONORABLE
- RECALL
- REMEMBRANCE
- SUSPICIOUS
- APPRECIATION
- VEIN
- DISCUSSING
- REGARDS
- SMALLEST
- REVIVED
- BASED
- ADMIRAL
- DESPITE
- SUBMITTED
- LARGELY
- BLOWS
- ENEMY'S
- YOUTHFUL
- COMPLAINED
- DEFENCE
- TEMPTED
- RADIANT
- DISTURB
- COLDLY
- SLEEVE
- SERVING
- EXAMINING
- PATRIOTISM
- FOLDS
- PASSIONATE
- OFFERS
- NIECE
- VEXED
- LEAP
- CROSSING
- POUND
- DRESSES
- PUSH
- TAP
- UNIQUE
- CONTINUING
- REQUIRES
- HAUNTED
- ECHOED
- REFLECTIONS
- MANAGER
- ACCOMPLISH
- STUMP
- MINISTERS
- POLISHED
- PERCEIVING
- COMMUNICATE
- BANQUET
- FACTORY
- STUDIO
- CHUCKLED
- DIGGING
- TUNNEL
- INSIGNIFICANT
- ALTER
- CRISTO
- ENTERS
- PROPOSITION
- MAGPIE
- MARCHING
- NICHOLL
- OCCUPY
- MATERIALS
- BET
- NEEDLE
- PERIODS
- RELATIVE
- WORLDS
- INTENT
- RECOLLECT
- STANDARD
- ACCEPTING
- HYPNOTISM
- HYPNOTIZED
- MYSTERIES
- DISPLAY
- CREATE
- POISON
- STUDIES
- NON
- NEGATIVE
- UNEXPECTEDLY
- GLITTERING
- ANALYSIS
- DISMAY
- ZEAL
- PROPRIETOR
- STOCKINGS
- CRACKED
- ENVELOPED
- GRANDEUR
- PLENTIFUL
- SUSTAINED
- MAGUA
- EXTREMITY
- PACIFIC
- ERECT
- CRIMSON
- HARBOR
- PORTER
- PROCEEDINGS
- DISGRACE
- CLOSET
- ROBIN
- RESEMBLED
- EIGHTEENTH
- TALENT
- SHOOTING
- DEVOTION
- SINS
- CANOE
- CABLE
- TRAVELLED
- TEMPTATION
- PIT
- CORRAL
- JEST
- TRIGGER
- BASIN
- QUEEN'S
- MARRYING
- SEPTEMBER
- PATTERN
- ERRAND
- QUANTITY
- CREAM
- ALLOWING
- SPARKLED
- BOAST
- EQUIPPED
- ELECTION
- ARTS
- MOUTHS
- WHARTON
- INTERRUPTION
- HORSEMEN
- INDIA
- REACTION
- DRUNKEN
- DROUET
- CAUTIOUSLY
- UNREASONABLE
- WOLF
- SCREAM
- ENDEAVORED
- BEATS
- CHAP
- SOURCES
- GULF
- LIONS
- FISHERMAN
- SALOON
- SLEDGE
- MARTIANS
- CHEERING
- PISTOL
- RAIL
- MANAGEMENT
- COPY
- WRITES
- GUINEAS
- SWELL
- SANCHO
- TARS
- TUESDAY
- SCOUT
- AGNES
- RIFLE
- DANTES
- MORTON
- BARRY
- PINES
- BORG
- NATHAN
- CARSON
- DEASEY
- LYRA
- CUCUMETTO
- ABUNDANT
- KNOT
- SAVING
- SPENCER
- EASIER
- RICHARD
- DOUBTS
- DEARLY
- PLUMS
- NOD
- ATTENDING
- AWAITED
- VICE
- ROUGHLY
- FEROCIOUS
- ABANDONED
- GRATIFIED
- TWENTIETH
- PAINS
- RESOLVE
- BEHAVED
- FRIEND'S
- DELICACY
- BEECH
- ANTICIPATED
- HUSH
- REPUBLIC
- ORDERLY
- AFFORDED
- RESENTMENT
- UNDERTAKING
- THIRTIETH
- DEPENDED
- NAVY
- SCOTLAND
- PROTECTED
- ANCESTORS
- OWED
- DEBATE
- LIQUID
- POUR
- STRAINED
- INTRODUCTION
- CARRIES
- ASSOCIATED
- SIGHTS
- APPREHENSION
- VULGAR
- GROTESQUE
- PRIVILEGES
- REVERENCE
- DISMAL
- CHIMNEY
- GRIM
- SPECIMENS
- EMINENT
- MIRTH
- REFLECT
- TRANSFERRED
- WANDER
- WAIST
- ENVY
- COWS
- INTIMACY
- PERSONALITY
- BASIS
- SELFISH
- SPOIL
- FOUNDATION
- PEAKS
- SPOTS
- VEXATION
- CLOTHED
- BARBER
- MALE
- HONEY
- BRIDLE
- DELIBERATELY
- PATCH
- WEARINESS
- THICKET
- OHIO
- TOTALLY
- PILES
- RELIEVE
- WAKING
- CURE
- SURPRISING
- FOUNTAIN
- CELEBRATED
- INJURY
- RETIRE
- MIRACLE
- FIST
- COMMERCIAL
- GOPHER
- LANDS
- PATHS
- SUFFRAGE
- CLIMB
- COMPARISON
- PENCIL
- UNWILLING
- PROCESSION
- INSULT
- TRAVELERS
- STRETCH
- CLUNG
- RETREATED
- HARNESS
- SCENT
- COUNTLESS
- BELONGS
- PERPLEXITY
- GENEROSITY
- CHARMS
- READERS
- ARGUMENTS
- TESTIMONY
- EXPERIMENTS
- VITAL
- OCCASIONAL
- CLINGING
- BROWN'S
- RESISTED
- KNOCKING
- CASTING
- SWEEPING
- SUBDUED
- SUBTLE
- APPLAUSE
- MARVELOUS
- ESTABLISH
- BLOWING
- BRUTAL
- SPARKLING
- CONFOUNDED
- RACES
- OFFENDED
- BITS
- EGYPT
- MICE
- SAVAGES
- MOOSE
- AREA
- BOTHER
- CAPITALIST
- MISSISSIPPI
- JAR
- NEWLY
- PERISH
- ANGELS
- PICKING
- HAWK
- HONESTLY
- USES
- HEED
- REGIONS
- SHOTS
- HOMEWARD
- PILOT
- BORROWED
- TASTED
- FURNISH
- EXHAUSTION
- KEYS
- ALLEN
- WEALTHY
- FORTNIGHT
- MEMORABLE
- MEN'S
- S
- ORLEANS
- RESEMBLING
- DECAY
- BLAZE
- UNUSUALLY
- PACES
- ROGER
- PICTURESQUE
- CHECKED
- HUNTED
- THEREUPON
- EXTENSIVE
- BROTHER'S
- PREVAILED
- ARISE
- COMMONLY
- COMMENT
- SOBER
- STATIONED
- THEREAFTER
- WALLACE
- FRAGMENTS
- ACCOUNTS
- PLACING
- LEADERS
- STRUCTURE
- SUBSEQUENT
- MYLES
- SUBSTITUTE
- RAFT
- FORMATION
- DEFEATED
- NEIGHBORING
- PUDDING
- AMPLE
- APPOINTMENT
- LOCATED
- SICKNESS
- TIGER
- SHALT
- JUDITH
- HULL
- RIVAL
- UPROAR
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- HANGED
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- COMPLAINTS
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- SEVERELY
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- COMFORTED
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- GALE
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- FAVOURABLE
- CONQUEST
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- BETRAY
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- PARTIAL
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- BROWS
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- JUSTIFIED
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- SHIVERED
- TRAVELLERS
- LATEST
- STAMMERED
- CROOKED
- PLEADED
- EMPLOY
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- NIGHT'S
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- UNITY
- INTENSITY
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- VICINITY
- BATHED
- PLEASANTLY
- TRIFLING
- APPROPRIATE
- THICKLY
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- DISPATCHED
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- MULE
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- JOYFULLY
- ENGAGE
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- FACTORIES
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- BLOCKS
- LAMPS
- ACQUAINTANCES
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- WAVERING
- SQUIRREL
- CEILING
- EXPERIMENT
- INDESCRIBABLE
- FORMAL
- EMPTIED
- INVARIABLY
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- CRANE
- CAGE
- APPARATUS
- INCREDIBLE
- ADVERTISING
- IRREGULAR
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- VINE
- GOAL
- SALUTED
- DEPENDS
- REPAY
- CIRCLES
- HARVARD
- DISCIPLINE
- PSYCHOLOGY
- STICKING
- NAUGHTY
- CONTINUOUS
- WONDERFULLY
- STAGGERED
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- COMMANDING
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- EXTINGUISHED
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- SHARED
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- CONVINCE
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- OBJECTIVE
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- REJECTED
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- EXPRESSING
- REFER
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- FAULTS
- JOYFUL
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- DOUBLED
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- IMMENSELY
- SCORN
- DREARY
- SYMPATHETIC
- DIFFER
- FRIGHTEN
- DENSE
- READINESS
- ENVOYS
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- WALTER
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- SQUADRON
- INTERFERENCE
- SOLUTION
- WELFARE
- SIXTEENTH
- EFFECTED
- ADVERSARY
- PROSPERITY
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- PERPLEXED
- PROFESSED
- OPPONENT
- INDIGNANTLY
- ACHIEVED
- OBSTACLES
- BOILED
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- INSTRUCTION
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- DOUBTED
- CLAIMS
- LAUNCHED
- HAMLET
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- THORNTON
- DEW
- MARIANNE
- DISGUSTED
- ZADIG
- ATTENDANTS
- REQUESTED
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- RECKONED
- MADNESS
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- STRAYED
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- TREASON
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- THEREIN
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- BEGGING
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- ORNAMENTS
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- REBELLION
- FLANK
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- PICKET
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- SECRETS
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- MEDIUM
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- AFFLICTED
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- PEEPED
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- PERPETUALLY
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- CULTIVATE
- ADDITIONAL
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- COMMANDERS
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- ESCORT
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- REPRESENTING
- INDUCE
- PROTECTOR
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- SHATTERED
- ANNUAL
- INTERNAL
- SUMMONS
- ASSIGNED
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- PROMPTED
- PEPPER
- INNUMERABLE
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- HARDNESS
- ATTAIN
- IMMORTAL
- PHILOSOPHER
- INSPIRATION
- HORRORS
- FROWNED
- TIPPED
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- GENIAL
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- HANDLING
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- CONFIDENTLY
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- ACCEPTABLE
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- BRAVELY
- INVENTED
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- RELEASE
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- WALLET
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- NERVOUSLY
- CLARA
- ACCESS
- PARTITION
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- CRAWLED
- VEGETATION
- DRIFT
- PANEL
- EQUIPMENT
- WITHDRAWN
- CATS
- SOUNDING
- RELEASED
- SPANIARDS
- WEARIED
- PROCLAIMED
- BEAUTIES
- ATTENTIONS
- TOAST
- REFERRED
- REWARDED
- ELDERLY
- ABNORMAL
- PERVERSE
- SMOOTHLY
- MISTAKES
- BEFOREHAND
- WITNESSES
- BODILY
- ENERGIES
- POSSUM
- SCARE
- RECOGNISE
- SCRAMBLED
- MAGNIFICENCE
- PARTIALLY
- LOVELINESS
- IMPELLED
- NOISY
- SEASONS
- INSOLENT
- SIMPLICITY
- DU
- TEARING
- HAPPENING
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- FLAMING
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- NOWHERE
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- LAPSE
- MISTOOK
- ALOFT
- ELEVATION
- PARTING
- DISAPPEAR
- EVILS
- DARKENED
- UTTERANCE
- DIES
- ABODE
- DELAWARES
- LANGUAGES
- SUBJECTED
- MUSING
- WRINKLED
- IMPOSING
- HUM
- SPLENDOR
- MAC
- CURLED
- EARN
- MUSED
- LITERARY
- SWEETNESS
- PERCHED
- EYEBROWS
- EXAGGERATED
- THURSDAY
- UNLOCKED
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- RAILING
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- DAMASCUS
- USAGE
- DECLARING
- WROUGHT
- CRUELLY
- GRACEFULLY
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- TUT
- INSECTS
- SCAMPERED
- CARDINAL
- HARDEST
- HOPPED
- GRAPE
- STEALING
- ACCUSE
- PEOPLES
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- RANDOM
- APPEARANCES
- TOLERABLY
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- HALT
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- EXCEPTING
- SULLEN
- UPWARDS
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- CHANNELS
- WIGWAM
- DETAINED
- CONSTITUTE
- VACANT
- BUD
- ATTEMPTING
- SUNG
- ATTACKING
- WHISTLING
- STATELY
- SEEDS
- RESULTANT
- HATCH
- PA
- GAUNT
- PHOTOGRAPH
- TOOTH
- BANISHED
- UPSET
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- ABBEY
- PUBLICATION
- PETITION
- DETECTED
- REFRAIN
- TERRORS
- PROMOTE
- GARDENER
- PLANTATION
- SAMSON
- SKULL
- CUTTER
- AUDIBLE
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- BREADTH
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- SUBSTANTIAL
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- REMOVAL
- LUCAS
- TORCH
- CONTINUES
- CUB
- GEORGIA
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- HEIGHTENED
- FEDERAL
- OWNERS
- WEDNESDAY
- CHATTERING
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- OXEN
- BREECHES
- ENTREATIES
- REJOICED
- KNELT
- TREVILLE
- CHILDISH
- STEALTHILY
- CONVEY
- RESOLUTIONS
- FLINT
- MECHANICAL
- SWING
- OUTFIT
- LEWIS
- PRODUCTION
- YOKE
- DAMNED
- GRAMMAR
- SPY
- GENSERIC
- SENATE
- IMPERIAL
- UNDERWATER
- NAUTILUS'S
- PROCEEDS
- VIRGIN
- ESSENCE
- CHEAP
- GRATIFICATION
- SKI
- TROUBLESOME
- ONESELF
- MEASURED
- CULTIVATION
- VENZA
- CURLS
- MARQUIS
- DERONDA
- SUMMER'S
- CAB
- GLARE
- CREVICE
- CANYON
- FRENCHMEN
- LAMB
- STUDENT
- BLINDED
- TRANQUILLITY
- KINGDOMS
- SUPPOSITION
- KNEELING
- EXPEDIENT
- PENNSYLVANIA
- CHAMBERS
- INSOLENCE
- SELECT
- ARTERY
- ROSTOV
- MARY'S
- PROJECT
- RESIGNATION
- SPEEDY
- DECKS
- PRODUCTS
- DISTRIBUTION
- TANGLED
- COMMISSIONER
- LAMENTED
- FULFILLED
- MANHOOD
- VILLONA
- DOYLE
- BRIGHAM
- FUEL
- INVESTIGATION
- MAIDENS
- MAXWELL
- PACKET
- GUB
- FIRS
- CHANCELLOR
- SHASTA
- PHILIP'S
- FUNDEVOGEL
- JEFF'S
- INSURRECTION
- CRANES
- COULSON
- CARAVAN
- POSTMAN
- LOCH
- INVENTOR
- HENSHAW
- VERONICA
- DIETRICH
- SHALMANESER
- ASSYRIAN
- BECHAMEL
- SOUSSIO
- MINKS
- HEADLONG
- AWED
- RACHEL'S
- BEES
- GRASSY
- WILLOWS
- DIRT
- DISHES
- PRESERVES
- BRISKLY
- SETS
- TICKET
- SHABBY
- BRUSHED
- EXCUSED
- EXECUTIONER
- ASSURANCE
- OCCUPYING
- ELOQUENCE
- POLITENESS
- WOVEN
- INQUIRING
- HUDDLED
- STERNLY
- BUTLER
- FALTERED
- DISLIKED
- ORNAMENTED
- ARBITRARY
- FOOTING
- INVALID
- WARRANT
- VISIONS
- SHILLING
- WARBURTON
- CORRESPONDING
- PROPOSALS
- REPARATION
- AMSTERDAM
- ECONOMY
- GENERALS
- JOINT
- PUNISHED
- PATRIOT
- INSPIRING
- ALLY
- TWELFTH
- FANTASTIC
- TREATY
- FEAT
- SECRECY
- SECURING
- REMONSTRANCE
- ACCEPTANCE
- GUARANTEE
- ATTRIBUTES
- COMPOSE
- MOAN
- TOPIC
- DISTANCES
- RICHER
- CREED
- DISCUSS
- DRAWERS
- COPIES
- ECCENTRIC
- CLUMSY
- CULTIVATED
- TOUGH
- PRAISES
- SOMBRE
- REINS
- UNLIKE
- CONFIDED
- INDICATION
- DIVIDE
- FLOORS
- HANGS
- REEDS
- TOES
- AWHILE
- INABILITY
- IMPRESS
- LOUNGE
- PHYSICALLY
- REFRESHMENT
- COMIC
- ARTISTS
- POETIC
- MATURITY
- ADJUSTMENT
- IMPOSSIBILITY
- COURTS
- EVE
- NORTHWARD
- BLANKETS
- GRAHAM'S
- CONVENIENCE
- CHALLENGE
- RAW
- YEAR'S
- INTERPOSED
- PENSIVE
- TWIGS
- ACCUSATION
- IMPRISONMENT
- EDGES
- RHEUMATISM
- JELLY
- TIPS
- D
- SHEETS
- MERITS
- PLANT
- LUSTRE
- ALIGHTED
- SIGHS
- F
- N
- GRIEVED
- ABOMINABLE
- FESTIVAL
- MALICE
- ALMIGHTY
- PERSIAN
- PENETRATE
- SWEAT
- DESERVED
- VIRTUOUS
- UNJUST
- PENSION
- COMMIT
- CREEPING
- SITE
- BLAUSSER
- LL
- SEMI
- MASSACHUSETTS
- WISELY
- LAVA
- NATURE'S
- GRUMBLED
- DIG
- DESIGNED
- TRIALS
- RECEIPT
- PERSPIRATION
- RECEIVER
- PREFERENCE
- CORRUPT
- IMPRISONED
- LIGHTER
- COMPASS
- EXPENSES
- ANKLE
- ECHOES
- QUIETED
- CROUCHED
- TUBE
- WHIRLING
- PENETRATING
- NOBLES
- CEREMONIES
- PROPORTIONS
- ARDENT
- MESSAGES
- CORDIALLY
- LOYALTY
- PSYCHOTHERAPEUTIC
- DEPRIVE
- CRITICS
- STRUGGLES
- TYPICAL
- SUPPRESS
- PROBABILITY
- REFORM
- OL
- LANGUID
- INTENSELY
- QUIVERING
- RICHLY
- GARMENT
- INDISTINCT
- RESOLUTE
- HABITUAL
- CONJECTURE
- GREEDY
- APPROVAL
- INTOLERABLE
- LEND
- OMINOUS
- DANCERS
- CLUTCHED
- NIGH
- SHUTTING
- PLUNDER
- TENDERLY
- CURVE
- SCREEN
- TEMPERED
- INDEFINITE
- CRUST
- HINTED
- FRUITS
- HUMBLY
- HURON
- CAPTIVE
- BLANKET
- PRIVACY
- DELAWARE
- DEVOURED
- INHERITED
- MARGIN
- PATENT
- CORRECTED
- OAKS
- SLIPPERS
- ASCRIBED
- ROCKING
- WASHING
- PROFITS
- CUSTOMERS
- TUCKED
- MORTALS
- TOM'S
- IMPROVE
- LADD
- SHIRTS
- GLOWED
- CONVEYED
- GLEE
- LID
- SATIN
- BELIEVERS
- COMPLAIN
- CORDS
- INSENSIBLE
- ALLIED
- COLOSSAL
- NUT
- PRETENSIONS
- CORPSES
- SPIED
- ERRORS
- FURNACE
- SHAVE
- DEVILS
- WEB
- ACCORDANCE
- DISCOVERING
- WORLD'S
- ACCOMPANYING
- TENSION
- E
- RAPIDITY
- FEEDING
- JEMMY
- WHITENESS
- SCRAP
- BURY
- WARFARE
- BATTERY
- SWAYED
- RAPTURE
- HEART'S
- LOVELIEST
- CRESTY
- WEE
- SHRIEKED
- KICKED
- TOMMY
- LONGBILL
- SPOTTED
- FRANCISCO
- SALMON
- ASHORE
- CONSTRUCTED
- SIGNIFICANCE
- ASIA
- SOLOMON
- DISPLEASED
- SAFER
- CROWNS
- CREST
- HOSS
- SHAPELESS
- ASCENT
- FIEND
- BENEVOLENT
- NORTHANGER
- MODERATE
- VOLUNTARY
- CONTRADICT
- DIRECTING
- MELODY
- SHAWL
- FRIGHT
- BRUTALITY
- DESPAIRING
- ARAB
- DESCENDING
- CARGO
- MOANING
- STARE
- SOOTHING
- RESENTED
- KEENLY
- JACKSON
- WHISPERING
- HENCEFORTH
- DARKER
- ILLUSTRIOUS
- COMBATANTS
- TAX
- TROOPER
- DESTROYING
- REBEL
- DOST
- INFECTION
- GROVES
- STARVATION
- COMMUNITIES
- JEFFERSON
- SHREWD
- HIGHWAY
- PRETENCE
- TREACHERY
- DAGGER
- STROVE
- CORPS
- EXCELLENCY
- BUCKINGHAM
- SEALED
- PLANK
- MECHANICALLY
- RUSK
- WILDLY
- SHADED
- LOWERED
- PHENOMENON
- WHIRL
- RAILWAY
- POSITIONS
- MIRRORS
- BAGS
- INVASION
- WAGONS
- POPE
- FLEE
- TIGERS
- TANKS
- MOCKERY
- SACK
- CIRCULATION
- SPECTACLES
- CONTRIBUTED
- STEVENTON
- APPROPRIATED
- CONVICT
- SHOVED
- CURSED
- HARDSHIPS
- JORDAN
- FUGITIVES
- REFRESHING
- REPUBLICAN
- APPROBATION
- DESPISED
- SAINTS
- DISASTER
- SERPENT
- IRWINE
- THEY'D
- DIVORCE
- RHYMES
- PRINTING
- EDITOR
- LUNCHEON
- CAVITY
- DECREE
- SITUATIONS
- BANDS
- RUBBISH
- SPIDER
- AVAIL
- CONTAINS
- APE
- BLOODY
- DEJAH
- THORIS
- HARROW
- WINCHESTER
- SERMON
- DAM
- NOME
- JOURNAL
- CUBA
- BUSHY
- MALEAGANS
- WILT
- SLAY
- HORACE
- MING
- DARKENING
- WADED
- SWITCH
- FARLEY
- HO
- COMSTOCK
- INLAND
- FINN
- CHURCHILL
- OVEREND
- CAREY
- ORPHEUS
- ENGAGEMENTS
- SANCH
- BULLFROG
- LINA
- COLONY
- JUNIORS
- DISTORTED
- ABOLITION
- USHANT
- TWENTYMAN
- REMSEN
- HOMO
- LYRE
- GIDEON
- ASHUR
- PITT
- BOOKSTALL
- GROCER
- YORKE
- PATRICIUS
- GETTYSBURG
- DIZZY
- TWITCHED
- THERE'LL
- ADJUSTED
- AMAZING
- RISKS
- AGONIES
- FIR
- COPE
- MOODS
- EXPRESSIVE
- ELBOWS
- OVERBOARD
- PRETTIEST
- TIMIDLY
- BANKER
- TACIT
- RECOLLECTED
- BASKETS
- PROSPEROUS
- ASSASSIN
- PROCUREUR
- PRECAUTION
- SINCERELY
- CRIMINALS
- BENEDETTO
- HEAVEN'S
- CURLY
- MICHAEL
- IMITATE
- TRAGIC
- STACKPOLE
- TACT
- SERENELY
- FLICKERING
- ALBANY
- TREAD
- PATIENTS
- HUMILIATION
- HUMILITY
- DEFINITELY
- INSIGHT
- VETERAN
- BINDING
- FISHERIES
- BOTTOMS
- PENSIONARY
- GOVERNMENTS
- INDIES
- BLAKE
- ADMIRALTY
- PORTLAND
- STATEMENTS
- INSISTING
- DOCUMENT
- EXPECTATIONS
- DEALING
- CRUMBS
- PINT
- MULTITUDES
- INSTINCTS
- CLOVER
- RELATIONSHIP
- TYRANT
- FRED
- GROWLED
- BRICK
- HILLSIDE
- MUTTERING
- SNEERED
- FOUNTAINS
- NEGLECT
- IRRITATED
- QUICKENED
- PHAETON
- WHEELED
- SPECIMEN
- LOWERING
- TOUCHES
- BELLAH
- IMPLORED
- NECKS
- BEGGARS
- DAZZLED
- BECKONED
- DAIRY
- FEATHER
- JEGU
- BEAMING
- GAINING
- PICTURED
- SOLICITUDE
- EXERT
- FUNDAMENTAL
- HANDLED
- GAIT
- RECEIVES
- BAT
- DOINGS
- LITERALLY
- DISAPPEARANCE
- FUNDS
- COURTYARD
- WEARILY
- AUTOMOBILE
- FOREIGNER
- CHART
- EVENTUALLY
- VISITING
- UNNATURAL
- LEISURELY
- RETAINED
- CLERGYMAN
- GLEAMED
- GOVERN
- COUGH
- HANDY
- POPULARITY
- LAMENT
- QUITTING
- CONVERSING
- JUSTLY
- AVERSION
- PRETENDING
- DEFERENCE
- OPPOSE
- GREECE
- PRESCRIBED
- VIZIER
- KENNICOTT
- NAIL
- LAKES
- LUXURIES
- DICTATED
- LEAFY
- RECITAL
- CURTAINS
- DRIPPING
- THANKFUL
- DAWNED
- CHATEAU
- AFFLICTION
- SCRAPS
- BAH
- ACUTE
- OUTLINED
- GHASTLY
- MENACE
- DELIGHTS
- TACKLE
- STORED
- SUFFICED
- ADVANCES
- CLASPING
- HEARS
- RUGGED
- SILKS
- CONCEPTIONS
- ENGINES
- ELECTRICITY
- CONTRIBUTE
- CONCEALMENT
- SENTRY
- SQUEEZED
- SPLIT
- SURVEY
- CLUMP
- SPARKS
- ANCHOR
- TOSS
- RUSTY
- JOINING
- PLEDGE
- ENFORCED
- RESIGNED
- ROYALTY
- QUITTED
- MYSTIC
- FLUID
- OBJECTIONS
- INCLUDE
- CRAWLING
- REASONABLY
- AWAKEN
- PILGRIMAGE
- DAZZLING
- ARCHWAY
- FUTILE
- HARDENED
- EXPOSING
- SPECULATION
- OPPONENTS
- EXALTATION
- FAIN
- INGENUITY
- PRODIGIOUS
- NICELY
- MISUNDERSTOOD
- GUARDIAN
- APPEARING
- OVERHEARD
- DISSATISFIED
- BEDSIDE
- EARNESTNESS
- ATTRIBUTED
- HYPOTHESIS
- TERRESTRIAL
- PROVES
- GRAVITATION
- COOLLY
- SLEEPY
- BISCUIT
- PROVOKED
- PRESERVING
- ENTERTAIN
- SEVERITY
- FOE
- TERMED
- SUBSIDED
- RIVERBORO
- SIMPSON
- SHAN'T
- UNBOUNDED
- CONSCIENTIOUS
- CUSTOMER
- PRICES
- ARGUE
- IT'LL
- BEGINNINGS
- SLATE
- RASH
- INSTRUCTED
- FURS
- ADAM'S
- DISGRACEFUL
- FLUTTER
- ERRANT
- STREWN
- EMBRACING
- INTERRUPTING
- REVIVE
- AFRESH
- DESIRING
- ROSEBREAST
- PROJECTED
- BONY
- PORTRAITS
- FRENCHMAN
- BALANCED
- MARINE
- PLUCK
- CONCURRENCE
- TRUNKS
- SHALLOW
- NATURES
- DENIAL
- VOLUNTARILY
- UTILITY
- LUDICROUS
- TEMPT
- ALTERNATELY
- VALLEYS
- DISASTROUS
- VEILED
- RULING
- ROBBERS
- TALENTS
- O'ER
- ELMS
- DISCERN
- SEAMED
- SEVENTEENTH
- SCANTY
- VARYING
- WRECK
- BEE
- PRAISED
- FERNS
- SEPARATING
- STUFFED
- CHASED
- SLAP
- MOVES
- COOLNESS
- SWELLING
- ISOLATED
- STIFFLY
- SPLENDOUR
- TEND
- SNATCHED
- HOPELESSLY
- TUMBLE
- RAINBOW
- RUSTLING
- SHIFT
- CATHERINE'S
- DESIROUS
- ALACRITY
- ADVANTAGEOUS
- PRONOUNCE
- DECLINE
- ACTORS
- INCESSANTLY
- ANTONIA
- BATTERED
- STRIPED
- SHADES
- FASCINATION
- MOUSTACHE
- STREAMED
- POPULOUS
- BOWS
- SLID
- PALM
- BOYISH
- GLAMOUR
- CARTRIDGE
- COINS
- STRODE
- DINNERS
- PAT
- INDIGNANT
- FEARFULLY
- ELEGANCE
- ODDS
- CONQUER
- PROPOSE
- LANCE
- BRACELETS
- COWARDLY
- EXCESSIVE
- NEPHEW
- SUPERIORITY
- AZURE
- INHERITANCE
- SCHEMES
- BRETHREN
- REVERSE
- MATCHED
- ARISTOCRACY
- RENTS
- RECORDED
- MA'AMSELLE
- RESTAURANT
- POLITICIANS
- OPERA
- FINANCIAL
- FUNCTIONS
- SPORTING
- COMMENTED
- DECORATED
- MORN
- SMOTE
- VILE
- PLEASES
- OATHS
- ESCAPING
- BENEFACTOR
- DROPS
- GUARDSMEN
- INTOXICATION
- PISTOLS
- INTERPRETED
- CONFIDENTIAL
- FIDDLE
- REGAINED
- GASPING
- LIKENESS
- COACH
- DAME
- LEE
- BARLEY
- QUAINT
- CRASH
- DISCOVERIES
- BEHAVIOR
- JEROME
- ASSOCIATE
- EFFICIENCY
- FILM
- PENNY
- GILBERT
- IMPROVEMENTS
- EXCEEDING
- EMPRESS
- TEMPLES
- LARGEST
- WRITERS
- COFFIN
- FISHERMEN
- ARRIVING
- SHARKS
- WORTHLESS
- BELLY
- QUANTITIES
- WILLARD
- INTERNATIONAL
- SECTIONS
- INTERFERED
- ADDER
- BOUNDARY
- ARCHITECTURE
- REQUISITE
- RIVALRY
- IGNORED
- ANITA
- STRIPPED
- INTERPRETATION
- FLORA
- SHAW'S
- GLADNESS
- COTTAGERS
- MURDERERS
- RATTLE
- SNUFF
- INK
- DISPOSE
- DUCK
- AMMUNITION
- IDENTICAL
- MUSEUM
- SPUR
- FORTS
- ZONE
- STUNNED
- BLACKNESS
- ARTHUR'S
- WORRYING
- EMANCIPATION
- MISCHIEVOUS
- HEIGHTS
- HUNTERS
- ARTERIES
- MUSO
- LAWTON
- DUNWOODIE
- INTERVENTION
- GAYETY
- GRACES
- TERESA
- OX
- SENTIMENTS
- APPARITION
- EXCEED
- SHEVARDINO
- SINGER
- SHOWER
- MINNOW
- DISCHARGE
- DUCKS
- PICKETING
- PHILADELPHIA
- CELLS
- SUPERB
- COLONISTS
- SLEW
- TEXT
- STEPMOTHER
- MOUNTAINEER
- LATENT
- DILWORTHY
- BUFFALOES
- REGIME
- SYBIL
- FLIGHTS
- MORLEY
- ANNA
- BOURGEOIS
- BELVANE
- GOWER
- THROCKMORTON
- HORNBY
- SPRUCE
- SCIENTIST
- CONSUMPTION
- LOS
- ETERNITY
- HOVEL
- CUBAN
- BOULEVARD
- WEYMOUTH
- BOB
- ADELA
- KINRAID
- FAUCHELEVENT
- JESSICA
- CHUMS
- GARTER
- MENSTRUATION
- MENSTRUAL
- MENSTRUATING
- PHYSIC
- EAMES
- O'TOOLE
- MORGESON
- JOST
- GERTRUDE
- CLANCHARLIE
- WITHAN
- AGATHA
- BACH
- HAYNES
- FOX'S
- PEERS
- ADDERS
- AUGUSTA
- BROWNRIGG
- INTRICATE
- CARMODY
- STRAY
- PAINFULLY
- MELLOW
- ANGLES
- TELEGRAM
- SANDS
- COMFORTING
- OFFEND
- ACCOMPLISHMENT
- SHRINK
- REFLECTING
- SNUG
- PREPARATORY
- BRISK
- WHISTLED
- INFANCY
- BLISS
- CHEERED
- IMAGINATIONS
- MOMENTARILY
- IMPLY
- MERCILESS
- DISGRACED
- INDULGENCE
- INFECTED
- FOOTMAN
- PRECAUTIONS
- ASCENDED
- STRIKES
- DRAGOON
- STAIR
- FROCK
- THRILLING
- BEARDED
- SWEETHEART
- WOKE
- BAFFLED
- GLITTER
- SOWN
- PASSIVE
- DRUM
- HARBOUR
- DEALT
- CASUAL
- FOREMOST
- OFFENSIVE
- GRIEVANCES
- MISUNDERSTANDING
- NIGHTFALL
- UNIVERSALLY
- JUNCTION
- EXPERT
- MEDITERRANEAN
- CHAPTERS
- TEMPERAMENT
- BOLT
- CLERKS
- PERCH
- SIMMER
- PINCH
- BAKED
- SALAD
- SENIORS
- BEAMS
- DISFIGURED
- NOURISHMENT
- MEETS
- ABJECT
- RENDERING
- ENVELOPE
- JUSTIFY
- SHRUGGED
- KARA'S
- SURVEYED
- SECURELY
- OVERCOAT
- DOUBTFULLY
- DELIBERATION
- OCCUPANT
- DEL
- DAMN
- PROFOUNDLY
- PINS
- INFLICTED
- TOLERABLE
- HABITUALLY
- BORED
- GLIMPSES
- PIGS
- HEARTH
- CABBAGE
- BARBAIK
- HORSE'S
- TENDS
- FLEETING
- IMPLIED
- DISADVANTAGES
- RESTS
- PETTY
- MENTALLY
- BREED
- WIDER
- PENETRATED
- RESPONSIBILITIES
- CONCENTRATED
- ENDURING
- BUCK
- ADMINISTERED
- SENIOR
- BICYCLE
- BIRTHDAY
- WHEREBY
- BOWER
- HEREAFTER
- ULTIMATE
- SLUMBER
- ASCERTAINED
- CONVERSED
- RETAIN
- PEERED
- CONSPICUOUS
- BATHE
- ROBINSON
- DARESAY
- CONQUERED
- WEAVING
- DAZED
- SCRATCHED
- COLONIAL
- RAG
- FOREFINGER
- TOKEN
- RUB
- SAMPLE
- EXCELLENCE
- STOPS
- RETIRING
- TOILET
- RECKLESS
- RELATING
- CARESSES
- APPREHENDED
- SURPASSED
- SOLELY
- CONVERSE
- SACRIFICED
- SIGNIFY
- PLUNGE
- STALL
- MAJESTY'S
- VILLAIN
- BOLDNESS
- WIPE
- WINDY
- NURSING
- REARED
- ELEPHANT
- GOAT
- REFINEMENT
- INTEGRITY
- GINGERBREAD
- CAUTIOUS
- INSTITUTION
- ESTEEMED
- JERSEY
- NEGROES
- SETTLEMENT
- SLANTING
- RIDICULE
- UNLIMITED
- STUDDED
- BANG
- GRAB
- DECIDING
- IMMORTALITY
- DOME
- STARTS
- CHICKENS
- RELATIVELY
- GRADUATED
- IDENTITY
- TRUTHS
- MENACING
- LUMINOUS
- PLAYERS
- OBLIGE
- EXPLAINING
- AUNTS
- COSTUME
- ILLUSTRATION
- WHISKEY
- CURES
- GAINS
- FREED
- WARMER
- VAGUELY
- REALITIES
- PERCEPTION
- RIDDLE
- PANE
- PRICKED
- HAZE
- MERRIMENT
- ARDOR
- ETERNALLY
- FINELY
- FRESHNESS
- PATHETIC
- CLEARNESS
- PLAUSIBLE
- CONSISTENT
- RIGHTEOUSNESS
- BUSILY
- STROLL
- HARDY
- STROKED
- POSSESSIONS
- SHUDDER
- STROKING
- BRILLIANCY
- UNANIMOUSLY
- MANTLE
- WANDERINGS
- RADICAL
- PRINCESS'S
- ROWED
- PANGLOSS
- PRUDENT
- CAPTIVITY
- PLOT
- HOVERED
- ENDEAVORING
- BREEDING
- CHIEFS
- ANNOUNCE
- FORTIFIED
- WHITES
- KNIVES
- SCENTED
- FLATTERING
- FORESEE
- POWERFULLY
- LABORS
- CONSULTING
- PURSUITS
- CEDAR
- THANKSGIVING
- MEALS
- BARNS
- SUSAN
- TROTTED
- FITTING
- ASTONISHING
- BOUT
- DRIVES
- MORTIFICATION
- TURNIPS
- FLASHES
- YALE
- GASP
- INVADED
- JOURNEYS
- DEVOTE
- TYRANNY
- THIRDS
- ATTENTIVELY
- RECOVERING
- PROVISION
- WINKED
- MEEKLY
- CRAWL
- CIRCUIT
- TAVERN
- BLAMED
- SWITZERLAND
- EXPOSITION
- MECHANISM
- MORALLY
- ENDOWED
- MORALS
- HAPPIEST
- EXPRESSLY
- REPULSIVE
- COLDNESS
- MODES
- HONESTY
- GILDED
- ANCHORED
- CONSISTS
- WESTWARD
- NOTIONS
- DISTRESSED
- SINGULARLY
- FREIGHT
- RUSHES
- SLICES
- BELIEVES
- ONWARDS
- SENSELESS
- ASSAULT
- SPACES
- JO
- BULLY
- ROAMING
- OFTENER
- BARRIERS
- ALASKA
- DIAMETER
- BEAK
- GALLERIES
- OVERTOOK
- MUSKRAT
- WINGED
- TROT
- JAW
- THREATS
- AIRS
- POMP
- ANIMATION
- WARD
- IDLENESS
- ENDEAVOUR
- PITIED
- ALLEN'S
- BLUSHED
- EXPOSE
- COMMUNION
- DESIGNATED
- SUNKEN
- WHIPPED
- FISTS
- APPALLING
- AFT
- RIM
- JUNGLE
- STARBOARD
- DELIBERATE
- ILLUSIONS
- EXALTED
- ANOTHER'S
- EEL
- GAPING
- INTERRUPT
- SCRIPTURES
- CARLISLE
- DISGUISED
- KISSING
- POETS
- AUDACITY
- LODGINGS
- STROKES
- RECOURSE
- CONCEALING
- AMENDMENT
- KATY
- CANST
- FLOURISH
- HABITATION
- SINGLY
- LOSSES
- POSSESSOR
- BUTCHER
- OVERCAME
- OCCURRENCE
- CONTEMPLATION
- SIMILARLY
- INSECT
- TANNER
- GRIPPED
- KNAVE
- SAUCY
- THYSELF
- UNAWARE
- SIGHING
- TRAILING
- ENCOURAGING
- FIREPLACE
- RESUME
- LOUIS
- GLITTERED
- CONQUEROR
- STAG
- DISPUTE
- NOBLEST
- WONDROUS
- SHADOWY
- BRANDY
- VAULT
- DEJECTION
- VIGILANCE
- COCKE
- VIGOROUSLY
- ALFRED
- CONTEMPLATE
- SECONDLY
- HUSKY
- MASTS
- WHARF
- LORD'S
- URGING
- COLUMBUS
- PACED
- STAGES
- NERVE
- FOUNDATIONS
- BELISARIUS
- CHRISTIANITY
- ENCLOSURE
- DIVERS
- EXTRACT
- SCALES
- WOMEN'S
- BUYING
- ACHED
- STRIFE
- IMPROPER
- HANS
- PAIRS
- INSPECTION
- MAKERS
- SPECK
- VIVIDLY
- TOUR
- SLIDING
- IRONY
- FARTHEST
- CARLING'S
- OB
- DEAN
- CONTEMPORARIES
- APPROVE
- RATIONAL
- RESCUED
- DISTURBING
- ENDING
- PROSPECTS
- MURDEROUS
- BONNET
- SUNBEAMS
- EXCUSES
- OMITTED
- YOSEMITE
- TENAYA
- WESTMINSTER
- BUSIED
- BRIDE'S
- REFORMATION
- SUBURBS
- CELEBRATE
- CAMPS
- COIN
- HANDING
- RANCH
- PLAZA
- FOSSIL
- SHIP'S
- HEADING
- RACING
- BACON
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- TOADS
- REMORSE
- HANDWRITING
- BUTCHER'S
- ABRUPT
- SUMMITS
- IMPENETRABLE
- PLUNGING
- SHEPHERDESS
- DIMLY
- YELLED
- MODESTY
- HUNGARIAN
- ROOSEVELT
- DESOLATION
- CONVERTED
- YELL
- RITES
- JAGGED
- ARUJI
- SITGREAVES
- SENOR
- HENS
- MUTINEERS
- MUSKET
- CAMBRIDGE
- POORER
- SHAVING
- BLAST
- KITTY
- APPROACHES
- PECULIARITIES
- PREJUDICE
- MANLY
- OPERATE
- PLASMOID
- OFFENSE
- VACATION
- GARRISON
- CAPRICE
- KAY
- MARVEL
- MAPS
- RUTH'S
- TONGUES
- SEGOUIN
- COLLECTOR
- DOCUMENTS
- SHAMEFUL
- PREACHER
- ACTOR
- WADMAN
- AVAILABLE
- PHINEAS
- RAYMOND
- CHURCHWARDEN
- TRENCHES
- RAWLE
- ALEXEY
- ALEXANDROVITCH
- COLCHIS
- BEAM
- PATSY'S
- HUMOROUS
- DADDY
- CLAIR
- CONGREGATION
- GANGWAY
- FUSS
- GODMOTHER
- BARRICADES
- INSURGENTS
- MOLIERE
- ROYLAKE
- THOR
- SYLVIE
- MASKERS
- CHELTENHAM
- TILBURY
- AYRTON
- NATSIR
- BANDITS
- COONSKIN
- LYNDE'S
- NOTABLE
- KNITTED
- TURNIP
- BUGGY
- IRISHMAN
- UNUSED
- SCOTIA
- EXPLANATIONS
- MOUTHFUL
- AMOROUS
- SCANDAL
- EUGENIE
- INSURE
- ACHING
- MUSE
- SHUDDERED
- PIERCING
- WICKEDNESS
- PASSIONATELY
- NESTS
- TOYS
- PAN
- OBLIGATION
- BOARDING
- FAITHLESS
- GOODWOOD
- RIBBONS
- HARMONIOUS
- MAINLY
- ENTERTAINING
- DYED
- INQUISITIVE
- FASTEN
- LONGEST
- ODIOUS
- DIET
- MATTERED
- SAILS
- MAZE
- OBLIGATIONS
- LIVID
- PRONE
- FLATTERED
- LESSER
- CONSTERNATION
- DEMANDING
- SALUTE
- OUTBREAK
- DOVER
- HUMILIATING
- FORCING
- SUCCESSES
- CONDUCTING
- NUMBERED
- AMBIGUOUS
- AFFIRM
- SESSION
- PRINCESSES
- DULY
- ASSENTED
- CHOPPED
- JUICE
- JOYS
- MATURE
- BETRAYING
- LOVE'S
- LIFE'S
- TRIVIAL
- AMBER
- RELISH
- CONSUMED
- REMNANTS
- INSERTED
- PRESUME
- PRETEXT
- BLEAK
- DILIGENCE
- SALARY
- APPEALING
- BUREAU
- LATCH
- FRAMEWORK
- ACCIDENTALLY
- RELUCTANTLY
- ADVISABLE
- DISAPPEARING
- ANNIVERSARY
- GALLOWS
- DANGLING
- GREEKS
- CONFERRED
- SCORCHED
- PEAR
- SURVIVE
- REMNANT
- EDIFICE
- HONOURS
- LANES
- NEEDLES
- TENDED
- HOSPITABLE
- DELAYED
- INDICATING
- RINGS
- BESOUGHT
- OBSTINACY
- ENVIED
- SPOILT
- LO
- COALS
- LASTING
- CENTERED
- WILLINGNESS
- SATISFYING
- STITCH
- EXPOSURE
- CUTS
- POSSESSING
- SMELLS
- BULK
- SYSTEMATIC
- TRACT
- EXPLORED
- MIRACLES
- VANISHING
- ENMITY
- DILEMMA
- SHARPER
- ALARMING
- UNSCRUPULOUS
- CONTROLLED
- FETCHED
- LESSENED
- DRAWS
- PEBBLE
- BANKERS
- BOWELS
- DISEASES
- TOE
- DOSE
- NOISES
- TISSUE
- ANNOYANCE
- PROMOTED
- STARVE
- DIDST
- SULTAN
- SCHEHERAZADE
- RELY
- BEFALLEN
- CREATOR
- CONFIDE
- REVEAL
- TRAITOR
- DOMINIONS
- REPENT
- CONTRADICTION
- FRONTIER
- MOUND
- PAW
- ALIEN
- RICHEST
- EXPANSE
- DES
- POSTS
- WOODED
- BASS
- FAVORABLY
- NECESSITIES
- LOGICAL
- ROUTINE
- SPACIOUS
- CONVERSATIONS
- BOASTED
- STOCKS
- DEPRIVED
- PIOUS
- RELIGIONS
- GENEROUSLY
- SLEEPS
- PAVED
- FESTIVITIES
- PHILOSOPHERS
- CREDITED
- CONVENT
- EDGED
- SHRIEKS
- TRANSFORMED
- SUICIDE
- MATRON
- DIALOGUE
- ROSALIND
- TEASING
- COMPETITION
- OCCURS
- SHAFT
- ARGUING
- FROZE
- BRIGHTER
- LURKING
- DOTTED
- PRINCE'S
- ENLISTED
- THANKING
- FIFTEENTH
- FAVORED
- ARNWOOD
- PRINCELY
- DISCRETION
- ELOQUENT
- OPIUM
- ELDERS
- CREATING
- PSYCHOLOGIST
- FACTORS
- SUPPRESSING
- DISPOSAL
- WHITISH
- POPPED
- LIPPERTY
- RAT
- MUSCLE
- TRANSPARENT
- ORNAMENT
- BALCONY
- CONTROVERSY
- CURRENTS
- RESOURCE
- VENT
- RESTRAINT
- FROWNING
- ACCENTS
- COMMONPLACE
- PARALYZED
- IMPORT
- POSTED
- BENTLEY
- FARMHOUSE
- STABLES
- JOVE
- SOLVE
- CEASING
- CLING
- DIVING
- PARROT
- AFLOAT
- FEEBLY
- FRANTIC
- HORRIBLY
- PIASTRES
- PRE
- STABBED
- UNNOTICED
- WATCHFUL
- INWARDLY
- NEIGHBOR
- GIRDLE
- HEREDITARY
- PETTICOATS
- WIGWAMS
- SHIFTING
- OFFERINGS
- SPIES
- SIGNIFIED
- EXCEEDED
- SPORTS
- PRECEDED
- ISSUING
- ALTERATION
- TURKEYS
- SUNRISE
- PARENT
- BUFF
- GORGEOUS
- PETTICOAT
- TRIMMED
- ALADDIN
- THEME
- SMASH
- SITS
- PICKS
- GRUDGE
- SPLASH
- LOOSENED
- RECREATION
- SWARMED
- IRENE
- TENNIS
- CHORUS
- JOKES
- TRUDGED
- SENSIBLY
- DISTRIBUTED
- GRIEVOUS
- ENGAGING
- HASTENING
- PROCLAMATION
- REPAIRED
- VIEWING
- DISDAIN
- CLASSIC
- SCAMP
- CLAW
- ANCIENTS
- ARISES
- MINGLE
- BITTERNESS
- PURITAN
- STOREROOM
- CARNIVAL
- IMPERFECT
- ACQUISITION
- SHAKESPEARE
- SUPER
- BRAVERY
- ROASTED
- STALK
- STALKED
- WATER'S
- DEMONSTRATION
- UNCOMMON
- NOTORIOUS
- ROY
- DETERMINING
- DEGRADED
- ADORNED
- TINGE
- EXCURSION
- COMPACT
- TREACHEROUS
- SUCCEEDING
- FAVOURED
- DIMENSIONS
- SPRAY
- DEVOUR
- RAGED
- CHOOSING
- CONVENTIONAL
- INCESSANT
- HAROLD
- SCORNED
- WHIRLED
- HARP
- LEAPT
- AIRY
- TRIUMPHANTLY
- SIDEWAYS
- CHUCKLING
- PET
- DEVICES
- THORNY
- MATES
- WORMS
- REDTAIL
- DARTING
- HOOKS
- PEWEE
- MUDDY
- TEETER
- SETTLERS
- SCREECHER
- JOHNNIE
- LICK
- EGOTISM
- FRAGRANCE
- EMBODIED
- GODDESS
- STRAIGHTWAY
- NASH
- TENDERFOOT
- MIDDAY
- FAMILIARITY
- AUTOMATIC
- SIDEWALK
- CRUSHING
- CONGRATULATE
- ASS
- WIRES
- ENTRUSTED
- CONFINEMENT
- VAULTED
- EAGLES
- ADVERTISED
- PROOFS
- MONUMENT
- SKETCH
- FULLERTON
- IRRITATION
- BRIGHTENED
- PERSUASION
- GENERAL'S
- CONTINUANCE
- PERFORMANCES
- HENRY'S
- COSTLY
- LILY'S
- ACHIEVEMENTS
- OBLIGING
- EQUILIBRIUM
- SLEIGH
- KANSAS
- BOOM
- CHOKING
- RECESSES
- FEARLESS
- GLIDED
- TRUTHFUL
- CONFOUND
- HERBS
- MIX
- LIQUOR
- CHILLED
- MILBURGH
- DOCK
- MINIATURE
- FORGIVENESS
- UNIT
- WAIL
- THIEVES
- CHIVALRY
- MOONLIT
- DROOPING
- FLATTERY
- DREAMILY
- SKIRTS
- MAGICAL
- FLOURISHING
- CONCLUSIONS
- CONTRIVANCE
- SPRUNG
- FONDNESS
- DEPENDENCE
- GALLANTRY
- FORTIFY
- VIGOUR
- DISAPPOINTMENTS
- COMPLY
- PEYTON
- PROFUSION
- BIRCH
- BALMY
- FORSAKE
- COMMUNICATING
- LIMB
- DEGRADATION
- OCCUPATIONS
- REMEDIES
- SUPPRESSION
- OBSCURITY
- DIMINISHED
- RESORT
- AGRICULTURE
- VALANCOURT
- DREAMT
- ATTENDANCE
- IMPRUDENT
- ASSUMING
- INJURE
- BLAND
- THIEF
- LUSCINDA
- TRESSES
- DAMSEL
- SINCERITY
- SWOON
- AIDED
- DESIGNS
- HOWLING
- PEACEFULLY
- CAULDRON
- SCREAMED
- EMINENCE
- MUSKETEER
- GUARDSMAN
- COMPANIES
- HOSTESS
- GOVERNESS
- MELTING
- FINISHING
- SUPPORTING
- MANIFESTATIONS
- DESCRIPTIONS
- EARL'S
- BARRELS
- STILLED
- CEDRIC
- LUMP
- SPANIARD
- DICK'S
- LATITUDE
- CAPTAINS
- POSTURE
- PLUS
- DESTINATION
- SPECIALIST
- ALIGHT
- SPAN
- MARKHAM
- SOLEMNITY
- ATTILA
- VICTORIES
- COMPEL
- TRADER
- SUMS
- MECCA
- CLUSTER
- CEYLON
- WIDTH
- FEARSOME
- SNAILS
- VALVES
- YEARLY
- TIES
- MUNICIPAL
- ARDENTLY
- UNDO
- GEAR
- UNDERGO
- MANOR
- CASSANDRA
- MONTHLY
- SCRAMBLING
- INDICATES
- AISLE
- EXECUTE
- RELAXED
- DALE'S
- COMPOSURE
- UNEASILY
- CUR
- SALON
- WARLIKE
- PERFUME
- STATEROOM
- COMMOTION
- MURMURING
- INERT
- MATRIMONY
- NOVELS
- PERCEPTIBLE
- KICK
- MIDWAY
- ARISING
- PLACID
- ADVENT
- BRIGHTNESS
- SKIES
- HUE
- SHAFTS
- SHROUDED
- DART
- FROSTY
- CHOIR
- CANDY
- PARISHES
- ANDREW'S
- CARTS
- APPREHENSIONS
- VICES
- OVERWHELMING
- MURPHY
- TWIST
- AUSPICIOUS
- SHIELDS
- HEAPED
- STRATA
- NARWHALE
- FARRAGUT
- OVERFLOWING
- DECISIVE
- DENYING
- BALD
- SARCASTIC
- CHIMNEYS
- PROTESTANT
- LYNNE
- DILL
- FISSURE
- MORNING'S
- MYRIADS
- PRINTER
- SWEETLY
- CLAPPING
- SWAYING
- SALLOW
- SHRIEKING
- NOVELTY
- PLUTO
- K
- CHESHIRE
- TIN
- REVEREND
- ASSOCIATES
- JUDICIOUS
- SPECIFIC
- LIVERY
- DISPERSED
- FORBID
- HISTORIES
- PIGEON
- PILLAR
- SCIENCES
- TOWERING
- BUTTONS
- LEAGUE
- JARS
- JEDDAK
- COMAS
- BLOCKED
- LOAN
- SLICE
- CRUISE
- BLACKENED
- RESPECTING
- MEMOIR
- TITLES
- TUTOR
- SCHOOLFELLOWS
- RAZOR
- STUPOR
- INFLAMMATION
- REMEMBERS
- CONSTRUCTION
- CABINS
- PETERSBURG
- NAPOLEON'S
- PLAYFUL
- ACCENTED
- KISSES
- HURRICANE
- MUTILATED
- ASSYRIA
- LOCALITY
- DECEASED
- MANTELISH
- PAL
- OKLAHOMA
- FURTHERMORE
- BUCCANEER
- ASSERT
- DOUGLAS
- SWEEPS
- ACQUIRE
- RUFFIANS
- GOBLET
- DINSMORE
- DAD
- TOW
- DUBLIN
- FLASHING
- MASKED
- VICKERS
- SCOUNDREL
- SIMIAN
- POLITICIAN
- ACTUATED
- WOODHOUSE
- HIGHBURY
- CORNY
- PUTNEY
- HOSKINS
- ANTENNAE
- METER
- PEAK
- POKING
- BLOUNT
- TRUMPETS
- PHILLIPS
- PREJUDICES
- ANNE'S
- JOSHUA
- PLAYMATES
- PULPIT
- PUGWASH
- BEARERS
- MINISTRY
- SURVEYING
- BRAG
- MARSH
- L'OLONNOIS
- LICKED
- PROPOSITIONS
- STURDY
- CHILLY
- CLUCK
- STICKEEN
- TOLLER
- COSETTE'S
- MIRIAM
- CONSTITUTED
- EBONY
- LOWESTOFT
- HARMON
- SOU
- SURREY
- BAILEY
- BINNY
- YORITOMO
- ZEPPELIN
- PUBLICAN
- MACMURDO
- SEYFFERT
- WHITLOCK
- SIDNEY
- STRUBLE
- MON
- TED
- DIPPED
- SEWING
- UNSEEN
- BRIDAL
- HUMMED
- MYRIAD
- MEEK
- RETREATING
- BIDDEN
- EVERYDAY
- NOVA
- BURNT
- CRISP
- ROBERT
- EJACULATED
- JOGGED
- NOTING
- ORPHANS
- ORDEAL
- PLUMP
- WITCH
- PROCESSES
- CONTEMPLATING
- OCCURRENCES
- LOYAL
- SHUTTERS
- INSULTING
- CALMNESS
- IMPOSTOR
- READS
- DEPRESSED
- REPULSED
- PLAINTIVE
- UNTRUE
- UMBRELLA
- EMBARKED
- EASIEST
- LIBERTIES
- CORRESPONDENT
- BREACH
- MIDDLING
- STROLLING
- AUTHORS
- GENTLEMAN'S
- EXCEPTIONS
- KINDLED
- CONTEMPTIBLE
- IMPERFECTLY
- PRELIMINARY
- MERLE
- ENLIGHTENED
- PANG
- COMMISSIONERS
- BOUNDARIES
- ADHERENTS
- AGREEMENT
- MAINTENANCE
- SOVEREIGNTY
- AYSCUE
- FLEETS
- PROTESTS
- WITT
- COLONIES
- CONVOY
- NORTHERLY
- SUFFERER
- INTRIGUE
- SWORN
- UNAVAILING
- INFORMING
- ALTERNATIVE
- PATRIOTIC
- DIP
- VINEGAR
- CORNS
- ENTRY
- INFINITELY
- ANEW
- CLOWN
- X
- RACK
- BALANCING
- FAVOURS
- BEASTLY
- CHEQUE
- CHARITABLE
- INVESTIGATIONS
- SCREWED
- FROWN
- PILLOWS
- MATERIALLY
- HAIRS
- BOOMING
- WARRANTED
- MASTERED
- PARCHMENT
- OUTLOOK
- GRATIFYING
- REGRETS
- MIDSUMMER
- REGISTERED
- ILLUSTRATED
- ROWING
- ACCOMPLISHMENTS
- VIGIL
- ABOUNDED
- CORAL
- ENTREAT
- HATCHED
- OVERJOYED
- CAVES
- REGARDLESS
- OVERNIGHT
- BESET
- ISSUES
- LIFETIME
- ESSENTIALLY
- SELFISHNESS
- SKIRMISH
- HEADSTRONG
- WHINING
- TABOO
- RELIEVING
- MARKING
- DISSATISFACTION
- INSISTS
- DISHONEST
- STEER
- SAVAREEN
- UNACCOUNTABLE
- SHORTEST
- ADJACENT
- DIGGERS
- SILAS
- DIVERTED
- EXPLORE
- BURIAL
- CONGENIAL
- INFLUENCED
- MISUNDERSTAND
- REDDISH
- CIRCLING
- BECKONING
- AUTOMATICALLY
- ENTANGLED
- CANDLES
- POEMS
- PAIL
- DISCOMFORT
- NEEDLESS
- WAXED
- DATES
- GROANS
- DEMONSTRATIONS
- EXPIRED
- FORTITUDE
- RESISTING
- CORD
- HEAL
- ACRE
- CUPS
- THREATEN
- ACHIEVE
- FAIREST
- INSTALLED
- MODELS
- RENOWNED
- ENDURANCE
- FLITTED
- EXPERTS
- SCORES
- EXCITEDLY
- FARMING
- SYSTEMS
- BRIAR
- SIGNATURE
- CONSOLED
- IMMEASURABLE
- BANKER'S
- GILT
- FUND
- SIGNATURES
- FREEZING
- INCREDULITY
- MA
- RESTRAINED
- TOWERS
- PINCHING
- COOLING
- STEAMBOAT
- BEATRICE
- WILDER
- HURTS
- SPARK
- INVESTIGATE
- IMAGINABLE
- FABRIC
- FEMALES
- TOLERATE
- SLIDE
- PERFECTED
- STATIONARY
- ELABORATE
- PRINCIPALLY
- CURVED
- PITS
- TERRIFYING
- RUSTLE
- THICKER
- BEWILDERMENT
- CONDE
- CALCULATE
- COUNTRYMEN
- CHALONER
- GRENVILLE
- HANDKERCHIEFS
- INDEBTED
- TIMIDITY
- TALLER
- RESIGN
- MOURNFULLY
- ARGUED
- HYPNOTIZATION
- HYPNOTIZER
- PAMPHLETS
- INSANE
- SUPERFICIAL
- SERVES
- STATUS
- HYPNOTIZE
- REMOVING
- SUCCESSIVE
- LABORATORY
- QUOTE
- DAYTIME
- ETHICAL
- STRENGTHEN
- OVERTHROW
- PERSISTENT
- SUPERSTITION
- THOROUGH
- ABSURDITY
- VARIETIES
- WINK
- SUBSEQUENTLY
- DROWNING
- GIDDY
- MATTRESS
- PILED
- GESTICULATING
- INCOMPLETE
- JOYOUSLY
- ENGROSSED
- FRENZY
- IMPRESSIVE
- ORDINARILY
- INDULGE
- UNCERTAINTY
- VICIOUS
- ELEVATED
- MULTIPLY
- CUSTOMS
- WEARS
- LINKS
- SUBJECTIVE
- STRESS
- ADOPT
- HESITATINGLY
- INLAID
- CLAPPED
- NETWORK
- INEXPLICABLE
- ORGANIZED
- EXTINCT
- SPECULATIONS
- AERIAL
- ZERO
- EARTH'S
- WITHERED
- TRANSPORTED
- RESENT
- DROWN
- PEEVISH
- UNDERGONE
- SENDS
- ENRICHED
- REAPPEARED
- MEDITATION
- LINGERED
- STAGGER
- TRUSTING
- FORLORN
- DEFECTS
- COMFORTS
- PLUNDERED
- SELECTION
- TRANSLATED
- APATHY
- MESSENGERS
- EXCLAMATIONS
- RENOWN
- CONSULTATION
- ELASTIC
- ANKLES
- PRESUMED
- BURSTING
- CHORD
- MAPLES
- SASH
- GATEWAY
- TOSSING
- SUPERHUMAN
- VENICE
- MONUMENTS
- HARRIET
- APPEALED
- CHIPS
- MILKING
- PANTED
- WICK
- HANDSOMELY
- APIECE
- GLIMMER
- CONNY
- RATTLED
- GYMNASIUM
- GREET
- BREATHLESSLY
- MASCULINE
- LUGGAGE
- EDNA
- STRAINING
- SHEW
- MULES
- SINGERS
- ROBIN'S
- POTATO
- TWIG
- PAVING
- SPLENDIDLY
- FARTHING
- BRUSSELS
- PROWESS
- CROSSES
- WRONGS
- COINCIDENCE
- EUROPEANS
- PRIVILEGED
- NOTICES
- CLOTHE
- DOMAIN
- SECONDARY
- CLOAKS
- ATTITUDES
- MOCK
- LASTLY
- SHORTER
- FLOODS
- RAVINE
- INTERVENING
- RESEMBLES
- FAMISHED
- NEUTRAL
- ERRONEOUS
- CANNONS
- SUNDAYS
- BISHOP
- DEMONSTRATED
- ABIDING
- CONCESSIONS
- HEROISM
- DISCREET
- BOOTY
- PITEOUS
- ENACTED
- PITILESS
- WRECKED
- DOLLY
- NIBBLING
- RESOLUTELY
- ASSURING
- ADAPT
- GRINDING
- TRAPS
- SUPERNATURAL
- SPRINKLED
- CHAT
- COMBINATIONS
- ROBES
- LUXURIANT
- APOLLO
- IVY
- D'YOU
- FOREMAN
- TAME
- STRAP
- GALLOP
- MINER
- SPRAWLING
- LIAR
- GRINNING
- BIN
- CONTEMPTUOUS
- ENCAMPED
- ROAST
- SPOON
- UNDERGROUND
- TORMENT
- LAGREE
- REASSURED
- STRICTEST
- LUCKILY
- SILL
- REJOIN
- CIRCLED
- LOVER'S
- CHEERFULNESS
- MORLAND'S
- UNAFFECTED
- TETE
- HEIRESS
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- ACQUIRING
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- ASCEND
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- SHARPENED
- QUARRELS
- ASSAULTED
- TENANT
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- HEAVED
- GUINEA
- PERNICIOUS
- INSTINCTIVE
- SLY
- SUGGESTING
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- LOOKOUT
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- GRANDPAPA
- WILDEST
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- HARNESSED
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- CHEERY
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- BESTOW
- PROGRESSIVE
- TRANSFIGURED
- CONSECRATED
- UNOCCUPIED
- ENCOUNTERING
- OWAIN
- ELSIE'S
- ADELAIDE
- CRUMBLING
- ATHLETE
- SPURRED
- PARCHED
- DECREED
- REASONED
- ETIQUETTE
- GIT
- RIVIERE
- STEERED
- INCONSISTENT
- WADMAN'S
- SAUSAGES
- MILBY
- ANTIGONUS
- SOSIUS
- EXCURSIONS
- LABORED
- MARGUERITE'S
- STUNG
- BALLAST
- MAURICE
- MUSKETS
- STAPLES
- D'YE
- VERITABLE
- DRIFTS
- PIONEER
- IMMIGRANTS
- FERRY
- GRADUATES
- MEXICAN
- LINK
- STRUTTED
- THEREWITH
- WHICHEVER
- LAUDONNIERE
- ESCORTED
- ASTOUNDED
- RANSOM
- TANKERVILLE
- BUNCE
- BAKER
- ELECTORS
- HARTFIELD
- CONNEXIONS
- EXTRAVAGANT
- SIBYL
- TREASURER
- CORNELIA
- CARLYLE'S
- QUAKING
- VARY
- ELEANOR'S
- COUNTIES
- CLUE
- GRIZZLED
- MARION
- MOWBRAY
- IMPUDENT
- HATTON
- TURBULENT
- MANETTE
- MATHEMATICS
- FLOODED
- ARGO
- JASON'S
- STRATEGY
- TEXAS
- NEBRASKA
- INCOMPREHENSIBLE
- GRASSHOPPER
- GODFATHER
- FISCHER
- PANTALOON
- CRYSTALS
- ARABY
- CONTEMPORARY
- SIGNORE
- MAJOR'S
- DISREGARD
- DEALER
- SMOOTHED
- MARVIN
- JUG
- CHESTER
- MOURNED
- CURRANT
- PYES
- COMPOSITIONS
- GATHERS
- SLOANE
- COPLEY
- SUBORDINATE
- PRESENTING
- CANYONS
- TINTED
- MOTORS
- SCRIPTURE
- SABBATH
- SENTINEL
- HAVANA
- BENEFITS
- WAKEN
- PRECARIOUS
- CHAPERONE
- KETTLE
- CHANDELIER
- STRUCTURES
- EQUIVOCAL
- TER
- FAINTEST
- TRUDOLYUBOV
- ROUBLES
- MONTH'S
- ARISTOCRATIC
- ANTIQUE
- RUSKIN
- HINGHAM
- OBSERVANCE
- STRUT
- FOWLS
- BYSTANDERS
- HEAVING
- DRAINED
- FIGHTER
- CAPRON
- MARKEN
- EMBROIDERED
- DISAPPROVE
- PHEASANTS
- MOSQUITOES
- JACKALS
- CHECHEN
- SKIFF
- IMPETUS
- CONSTITUTIONAL
- NIVER
- EF
- VERBAL
- CONFINE
- PLANTATIONS
- COUNSELS
- BASKETBALL
- FRICTION
- PLUMBER
- AMBIGUITY
- BRAGTON
- GORE
- EXIT
- MORGUE
- LABORER
- CONFEDERACY
- CONFEDERATE
- HEPSEY
- MATERIALISTS
- PATIO
- COLYUMIST
- LYRIC
- BLASI
- MEEKS
- PHIL
- ABIGAIL
- RIYOS
- GENZABURO'S
- SAZEN'S
- KIYOMORI
- ARGUS
- ARCHIVES
- STEYNE
- GERALD
- GUNTER
- ANGLO
- NIGHTINGALE
- SHOREDITCH
- WAND
- RATIBOR
- AMEN
- REVENUES
- PROPAGANDA
- DAEMON
- HERACLIUS
- POFFENBURGH
- MERCER'S
- COLLIE
- ODIN
- RITZNER
- JUNG
- HERMANN
- ABOLITIONISTS
- ORIOLE
- HAMISH
- WEBB
- RANDOLPH
- AXEL
- LIEDENBROCK
- FRINGED
- REPUTED
- DECORUM
- NEIGHBOR'S
- DINT
- NEGLECTING
- HOUSEWIFE
- SOWING
- PLACIDLY
- SCANT
- THERE'D
- BIRCHES
- CRAB
- UNHEARD
- UPSIDE
- PERFORCE
- UNCANNY
- SLOPED
- PASSENGER
- FRECKLED
- MOONSHINE
- BLOOMING
- MISTY
- SIDEWISE
- ASCENDANCY
- RELATIONSHIPS
- CLASSED
- CONTIGUOUS
- BOLTS
- MADEMOISELLE
- SATIRE
- CONCIERGE
- STUPEFIED
- FORMALITIES
- CRUCIFIX
- GALLEYS
- ACCOMPLICE
- HARSHNESS
- SINNED
- CONDEMN
- DESPATCH
- ADIEU
- PAINED
- HOMOGENEOUS
- DETEST
- FREEMEN
- DOMESTICS
- HILARITY
- IMPLACABLE
- INTIMATION
- FICKLE
- NOTIFIED
- CAPRICIOUS
- GENTLEWOMAN
- DARNED
- BLOSSOM
- CONSISTENCY
- PRESCRIPTION
- POWERLESS
- EMINENTLY
- SARCASM
- SCAR
- PARLIAMENTARY
- NEGOTIATIONS
- RESTRICTIONS
- FORBIDDING
- INJURIOUS
- PARTISANS
- CESSATION
- DIPLOMACY
- CONTINUATION
- OBSTINATELY
- DIRE
- DRAGGING
- DISPUTES
- MASSACRE
- DIGNITIES
- WITT'S
- HOPELESSNESS
- NAVAL
- PUBLICITY
- WHITEHALL
- RENEWAL
- DEXTERITY
- FRY
- SALTED
- COOKED
- BEEF
- CIVIC
- ADDS
- PERMANENCE
- THROBBING
- SUFFERS
- BUDDING
- DISPOSING
- THROB
- DEFY
- FOREBODING
- FORGETFULNESS
- BLANDLY
- PROVIDING
- SORDID
- ADMIRABLY
- RUFFLED
- KILLS
- RUM
- MEDITATIVE
- UNKEMPT
- INFIRMITY
- BANGED
- TWITCHING
- WREATHED
- ANTAGONISM
- CHALLENGED
- LIMPLY
- HONOURED
- CHUCKLE
- REPROACHFULLY
- TESTING
- GRATUITOUS
- CRITICISE
- ARROGANCE
- TACITLY
- GOTHIC
- GRUMBLE
- JUDICIAL
- AFTERNOONS
- FOREGROUND
- COMPLACENCY
- TERMINATING
- PERILS
- SKIMMING
- SWIFTER
- CONTRIVE
- CHARIOT
- BUSTLING
- POTS
- MASKS
- HIDES
- INDIVIDUALITY
- APPEALS
- PATHWAY
- UNAVOIDABLE
- DECISIONS
- BUILDS
- BENDS
- ENVIRONMENT
- NERVOUSNESS
- CONCENTRATION
- CONTENTMENT
- LEVELS
- ADULT
- NOCTURNAL
- BANDIT
- FENCES
- PRISONS
- SHOVING
- SKIRTED
- BROKER
- ORATION
- HARRINGTON
- IMPROBABLE
- MARSHY
- LANDLADY
- MINISTER'S
- TELEPHONED
- KATHERINE
- OBSCURED
- MARIA
- TURMOIL
- REVEALING
- SMITHTOWN
- BASTILLE
- REPRESENTS
- CARLOS
- OMEN
- STIMULATION
- SHROUD
- UNCOUTH
- FLEEING
- IRONICAL
- NOISILY
- NASTY
- BULGING
- PHASE
- ODDLY
- FORMULA
- MECHANICS
- DELAYS
- FAINTING
- GERM
- DRUGS
- VARNISHED
- DISARMED
- ENQUIRE
- TRANSPORT
- SHEWED
- UNJUSTLY
- DIVERT
- ALLEYS
- VETERANS
- RAYMIE
- SPECULATE
- SLABS
- DRENCHED
- REFERENCES
- CLAIMING
- CLUSTERS
- CIGARETTES
- CLARK
- AVENUES
- ELM
- ALLEY
- SCOTTISH
- LECTURES
- CANAL
- JEALOUSIES
- INCONTINENTLY
- PLANNING
- CINDER
- GREASY
- HATING
- WATERY
- VAGUENESS
- BLOATED
- DOORSTEP
- GROWLING
- REDDY
- COACHES
- TRANSACT
- ROBBING
- SCRUPLES
- TAXI
- MATINEE
- SCATTERING
- STALE
- CRUTCHES
- PALLID
- AMORY
- ASPECTS
- STINKING
- PATTERNS
- REITERATED
- PATHOS
- QUESTIONER
- WARMING
- COLDER
- HONEYMOON
- BLENDING
- ABSORBING
- HAULED
- HERMETICALLY
- CHRONOMETER
- SOLIDLY
- SATELLITE
- HEIRS
- MARVELS
- TRANSMITTED
- COSTS
- DASHES
- VEHICLES
- MANUFACTURE
- UNANSWERABLE
- TEMPORAL
- TESTED
- PERSECUTION
- DETOUR
- SLING
- PARTICLES
- MAGNET
- CLUMPS
- HALFWAY
- FIRMER
- AU
- REVOIR
- CHAMPAGNE
- ADVERSE
- ADVENTURERS
- ACCLAMATIONS
- PAYMENT
- RESPLENDENT
- IMPETUOUS
- OSWALD
- REPEATEDLY
- HYPNOTIZING
- REQUESTS
- TECHNIQUE
- CONVICTED
- CENSURE
- MEANINGLESS
- DISTURBANCES
- OVERSHADOWED
- SYSTEMATICALLY
- SUPPLEMENT
- DISORDERS
- EMOTIONAL
- LOSES
- PSYCHICAL
- REFORMERS
- EMPHASIZE
- TRAIT
- NATUREDLY
- DAWNING
- TRAVERSE
- WATERFALL
- PEBBLES
- TINT
- TRIPPED
- VISTA
- ARCHITECTURAL
- NEGLIGIBLE
- MALICIOUS
- ADROIT
- FASTIDIOUS
- CORRECTNESS
- FICTITIOUS
- GNAWING
- DESPOTIC
- IMPROMPTU
- FUSSY
- SUPREMACY
- UNANIMOUS
- INCONCEIVABLE
- INDULGING
- STUBBORN
- MALIGNITY
- SUPERFLUOUS
- UNFLINCHING
- LASH
- LEADEN
- DISTRUST
- MINUTELY
- PREGNANT
- GOODLY
- INTRODUCING
- DANCES
- LOBBY
- LIMITLESS
- DAVID'S
- SCHEMING
- MAGAZINES
- REPLACE
- PARALYSIS
- ACHE
- GLEAMS
- CONFIRM
- INEQUALITY
- COOLED
- AFFIRMATIVE
- OUTSKIRTS
- HEATH
- CONCEDED
- IMPUDENCE
- EXILES
- EXILED
- PRESENTATION
- QUARRELLED
- DERVISH
- DUSKY
- DUNCAN
- LODGES
- ACCORDED
- EXCLUDED
- REPRESSED
- RESUMING
- SICKENING
- EMULATE
- SQUAWS
- ADO
- HATCHETS
- SOFTEN
- STERNNESS
- SCOUTS
- DESPATCHED
- FEARLESSLY
- PROJECTS
- ADVENTUROUS
- PREMIUM
- SEESAW
- ATTIC
- REBUKED
- MANUFACTURED
- PUNCTUALLY
- SAMPLES
- MUSTACHE
- VEGETABLE
- BLACKSMITH
- DELIGHTEDLY
- GINGHAM
- COBB
- SOMETHIN
- RECITE
- SATISFACTORILY
- SWEARING
- PROFANE
- BLOODHOUNDS
- UNRULY
- LYDIA
- ADORED
- REVERENT
- ACCOMMODATE
- COATED
- PROPPED
- CONDUCTOR
- CHAUFFEUR
- TRIFLES
- LINING
- HORRIFIED
- RICE
- FANCYING
- BESEECH
- DISCONSOLATE
- REQUITE
- WRONGED
- AWAITS
- QUESTIONING
- COCKED
- CROSSLY
- BLOND
- PANES
- WATERLOO
- FLANDERS
- BELGIAN
- TATTERED
- TEMPERATE
- SCREW
- BURDENED
- PASSERS
- OPPRESSION
- PHYSICS
- DISGUISING
- PANTOMIME
- SERMONS
- INMOST
- SPIRITUALITY
- PREACHERS
- GROANING
- OPERATING
- MINGLING
- IMPLIES
- YEA
- SAXON
- PROXIMITY
- SHUDDERING
- APPLIES
- MOCKING
- OVERLOOK
- METAPHOR
- FALKLAND
- OVERHANGING
- VEHEMENTLY
- WRETCHES
- MAGELLAN
- DER
- COUNTENANCES
- COUGHED
- YIELDS
- WATERFALLS
- SOLITUDES
- WINTER'S
- INCONSIDERABLE
- EASTERLY
- ISLES
- CONSIST
- JERK
- MERCILESSLY
- BEHOLDING
- INVENT
- OLYMPIANS
- EXPLORING
- RESPONSIVE
- HAUNTS
- UNDENIABLE
- DISPLACED
- CIVILISATION
- PILGRIM
- DISCUSSIONS
- PRETENCES
- ODOUR
- BELATED
- TRUANT
- FROLIC
- VESTRY
- FOOTHOLD
- SNEAK
- AIDS
- MENDED
- CHAFED
- SCARRED
- LULLED
- CONGRATULATIONS
- FLYCATCHER
- OLIVE
- APOLOGIZED
- BUG
- DISTRICTS
- NEWER
- MOUNDS
- CHINOOK
- WASTING
- NETTING
- PADDY
- MODIFIED
- LENGTHENED
- OCCUPIES
- SHAKES
- SOMEBODY'S
- TIRELESS
- CRACKERS
- LAME
- BUNK
- APPARITIONS
- BROOKLYN
- ARC
- ORIENTAL
- RETORT
- TUSH
- EXPLOSION
- PERSUADING
- FAVORS
- CLAMPED
- FIERCENESS
- DISADVANTAGE
- JOURNEYED
- DIMINUTIVE
- HANDSOMER
- BARRED
- GALLOPING
- OBSOLETE
- DOLLS
- WATERING
- SALLY
- SONNETS
- PRELUDE
- REPROOF
- DEJECTED
- MALADY
- SYLLABLE
- PARTIALITY
- ECSTASIES
- BESTOWING
- TRANSACTIONS
- AIMING
- SUSPECTING
- AVOWAL
- DICTATE
- STRIDES
- MARSHALL
- DOCILE
- LONESOME
- APPREHENSIVE
- SIMPLER
- VOYAGES
- FURIES
- TORMENTED
- UNINTELLIGENT
- RECOVERY
- TROPICAL
- SOFTNESS
- PALMS
- WHITEWASHED
- PILGRIMS
- TREADING
- UPHELD
- POUNDED
- LIGHTHOUSE
- HISS
- COLLISION
- BOLTED
- MOANED
- THICKNESS
- VICIOUSLY
- REACHES
- MEAGRE
- NARROWER
- ENLARGED
- HETTY
- CLOUDLESS
- VERGE
- SEASONING
- MORTAR
- CRUMB
- LOBSTERS
- ONIONS
- REALISE
- TINGLING
- ENDEAVOURING
- CAREW
- CONTENTEDLY
- DENUNCIATION
- WEIGHING
- BEADS
- WORKMANSHIP
- WEEDS
- DELIRIUM
- HELEN'S
- SUH
- MOTIONING
- SHEEPISH
- PSALM
- GALILEE
- THRONGED
- RANGED
- PIPING
- SILKEN
- CECILY
- MAGAZINE
- PLEDGED
- COMERS
- EVERMORE
- BRAVER
- GRASSES
- TRIES
- PROFESS
- INSTANTANEOUSLY
- PENETRATION
- MARGARET'S
- WILLOUGHBY
- CONDEMNATION
- SEEM'D
- BABYLONIANS
- FACTIONS
- ELECT
- ENIGMAS
- CANDIDATES
- NARROWLY
- COMMENCE
- SWAY
- COMBATANT
- TILTED
- THO
- DISTINGUISHING
- HURST
- DISH
- WRETCHEDNESS
- NETHERFIELD
- MENTIONING
- TAPPING
- EMERGENCY
- SHARING
- ARRESTS
- QUAKE
- DUN
- VAPOURS
- DESTRUCTIVE
- BORDERING
- CONTAGION
- DISTRESSES
- COMMENCEMENT
- FORBADE
- ASIATIC
- REVENUE
- EXPENDITURE
- LUXURIOUS
- SUMPTUOUS
- SCREENS
- ENTERTAINMENTS
- FAVOURABLY
- ANOMALY
- DECORATIONS
- INNATE
- FARCE
- COMETH
- WOULDST
- TRUSTY
- CARDENIO
- HEEDLESS
- LASTS
- OPPRESSIVE
- INCLINATIONS
- ATTIRE
- UNAWARES
- FERKO'S
- GLUTTON
- TORMENTS
- FOURS
- HEALED
- PACING
- LIME
- PARTISAN
- LACKEY
- COMPRESSED
- SWELLED
- INSPIRE
- CROWNING
- MAIDS
- STATUES
- CELESTIAL
- VISIONARY
- PAPA'S
- PROCEDURE
- KNOLLYS
- HELPLESSNESS
- COMICAL
- EMBODIMENT
- NOTICEABLE
- ENORMOUSLY
- INVARIABLE
- OUTRAGEOUS
- CRUSADE
- ORIENT
- SQUIRES
- JESTS
- SEMBLANCE
- LOOSED
- SLOPING
- HAVISHAM
- FAUNTLEROY
- SWELLS
- CREAKING
- YER
- LONGITUDE
- BROADER
- METAMORPHOSIS
- LIMITATION
- PRIMARILY
- APPLICABLE
- QUICKEN
- REBUILD
- WHATSOEVER
- TRADITIONS
- LIMITATIONS
- SANITY
- RUGS
- SHOULDERED
- COMPARTMENT
- POCKETED
- ADDRESSES
- FORMALITY
- VICAR
- TRACING
- PONDEROUS
- HUNS
- SHEPHERDS
- GAUL
- LEO
- BONIFACE
- CAPTURING
- INTRUSTED
- MOSQUE
- CHANTING
- ASSUREDLY
- CLAMS
- PRICED
- INDIVIDUALLY
- SORTING
- REDUCE
- ESTIMATED
- BELLIES
- PRONOUNCING
- BLINKING
- SHAPELY
- SPAT
- BLONDE
- VIRGINAL
- FLOURISHED
- TREATIES
- LOFT
- TRIPLE
- CRACKS
- CLOSEST
- FASTENING
- BLINDNESS
- FLANKED
- RECTOR
- UNDISTURBED
- ACCOMMODATION
- TOPOGRAPHY
- FATED
- ABOLISHED
- FACILITIES
- MISTRESSES
- TEDIOUS
- PATTEN
- DAMSELS
- EFFECTUAL
- COWPER
- HEATHEN
- ORCHESTRA
- DISREPUTABLE
- RISKED
- AGILITY
- INSPECTOR
- MERCANTILE
- ICILY
- MOYNE
- CLEW
- CLUTCHING
- PURSER
- LECTURER
- HALJAN
- EXIGENCIES
- BLACKSTONE
- EXOTIC
- FRANK'S
- DASHING
- TORTURED
- METALLIC
- LUNGED
- FRISKY
- MEANLY
- DELICACIES
- PUFFS
- SIXPENCE
- QUIVER
- AVERTED
- IMPLORINGLY
- ABHORRENCE
- MANCHESTER
- GLADE
- HARLEY
- DEPRESSION
- GRANDLY
- ANTICIPATIONS
- OVERPOWERED
- CICERO
- CONCERTS
- POSTPONED
- SHYNESS
- FOOTLIGHTS
- YOUTHS
- WAKED
- BROTHERHOOD
- QUIVERED
- COMPLEXITY
- SUPPLYING
- TREMENDOUSLY
- MILDLY
- UNEARTHED
- SHEPHERD'S
- ENTRENCHMENTS
- CHEMICAL
- SWARD
- EXULTATION
- BREEZES
- PLIGHT
- LOVINGLY
- MOLE
- SWAN
- RIDGES
- MOSSES
- FETCHING
- INTERPRETER
- FRESHMAN
- REPRESENTATION
- MASTERPIECE
- EXERCISES
- THRASHING
- STIRRUP
- VESTIBULE
- WALNUT
- RUMOURS
- COUNCILS
- DRURY
- HOLBORN
- CERTIFICATES
- SURROUND
- EXCLUSIVE
- TRADES
- KESEBERG
- GRANDPA
- STARVED
- CHARGES
- SHUNNED
- CALENDAR
- WILLOW
- ASTOUNDING
- COLOMA
- ANNOUNCING
- FOOTED
- BUNCHES
- HOOKER
- FRAGRANT
- TIDY
- RESORTED
- GOVERNORS
- TORPEDO
- PINNED
- WAG
- PIERCE
- TUSKS
- ENJOYS
- SURPRISES
- TIRESOME
- DEPARTING
- CLASSIFIED
- ANNEX
- ABREAST
- LAND'S
- CRAMPED
- UNEXPLORED
- MUTINY
- SQUEAKING
- UD
- SQUIRE'S
- RATS
- AFORE
- CHRISTENED
- WAGE
- INFLICT
- FORFEIT
- AMOUNTING
- DEEM
- PERSIST
- CHASM
- TWISTING
- TRAMPLED
- GROUNDLESS
- RANGES
- GRANDCHILDREN
- RESEARCH
- LORE
- RESEARCHES
- GOSPEL
- GOOCH
- GEMS
- WEAVE
- DRILLED
- MASSED
- FLUCTUATING
- GESTICULATED
- NOISELESSLY
- REVOLUTIONARY
- LABOURS
- DIALECT
- ILLUMINATION
- WARREN
- TRUCK
- SERFS
- THUNDERSTORM
- UNSPEAKABLE
- ORACLE
- FEASTED
- ANT
- WHEREFORE
- COWARDICE
- PINING
- SHRINE
- FLAVOR
- LACKS
- FRANKLIN
- SAGE
- TENANTS
- HORDES
- CONVERSION
- TAMED
- STUMBLING
- MADMEN
- CAUSELESS
- BELIEFS
- BUDDHIST
- UMEGAE
- BRIM
- RUINOUS
- MEDITATING
- ROKURO
- KUBI
- CONFISCATED
- QUICKNESS
- TESTAMENT
- CONTEND
- CONSIDERATE
- FRAGILE
- STEED
- DISCOMFITURE
- RELICS
- DISMAYED
- DESCENDANTS
- SANTA
- HELMET
- PLAYER
- HALTING
- LICENTIATE
- GEESE
- PINIONED
- INFURIATED
- SOLA
- KOVA
- GRATING
- ZODANGA
- CUSHION
- KANTOS
- EMBERS
- CAP'N
- KNOWED
- WIPING
- SCRAMBLE
- BLUNDERED
- ENSURE
- SNATCH
- BAFFLING
- SUNDOWN
- PAINTERS
- CONFUSEDLY
- STRAIGHTENED
- BRUTES
- PREFACE
- FRAMLEY
- ACCUMULATED
- UNWILLINGNESS
- ARCHBISHOP
- SALUTARY
- UNDRESSED
- MOANS
- HOW'S
- COLLAPSE
- RHYTHMIC
- GNAW
- ENJOYABLE
- GRANDADDY
- DEY
- MEMPHIS
- BOGUCHAROVO
- QUARTERED
- KOLOCHA
- OBJECTIONABLE
- COLLINS'S
- RESERVES
- DIFFIDENCE
- PULSES
- UNCOMFORTABLY
- UNALASKA
- MISSIONARIES
- ZEALOUS
- POP
- COMPARING
- MARSHAL
- SKINNED
- ATTRIBUTE
- GEOGRAPHICAL
- INERTNESS
- VARIATION
- ROSS
- GRACE'S
- EMERGE
- GUST
- ELEPHANTS
- ACCUMULATIONS
- ENGINEERS
- AD
- LOUISIANA
- GAMBLE
- UNHAPPILY
- PICKETED
- BRANNAN
- UTAH
- EVA
- OBSTRUCTING
- O'BRIEN
- GANNON
- BRAZILIAN
- NAP
- CUTLASS
- SETTLEMENTS
- CAMPEACHY
- IMPERTINENT
- RECKLESSNESS
- STING
- DREAMY
- UTTERANCES
- PETALS
- ATOM
- OMNIPOTENCE
- IMPARTED
- DIFFUSED
- UNINTERRUPTED
- GUENEVER
- MAYING
- COVENANT
- CONVEYING
- FIREWOOD
- DISCOURSED
- BABES
- AMOUNTED
- SUFFUSED
- UNKIND
- FUSILLADE
- FOOTMEN
- RIFLES
- LUSTROUS
- WRITHING
- CHARGING
- CARPETED
- THUMPING
- SUAVE
- CHUBBY
- UNMISTAKABLE
- RESONANT
- STEPHEN'S
- JOVIAL
- DI
- EXPLOITS
- COLONEL'S
- VOLUNTEERED
- OFT
- BOWLING
- TONGS
- LADYSHIP
- CORPORAL'S
- CZERLASKI
- NOUGHT
- SYRIAN
- ENDANGERED
- OFTENTIMES
- ANOINTED
- DEVELOPMENTS
- DEFECT
- SLEEPLESS
- CONSOLE
- BRAVADO
- TYING
- STEERING
- EXULTANT
- PERPENDICULAR
- BURGESS
- STOOP
- EMITTED
- STEILACOOM
- MT
- JUAN
- TRINITY
- YEAST
- BEQUEATHED
- ROUNDS
- PLASTERED
- BARRINGTON
- ELTON
- BODY'S
- NICER
- DISSIPATION
- PALLOR
- SCRAWL
- ARMCHAIR
- HURRAH
- TORTOISE
- DUCKED
- JERRY'S
- BAPTISM
- STOMACHS
- INFANTRY
- TAUBE
- SPINE
- DECLINING
- MICK
- MUTTON
- DARNAY
- SCAFFOLD
- DISTASTEFUL
- PANELS
- WORKMEN
- PUMPS
- STRIPES
- SEVERED
- BOON
- TALLEST
- FILE
- MOSES
- JUDICIARY
- FOLD
- LIBERATION
- INSINCERITY
- WRONGLY
- CAPITOL
- STUYVESANT
- BOXING
- EVERGREENS
- HARLEQUIN
- LEANT
- THEO'S
- OCTAGON
- NECKED
- BELGIUM
- GIRLISH
- PAMELA
- POLTON
- FATIGUED
- AFFORDS
- REUBEN
- MEMORANDUM
- MONASTERY
- PATRICIA
- DANNY
- PLEADING
- SUSPICIOUSLY
- PANTRY
- STRAWBERRY
- UNACCUSTOMED
- HARRIS
- FADE
- PAUL'S
- CLAP
- SKETCHES
- CHRISTOPHER
- SLICK
- UNDERSTANDS
- JIST
- LOPEZ
- RATTLER
- IGNORE
- JACKETS
- COLOSSEUM
- FLAKES
- STIFLED
- FEEBLENESS
- SCULPTURE
- ETHEREAL
- SEATTLE
- VICTORIA
- RUNG
- JEHU
- LAYS
- SPAKE
- PADDLING
- PRECEDE
- CLEMENT
- EASEL
- PROJECTILES
- TRAVERSING
- MANIFESTATION
- SICILY
- EPITHET
- FORESTER
- MILLER'S
- GILL
- ABSORPTION
- CARNEGIE
- ROCKEFELLER
- FACILITY
- REIGNS
- PREDECESSORS
- CONCRETE
- CHRONIC
- RELIANCE
- MAINE
- OILED
- PEWS
- PURITANS
- FRINGE
- EDUCATIONAL
- PATRIARCHS
- SPECKLE
- COCKLETOP
- ARRANGING
- DISPATCH
- SENTIMENTALITY
- PETTED
- GARDENING
- ENLIGHTENMENT
- WRANGELL
- GLENORA
- MORAINE
- YOUNG'S
- BUSTLED
- VOLLEY
- THORBIORN
- KODAK
- MAITLAND
- CROPPER'S
- ELUSIVE
- ROMISH
- IMAGINES
- TEREK
- PRO
- TARTAR
- ROBSON
- FOSTER
- MILLVILLE
- WEGG
- HUCKS
- THOMPSON'S
- COMEDY
- AFFIRMED
- HOSE
- WID
- WOODYARD
- SOWERBY
- CRAWLEY'S
- ARABIN
- VERBS
- FANNICOT
- REPRESSION
- NESBIT
- LEGACY
- VARIATIONS
- STIMULI
- CHORDS
- OSTLER
- REGULATIONS
- CRIANAN
- HERALDED
- ACADEMICAL
- PREUSS
- GLASGOW
- MANDERSON
- DIRHAMS
- STERLING
- TICKETS
- GREGGORY'S
- FAMILIARLY
- MAXIMILIAN
- CARTERET
- INDOLENCE
- HEREDITY
- REGIS
- WAN
- VERRY
- ROSVILLE
- HOISTED
- TEACHES
- CENTIPEDES
- CONWAY'S
- RODGERS
- GRIMSHAW
- ARAMAEANS
- REVOLTS
- AHAB
- BECKY
- PAYNE
- DENOUNCE
- MAUMEE
- ANKLET
- ANGELES
- ATTIRED
- KEATS'S
- BRAWNE
- ATTAINMENT
- BRYAN
- FERMAIN
- COUNTERS
- GOVERNING
- CENSORSHIP
- OSSOLI
- RITA'S
- RITA
- ZENZA
- BOBO'S
- HAUNTING
- PRODUCTIVE
- SEDUCED
- BAGPIPES
- FALCON
- WEBBED
- GLUTEN
- FONTAINEBLEAU
- BEAN
- SPOOKS
- CHICHESTER
- TRUMPETED
- ERADICATE
- SWEETWATER
- ROMANIANUS
- PLANTAGENET
- OLDACRE
- GRANNIE
- AUXILIARY
- SORREL
- DEFTLY
- MINUTE'S
- RAMBLING
- FURTHEST
- RAPPED
- HAIRPINS
- MARILLA'S
- HEADACHE
- SUCK
- IMPORTED
- BELL'S
- FULNESS
- TENSE
- LOCKING
- YELLOWISH
- VIVACITY
- SCOPE
- DIMPLES
- PITYING
- PLUMES
- KID
- TALKATIVE
- PATERNAL
- ENDEAVORS
- APPELLATION
- CHATTING
- GRADE
- INTRIGUES
- IMPERTINENCE
- TRADESMAN
- PREOCCUPIED
- VAGABOND
- REFLECTS
- INACTION
- CYPHER
- HOOP
- TINK
- LONGINGLY
- TIGHTENED
- MUMMY
- JEERED
- NEVERLAND
- TINKER
- ARTFUL
- SQUEEZING
- FALTERING
- GLORIOUSLY
- SHAMELESS
- LISTENS
- ENQUIRY
- EXPERIMENTAL
- EXTREMES
- ESTABLISHMENTS
- LIVERPOOL
- CLEVERLY
- LISTENER
- INFALLIBLE
- CASPAR
- SULTRY
- FADING
- MEDITATIONS
- DISMISSAL
- DECAYED
- COMPASSES
- PICTURING
- PREDICTED
- SUITOR
- CHERISH
- TINGED
- SORES
- REFUGEES
- AMBASSADOR
- UNTHINKABLE
- COUPLED
- REDRESS
- PORTS
- STATESMEN
- DOWNS
- MERCHANTMEN
- CRUISING
- ADRIAN
- STIFFENED
- HOSTILITIES
- ABUSED
- THAMES
- NORTHWARDS
- RUYTER
- PLYMOUTH
- STRAITS
- EMISSARY
- SUPPLICATION
- FORCIBLE
- MARITIME
- EXTINCTION
- INSTIGATION
- STRANGEST
- DIPLOMATIC
- MOMENTOUS
- BRIBE
- AVOIDING
- CLAMOUR
- DEFECTION
- LABORIOUSLY
- TRIUMPHED
- THICKEN
- CLEANED
- CARROTS
- THICKENED
- LEMONS
- GRATED
- STEWPAN
- STANLEY
- TINTS
- INCUR
- CIVILITY
- RUSTIC
- RUDELY
- WREATH
- SWEETER
- MADMAN
- MUTUALLY
- INFERIORS
- IDLY
- AMPLY
- HACK
- REPLENISHED
- GLARED
- FOREARM
- STORMED
- SNARLED
- COMPRISED
- DODGE
- RAFTERS
- APOLOGETIC
- WHITENED
- GOINGS
- CHAFF
- RENDERS
- COUSIN'S
- ORIGINALITY
- HEDGEROWS
- APPRECIABLE
- VACANCY
- FRAY
- ADORN
- EXCELLED
- BEWAILING
- FATTEN
- SPELLS
- ASUNDER
- LOAVES
- CHERRIES
- UNTIDY
- FARMER'S
- CHARACTERIZED
- HELPS
- STUMBLE
- CONFLICTING
- CONTESTS
- TRADITIONAL
- OUTLET
- HYPOCRISY
- IRRITABLE
- COUNTS
- UPS
- IMPERFECTIONS
- REVERSED
- RECIPIENT
- PROGRAM
- SAVES
- EMBARRASSING
- BENEDICT
- PIETY
- BLUBBER
- BREVITY
- BUMP
- EFFECTUALLY
- SUBSTANCES
- INTACT
- DEEPENING
- INTERMISSION
- CORONER'S
- ADVERTISEMENTS
- PAREDES'S
- PUZZLING
- TRAMPING
- SPADE
- GRANDFATHER'S
- BREAKFASTED
- MORBID
- JUSTIFIABLE
- YESTERDAY'S
- INCONGRUOUS
- SUBTLER
- CLEANSING
- EXCAVATION
- JET
- SCARF
- HANDLES
- UNHEEDED
- EXCLAIMING
- CANCER
- DRUGGIST
- GUARANTEES
- ADULTS
- WRIGHT'S
- BALM
- ASTHMA
- STRAINS
- HOLDER
- WITHER
- RESTORES
- ITCHING
- BENEFICIAL
- SPOONFUL
- PURIFYING
- INNOCENTLY
- AGREEABLY
- OMIT
- ENDEAVOURS
- EMBRACES
- TURKISH
- HEBREW
- LEPROSY
- APPLYING
- INTIMATED
- RAISES
- PREJUDICED
- SINBAD
- CONDESCENSION
- INVADE
- BINS
- EMBLEMS
- MINNEAPOLIS
- LAYER
- TICKLED
- LOCATE
- ENTERPRISING
- BRIGHTEST
- UNIFORMS
- PIKE
- LAWNS
- COMMODIOUS
- SKILLED
- SPHERES
- HAMPSHIRE
- AVIATORS
- PASTOR
- CYNICAL
- ARTIST'S
- FICTION
- HEMMED
- DEVOTING
- SPURIOUS
- DEMURE
- DEVOID
- COILS
- SENSITIVENESS
- JOINS
- DISOBEY
- WADDLING
- GREETINGS
- SQUIRRELS
- PRAIRIES
- UNMOVED
- PROVERB
- MORCERF
- GRIEFS
- THUNDERBOLT
- WIDENED
- COUNT'S
- HOSPITALS
- CLAMOR
- GLIMMERED
- CABS
- COLLARS
- MIRACULOUSLY
- RESTAURANTS
- COARSENESS
- REACTIONS
- GRANDER
- INSINCERE
- MERGED
- BAYONET
- CONICAL
- FLOATS
- LAUNCH
- HUMPH
- DISC
- DECREASED
- REVOLUTIONS
- ARITHMETIC
- LEASE
- SENSATIONAL
- ADVERTISE
- PROPHESIED
- MOVABLE
- HIGHWAYS
- MOISTURE
- THREADED
- HERETOFORE
- CRASHED
- LOOP
- BUBBLE
- RELUCTANT
- NEEDLER
- FLAPPED
- SWIRLED
- WARY
- AWAKING
- SNARLING
- SPITTING
- JERKING
- TURENNE
- TACTICS
- SUBURB
- FRONTIERS
- REVERSES
- STIRRUPS
- OBEISANCE
- REIGNING
- RESIDE
- REBUILT
- EFFACED
- REBUILDING
- CORRESPONDED
- REVERE
- FETE
- INFIRM
- CURATIVE
- DISEASED
- LAYERS
- OVERWHELM
- DESTROYS
- DIRECTS
- HOURLY
- OVERPOWERING
- ADDICTED
- IMMORAL
- INCREASES
- METAPHYSICAL
- THERAPEUTIC
- AGENCIES
- IMITATING
- MISGIVINGS
- PERSUASIONS
- OVERCOMING
- TRANSITION
- POSSUMS
- LONE
- WHITER
- YOUNGSTERS
- POOH
- CLEVEREST
- RECUR
- WHISPERS
- RECEDING
- TROUGH
- APPLIANCES
- REELED
- INTERVENED
- TRANSVERSE
- RAILED
- SLEEPER
- THEOLOGICAL
- FRUITFUL
- EXTRAORDINARILY
- FANATICAL
- CONVICTIONS
- SOULED
- IMPARTIAL
- CONCEIVABLE
- LUST
- PERSPECTIVE
- INVINCIBLE
- EXTORTED
- ACKNOWLEDGING
- NOTORIETY
- SCANDALOUS
- CONTINUITY
- FORESEEN
- SORRENTO
- TOURISTS
- VOLCANO
- REGISTER
- ARISTOCRAT
- POSE
- ENJOINED
- WOMANHOOD
- QUARRELSOME
- DISMISSING
- UNIMPORTANT
- EXPANDED
- EXPECTANCY
- WRIGGLED
- GRIPPING
- NORTHEAST
- JETS
- MERIDIAN
- LUNAR
- SAVANT
- RESTRICTED
- ABUNDANTLY
- DIFFERENCES
- VAPOR
- PERSISTENTLY
- RAREFIED
- EMBARRASS
- GALLANTLY
- ABIDE
- GUSH
- SHALLOWS
- CHAMBERLAIN
- NIGHTCAP
- CACAMBO
- GALLEY
- DECENTLY
- IMPALED
- PORTE
- RAVISHED
- DISSIPATED
- MEDDLE
- JEHOIADA
- POMPEY
- NERO
- PASTRY
- EL
- POPE'S
- UNCAS
- HURONS
- STALKING
- RETRACED
- ADVENTURER
- RIVETED
- CHEATED
- CONTRADICTED
- CHILDLESS
- IMPENDING
- BLACKER
- SCALP
- POLITIC
- IMPLEMENTS
- ORATOR
- BEANS
- ACQUIESCENCE
- YENGEESE
- HUNTS
- DISCARDED
- SHORN
- PATRIARCH
- AFFINITY
- MEDALS
- REVERENTLY
- PERILOUS
- SAGACITY
- DISTRIBUTE
- INFANTILE
- AWARDED
- RECLINING
- PRACTICED
- TUB
- ACQUIESCED
- CIDER
- COLT
- MITE
- REPLIES
- REBECCA'S
- SHAVEN
- DIMPLED
- ACID
- DEARBORN
- GRAPPLE
- CLASH
- HASH
- BURNHAM
- RAISINS
- ELLEN
- S'POSE
- RIB
- MINCE
- CURVING
- JOLTING
- BESIEGED
- CAVALCADE
- DOWAGER
- ACTIVELY
- TRIO
- LUGGING
- JERMYN
- UNPACK
- POPPING
- WATCHES
- PENCILS
- THRILLED
- SUSPENDERS
- FLOCKED
- HEIGHTEN
- ABOU
- BANISH
- UNITING
- WITHSTAND
- APPAREL
- SYRIANS
- SURPASS
- SQUEAKY
- SPARROW
- AGILE
- WENCH
- FISHWIFE
- VOLTAIRE
- DANGEROUSLY
- COMPOSITE
- RICKETY
- ATTACHING
- FURROWS
- TALKER
- ASTUTE
- COMER
- DISAGREEMENT
- PENNILESS
- BAREFOOTED
- VENOMOUS
- TILLAGE
- UNFORESEEN
- CULTIVATOR
- CONCURRED
- FIRMAMENT
- MOTLEY
- BLUNDERS
- FLATTER
- GRADATIONS
- RELINQUISHED
- OMISSION
- PRESUMPTION
- PRACTISE
- IGNOBLE
- NAPLES
- BARBARIANS
- STRIVE
- SCULPTOR
- INCLINES
- THINKER
- INHOSPITABLE
- STUNTED
- BARED
- ARTICULATE
- EDUCATE
- MINSTER
- BEARDS
- HANDSOMEST
- REPAID
- CONFINES
- BOISTEROUS
- RAINING
- INTIMATELY
- BROILED
- OTTERS
- SUBSISTENCE
- BRAZIL
- UNCLES
- HERDS
- QUALIFICATIONS
- AIMLESS
- SUBMITTING
- ANON
- EGO
- GIANT'S
- OUTDOOR
- FRISKING
- BRIMMING
- MUFFINS
- UNICORN
- RACED
- SHOWERS
- HYPOCRITE
- EVADE
- RALLY
- IRRESPONSIBLE
- FULLEST
- CALVES
- ANTICS
- BUBBLING
- VALOROUS
- FRETTED
- WRISTS
- BHAER
- SURVIVORS
- HARASSED
- SHIPPING
- BON
- JOSIE
- HEROINES
- STRENGTHENED
- MATRONS
- TOILING
- REAPED
- DINGY
- SHORTCOMINGS
- CHEBEC
- FEATHERED
- LATTER'S
- CLICK
- SITES
- STRUNG
- MOUSING
- UNPLEASANTLY
- FARMYARD
- OUTSET
- PEACH
- RIPPLE
- IMMOVABLE
- CARVINGS
- BURSTS
- HEADLESS
- RESEMBLE
- SPIRITED
- FAULTLESS
- LOGAN
- OVERLOOKED
- METHODICALLY
- THAT'LL
- YOUNGSTER
- RUNNIN
- FIGHTIN
- RELIGIOUSLY
- OUTSTRETCHED
- LOATHING
- HUSKILY
- GENT
- STEALTH
- ROT
- CAVALIER
- CONTRACTING
- GRATE
- DISOBEDIENCE
- SUN'S
- REVENGED
- DEPRIVING
- MYRTLE
- EXPIRE
- INHABITANT
- DRAGONS
- SERVICEABLE
- PIANOFORTE
- SENSIBILITY
- BARONET
- FRETTING
- COMPREHENDING
- PROFESSIONS
- INADEQUATE
- RUINING
- ACQUIESCE
- CHAGRIN
- PROBABILITIES
- TROPHIES
- STALLS
- ALLUDE
- CAIRO
- VIRTUALLY
- KIRKPATRICK
- SHRIVELED
- DETECT
- SPASM
- TRICKLING
- WRENCHED
- MOLLY
- CUTTERS
- BRUSHING
- SNOUT
- PLASTER
- AROUSE
- ZEST
- BEWITCHING
- PAGEANT
- SERENITY
- BILLET
- ALLOWANCE
- WALES
- CURSE
- SHUFFLE
- SKIPPER
- WRINKLE
- NAUTICAL
- REQUIRING
- TON
- SPECKLED
- SUBJUGATION
- CONVULSIVE
- PROPAGATED
- PERCEPTIBLY
- NEEDFUL
- DISCOURAGING
- SNOWFIELD
- MOURN
- REMINISCENCES
- ACCOMPANIMENT
- ALEXANDRIA
- NETTLE
- NECESSITATES
- SEASONABLE
- HARVEY'S
- MOISTEN
- BROTH
- LIDS
- THRUSTS
- SPOILS
- PRAWNS
- MOISTENED
- COMPROMISED
- OVERLOOKS
- COMPATIBLE
- NEARED
- AEROPLANE
- ALIBI
- FO
- PHARISEES
- NEEDING
- JUDAS
- OVERHEAR
- SNEAKED
- DEMORALIZED
- PEG
- BOWEN
- SPECTRAL
- SIMULTANEOUS
- LEAPS
- SPRUCES
- RUMBLE
- PRETENDS
- CASTS
- NAMELESS
- COMPLIED
- FORGIVEN
- TRIPPING
- IMPEDIMENT
- AFFRONTED
- EASED
- HOTTAM
- LANCES
- CONFER
- IMPERCEPTIBLY
- SUBSTITUTED
- IGNOMINY
- REVOLVING
- MALIGNANT
- EXCESSIVELY
- PEMBERLEY
- PROTESTING
- CONDESCEND
- REBELLIOUS
- DRAGOONS
- LOCUSTS
- STATURE
- SCRUTINY
- TREPIDATION
- DANK
- AVALANCHE
- VERDANT
- UNSETTLED
- GENERATED
- UNPRODUCTIVE
- LEGISLATORS
- ASSIGN
- SUBDUE
- BANKRUPT
- MEXICO
- BOSOMS
- WILDS
- HOLDERS
- LEVIED
- METROPOLIS
- MARSEILLES
- ITALIANS
- PERISHING
- SHARES
- CONDESCENDED
- TRIFLED
- REPENTANCE
- RECTOR'S
- MONROE
- CRAVED
- BUZZ
- TILES
- PROFESSIONALLY
- THEATRES
- LUXURIOUSLY
- MOTHS
- CHATTER
- ELATED
- FEATS
- WELCOMING
- DONNED
- DOUBLET
- YEOMAN
- CRACKING
- CHIDE
- AFOOT
- UNDONE
- KNOWEST
- ROUGHNESS
- DISHEVELLED
- FALSEHOODS
- SIRS
- PRESSES
- DISPARITY
- FOREGO
- FERVOUR
- BETROTHED
- DISTRACTION
- TRAVELS
- DISCONSOLATELY
- TROUBLING
- BLOOMED
- YELLING
- BELONGINGS
- JUSSAC
- CARDINAL'S
- RECOIL
- HURLING
- AJAR
- RENDEZVOUS
- DESIGNATE
- MYSTIFICATION
- THRICE
- PISTOLES
- DEMON
- SEEKS
- MOONBEAMS
- INTIMATIONS
- BRILLIANTLY
- SEPULCHRE
- HORIZONTALLY
- SWEDENBORG
- COLOURING
- RAMBLE
- MEDICINAL
- DERISIVE
- THEOLOGY
- MISGIVING
- DIVINED
- COVERLET
- VOLUNTEERS
- NEWSBOY
- SOLICITED
- EXHIBIT
- CONTRADICTORY
- HYPNOTISTS
- GROSSLY
- CONFESSIONS
- BEAUMONT
- GEORGE'S
- CORRIDORS
- ARM'S
- COUNTESS'S
- TOOL
- LINEAGE
- SYMPATHIZED
- WOT
- SHA'N'T
- DORINCOURT
- EARLS
- DEBRIS
- CABLES
- METALS
- SOUVENIRS
- FERDINAND
- DARKEST
- CONCENTRATE
- TRANSFERENCE
- THIRDLY
- INCOMPATIBLE
- DISCORD
- QUARRELING
- TEACHINGS
- DERYCK'S
- PEARLY
- DERYCK
- COMPLICATIONS
- COPPERS
- THANKFULNESS
- STEAMING
- DEFINITION
- REGULATION
- GREEDILY
- PERUSAL
- RINGLETS
- ECSTATIC
- PALENESS
- THEODOSIUS
- VISIGOTHS
- COASTS
- IMPROVING
- REFORMED
- MOSLEMS
- CAMELS
- SURRENDERED
- CIRCUMFERENCE
- GAUGE
- PANAMA
- REWARDS
- ARRIVES
- GENUS
- ATLAS
- SCHOLARLY
- MUSSELS
- SHIMMERING
- EMBROIDERY
- DIVIDING
- AUTHENTIC
- UGLINESS
- SMELLED
- HOMAGE
- FERVOR
- HEM
- IDEALISM
- PERMITTING
- RUDIMENTARY
- FOREIGNERS
- ADVOCATE
- UPHOLD
- JANGLING
- MAHDI
- RAIDS
- UNWISE
- GABLE
- SHOD
- STRAPS
- POLAR
- GLOVE
- PRIZED
- FREEZE
- ANIMAL'S
- SCISSORS
- DAMAGED
- COMMODITY
- SINKS
- ENTAILED
- AUSTEN'S
- THEATRICALS
- OBSERVES
- INVENTIONS
- HARPSICHORD
- FAN
- FANS
- PREMATURELY
- SPIN
- GARDENERS
- MANUAL
- CONTRASTED
- GROOMS
- DEFAULT
- STIMULATED
- UNDERWORLD
- CRITICALLY
- THINNED
- FEVERISHLY
- SPECULATIVE
- TOPPED
- PLEAD
- OMINOUSLY
- REPOSING
- GENUINELY
- FRACTION
- HOUND
- PACKAGE
- GRANTLINE
- FERROK
- SHAHN
- CORDON
- EAVESDROPPER
- PROWLER
- CRESCENT
- IRONICALLY
- ENCASED
- INADVERTENTLY
- MATHEMATICAL
- FANCIFUL
- ALLURING
- SATAN
- MUSTER
- SNEAKING
- SHAC
- PATCHED
- GLIBLY
- INTRUDER
- WOMANLY
- UNSATISFACTORY
- CARESSING
- OBSERVANT
- FLING
- INCONVENIENTLY
- SNEERING
- SCHOLARSHIP
- HINDERED
- COLORING
- COMPETENT
- THORNS
- ROSEBUD
- SHADOWED
- STAID
- ANCESTRAL
- WAREHOUSES
- FEASIBLE
- COMPARATIVE
- BENEFICENT
- PLASTIC
- DISCIPLINED
- ROUT
- RARITY
- JOURNALIST
- ACCESSIBLE
- WAISTCOATS
- DEFICIENT
- MEANINGS
- GAIETY
- FOOLISHNESS
- DISCOVERS
- IRRESISTIBLY
- INSCRUTABLE
- VANISH
- CHRISTENDOM
- BULWARK
- QUARRY
- WRAP
- POSSE
- HADST
- STAKES
- NARROWED
- HOUR'S
- SEAWARD
- BITTEN
- QUARRIES
- RUDDY
- SCREENED
- INSTRUCTING
- VINTAGE
- NEEDLESSLY
- FURTIVELY
- CHRIST'S
- GILES
- MAYOR'S
- ADVISING
- BEFALL
- SOUTHWARK
- GENERALITY
- INTERVIEWS
- ADOBE
- INQUIRINGLY
- BEREAVED
- CHUNKS
- LISTENERS
- SHOPKEEPERS
- WADDLED
- COWARDS
- UNCOVERED
- BIER
- VALLEJO
- CHEROKEE
- MODELLING
- SKILFULLY
- MUSLIN
- UNFEELING
- ELECTIONS
- REGAL
- CHIVALROUS
- RETAINERS
- EXPEDITIONS
- ABSTAINED
- FACTION
- SATE
- CHARIOTS
- HYPOTHESES
- HOTLY
- UNTOLD
- TITANIC
- RATES
- PLOTS
- COLLECTIONS
- VERSED
- SPERM
- BUCKLING
- FRIGATE'S
- PIER
- HORIZONTAL
- PROPELLER
- SEEKERS
- PASSAGEWAY
- FORECASTLE
- RAILINGS
- CORRESPONDS
- WATERWAYS
- MONSTER'S
- PLOWED
- TRAILED
- UNDULATED
- SEEKER
- BAILIFF
- POYSER'S
- CURTSIED
- REDDER
- PASTURES
- ABSTRACT
- EXPLANATORY
- EVERYBODY'S
- HILARIOUS
- MASSEY
- GRIEVE
- REVENGEFUL
- MORRIS
- SETH
- PROSPECTIVE
- FORFEITED
- REMONSTRATED
- SOLICITORS
- DATED
- MILADI
- INTERPOSE
- CRACKLING
- COMPLIMENTARY
- OVERTAKEN
- REPROACHFUL
- SARCASTICALLY
- DESCRY
- HOWLED
- JOKING
- BLIZZARD
- SASTRUGI
- DOWNHILL
- UNEVEN
- ERECTION
- MOUNTS
- OSCAR
- NILSEN
- CHASMS
- DRIVERS
- EVOKED
- PHOTOGRAPHER
- UNDERLYING
- BASES
- ELIOT
- WHEELER
- SOOTHED
- WAGGING
- REPRESSING
- PONDERING
- THUNDERING
- EMBOLDENED
- RECEDED
- TRIBUTARY
- DISLOCATION
- DEXTEROUS
- PARAPET
- DISOBEYED
- SWATHED
- THREADS
- SUBTERRANEAN
- VISTAS
- TROTTING
- ALTARS
- ECLIPSED
- JUNO
- UNLAWFUL
- PAINTINGS
- PRODUCTIONS
- ADORE
- UNDUTIFUL
- PIGEONS
- ODORS
- OBEDIENTLY
- PRECIPITATE
- JUPITER
- NUPTIALS
- DWELLS
- TEEMING
- MOUTHED
- LIQUORS
- GIN
- MONTREAL
- ALGONQUIN
- SUBSIST
- FORTIFICATIONS
- ATHLETIC
- CHIRP
- UNPERCEIVED
- REPOSED
- CHEERILY
- CHATTED
- FOOLISHLY
- ADEQUATELY
- ILLUSTRATE
- SIGNIFICATION
- NARRATION
- RESIDENT
- DELUDED
- WOODCUTTER
- FUGITIVE
- MONSTROUSLY
- JUDGES
- COMPLIANCE
- TENEMENT
- DESPISABLE
- INTERFERES
- STAIRWAY
- PIAZZA
- ROANOKE
- DOMAINS
- UNGOVERNABLE
- COMBATS
- SCOUNDRELS
- TOILS
- BLESSINGS
- TOOTHACHE
- INACTIVITY
- VIRGINS
- MOURNERS
- DROLL
- CAMACHO
- QUITERIA
- MOODY
- BLITHE
- SINNER
- PROTRUDING
- ANNIHILATE
- JED
- HORDE
- DISORDERED
- RAID
- IDIOT
- GROPING
- SOLDIERY
- DISCERNIBLE
- KAN
- DISCLOSING
- PATROL
- CITY'S
- CONSPIRATORS
- BUNGLED
- GIBBET
- SHIPMATES
- STRANDED
- FLICKERED
- HUMANE
- AVAILED
- TRANSMIT
- ESSAYED
- INDEPENDENTLY
- ORIFICES
- WOMB
- RETAINS
- INVENTING
- DUKE'S
- UNFINISHED
- APTITUDE
- NAUGHT
- ACCURATE
- CONSERVATIVE
- THRASHED
- ICONS
- ADJUTANT
- UNDRESS
- UNDRESSING
- CREAKED
- KITTEN
- FLOPPED
- BUZZING
- SPHINX
- BODIED
- GAL
- RUTHLESS
- ARRIVALS
- CAPS
- TRAITORS
- PIERRE'S
- IDIOTIC
- HOSTEL
- ENTRENCHED
- CONGRATULATED
- STRANGENESS
- RECURRENCE
- GROUPING
- INHERENT
- COMBINING
- RESPECTIVELY
- SOCIABLE
- SEQUEL
- SLANG
- MOLLIE
- CHINIK
- RADISHES
- PRICKS
- PURER
- OBSERVERS
- ANNALS
- SUCKED
- MINNOWS
- ADAPTATION
- PONDS
- HEAVIEST
- ALABASTER
- ITEM
- REPULSION
- REPUTE
- INTERMEDIATE
- INVESTIGATED
- SWARMING
- NANCY
- LOADING
- CLYTIE
- TONED
- SPOOK
- LAB
- MANON
- REASSURINGLY
- IMPRISONING
- MOREY
- SPRINGS
- FLORIDA
- OCCOQUAN
- EXTORT
- DISCREETLY
- BRITAIN
- RECRUITS
- JAMAICA
- PIRATICAL
- BIOGRAPHER
- PERSECUTIONS
- MODERATION
- CAPTORS
- REVERED
- WRITINGS
- TAPESTRY
- VERDURE
- HAPHAZARD
- INDIVISIBLE
- CONSUMING
- ANTICIPATION
- SYMBOL
- HORSED
- SEEST
- AMBUSH
- SPORTIVE
- TAUNTS
- LEOPARD
- TRAPPINGS
- VALOR
- UNWILLINGLY
- WAST
- PRANCING
- AVENGED
- HEAVINESS
- OBSERVANCES
- REMARKING
- HOWARD
- CARRINGTON
- LUCY'S
- LETS
- PORTICO
- LABELLED
- ROADWAY
- PONIES
- PIKES
- NEWCASTLE
- NICETY
- SWIFTNESS
- SURMOUNTED
- SHIN
- MOCKED
- ENCIRCLED
- GOODWIN
- REVELATIONS
- BUTT
- PORCELAIN
- FRAMES
- EVENTFUL
- POPLAR
- ROD
- DRAWLED
- MOUNTAINEER'S
- HEERD
- FER
- GUT
- GRATEFULLY
- NODS
- INVESTMENT
- ROUTH
- CONGRESSIONAL
- FRAUD
- BUCKSTONE
- COUNTRY'S
- CONFIDING
- ABLEST
- VOICED
- VOWS
- SECLUSION
- SLAB
- PROPHETIC
- ILLEGITIMATE
- BAREHEADED
- DEI
- REASSURE
- PRELIMINARIES
- PLUG
- CRITTER
- OFFICER'S
- BENNETT
- PREVALENCE
- DOWNWARDS
- VENERATION
- BESPEAK
- SLOP
- COMPLETION
- PREDICTION
- GRIEVOUSLY
- DISCRIMINATE
- ATTORNEY'S
- UNSUCCESSFUL
- BRIDMAIN
- RESPECTABILITY
- DEFENSE
- DOMINION
- DEDICATED
- ARABIANS
- VENOM
- SOONEST
- DISCOMFORTS
- DEPRECATION
- LIAISON
- MISTRESS'S
- BORROWING
- CHEAT
- RETREATS
- RIPENING
- NAVIGATE
- LESLY
- TAUNT
- STIFLING
- RUSSEN
- LYON
- RUDDER
- BOAT'S
- CRAG
- TUMULTUOUS
- OUTLYING
- EVERGREEN
- PUGET
- DEEPS
- DREAMLAND
- SLACKENED
- OUTBURSTS
- MISHAP
- SHOVEL
- RECRUITING
- POTTER
- RIOTS
- BENIGN
- IMPOSITION
- ADROITNESS
- DESPOTISM
- BISHOPS
- REMINDING
- DECLARES
- ISABELLA'S
- HANNAH
- CONNEXION
- DECEASE
- INSTITUTE
- SAVIOUR
- PROVOKE
- DENS
- UNSTEADY
- SODA
- HUSHED
- TECHNICALLY
- ACCUMULATION
- PROCLAIM
- IMPERCEPTIBLE
- SEVERN
- BARGAINED
- DECISIVELY
- RENOUNCED
- UNACQUAINTED
- NOIRTIER
- PALAIS
- AIDE
- SUFFOCATION
- SAILOR'S
- HISSED
- WAKEFULNESS
- BARBED
- LAD'S
- TICK
- TRENCH
- ELIGIBLE
- PAVEMENTS
- PRECIPITATED
- DELEGATES
- LORRY
- ENTREATY
- ASPHYXIATION
- TENACITY
- ABSORB
- COUNTERACT
- AGONIZING
- DEPICT
- THROES
- TIPSY
- SHARK
- DEGRADING
- LAUREL
- MOUTHFULS
- FORKED
- WRIGGLE
- BAYONETS
- SENATORS
- REPUBLICANS
- DEMOCRATS
- CAROLINA
- WARDEN
- DISAPPROVAL
- LANDSCAPES
- LAURELS
- WRAPS
- COLUMBINE
- OUTSTANDING
- TRANSFORMATION
- PALER
- BROOME
- BENJAMIN'S
- MULTIPLICITY
- ASSISTING
- SOLACE
- CONSTRUCT
- CORROBORATION
- TOUCHSTONE
- SYLLOGISM
- AMALFI
- RIG
- DEARS
- AMBLED
- RAINED
- VERANDA
- SCOWLED
- OUNCE
- SKIPPED
- HARRISON
- LASS
- WORKMAN
- STUMPS
- ANNETTA
- UNPROFITABLE
- TRANSFIXED
- POINTER
- PLOUGHING
- MUNSON
- FITFUL
- BLUISH
- CELIA
- WADE
- PEAKED
- CARESSED
- SULLENNESS
- WAGED
- BLOODSHED
- GLOOMILY
- HIPPOPOTAMUS
- CY
- BILLY'S
- LICKING
- SISSON
- CUMULI
- HEAVIER
- PILING
- BENUMBED
- MOUNTAINEERS
- FILMS
- SPUTTERING
- PROFUSELY
- WAHSATCH
- OQUIRRH
- LILIACEOUS
- CRUMBLED
- BUTTERFLIES
- FRITILLARIA
- SHOOTS
- DOLEFUL
- HEARKENED
- BUSHELS
- FRIED
- MOORED
- INDEFATIGABLE
- CAYOS
- OBLITERATED
- TRUDGING
- CURTLY
- WAREHOUSE
- NUDE
- SWEDEN
- WOOD'S
- EVIDENCES
- CONCLUDING
- SCATTER
- EXCEPTIONALLY
- SOUL'S
- BUCKETS
- TILED
- ECONOMICS
- BANANA
- MULLINS
- SMELTERS
- CONSPIRACY
- MYRA
- CUBANS
- STEPHANUS
- COMBEFERRE
- MONDETOUR
- PHASES
- CONTRADICTIONS
- DOGMA
- JAVERT
- FORKS
- MONTPARNASSE
- SOUS
- THENCEFORTH
- UPRISING
- FATHOMS
- COWBOY
- NEWCOMB
- WAGGED
- MILL'S
- INDUCTIVE
- OBNOXIOUS
- PRESCRIBE
- PREFERS
- AGGREGATE
- HOSTILITY
- MONARCHICAL
- CERTIFICATE
- SWAGGERING
- CONCEALS
- UNPARALLELED
- TIMBERS
- CEREMONIAL
- ARCHED
- COURTED
- CHICKS
- CHANTY
- BOLDEST
- SCRATCHING
- HOWLS
- SCOWLING
- LARBOARD
- HAMPER
- CHOKE
- RUBIES
- CRISTEL'S
- COMPONENT
- COMPONENTS
- CHATEAUBRIAND
- STODDARD
- CRISTY
- INFIRMITIES
- WEEK'S
- SERVANT'S
- SIBONEY
- CHAPPARAL
- CONNECT
- LLEWELLYN
- KANE
- CAVALRY
- FORETOLD
- COWBOYS
- CARBINE
- CRITICISMS
- OCCUPANTS
- FIGHTS
- THORHILD
- THORSTEIN
- ERIC'S
- FISHED
- WINELAND
- ASGARD
- UNQUESTIONABLY
- EXACTED
- SADDLED
- CROPPED
- BARKING
- HEPBURN
- MONKSHAVEN
- HESTER
- CHANTED
- MANUFACTURER
- FOOTSTEP
- TIBERIUS
- CRAZED
- CHASTITY
- GILLENORMAND
- WIGS
- REBUKE
- SOOTH
- SANCY
- SCEPTRE
- COO
- PLUME
- PHRASEOLOGY
- MODIFICATIONS
- PRICELESS
- ACES
- HALLO
- PUCK
- PURCHASING
- BARCHESTER
- INTREPID
- COLLISIONS
- COURFEYRAC
- BLINDING
- ALLISON
- CULPRITS
- SAVELL
- SOPHOMORE
- ANNABEL
- CROSBY
- SCORED
- WHOOPING
- VERSAILLES
- PRUDE
- PIPED
- OCCULT
- HAMS
- DEMEANOUR
- HERB
- MONTEZUMA
- FLESCHE
- WEBSTER
- PRINCETON
- NEILL
- CRITIC
- ABU
- HASSAN
- ZIYADI
- GUESTWICK
- HANDIWORK
- JULIA'S
- THEATRICAL
- KEMP'S
- HARLOW
- IMPULSIVE
- MAGISTRACY
- CHIMERA
- SOOTHE
- FIDDLER'S
- LACHENEUR
- THROWS
- SOMERS
- POTHIER
- HUSBANDMAN
- LEGISLATOR
- MELCOMBE
- GROWL
- PORRINGER
- URSUS
- DOUGHERTY
- CHEEKED
- RIGOROUS
- PALELY
- FRESHMEN
- COUNSELLED
- GROTTO
- TOPICS
- BALDWIN
- ETA
- PYROXYLE
- HERBERT'S
- ADAD
- HITTITES
- ARAMAEAN
- INVADING
- KALKHI
- TEUTON
- MEMORIALS
- COMBED
- ROGERS
- VOTERS
- CYNTHIA
- FAIRFAX
- INTRUDE
- VERDICT
- POINDEXTER
- ERRED
- PALANQUIN
- ANKLETS
- LIED
- OWNING
- HATBORO
- KILBURN
- CALIFORNIAN
- RICHMOND'S
- RUBEZAHL
- COLVIN
- CURFEW
- WAPENTAKE
- QUORUM
- GENEVA
- CONFRONTATION
- MAYNARD
- BURGLARS
- TUNES
- MILAN
- CULTIVATING
- CAPITALISM
- ANTIPATHY
- LEDGER
- SAFIE
- EMPIRES
- SUPERINTENDENCE
- PALESTRINA
- FROSINONE
- CAVERN
- MONOCHORD
- LEGATO
- RUBATO
- PRELUDES
- CURRANTS
- ASP
- GRAHAME
- SHRIMPS
- IMPLICITLY
- CHAUVELIN'S
- LUTHER
- ENIGMA
- PROPERTIES
- LASHER
- NACKERSON
- MAHOMET
- SARACENS
- CHERSON
- PHILIPPICUS
- PORSENNA
- CHASKEY
- PRINTZ
- PLANCHET'S
- TRUCHEN
- ROCHELLE
- INDISCRETION
- UNWITTINGLY
- STEAK
- PETRIFIED
- INTUITIVELY
- BUTTERFLY'S
- ORDINANCE
- BRIGGS
- CRAYFISHES
- NEWFOUNDLAND
- BELOSTOMA
- OUCH
- FEELERS
- TAPPAN
- STOWAWAY
- SUTHERLAND
- AUGUSTINE'S
- PEARS
- MALLESON
- BERNARD'S
- KOREAN
- ANTUNG
- PURVIS
- BREAKER
- THOU'LL
- QUATERNARY
- GEOLOGISTS
- ARCHAIC
- EGOISM
- DECENCY
- SOW
- PATRIARCHAL
- PRIM
- STREAKS
- DISAPPROVED
- SPENCER'S
- SCHOOLING
- JOB'S
- BRUNSWICK
- SHINGLES
- BLANKLY
- WINCEY
- BRAIDS
- AWKWARDLY
- SHYLY
- PULLS
- SEASICK
- PROWL
- DETESTED
- MAGNITUDE
- GUARDIANSHIP
- REPELLING
- RECURRED
- INCURABLE
- EXTRICATE
- CORE
- IGNOMINIOUS
- INFLEXIBILITY
- SUPPLICATE
- MERCHANDISE
- MAGNIFY
- IMPERTURBABLE
- IRREPARABLE
- RUMORS
- UNPUNISHED
- RUNAWAY
- SIREN
- ASSIZES
- BEHAVING
- UNWORTHILY
- ABSURDLY
- KENNEL
- CROWING
- DARN
- SHRINKING
- GENIALITY
- MENIAL
- CONCESSION
- LISTLESS
- IMPARTIALLY
- PAINTS
- SOFTENING
- RECTITUDE
- PALAZZO
- CRESCENTINI
- HOVERING
- RECOGNISABLE
- JUDGEMENT
- THOROUGHNESS
- REPUBLICS
- STRICKLAND
- ABUSIVE
- DEFENSIVE
- STRENUOUSLY
- EMBASSY
- PURSUANCE
- NETHERLANDS
- STRENUOUS
- CREWS
- INTERCEPT
- BALTIC
- SOUTHERLY
- SHETLANDS
- INFERIORITY
- SEAMAN
- WARSHIPS
- PRIZES
- DENMARK
- ASSUMPTION
- INSISTENCE
- CONTESTED
- BLOCKADE
- NEGOTIATION
- OBSESSED
- ACCESSION
- INGRATITUDE
- GUARANTEED
- ENVOY
- CIPHER
- FORWARDED
- VALIDITY
- INSURMOUNTABLE
- DRAIN
- SAUCEPAN
- SPRIG
- CELERY
- NUTMEG
- MALT
- TABLESPOONFULS
- UNITES
- PORTRAY
- SAVOR
- CHILLING
- MUSES
- CANKER
- PRECINCT
- RECOUNT
- SOOTHES
- EXPANDS
- EMPLOYER
- MIGHTILY
- GINGERLY
- KNOB
- STUDS
- NEWCOMER
- KNUCKLES
- STRANGER'S
- GLOVED
- AGONIZED
- SEALING
- THUD
- SHOCKING
- HALLWAY
- DETAINING
- PATAGONIA
- CLANG
- ARCHER
- DULLEST
- GOUTY
- MORSELS
- INCIDENTAL
- DRYNESS
- IMMODESTY
- LICENCE
- CENTRED
- CARICATURE
- FLAGGED
- STEEPED
- MANOEUVRE
- TRIUMPHS
- SPONTANEOUS
- BAS
- HARMONIOUSLY
- LUGUBRIOUS
- PREVISION
- CAPACIOUS
- TIMBERED
- PROFUNDITY
- ENDURABLE
- HURL
- RIVERSIDE
- LAZINESS
- SONOROUS
- BLEST
- MYSTIFIED
- MISTAKING
- INTERTWINED
- SHIPWRECKED
- CRICKETS
- ROSETTE
- ELSE'S
- OVEN
- DWARFS
- CLOTHS
- PANS
- LONGINGS
- INTENSIFIED
- CRIPPLED
- GLORIFICATION
- REPUGNANCE
- EXPLOIT
- RELAXATION
- DEGENERATE
- ACHIEVING
- FAIRNESS
- DEVELOPS
- DOSES
- PARALYZING
- RETALIATION
- INCREASINGLY
- KEEPERS
- SPELLING
- SMACKS
- BENEDICTION
- PULLMAN
- TREMORS
- JAY
- EFFULGENCE
- ELASTICITY
- IMPLEMENT
- CANNED
- UNTROUBLED
- PAWN
- BRIMSTONE
- FIREWORKS
- SPECULATING
- LAPIERRE
- ASTIR
- JONATHAN
- HERBAGE
- PUDDLE
- UNRAVEL
- WOODLAND
- PROCLAIMING
- WESTCHESTER
- TORONTO
- DETENTION
- BURLY
- FUNEREAL
- OUGHTN'T
- AFTERMATH
- TINKLING
- MANNERED
- TEMPORARILY
- FUTILITY
- LOPPED
- GRUNTING
- IGNORES
- SETTLES
- SURER
- ABOMINABLY
- IMAGINATIVE
- LOUNGED
- UNCTUOUS
- MUTELY
- WEIGHTS
- MORSEL
- DOUBLY
- NAMING
- PALATE
- LABORING
- SCALD
- ARNICA
- TUBES
- RHEUMATIC
- NOSTRIL
- USEFULNESS
- PROMOTING
- WRAPPER
- UNSURPASSED
- TONIC
- DUSTS
- VARNISH
- PERFUMES
- CIMETER
- DISCONTINUED
- DINARZADE
- RESOLVING
- MISTRUST
- CONJURING
- ACQUAINT
- WIDOWER
- ARABIC
- POTION
- RAVED
- PROVEN
- ELEVATOR
- LOADS
- RUMORED
- UNANNOUNCED
- WUTHERSPOON
- CROSSROADS
- REGRETFUL
- MINNESOTA
- MORTGAGED
- CIGARS
- ORATORICAL
- PUNCH
- BOUNTIFUL
- HEARTHSTONE
- CAMEL'S
- CRABS
- CHEWING
- WESTERNER
- FOOTSTOOL
- BOOST
- FAMED
- EQUALLED
- PROMOTER
- FILED
- ANXIETIES
- STUDIOS
- EXPLORERS
- METHODIST
- SUPPERS
- AMBASSADORS
- INFIDEL
- SCIENTISTS
- ENLIST
- MILITANT
- SUFFRAGIST
- GRUNTED
- BANTER
- FLATS
- FRENZIED
- SARDONIC
- GROCERY
- PROTECTING
- TIMOROUS
- POISE
- INVOLVING
- IMPERSONAL
- STRAIGHTENING
- ALLEVIATE
- BEAUCHAMP
- AFFABLE
- PURITANICAL
- DISHONORED
- FINANCE
- DEMOLISH
- DEPOSIT
- RALLIED
- PORTFOLIO
- CHARITIES
- WIDOWS
- NABOB
- DEFICIENCY
- MONOTONY
- SKYLIGHT
- CLATTER
- COMPOUNDED
- BORES
- SQUALID
- SUNFLOWERS
- SUMMERS
- STICKY
- DEPOSITS
- MAGNIFIED
- DISTASTE
- DODGING
- UMBRELLAS
- ADOLESCENCE
- CALORIES
- MIND'S
- SUE
- JILL
- INSTRUCTOR
- THOUGHTLESS
- HYDROGEN
- MURCHISON
- COLUMBIAD
- HUMORED
- DEADEN
- PULSATION
- BALTIMORE
- RAMMED
- INTERPLANETARY
- CLASP
- STANDARDS
- AXIS
- CEREALS
- WEIRDNESS
- VITALITY
- TRASH
- ANNOUNCEMENTS
- BRIEFITES
- PARTAKE
- TROLLEY
- WIZARD
- CONSTRUCTING
- WHEREON
- TRANSLATION
- KERM
- CHER
- GLORIFIED
- RYNCH'S
- MOLD
- JUTTING
- WATCHERS
- SPACER
- GROUPED
- WEBBING
- ANGLED
- CLAWED
- THIGHS
- SHIED
- FRANTICALLY
- WORCESTER
- BEVERLEY
- EDWARD'S
- EXPELLED
- DISBANDED
- MERITORIOUS
- RUSSET
- DESERVEDLY
- RIDDLES
- HAMPTON
- BRIDES
- DETAILED
- TELEPATHIC
- SYMPTOM
- ELABORATION
- LEGISLATURES
- RECOGNIZES
- SUGGESTIBILITY
- REENFORCED
- NATURALISTIC
- THERAPY
- ARGUES
- THERAPEUTICS
- INALIENABLE
- URGES
- POSTULATES
- INJECTIONS
- PERVERTED
- OVERLOOKING
- UNINTENTIONALLY
- ABSTAIN
- UNDESIRABLE
- CLINIC
- FULLNESS
- UNDESERVED
- VIEWPOINT
- MASTERING
- UNSTABLE
- PO'LY
- UNS
- BABYHOOD
- OVERHUNG
- FLUX
- GULFS
- EPOCHS
- PERPLEXING
- PANORAMA
- UNFAMILIAR
- WAKES
- CONFUSING
- MAXIMUM
- TRANSLUCENT
- COLOURLESS
- SURGING
- REMOTELY
- DEEPENED
- ENORMITIES
- ESPOUSED
- ALLEGIANCE
- EXPLOSIVE
- EXASPERATING
- UNRESTRAINED
- LUCIDITY
- FEIGNING
- UNSUSPECTED
- EXPEDIENCY
- GROOVES
- DIVERSITY
- ABSTRACTION
- UNDISPUTED
- SICKLY
- INSPIRITED
- INSUFFERABLE
- IRRITABILITY
- IRRELEVANT
- CONSTRAINT
- BLUNDER
- CONSOLING
- INSUPPORTABLE
- REQUIREMENTS
- MARVELOUSLY
- RASHNESS
- UNMISTAKABLY
- PARAMOUNT
- PREDICT
- PROTRACTED
- VEXATIOUS
- LOUISE'S
- IMPOSTURE
- WELDON
- ASSORTED
- MOROSE
- ECSTATICALLY
- BARNYARD
- WETTING
- INSISTENT
- VOLCANIC
- SATURATED
- INCANDESCENT
- HABITABILITY
- SOLAR
- DIFFUSE
- CALCULATIONS
- DIMINUTION
- LINEAMENTS
- SIEVE
- TUNIC
- SNAPPISHLY
- DEVOTEDLY
- ROGUISH
- STEEPER
- BLACKED
- WETTED
- BUMPED
- SIPS
- BUBBLED
- PUNISHING
- UGLIER
- SIGNIFIES
- MUFTI
- PEEL
- ORANGES
- SUPPING
- ASSASSINATED
- DARTS
- LAUDABLE
- ILIAD
- PROFITING
- DEPORTMENT
- ENTERTAINERS
- EMPIRICISM
- INHALE
- WEED
- INHALED
- EDDIES
- SQUAW
- PRECEDES
- TERRITORIES
- LOUNGING
- IMPRESSIVELY
- ASSURANCES
- SALUTATIONS
- FRUGAL
- TOMAHAWKS
- REPULSE
- CORA
- SWALLOWS
- AUDITORS
- EVASIVELY
- SLAUGHTERED
- INSTANTANEOUS
- DUR
- INSINUATION
- DISAPPROBATION
- TEMERITY
- UNCONCERNED
- VIGILANT
- FASHIONS
- WORKINGS
- ADOPTION
- APPETITES
- FAVORITES
- HOUSEWIVES
- JUNCTURE
- WHOLEHEARTEDLY
- PERKINS
- PLUSH
- CEILINGS
- HUES
- SYNDICATE
- TRUSTWORTHY
- INGREDIENTS
- REEL
- MARKETS
- INTERVIEWED
- IMPERIOUS
- BLINDS
- UNFOLDED
- PRONOUN
- INEXPERIENCED
- CONFIDENTIALLY
- CORROBORATE
- UNBELIEVABLE
- NICKNAME
- WRESTLED
- BEREFT
- ALLERS
- SAG
- APPROVINGLY
- BAREFOOT
- BELLE'S
- FESTAL
- GLISTENED
- ENCHANTING
- INFER
- INUNDATED
- DRIVER'S
- PATTY'S
- HILLIARD
- INITIALS
- BRIDESMAIDS
- FERVENTLY
- CHRONICLE
- PADS
- SNIFFING
- DAINTILY
- MAE
- BRISTLED
- JALIB
- MOSQUES
- MAT
- SUSTENANCE
- TESTIFY
- HAROON
- FRAILTY
- ENLARGING
- EXTOLLED
- ALIGHTING
- WAITS
- INCAPACITY
- DURST
- OVERCHARGED
- JAAFFIER
- EGYPTIANS
- CONTRACTS
- WREN'S
- GRAYISH
- REDWING
- BLACKBIRD
- COAXED
- BLOTCHES
- FLARING
- FLEMING
- ASTRIDE
- EBB
- DISPENSE
- GRIEVANCE
- CALAMITIES
- LEAVEN
- DWARFED
- PURSES
- LIGHTEN
- RUSE
- CREDITORS
- HOSTELRY
- DEFINE
- DISPOSES
- SUNS
- ATTACH
- BEHAVES
- LOFTIER
- CULTURED
- UNINTERESTING
- MYSTICAL
- PEDANT
- PLEBEIAN
- COSTUMES
- PREFERENCES
- DESPERATION
- ANDREWS
- VALUATIONS
- UNINTELLIGIBLE
- SUFFICIENCY
- EVOKE
- BOUNDLESS
- CREATIVE
- UNDERGOING
- SHRED
- MIRE
- HAMMER
- GRANTING
- SANCTIFIED
- SANCTITY
- EXPRESSES
- QUESTIONABLE
- FUEGIAN
- PATTING
- SLAPS
- GUTTURAL
- GRIMACES
- INSTRUCT
- INDUCEMENT
- POX
- TACITURN
- SIMPLEST
- HARANGUE
- MOUNTAINOUS
- PEAT
- DESCENDS
- SWAMPY
- PREDOMINANT
- ENLIVENED
- DWINDLED
- SUCCEEDS
- HABITATIONS
- LACED
- SCREAMS
- SUPERSTITIOUS
- PERSONIFIED
- LABORIOUS
- PERPETRATED
- BYRON
- DECREASE
- EMERGING
- KEN
- INANITY
- CHASING
- POPLARS
- PUDDLES
- MUFFIN
- THRONGING
- GRIZZLY
- VALIANTLY
- LION'S
- PANT
- STILE
- RAMBLED
- BILL'S
- LAWLESS
- HEDGEROW
- AIMS
- ALTERNATE
- CHANT
- WHIPPING
- CLIPPED
- HOMEWARDS
- DAINTIEST
- BROTHERLY
- REBELLED
- DESPAIRED
- PLAYMATE
- ECLIPSE
- SUFFERERS
- CONGRATULATION
- POEM
- OATS
- PLEASANTER
- WINTRY
- VIOLINIST
- ORDERING
- PENITENCE
- SOAR
- KINGBIRD
- RACKET
- CRESTED
- SHADING
- RESTFUL
- UGH
- WOODPECKER
- NUTHATCH
- NESTING
- SCREECH
- BITES
- BRUSHY
- SWAMPS
- PEBBLY
- MARKINGS
- BOBBING
- DIFFERED
- SURFACES
- RAPIDS
- BALES
- DISAPPEARS
- FRONTS
- HEWN
- CLAN
- JARGON
- GREEN'S
- DAWDLED
- SKIP
- TWIXT
- BREATHES
- THROBS
- INEXHAUSTIBLE
- WARPED
- ALBUM
- DECREES
- SCULPTURED
- JOG
- SNAPPER
- STEVE
- BULLDOG
- UNSADDLED
- FRYING
- THINKIN
- GLIMMERING
- MADDEN
- PRETTINESS
- FLESHLESS
- DEATH'S
- TREMOR
- REALIZING
- DESERTING
- WINEGLASS
- SPITEFUL
- FAIRY'S
- LOCRINOS
- BIRD'S
- FAREWELLS
- PRISONER'S
- JEWELLED
- FONDLY
- APPEASED
- REJOICINGS
- RAVENS
- MORLANDS
- LANK
- ENJOYMENTS
- PROPENSITIES
- QUOTATIONS
- HUMOURED
- NEEDLEWORK
- FRET
- ESSAY
- AVOCATIONS
- APOLOGIZE
- SILENCING
- WOODSTON
- AFFRIGHTED
- LATTERLY
- SHREWDNESS
- NECESSITOUS
- MURDERING
- PITIABLE
- SANCTION
- REPLETE
- RETROSPECT
- LUDLOW
- EXHILARATING
- EXCLAIM
- PYRAMIDS
- SHRUNKEN
- ADMITTEDLY
- SOLICITOUS
- GARDENER'S
- DAPPER
- SALESMAN
- DAINTIES
- SACKING
- NELLIE
- CRASHING
- HUSSY
- HIDEOUSLY
- DISCOLORED
- DENVER
- FIBRE
- UNBRIDLED
- SPAR
- UNREST
- LUCID
- OCEANS
- RAILS
- CLEARINGS
- UNROLLED
- WISP
- PUNKAHS
- POIGNANT
- PAD
- SPLASHING
- BULKHEAD
- VIVIDNESS
- REPRODUCED
- PERDITION
- ASSESSOR
- SUNBURNT
- DRAPERIES
- BUTTONED
- INCOHERENT
- AUDIBLY
- ELONGATED
- HARBOURED
- EXPANDING
- BINDS
- MEDITATE
- DINAH'S
- TREADS
- WANED
- NAG
- BLURRED
- MUSICIAN
- MILDER
- SHELTERING
- SOFTER
- STARTLE
- QUARTS
- RECIPE
- EIGHTHS
- BATTER
- KETCHUP
- PRICKLY
- RIFLED
- MUSHROOM
- FRAMED
- UNSTEADILY
- CARESS
- CYCLING
- DANGLED
- LENGTHEN
- CAREWORN
- OCUMPAUGH'S
- EXAGGERATION
- BURDOCK
- DISHONESTY
- FELDERSON
- DINNAH
- DONKEY'S
- HOSANNA
- PALESTINE
- PASSOVER
- NAZARETH
- CLINKING
- BAPTIST
- ANECDOTES
- BEAU
- INTOLERABLY
- CONCEITED
- PURR
- THRIFTY
- BLAIR
- SPICE
- PICNIC
- PRECINCTS
- WAVERED
- SOUNDLESS
- MARIANNE'S
- HACKNEYED
- BLASTED
- PLAIT
- EYEING
- JENNINGS
- RAILLERY
- NEWNESS
- ARCHNESS
- HUMBLED
- PERFECTIONS
- HABITUATED
- VICTORS
- VALOUR
- QUERIES
- AMUSEMENTS
- CANDIDATE
- ILLUMINATE
- RETURN'D
- MAGUS
- IMPERIOUSLY
- HOUSINGS
- FEINT
- ANTAGONISTS
- SADDLES
- COMMENCING
- BINGLEY'S
- LOUISA
- AGREEING
- MEANNESS
- APOTHECARY
- LIZZY
- UNVARYING
- WITTICISMS
- WILY
- ASSEMBLING
- FRATERNAL
- UNAVOIDABLY
- DRYLY
- SUBDUING
- QUENCHED
- POURS
- ELEMENTARY
- SUBSERVIENT
- PINNACLES
- THUNDERS
- WRECKS
- DISORGANIZED
- INSIGNIFICANCE
- EPIDEMIC
- LAMENTATION
- TAMPERED
- PARAGRAPH
- GRAVEN
- RESIDENTS
- NECESSARIES
- PLOUGH
- WORTHIER
- MANUFACTORIES
- DIMINISH
- LIKELIHOOD
- CHEVALIER
- GRUFF
- TURRET
- ASSERTIONS
- EXERTED
- LAVISHLY
- STRAIGHTEN
- BARTLETT
- CARYOE
- CHARLIE
- THUMBS
- DISTINCTNESS
- EXPENSIVELY
- TREND
- TWINKLE
- TILTING
- QUARTERSTAFF
- VENISON
- NOTTINGHAM
- TANNED
- WILES
- TROD
- DRUBBING
- SMITE
- THUMPED
- EVENLY
- INNS
- CARDED
- GAITERS
- VOUCH
- DOROTHEA
- VASSALS
- INDUCING
- BETROTHAL
- IDLERS
- STAB
- PRECIPICE
- PANGS
- SCORCH
- WOLF'S
- PLOUGHED
- PERFUMED
- FOAMED
- LAMBS
- EDICT
- GASCON
- APPRENTICESHIP
- MELEE
- CALMING
- LOUVRE
- BLOCKHEAD
- DILATED
- LIBERALITY
- BONACIEUX
- CONJUGAL
- MAJESTIES
- GOLDSMITH'S
- GEM
- CADET
- SHUN
- FLIRTATION
- KNOWL
- SITUATE
- REVERIE
- DEVOURING
- COMMISSIONED
- FLUENT
- MADAME'S
- COUGHING
- HAUNT
- ACCOMPANIES
- KINDEST
- INMATE
- CRACKY
- FUMBLING
- OPERATES
- EXHIBITS
- PHRASES
- UNLIKELY
- OBEYING
- SUBJECT'S
- GLASSY
- SIMILARITY
- DILATE
- COOPER'S
- MAGNETISM
- RESPOND
- PARADOXICAL
- PROGRESSED
- TAM
- MUSKETRY
- PRESUMABLY
- UNEDUCATED
- SINGLED
- TOUGHEST
- WALLED
- SODDEN
- CROWS
- VICTUALS
- INFIRMARY
- LATTICE
- BRUSHES
- BUSTLE
- HORSESHOES
- LONGS
- PARTNERSHIP
- NEWPORT
- BEN'S
- AX
- STRANDS
- TRANSMUTE
- ALEMBIC
- EXPLORER
- DRAUGHTS
- NOBLER
- ARROGANT
- WONDROUSLY
- SMOOTHNESS
- INCORPORATED
- NOTABLY
- SHRINES
- UNCRITICAL
- EXTENSION
- GODDARD
- WEAKENS
- ADJUST
- SIMPLIFY
- INFECTIOUS
- LUSTY
- NEATNESS
- STEAMER'S
- WIMPOLE
- SCANNED
- PRACTITIONER
- KNOWETH
- BLANDISHMENTS
- PORTERS
- CIRCULATED
- WILDFELL
- NIGHTLY
- FROLICSOME
- CEMENT
- PERUSE
- PROSPERED
- BRILLIANCE
- BLANCHED
- HOOFS
- SALUTING
- MILLWARD
- VICARAGE
- DEPRAVITY
- BEVERAGE
- REPREHENSIBLE
- INVADERS
- BARBARIAN
- MAXIMUS
- TIBER
- SACKED
- SEAPORT
- RAVAGES
- TRUCE
- NARSES
- CARAVANS
- KORAN
- MEDINA
- ATROCIOUS
- MULTIPLE
- HAZARDS
- CALCIUM
- NATURALISTS
- SECRETE
- INSIDES
- SOLIDIFYING
- OYSTER'S
- STERILE
- DUBIOUS
- EXTRACTING
- TWOFOLD
- BASTARD
- OPAQUE
- STRAINERS
- CLEOPATRA
- COLLECTS
- SAGGING
- HAIRLESS
- LINGER
- ED
- SUPERVISION
- DISCHARGING
- DAYTON
- COMELY
- PITFALLS
- STRIVEN
- FRAUGHT
- THREESCORE
- MASSACRES
- ARMENIANS
- VIOLATED
- EDITORIALS
- SUDAN
- RAPE
- FULFILMENT
- NORWEGIAN
- TARRED
- ANTARCTIC
- STAYS
- PROPORTIONATELY
- BRIDGES
- WORKSHOPS
- SLACK
- TEMPERATURES
- DAMPNESS
- SEAMS
- DISINCLINED
- SMARTING
- TALLOW
- LENGTHS
- ALTERATIONS
- FISSURES
- PHOTOGRAPHIC
- RELIABLE
- PEMMICAN
- WISCONSIN
- BEACON
- ATTAINING
- NOOKS
- PRETTILY
- PROPRIETORS
- UNDISTINGUISHED
- REPRESENTATIONS
- QUARTERLY
- EDMUND
- RESTLESSNESS
- SAVOURY
- PRACTICABLE
- MINORITY
- ENTRUST
- UNEQUIVOCALLY
- DERIVES
- SUPPORTS
- CLOG
- FINED
- SPINDLE
- LOOSELY
- JADES
- PRESIDED
- FATES
- SKULKED
- TEXTURE
- HUNGRILY
- ATTRACTING
- POCKETBOOK
- GROOMED
- WOWZER
- POKE
- GETTER
- LURE
- IRRITABLY
- POMPOUS
- MAHOGANY
- MELODRAMATIC
- GRUFFLY
- ASHEN
- HELLISH
- KNOBS
- BANK'S
- STEAMSHIP
- DENIALS
- BABBLING
- REELING
- TWEEZERS
- GRANTLINE'S
- SUITE
- SOMBER
- PLOTTING
- JERKIN
- PANTS
- SWAGGER
- RASP
- HERITAGE
- TRAJECTORY
- SNAKY
- JOCULAR
- LASHES
- INTOXICATED
- RADIO
- ASSAILANT
- SIZZLING
- STARLIGHT
- DRAMAS
- LEVER
- STARCH
- UNEXPLAINED
- HOTTEST
- RHETORIC
- UNSHAKEN
- CONSTANCY
- PATS
- REPRESS
- QUETCHAM
- ALLOWANCES
- OFFSPRING
- FIXEDLY
- QUICKEST
- RASHLY
- REVISED
- HOLINESS
- INSTITUTIONAL
- CHIP
- HELSTONE
- FERN
- REVELLING
- URGENCY
- UPLAND
- AUTUMNAL
- GORMANS
- LABOURERS
- BACKGAMMON
- DIXON
- ALIAS
- HALES
- MORTIFIED
- ALTERCATION
- ASPERITY
- MITIGATE
- OVATION
- BARRELLED
- SAGACIOUS
- INCENSE
- KEYED
- SOARED
- MILLY'S
- ODDEST
- ENDLESSLY
- IMPERTURBABLY
- STOCKING
- MASKEW
- EXCISE
- OFFING
- GENTLEST
- SMUGGLERS
- PURBECK
- TWINGE
- SNAIL
- TWOULD
- BRAMBLES
- STRIDE
- RUBBLE
- SUFFOCATING
- HATCHWAY
- WOES
- TUMULTS
- SINEWS
- COMMOTIONS
- SCANDALIZED
- CARNAL
- GOATS
- BOLING
- JEALOUSLY
- ALMA
- MATER
- COLLEGES
- SAKI
- SAKI'S
- BLEAR
- DIPS
- TEASE
- VANKA'S
- RUBLES
- OLGA
- IGNATYEVNA
- WRAPT
- FRIGHTFULLY
- DOG'S
- TOKENS
- VARIABLE
- ABATEMENT
- DISTEMPERS
- SADDLER
- OVERSEER
- PORTUGAL
- APPOINT
- IRRESOLUTE
- NOISOME
- WHEREOF
- INEXPRESSIBLE
- FALLON
- FRESHLY
- DRYING
- HAPPENINGS
- SORROWING
- CUPBOARDS
- PUNISHMENTS
- PANTHER
- LIEUTENANTS
- DESERVING
- HUGGED
- TRIMMING
- FRISBIE
- TUFT
- NEUCHATEL
- ENTRANCED
- HERALDS
- FRAU
- MARIE
- SLIPS
- SENORITA
- CONTRIBUTORS
- TOWERED
- SAP
- WILDMAN
- FLETCHER
- AUGURED
- FEUDAL
- SHAFTESBURY
- VULNERABLE
- CANONS
- PUNCTILIOUSLY
- CONGREGATED
- TRANSACTION
- EDINBURGH
- REVOLTED
- SUPPORTERS
- RAM
- SURVEILLANCE
- PRUSSIA
- SOUNDINGS
- TENFOLD
- RAMS
- DEBATED
- CRYSTALLIZED
- PURGE
- ENERGETICALLY
- FURNACES
- STOIC
- DEFIED
- FANATIC
- KIT
- AFTERDECK
- MAJESTICALLY
- FERRIES
- TENDERS
- WHARVES
- THROATS
- HAILING
- NEGOTIATE
- ISLET
- AMAZINGLY
- BLACKISH
- DENSELY
- CHESTS
- TACK
- STRIPPING
- FICKLENESS
- BUNKER
- DONNITHORNE
- HEV
- HEARER
- CITED
- DISAGREE
- PITCHING
- TONGUED
- MARTYR
- MICHAELMAS
- CALLOUS
- RETRIBUTION
- RUMINATING
- WOUNDING
- LEVISON'S
- WOOLEN
- UNDECEIVED
- CALMER
- AMICABLY
- CAMPING
- UNABATED
- BRAKES
- GUSTS
- SNOWFALL
- TOILED
- FORENOON
- IRREGULARITIES
- BJAALAND
- THORVALD
- ABYSSES
- THOUGHTFULNESS
- CREVASSE
- BALLADS
- JINGLES
- RHYME
- PUSSY
- PUSS
- DESCENDANT
- FLEET'S
- DISMISS
- CRADLES
- RESIDING
- EDITIONS
- DECADE
- ILLINOIS
- NESTLED
- PROFICIENT
- RUFFLES
- HAMMERING
- DEPUTED
- THAWING
- INDISTINGUISHABLE
- BAWLING
- ALCOVE
- OBLIQUELY
- BALCONIES
- ROBED
- ARCHES
- DIPPING
- BARGES
- LONGITUDINAL
- UNDULY
- TESTIFIED
- NOTICEABLY
- VICTORIAN
- LABOURER
- CHINKS
- AISLES
- MASONRY
- VAULTS
- ILLIMITABLE
- YELPING
- DETERMINATE
- USURP
- NUPTIAL
- ZEPHYR
- PERFORMERS
- LUTE
- PREYED
- SICKLES
- FROWNS
- INEXTRICABLE
- WOOLLY
- PROSERPINE
- REALMS
- ALLEGORY
- ALLUDES
- WOVE
- SAPPHIRE
- HOLIEST
- NIBBLE
- ROQUEFORT
- CUBE
- BUNG
- ANDRE
- PALATABLE
- UNCHANGING
- HUDSON'S
- BOUNTY
- INVETERATE
- LIGHTENED
- ONTARIO
- ROAMED
- WINDINGS
- JESUITS
- UNMOLESTED
- RICHELIEU
- DESTINIES
- DOWNFALL
- HINDRANCE
- DULLED
- VULTURE
- FORESIGHT
- ACUTENESS
- REFRAINED
- FLUENTLY
- SECT
- BAMBOO
- NAZORAERU
- GOBLIN
- HERMITAGE
- CAPACITIES
- DEWS
- HAUNTERS
- WICKEDLY
- SUTRAS
- RECITING
- PLUCKING
- BRIGHTENING
- GOBLINS
- SUWA
- SAMURAI
- TRANSITORY
- EXCLUDE
- CROWED
- SPINSTER
- DENOTED
- DEIGN
- ENGENDERED
- MOIST
- EXHIBITING
- CONSTRUED
- DEARER
- REBELS
- COMPLETING
- CASUALTIES
- COSTING
- CHASTISEMENT
- LINEAGES
- PYRAMID
- MAINTAINS
- MONARCHS
- MEDES
- WILLS
- ERRANTRY
- PROVERBS
- CODICIL
- EMERGENCIES
- JINGLE
- BASILIO
- MATRIMONIAL
- NOONDAY
- NOTARY
- AFFIDAVIT
- EXCELLENCES
- SLOTH
- SNORING
- ENVYING
- THRESHING
- SUCKING
- INTERMEDIARY
- PRESERVER
- BACKING
- ACCOMPLISHING
- EMITTING
- FOREBODINGS
- PINNACLE
- CHAINED
- WARHOON
- THARK
- VANQUISHED
- INCUBATOR
- MANIACAL
- FIENDISH
- ABASHED
- STOCKADE
- LOCKER
- DAVY
- JONES'S
- SURVIVING
- SNORED
- SPOUT
- STRANGLING
- NICK
- MAROON
- GUNN'S
- SUPERSTITIONS
- LOWLANDS
- INGRATIATE
- GUNSHOT
- EXPOUND
- COMETS
- NECESSITATED
- FEIGNED
- DEMONSTRATE
- TRAVERSES
- OBSERVABLE
- INANIMATE
- DEDUCING
- KINDLING
- FERMENTATION
- DISSECTED
- VIZ
- CONVENIENTLY
- POUCHES
- ENTRANCES
- AMOUNTS
- SURGEONS
- ESCAPES
- THINNER
- HUMORS
- PUBLISHING
- SPONTANEOUSLY
- ANALOGOUS
- ASTRAY
- FIFTHS
- MESSRS
- BIOGRAPHY
- PLAYGROUND
- SLOWNESS
- EXHIBITIONS
- TRADESMEN
- BATTELS
- DUNG
- SCHOOLFELLOW
- SCHOSS
- DISTRACT
- NATASHA'S
- MYTISHCHI
- QUILT
- SUPPLE
- EARTHEN
- GOSPELS
- WHIFF
- RESTRAINING
- SOMNAMBULIST
- IMPUNITY
- NIGGER
- MARSER
- LARD
- SALOONS
- NATCHEZ
- FORGETFUL
- REPLYING
- ALPATYCH
- OBDURATE
- SHOVE
- BROADSHEET
- ARSENAL
- SHUFFLING
- RIOTING
- BANTERING
- BURGHERS
- MOZHAYSK
- SMOLENSK
- HIGHROAD
- UTITSA
- STIFFNESS
- EXPECTS
- SUSPECTS
- CHARLOTTE'S
- PLEASANTEST
- EXERCISING
- UNACCENTED
- DUPLE
- VARIES
- GOLOVIN
- MINERS
- ARCTIC
- SEAWEED
- IMPEDED
- MAILS
- COT
- LEGION
- LACES
- BOARDED
- JENNIE
- MIGRATION
- DATUM
- SEGREGATION
- QUASI
- SCOOP
- WHIRLWINDS
- INDIGENOUS
- ZOOLOGIST
- ORTHODOXY
- DISCHARGES
- STICKLEBACK
- HERESY
- CRUCIFIXION
- RIGOROUSLY
- ORIGINS
- PRECIPITATION
- ARCHAEOLOGISTS
- ORTHODOX
- CONCEIVING
- CORNWALL
- UNINTERESTED
- LIZARDS
- DAMNATION
- TENNESSEE
- HOGS
- HOG
- NIGGERS
- PARSONS
- DURHAM'S
- WHO'LL
- SPORTY
- CLIMATES
- FOREFEET
- INFAMOUS
- SOMEWAYS
- CARPENTERS
- WIDOW'S
- BUD'S
- TATE
- ENGINEERING
- HANDICAP
- GESS
- HOLATI
- FEDERATION
- PLASMOIDS
- INDUSTRIAL
- RAIDER
- TARGET
- PROFESSOR'S
- HUH
- WINTERS
- OREGON
- BETTY
- COSU
- LOGICALLY
- DEXTEROUSLY
- HERNDON
- MATS
- CLIENTS
- ADMINISTRATION'S
- STRAITJACKET
- CLOSETS
- BAYING
- HABEAS
- CORPUS
- NORFOLK
- LEGALITY
- VIOLATION
- CONTENDED
- MILFORD
- FOLLIES
- EMBLEMATIC
- RESORTS
- WHACK
- EXTENUATING
- ROC'S
- OUTLAWED
- SHIPPED
- PIRACY
- DOUGHTY
- MERIDA
- IGNOMINIOUSLY
- UNFOLDING
- STRAIGHTFORWARD
- AWKWARDNESS
- ALOOF
- BETHINK
- PERMISSIBLE
- GLEANED
- FATHERLAND
- ENTITY
- BEFELL
- SENESCHAL
- FORSOOTH
- DOLEFULLY
- SPURS
- COMMANDMENT
- ASKEST
- KNIGHT'S
- HEREUPON
- APOSTLE
- JACOB
- PORING
- COAXINGLY
- AILS
- ADDITIONS
- DUET
- LESLIE
- ENNA
- SMOOTHING
- VACATED
- BONBONS
- INTERSPERSED
- UNWHOLESOME
- VENTNOR'S
- TIMED
- SUMMONING
- JAVELINS
- WEIRDLY
- DISQUIETING
- GLOBES
- RECTANGULAR
- CLICKED
- PARODY
- SMITING
- ROCKED
- ANNIHILATION
- NORHALA'S
- WRAITHS
- ENIGMATIC
- BILLOWS
- COUNTERPART
- SHRUBS
- SNUGLY
- QUEERLY
- THAR
- FISHIN
- WAYWARD
- PUFFING
- WHAR
- MOTTLED
- GEE
- PEAL
- BUMPING
- TRANSLATE
- ROWBOAT
- YACHT
- BOHEMIAN
- FLINGING
- DISSOLVED
- DOMINICAN
- CLOISTER
- SUPERIORS
- DEATHBED
- MEDICI
- RIGHTFUL
- CALIBRE
- LUCRETIA
- REFEREES
- RAGING
- SCOUTING
- WYANDOTTE
- WILCOX
- ROCHESTER
- BUNTLINE
- LIZZIE
- SHANDY
- CIVILIAN
- CHATTELS
- CITADEL
- TURN'D
- ALL'S
- TRIM'S
- HUMOURS
- FLIMSY
- DISTINCTIONS
- WIDOWHOOD
- RECALCITRANT
- MAGNATE
- STOCKED
- PAPPUS
- JUDEA
- MACHAERUS
- JERICHO
- DEMOLISHED
- AUXILIARIES
- ADVERSARIES
- CUSTODY
- EMPTYING
- SLAYING
- FAINTNESS
- WITHHELD
- FREEING
- INTERPRETING
- MODERATED
- PREPOSTEROUS
- COURTLY
- INDIGNITY
- CHIRPING
- FIREARMS
- RILEY
- GALLED
- DRAFTED
- PENAL
- PRECEDENT
- MUZZLES
- KNELL
- SURVILLE
- VETCH
- BILLOW
- OUTSPREAD
- SPRAINED
- COX
- DESCRIED
- HELM
- SPURTS
- WEARISOME
- THEODORE
- TACOMA
- CONSEQUENT
- IMPROVISED
- CONTENTION
- COWLITZ
- OLYMPIA
- DRENCHING
- SELLS
- BUYS
- AGRICULTURAL
- PATRONS
- SHIRLEY
- GRAZING
- CAUTIONED
- UNCOMPLAINING
- NARRATED
- RECOUNTED
- BREVET
- CANAAN
- PLUMB
- MACKENZIE
- DEDICATION
- CITIZENSHIP
- DISTINCTIVELY
- EQUALS
- FROWSY
- KINGSHIP
- ATROCITIES
- ANT'S
- ATOMS
- ABOLISH
- WORSHIPPING
- FOODS
- PUNY
- PARASITES
- ADAPTABILITY
- RHINOCEROS
- QUIVERS
- ARQUEBUSIERS
- ROBUST
- SOUTHWESTERN
- HOGSHEAD
- JOURNEYING
- EXTORTION
- BRETON
- EXASPERATED
- OUTCRIES
- BROWBOROUGH
- COUNTESSES
- BROUGHTON
- DAUBENY'S
- KENNEDY
- INEFFABLE
- TAYLOR'S
- TUMBLER
- VIRGINIA'S
- NORMAN
- ORGANIZE
- PARASOL
- WINSLOW
- TREASURED
- CAPTIVATING
- PROPOSING
- GRAFT
- SOLICITOR
- REFLECTIVE
- ROTTENNESS
- CROOKEDNESS
- BUTCHERS
- ATTRIBUTING
- UNDERTAKER
- LEAKED
- BULKY
- CLIENT
- DEVISE
- DISSENTING
- BUCK'S
- SCRAPE
- REMONSTRANCES
- SLIMY
- DAMAGES
- YELLOWS
- LOFTILY
- OUTCAST
- ABBE'S
- CHISEL
- FARIA
- PUZZLES
- ENABLES
- OVERFLOW
- REPROACHES
- IMPERFECTION
- NOBLENESS
- INFATUATION
- FREDERICK'S
- CANDID
- SUFFOCATED
- PORTABLE
- CIRCUMSTANTIAL
- BOILER
- BELLIGERENT
- BOMBS
- CANNONADING
- BITTEREST
- HOAX
- SIGNALS
- SOCIALLY
- ENFORCE
- NATION'S
- SOMME
- HAYDEN
- CIGARETS
- CELLARS
- LITHE
- JACKASS
- CARRION
- PETS
- EVREMONDE
- DOGGED
- EMIGRANT
- ENTHUSIASTICALLY
- REDDENED
- REGRETTING
- PRIOR
- CREWMEN
- CUBIC
- DIOXIDE
- TRACTS
- BATTERIES
- YAWNS
- MUTTER
- ALLOTTED
- HEADACHES
- TOWED
- POUNCE
- BAROMETER
- MOLLUSKS
- OPULENT
- JOINTS
- SNORE
- ARGONAUTS
- COMPOSEDLY
- PLOW
- HARROWED
- SPROUTED
- RECOMPENSE
- WREATHS
- POTENTATE
- ENTANGLEMENTS
- ASPIRATIONS
- TUMBLING
- VERNON
- FLURRY
- TELEGRAMS
- LEGISLATION
- WIDESPREAD
- PROPHECIES
- PRESIDENTIAL
- PRESIDENT'S
- SAVORED
- CZAR
- NA
- DERIVE
- MOLESTED
- ADMONITION
- MONTANA
- CALIBER
- SWINDLED
- DICKENS
- REDDENING
- BURGLAR
- CLAUS
- NUISANCE
- CONVENTIONS
- THING'S
- PASTE
- SCHOOLBOY
- UDO'S
- CASUALTY
- GINGER
- BARODIA'S
- IMPULSIVELY
- INTERCHANGED
- AHA
- REPAIRING
- ANASTASIA
- MARRIES
- BERLIN
- FLUTE
- IMPERATIVE
- GHOSTLY
- SMASHED
- CONCUSSION
- DISAPPOINTING
- COLLEAGUE
- REUBEN'S
- IL
- SIRENS
- HORSEMAN
- WATSON
- SCORNFULLY
- MERRICK'S
- STUBBY
- COZY
- MISCHIEVOUSLY
- PAUPER
- COMPLACENTLY
- BUNS
- ISHAM
- AMALGAMATED
- NAPKIN
- AESTHETIC
- BLYTHE
- NICEST
- SMALLPOX
- GRAVEYARD
- WAILED
- DISLIKES
- WRATHFULLY
- IMPROVERS
- LUCKLESS
- MUDDLE
- TABLECLOTH
- EMERSON
- YANKEE
- JONAH
- WAKEFUL
- ANGELIC
- SCHOOLROOM
- SMOKY
- PROVOCATIVE
- SQUEAKED
- HUMILIATED
- QUIETER
- WHISKY
- SECULAR
- CALMED
- FACILITATE
- CLOCKMAKER
- SAWDER
- MARRED
- LOVABLE
- ENTITLE
- ORLANDO
- PATRONAGE
- EDITORS
- GRESHAM
- EXCHEQUER
- MENAGERIE
- CLOWNS
- NORTHUMBERLAND
- FAY
- WINKING
- WAYSIDE
- JINGLING
- SPLENDORS
- PRANCED
- BOTHERING
- TIMBERLINE
- SNOWS
- CHAPARRAL
- BUTTE
- BOULDERS
- PACKS
- ICEBERGS
- HOLLOWS
- SACRAMENTO
- SPRINGTIME
- BOXED
- BOOMED
- BRUISE
- MITIGATED
- OUTPOURING
- DENSER
- GLACIAL
- RAVISHING
- DORMANT
- DAISIES
- INACCESSIBLE
- TERMINUS
- CHARMINGLY
- RAILROADS
- RIDES
- CONES
- FIRELIGHT
- RAVENOUS
- BAIT
- PAPOOSE
- REDEMPTION
- DIGNITARY
- UNWIELDY
- TORCHES
- CRAFTY
- RETRACT
- YOUTH'S
- EMPHASIZED
- DISHEARTENED
- REBS
- BRIERS
- TALKIN
- VEXATIONS
- STATUTES
- TAILORS
- UNRIVALLED
- GNATS
- KNOCKS
- LECTURE
- HAWKS
- WHATE'ER
- INHERIT
- STEWARDS
- VENGEFUL
- DUELS
- SAK
- PUPPY
- DOYLES
- CUSHIONED
- SNORT
- MULTI
- UNMARRIED
- CAT'S
- FEASTING
- TALERS
- COBALT
- ENDOWMENT
- ASBESTOS
- FIZZLECHIP
- HARDWARE
- CASKET
- OUTLANDISH
- TRICKERY
- BANANAS
- DRAFT
- CALCULATION
- AUCTION
- MATTRESSES
- OMNIBUS
- GUTTER
- BRUTUS
- CAESAR'S
- FREDERIC
- WEARER
- MINUS
- ADORABLE
- INTERSECTION
- USURPATION
- PURCHASES
- EPONINE
- UNDERWENT
- FARTHINGS
- CORKS
- JACQUES
- SOCIETIES
- RENDING
- CHANVRERIE
- INFAMY
- PROTESTATION
- INESTIMABLE
- WHEWELL
- DEMOLISHING
- INTRINSICALLY
- INSUBORDINATION
- STUPIDER
- ANTON
- ANTONITCH
- SERVITUDE
- PURPOSELY
- SHAM
- REBUFF
- SIMONOV'S
- APOLLON
- PALTRY
- FUNKED
- HOMESTEADS
- EPHEMERAL
- TRAILS
- HINGHAM'S
- BELFRY
- DERBY
- RIPPLES
- SNOWBALL
- BROOD
- CHICK
- DIGEST
- MOW
- IMPLORING
- SCUFFLING
- GLIDE
- BISON
- PLUMAGE
- SNARL
- TIGER'S
- CAPERS
- BOA
- KEEPER'S
- BUOYANCY
- RECALLING
- SLUGGISH
- ADRIFT
- VASTNESS
- REVOLVED
- VAPOUR
- POISED
- UNRECOGNIZABLE
- REMINDER
- BLAMING
- NATIONALITIES
- ALEXANDER'S
- INTERACTION
- MISDIRECTED
- BOURBONS
- STAEL
- TALLEYRAND
- MURDERS
- INTRINSIC
- LADDERS
- NATHAN'S
- TRIBUTARIES
- TERMINAL
- STEADYING
- ROYLAKE'S
- UPPERCLIFF
- DRAINS
- FALSELY
- GLOODY'S
- STEPMOTHER'S
- DIMMED
- EARTHY
- NOSEGAY
- WARNINGS
- SANTIAGO
- UNDERGROWTH
- SCRIBBLED
- ROUTED
- SOLDIER'S
- THORGEST
- GUNNBIORN
- ODIN'S
- TIGHTS
- ERICSSON
- WHIZ
- CLATTERED
- PRANKS
- PRESS'D
- PITIES
- DUNKIRK
- PRITHEE
- ACQUITTED
- FRIAR
- DISSERTATION
- PATHETICALLY
- PREDESTINED
- HALTER
- OLENIN'S
- ABREK
- LUKASHKA'S
- KUNAK
- LUKE
- MARYANKA
- DRABANT
- VANYUSHA
- UNDREAMT
- WILLIAM'S
- SYLVIA'S
- YO'R
- IDENTIFY
- EXPEND
- LUNNON
- INTERROGATOR
- LOITERING
- LIGHTNESS
- IVER
- DEMONS
- RANGING
- FOUNDING
- SCENTS
- RUMOR
- INDISPUTABLE
- EVOLVED
- TRODDEN
- MADELEINE
- SHROVE
- METAPHORS
- PROUVAIRE
- COQUETTE
- ERR
- BROOCHES
- PRELATES
- WUZ
- KASE
- DAR
- LEER
- BRAGGER
- PADDLES
- LUFTON'S
- SCOWL
- DAMASK
- GRANTLY
- CROUP
- ARTISANS
- PRECOCIOUS
- MARVELLOUSLY
- INTRACTABLE
- O'MALLEY
- IMPLICATED
- HARLOWE
- THEMES
- HUSTLED
- SCION
- BOUDOIR
- KNOTS
- PARBLEU
- BOUQUET
- CATASTROPHES
- DIAGNOSIS
- DEALS
- HYSTERIC
- WIDEST
- PRESCRIPTIONS
- REDUCTION
- STIMULATE
- BRAITHWAITE
- WORTHILY
- TREMULOUSLY
- PANEGYRIC
- BANDAGED
- WIRED
- VOLOR
- UNREASONABLY
- DRAPERS
- HALLIDAY
- NOBBLER
- DIGGINGS
- FRAZER
- POLLUTION
- LAURENT
- VIOLIN
- WAISTBAND
- FINDLAY
- INCE
- CONTINGENCIES
- TWEED
- ULSTER
- CAIRNGORM
- BOULDER
- VERACITY
- FUNNEL
- ATHLETES
- RACIAL
- SIOUX
- INFLUENTIAL
- ASSIDUOUSLY
- AROUSING
- PRECEPTS
- SECTARIES
- CURTIUS
- RIVULETS
- IMPRACTICABLE
- FOUNDERS
- LOWELL
- MORTON'S
- DILLSBOROUGH
- SWITCHED
- MARVELLED
- SHAHRAZAD
- WIGHT
- KHORASANI
- SUCCOUR
- HATTIE
- SKAGGSY
- ALLINGTON
- ENVIES
- CROFTS
- ANIMOSITY
- AIL
- RECITATION
- QUAINTLY
- NORA'S
- LEVICE
- HARMLESSLY
- FREIGHTED
- COLLECTOR'S
- WHEW
- MUSHA
- ENDOWMENTS
- INARTICULATE
- CATHOLICS
- WEALTHIER
- PAPISTS
- BOTTLED
- COLLOQUY
- HOOPER
- DICKY
- BLINDFOLDED
- HOLMES
- THUMP
- PIPER
- LECOQ'S
- EUGENE
- OAKEN
- LOITER
- POLYTE
- UNREASONING
- JOSIANA'S
- STUART
- ESSEX
- CUPIDITY
- FADES
- DIMINISHES
- DEFORMITY
- HURTFUL
- PRICK
- POULTRY
- PUGILIST
- NECKLACES
- CHARTERS
- GIBRALTAR
- MULTIPLICATION
- VALUATION
- DISUSE
- SHEDS
- SHREDS
- DUKES
- SUB
- SHREWDLY
- BARGAINS
- EMBITTERED
- FATHERLESS
- GARRET
- CASSY
- GRAND'THER
- SPONGE
- MERCE
- SAFFRON
- UNFALTERING
- OSBORNE
- WHOOPED
- HELIOTROPE
- PHENOMENAL
- CORLEONE
- ATTICS
- BURNISHED
- ALCOVES
- PROW
- LUMBERING
- TYPEWRITER
- STOCKTON
- UPLIFT
- SEDATIVE
- NOVELIST
- CHESTNUT
- WHISTLES
- WINNINGS
- DIETERLI
- GARRETS
- MARDEN
- WALLACE'S
- PLAYFELLOWS
- UNCHRISTIAN
- INSTRUCTIVE
- GNOMES
- MOULDERING
- CORK
- YE'D
- CAROLS
- HOWLAND
- METROPOLITAN
- INKLING
- WITCHES
- MISDEEDS
- MINAMOTO
- PROVIDES
- TOKIWA
- IOLCUS
- INITIAL
- GRAZE
- PALISADE
- HEMORRHAGE
- ACCUMULATE
- EFFICACIOUS
- PREMATURE
- SYRO
- CAPPADOCIAN
- MUSKI
- URARTU
- ARBELA
- SURU
- BRAKE
- OMRI
- JORAM
- HAZAEL
- SHAMSHI
- CAMPAIGNS
- MONSTROSITY
- AIRSHIPS
- FOSTERED
- STYLED
- MUSTY
- MANUSCRIPTS
- TYPOGRAPHICAL
- PUBLISHER
- MARCY
- DOBBIN'S
- EMMY
- DOBBIN
- ADMIRER
- BEFRIENDED
- INEXPERIENCE
- CALTHORPE
- CASSIUS
- CALHOUN
- MISSILE
- MORTGAGE
- VENTURES
- EUCLID
- SHOWMAN
- FAIX
- SAXONS
- DANES
- EMPERORS
- SUARD
- CHRONICLED
- SUFFRAGES
- PENSIONER
- ACCUMULATING
- MAINTAINING
- IMPAIRED
- MISLEAD
- AUGMENT
- TRAMPED
- STARLING
- CAVELL'S
- WALLFLOWERS
- DOMINATION
- EVOLVE
- EDWIN
- UNDIGNIFIED
- GRATIFICATIONS
- LEEDS
- UPPERMOST
- TAPERING
- CARRUTHERS
- KENSINGTON
- ORANMORE
- TERMINATED
- IRRETRIEVABLY
- BEQUEST
- JASMINE
- STORK
- VIRILE
- FENWOLF
- CHATHAM
- AVE
- BARKILPHEDRO
- CORONET
- COUNSELLORS
- MEDWORTH
- NORMANDY
- POACHERS
- CABINETS
- HOST'S
- SPITS
- STACKS
- PROPHETSTOWN
- BELLERS
- MASHED
- INDUSTRIES
- CLEVELAND
- CONTINENTAL
- PLUTOCRACY
- CAPITALS
- PROPAGANDISTS
- FANATICISM
- WHEREWITH
- CHAOTIC
- BIDS
- MOTTO
- AUTOGRAPHS
- AUTOGRAPH
- CHINK
- GUITAR
- POISONER
- PAPAL
- HORATIUS
- CARLINI'S
- DERIDED
- CLAVIERS
- JACKS
- FORESHADOWED
- FUGUE
- MOZART
- FUGUES
- HARMONIC
- CONFLICTS
- ELSNER
- ASSIGNING
- ENRICHING
- FUNDED
- MICHABO
- CLOUDCREST
- O'SHAUGHNESSY'S
- JARRED
- TILLIER
- POTTED
- PERCY
- ECCLESIASTICS
- DEVOUT
- PONTIFF
- EMENDATION
- AVOIDANCE
- WRUNG
- MARTYRS
- SPORTSMAN
- BULGARIANS
- DEFEATS
- THEODOTUS
- BEHEADED
- BAKING
- RATION
- DULCET
- SWEDES
- HELSENBURG
- PROTESTANTS
- UNDOUBTED
- PORTHOS'S
- PURRING
- COMPLIMENTED
- CONJUNCTURE
- IMPETUOSITY
- PREROGATIVE
- POPERY
- HUGONOTS
- IMPEACHMENT
- PARLIAMENTS
- SUBJECTION
- USURPATIONS
- BUTTERNUT
- A'RONY
- HEZ
- PYTHAGORINA
- FLATIRONS
- COONSKIN'S
- SPINAL
- GRUNTS
- DUELLO
- HOBBY
- DECANTER
- SPECTERS
- CLANK
- WRAITH
- YAK
- PROLONG
- PUFFY
- BARMAID
- JESSIE
- BOURBON
- BOGNOR
- CHARMING'S
- SLAPPED
- BLENNIES
- TADPOLE
- RAD
- DOTS
- CHURCHWARDENS
- PEARSON
- BATSY
- PRIMAL
- SIMON'S
- CONNISTON'S
- TOBACCONIST
- MIGHTIEST
- YALU
- MANCHURIA
- SLAV
- TRAININ
- THOT'S
- AMNESIA
- INCEST
- AFFECTIONAL
- ALDERS
- CASCADE
- PROP
- MISSIONS
- VOLUNTEER
- BETOKENED
- PONDER
- AFTERNOON'S
- EMBOWERED
- HOMESTEAD
- PROVERBIAL
- DISTRUSTFUL
- MATTHEW'S
- AGGRESSIVELY
- KANGAROO
- AUSTRALIA
- ASYLUMS
- REGULATED
- INNOVATION
- HOPETON
- SPRY
- CHORES
- PRIDED
- QUALMS
- POISONING
- PESSIMISM
- STEADIER
- FILMY
- UNGAINLY
- STATIONMASTER
- BEARDING
- SHUFFLED
- LIPPED
- SCRAWNY
- DEFERRED
- GORGEOUSLY
- BANKRUPTCY
- EUGENIE'S
- DISSENSION
- GRISETTES
- CLEAVING
- BETRAYS
- ANDREA'S
- INVOKE
- PROVENCE
- LAZARETTO
- ELEGANTLY
- COMPLAINING
- MISCHANCE
- INSULTINGLY
- EGOTISTICAL
- EXTENUATE
- CORRECTION
- LUCCA
- DISGUISES
- WEAKNESSES
- PERVERSITY
- PARDONABLE
- HALVES
- NIBS
- UNFAVOURABLE
- TOOTLES
- CRAFTILY
- WENDY'S
- COMFORTER
- TARTLY
- NURSE'S
- NANA
- ADMITS
- DARNING
- FORLORNLY
- FORGETS
- RECIPROCITY
- DISPLEASES
- DETESTING
- INTERVIEWER
- WAITER
- HANDSHAKE
- DISBELIEF
- BUNCHIE'S
- FINITE
- MISSIVE
- ALTERNATIVES
- CRITICISED
- CASHMERE
- WATERPROOF
- SKETCHED
- PERSISTING
- GENOA
- INTERLUDE
- MUNIFICENT
- SALIENT
- APPRECIABLY
- WOO
- DECADES
- DORESLAER
- ROYALIST
- FORMALLY
- COMMONWEALTH
- EPITHETS
- COALITION
- OFFENDERS
- ENGLAND'S
- SQUADRONS
- SEIZURE
- PRIVATEERS
- ADMIRALTIES
- REINFORCED
- COMMERCIALLY
- PENN
- ENCOUNTERS
- INTERCEPTING
- CALAIS
- CLAMOROUSLY
- SHOAL
- KENTISH
- DUNGENESS
- INDOMITABLE
- GALEN
- LEGHORN
- ADEPT
- CONCILIATORY
- COINCIDED
- CROMWELL'S
- RUMP
- DEANE
- SOLIDITY
- INSTITUTED
- REDUCING
- UNA
- REFIT
- REARGUARD
- CHICANERY
- RATIFIED
- RATIFICATION
- PEREMPTORY
- OUTCRY
- DECIPHERING
- GARNISH
- SLICED
- CUPFUL
- MUSHROOMS
- MINCED
- TEASPOONFUL
- YOLKS
- BAKE
- ESTABLISHES
- IMPUTATION
- CENSORS
- GLOWS
- COMMEND
- BEHOLDEN
- ADHERENCE
- REMEMBRANCES
- DEROGATORY
- VISITATIONS
- DRUGGED
- INFUSE
- REHABILITATE
- PEACEMAKER
- MEREDITH
- REMINGTON
- HEALS
- UTILIZE
- BLACKMAIL
- AIRILY
- DEPRECATING
- BODYGUARD
- SECRETARIES
- DEFERENTIALLY
- SPANNED
- SALVER
- PEREMPTORILY
- LOOSENING
- VALET'S
- MANIPULATION
- UNNECESSARILY
- PURSED
- DROOP
- EYELID
- SUPERSCRIPTION
- INDOOR
- BEALE
- UNPAINTED
- NOBODY'S
- SITTIN
- PEASANT'S
- MANACLED
- ASSASSINS
- ALLAYED
- SANDWICHES
- TESSELATED
- SARDONICALLY
- FUNK
- SYMBOLIC
- PASTEBOARD
- NATIONALITY
- TOUCHETT'S
- SAVOUR
- LIVELIEST
- ACCUSING
- TASTELESS
- INCENTIVE
- FORECAST
- DISCREDIT
- REFUTING
- DEVOLVED
- PERSISTENCY
- LATTICED
- PORTMANTEAU
- VOCABULARY
- FELICITIES
- FESTIVE
- CANDLESTICK
- PONT
- FORERUNNERS
- TINKLE
- WITCH'S
- TOAD
- TIGHTENING
- DISMOUNTING
- BUCKLES
- GORSE
- TRANSFORM
- MILKED
- PAILS
- RYE
- SQUINTING
- SHUTS
- DELIVERY
- APPRAISE
- HINDRANCES
- PERSONALITIES
- WASTES
- COOPERATION
- CLASHES
- PRECEDENCE
- FAULTY
- CRAVE
- BOSS
- INFLUENCING
- SCRUTINIZED
- EMOTIONALLY
- WAVER
- SHAKY
- MANNERLY
- WEDGE
- WIDENS
- DISLODGED
- PERPLEXITIES
- SCALING
- FRIGHTENING
- MANNERISM
- WANE
- UNTURNED
- HELPFUL
- UNHEALTHY
- RELINQUISH
- MATURING
- UNATTACHED
- TOPERS
- OUTLAW
- HARBORS
- KILLERS
- INSANITY
- MENU
- PHYSIOGNOMY
- SENSORIUM
- FILAMENTS
- DIVESTED
- ADMINISTERING
- SHINGLE
- DISTILLED
- BOWERY
- BRACELET
- CRANIUM
- HADES
- BUM
- PEACOCK
- UNINJURED
- MORASS
- INCONCLUSIVE
- MINUTENESS
- CONVENED
- PROSECUTE
- HAMILTON
- OVERALLS
- BLACKBURN
- LESSENS
- HOWELLS'S
- UNCOMMUNICATIVE
- GROPE
- COURTHOUSE
- ROTUND
- PANAMANIAN
- ARRAIGNED
- BYGONES
- PERMITS
- MOURNER
- UNCOVERING
- IMMATERIAL
- CUFFS
- EVERYTHING'S
- SPASMODICALLY
- SOMBRELY
- NECKTIE
- INSTABILITY
- HOPEFULLY
- CROQUET
- ULCERS
- SCROFULA
- CAUSTIC
- PORES
- MARES
- INFANTS
- HYSTERIA
- CLEARS
- ACHES
- ASHLAND
- LUXURIANCE
- INVIGORATES
- DEBILITY
- KIDNEY
- LITE
- WOODWORK
- PIANOS
- ENAMEL
- SCRATCHES
- ETHER
- CORRESPONDENTS
- INCOMMODED
- BEWAILED
- REGULATING
- MERCHANT'S
- BARRENNESS
- BELLOWED
- FITTER
- COMBATING
- BELLOWING
- COMPASSIONATE
- LANGUISHING
- LEPROUS
- POTIONS
- ENRICH
- PERSPIRE
- VIZIER'S
- DILIGENT
- CRUCIFYING
- EZRA
- POSTMASTERSHIP
- SINECURE
- BARTENDER
- SOLICITATION
- STERNER
- DIVULGED
- TOWN'S
- JUBILANT
- IOWA
- TOWNSMEN
- MINNIEMASHIE
- SAUCERS
- INVESTMENTS
- NELSON
- HAYDOCK
- FISTED
- BULLING
- MOINES
- MATCHLESS
- DAUNTLESS
- QUESTING
- APPLAUDING
- DISILLUSIONS
- MADRID
- DOMESTICITY
- MAGNOLIAS
- CURTAINED
- SHANTIES
- TINCOMB
- TABERNACLE
- PLAID
- FLIPPANT
- NEWSPAPERMEN
- BUREAUS
- CONTAMINATION
- LADYLIKE
- CHEMISTS
- ENVELOPES
- CHESAPEAKE
- SCOFFING
- ENTHUSIASTS
- DUDES
- MULTITUDINOUS
- INVEST
- EAVES
- ANECDOTE
- UNBELIEVABLY
- OBSEQUIOUS
- ARCHITECTS
- TRACEABLE
- REFUGES
- PETTINESS
- AFFLICT
- TUNNELS
- CLOUDY
- CAMBRIC
- PUNCTUAL
- CRAPE
- AVIDITY
- MORREL
- VILLANY
- SIGNING
- NEAPOLITAN
- WRESTED
- THOMSON
- TRANSACTING
- QUITS
- REPELLED
- CREDITOR
- GUILTILY
- DISCOUNT
- FOI
- CREEDS
- OPALESCENT
- PAWNED
- INTERLACED
- SNIFFED
- FETID
- USHERS
- SQUAD
- CAPES
- DETACHMENT
- SUBWAY
- LEERING
- PHANTASMAGORIA
- BLISTERED
- NIGHTMARES
- ENVELOPING
- SPOONS
- HERDED
- CYNICALLY
- ACCUSATIONS
- AUTO
- PASSIVELY
- SIMPER
- GROTESQUELY
- WORRIES
- APPENDICITIS
- FROGGY
- PARKER'S
- DOUGHNUTS
- RECEPTACLE
- DIVAN
- NICHOLL'S
- COMMUNICATES
- CANINE
- BETS
- LAUNCHING
- SUSTAINS
- REVOLVES
- DIZZINESS
- BEAUTIFYING
- TRANSPORTATION
- UTILIZED
- GASEOUS
- HOISTING
- EXPERIMENTING
- PROPELLED
- EXCEL
- FENCING
- SWEATING
- FEATURELESS
- LIMBED
- POTENTIAL
- DOWNSTREAM
- CROUCH
- SHRUGGING
- BETRAYAL
- TIMELY
- FINGERING
- BEAST'S
- THANKFULLY
- PRUDENTLY
- AROMATIC
- LURKED
- LURCHED
- RIVER'S
- TRAPPED
- VINCENNES
- LEVEE
- ENLISTING
- HUMPHREY'S
- INTENDANT
- OVERTURES
- NOMINATED
- KERCHIEFS
- GALLANTS
- ASHLEY
- DISCONTENTED
- PROFFERS
- HEATHERSTONE'S
- MONEYS
- PRESENTATIONS
- EMPOWERED
- MISUSE
- INTERDICTED
- AWAKENS
- ANTAGONISTIC
- OVERRUN
- PSYCHOTHERAPIST
- AUTOBIOGRAPHY
- USER
- ELIMINATE
- WILLFUL
- VALUES
- INBORN
- UNFIT
- ADJUSTING
- PSYCHIATRY
- PSYCHOLOGISTS
- FREEST
- REENFORCEMENT
- EXPANSION
- HAMPERED
- ADMINISTER
- REMODELING
- INATTENTIVE
- EDUCATORS
- UNTRAINED
- ARTIFICIALLY
- RETARDED
- ANTISOCIAL
- SUPERFICIALLY
- INTRODUCES
- MARSUPIALS
- CHATTERER
- REPROVINGLY
- BRAMBLE
- BLACKY
- CONFLUENCE
- SYNTHESIS
- SUBCONSCIOUS
- RECUMBENT
- CONTOUR
- PASSER
- BREAKERS
- MINIMUM
- MISCALCULATED
- ARMCHAIRS
- STIMULUS
- TRANSPARENCY
- VIANDS
- SUBTLY
- ANOMALIES
- INSENSIBILITY
- EXCESSES
- INCREDIBLY
- FERTILITY
- ARGUMENTATIVE
- DICTION
- PRESENTIMENTS
- NEIGHBORLY
- SILENCES
- DISCOMFITED
- VOLUBLE
- BEMOANED
- ALLEGE
- PARADES
- CULPABLE
- INTERROGATION
- EJACULATION
- EXTENUATION
- INCITED
- INCOMPARABLE
- CLUMSINESS
- PETULANCE
- RIOTOUS
- MALEVOLENCE
- EMBARRASSMENTS
- ASSUMES
- DEFIES
- DISSIPATES
- IMPOSES
- LAX
- DULNESS
- MANIFESTLY
- MICROSCOPIC
- MINISTERING
- NERVELESS
- VARIANCE
- OMITTING
- COMMONPLACES
- ADJUSTMENTS
- DIAMETRICALLY
- FRAGMENTARY
- MISINTERPRETATION
- PEDDLING
- PELTING
- SHIFTS
- TITULAR
- TARANTELLA
- TWIRLED
- ADOPTS
- OBSTRUCT
- ANTECEDENTS
- FLORIANO
- CONTE
- UNMASKED
- IMPOSTORS
- ENTANGLEMENT
- HARSHLY
- CORRUPTED
- MATERIALISTIC
- TILLING
- STOUGHTON
- BAWLED
- GROOMING
- PRODUCTIVENESS
- FERMENT
- SAUL
- SCRUPULOUS
- HEMISPHERE
- FORTIETH
- FURROWED
- RELIEFS
- HERSCHEL
- REGULARITY
- CONTRACTION
- IRRADIATION
- SELENITES
- ALTERNATIONS
- MOON'S
- GEOLOGICAL
- UNFATHOMABLE
- COMPLEMENT
- RADIATION
- INSUFFICIENCY
- ROTATION
- EVAPORATION
- BEWITCH
- VISIBILITY
- BRAN
- HATCHING
- SWIMMER
- BEWITCHED
- STEADINESS
- NONCHALANCE
- TUGGED
- CANDIDE
- INCAS
- FRETFUL
- PASHAS
- DISTRACTING
- INQUIETUDE
- CITRONS
- PISTACHIO
- UNADULTERATED
- TURK
- JEROBOAM
- SYRACUSE
- DISPUTING
- INQUISITION
- DORADO
- COWARD'S
- SAGAMORE
- ADMONISHED
- TERMINATION
- UNQUESTIONED
- PARTICIPATION
- MOLDED
- HEYWARD
- TOMAHAWK
- REED
- FUMES
- EYEBALLS
- TRANSMISSION
- MONTCALM
- INROADS
- MOHAWKS
- EMISSARIES
- SHARPEN
- ABORIGINES
- BRIGHTEN
- TRINKETS
- APTLY
- COEUR
- SYLLABLES
- TARDY
- CONJUNCTION
- VENERATED
- USURPERS
- DEEMING
- ESCORTING
- PUMPKINS
- GROPED
- TRUNDLED
- EXCELSIOR
- CIRCULARS
- PREMIUMS
- BOOKCASE
- RAPTUROUSLY
- CRINKLED
- LAUNDRY
- ORNAMENTAL
- HYSTERICALLY
- INDULGENT
- SAWYER
- WATSON'S
- CARMINE
- BLOOMS
- GENIALLY
- CORROBORATED
- CONSCIENTIOUSLY
- BLACKSMITH'S
- FIGURING
- LILAC
- ROWENA
- RANDALL
- COSILY
- PICTORIAL
- ELOCUTIONIST
- COUPLET
- MINNIE
- SHAKER
- TERSELY
- BORROW
- PEPPERMINTS
- SAGACIOUSLY
- CULLOUGH
- PRINCIPAL'S
- DIMPLE
- CRAM
- OUTRAGED
- FLUSTERED
- SEASIDE
- FLIRT
- FLIRTING
- HARPER
- MATCHING
- TROOPING
- ROSALIE
- BAGGY
- MERTELLE
- TRENT
- PUNCTUATED
- KOOLLOOB
- ALEPPO
- PERSECUTES
- REQUITAL
- AYOUB
- RIGOUR
- RUSHEED
- INGENUOUS
- HEAPING
- EQUIPAGE
- EUNUCHS
- DEPLORABLE
- AYOUB'S
- SOEVER
- FATALITY
- FETNAH'S
- JEWELLER
- HAZARDING
- INDIGNITIES
- PERSIANS
- SQUEAK
- SLYLY
- SPUTTERED
- SHEEPISHLY
- BUGS
- TANAGER
- REDCOATS
- FIFTIETH
- FORTIES
- FAIRS
- SKIMMER
- ROMANCES
- DELILLE
- MATERIALISM
- EXUBERANCE
- HUSSARS
- CABARET
- BONAPARTIST
- AVENGING
- DECEPTIONS
- MASTODON
- INADMISSIBLE
- CONCORD
- ADORATION
- PYRENEES
- OTTOMAN
- WEDDED
- DUSTED
- SPIDER'S
- SPIDERS
- NIHILO
- ARISTOTLE
- DISCOVERER
- FLOTSAM
- UPROOTED
- SWIRLING
- UNEQUIVOCAL
- CHIME
- SUBTLETY
- CONDEMNING
- INDEMNITY
- FUNDAMENTALLY
- SEDUCTIVE
- UNEGOISTIC
- RETIREMENT
- UNCONDITIONALLY
- SEDUCTION
- GALIANI
- D'EPINAY
- HYBRID
- MASQUERADES
- CLASSICAL
- FLORENTINE
- TRANSCENDENTAL
- DIVINING
- CIVILIZATIONS
- MOORISH
- HALCYON
- COMPULSION
- NAIVETES
- ELEVATIONS
- FORGED
- INSIDIOUS
- PERFECTING
- OVERSPREAD
- ADVOCATES
- DISCLOSES
- PONDEROUSLY
- HELVETIUS
- INSINUATED
- MORALIZING
- PONSONBY
- BEAGLE
- DIEGO
- SQUALLS
- DOMESTICATED
- GUANACO
- CLICKING
- YAWNED
- AWRY
- KEENER
- WALTZING
- CHARTERED
- FUEGIA
- RIO
- PARTAKEN
- PORTUGUESE
- ZOOLOGICAL
- INLETS
- BAYS
- ALPINE
- GOEREE
- PUTREFYING
- TROPICS
- FAGUS
- COMMEMORATION
- MOORLAND
- SLEET
- PUFF
- SURGE
- TUFTS
- LOINS
- TRICKLED
- SUCKLING
- TEMPESTUOUS
- PUTRID
- QUARTERMASTER
- DIALECTS
- UNCEASINGLY
- BACKBONE
- PERU
- EXPLODE
- STEREOTYPED
- INFREQUENTLY
- MOCCASINED
- WHOOPS
- SCENTING
- IMPARTING
- ESTRANGEMENT
- TRACKLESS
- MASTERFUL
- SWISH
- PULSED
- PLAYIN
- MAGNANIMOUSLY
- DUBIOUSLY
- EDWARDS
- MUMMER
- CLODS
- DISSENT
- RELAPSE
- RAREST
- UNPRINCIPLED
- JIGGING
- UNWINKING
- FATUOUS
- RECONCILING
- VICAR'S
- SQUEALING
- WHISK
- WAGGISHNESS
- EMIL'S
- HAMBURG
- NAT
- CAMERON
- FIREFLY
- LURED
- DEMI
- SCRUBBING
- FARING
- SOWED
- TARES
- DILIGENTLY
- THINLY
- BERGMANN
- PENANCE
- COFFINS
- CHEBEC'S
- BREASTED
- FEARLESSNESS
- TINIEST
- SCRAPPER'S
- DRONES
- EYESIGHT
- NOTCH
- BEDCLOTHES
- WOODPECKERS
- WINSOME
- YANK
- CHICKADEE
- KILLY
- SPOOKY
- NESTED
- MORE'S
- STRAWS
- SHAMEFULLY
- FEEDS
- SQUATTED
- RAFTS
- DISASTROUSLY
- FIBRES
- VANCOUVER
- SKATING
- MINK'S
- MOULDED
- MOBILE
- BABY'S
- SWERVING
- RETINA
- PLIANT
- DISCONNECTED
- BRIGHTENS
- UNRESPONSIVE
- SCULPTURES
- MASTERPIECES
- BACCHUS
- ELDARA
- DREW'S
- NOTHIN
- GETTIN
- FODDER
- PA'S
- SPEAKIN
- NARRATOR
- RINGER
- DRIVIN
- PINTO
- LOOKIN
- RELAX
- SLIT
- CLUMSILY
- BLINKED
- FOLLOWER
- TOLLIVER
- BROODED
- EAVESDROPPING
- OUTRIGHT
- WRINGING
- CORNFIELD
- PLENTIFULLY
- BLACKBERRIES
- OUTWITTED
- HOOF
- SPILT
- INJUNCTIONS
- ODOURS
- VIOLINS
- SOUTHWARDS
- QUAILED
- CROAKING
- FOAL
- UNPROPITIOUS
- SPINNET
- PROFICIENCY
- CLEANLINESS
- CONFIRMATION
- RAPTURES
- PERVERSENESS
- SEDENTARY
- RICHARD'S
- CRAVATS
- OCCURRING
- MISCONDUCT
- UNLOOKED
- MILDNESS
- REJECTION
- MISLED
- COMMUNICATIVE
- WHOMSOEVER
- ALLENS
- SUDDENNESS
- SPURNING
- CONJECTURES
- INTIMIDATE
- HEREFORDSHIRE
- WEIGHTED
- MUNIFICENCE
- CHESTNUTS
- CONSERVATORY
- CONQUESTS
- DISCRIMINATIONS
- FREQUENCY
- FIDGETY
- PICCADILLY
- ATHENS
- CONSUMMATION
- INCOMING
- CONFERRING
- SMARTEST
- ANSON
- WILLY
- O'REILLY
- JEWELRY
- ACTRESSES
- PRODIGIES
- SUBSERVIENCE
- TOY
- MULATTO
- CUFFED
- BOXWOOD
- UNMERCIFULLY
- MEDDLING
- PRIMEVAL
- EXPERIMENTED
- GLOATING
- WALTZ
- DUSAK
- GIGGLING
- ROOMFUL
- TOPAZ
- COLOGNE
- WHELP
- DISGUSTINGNESS
- INTERROGATE
- WAYMORE
- RAKE
- DEBAUCHERY
- ESCAPADE
- INDEFINABLE
- COMPLICATION
- LAMENESS
- DEBAUCHERIES
- ROADSTEAD
- DREAMERS
- ATTUNED
- SIAMESE
- CHINAMAN
- GANGWAYS
- CONFINING
- CREVICES
- GRIME
- TURBAN
- ASTERN
- UNBELIEVERS
- VISCOUS
- AWNINGS
- IMMENSITY
- FLICKED
- ENSLAVED
- COLLIDED
- AWASH
- SCORNFUL
- BLOTTING
- FOREPEAK
- METICULOUS
- TRUTH'S
- EDDIED
- VOLUMINOUS
- DRILL
- DRAPED
- USELESSLY
- DELIBERATING
- COURAGEOUSLY
- DOWNRIGHT
- SOLIDARITY
- AGGRIEVED
- OAKBOURNE
- BLIGHTED
- WRAPPING
- EFFULGENT
- OUTGROWTH
- SEARCHINGLY
- SLOMAN'S
- CHATTY
- SPAWN
- TAMMY
- CONDIMENTS
- OZ
- FLAVOURED
- FORCEMEAT
- MALES
- ADHERING
- BIVALVES
- ANCHOVY
- STEW
- LISTLESSLY
- SHIELDING
- MOCKINGLY
- CLIPS
- SPLASHES
- CYCLIST
- DOER
- SURPRISINGLY
- QUARRELED
- JUPP
- UTTERS
- COLLAPSED
- IMPERVIOUS
- MUTTERINGS
- VALERIE
- ENGULF
- SLANTED
- STUTTERED
- LAK
- YAS
- MO
- FARNSWORTH
- UNTIED
- GENTILES
- DOVES
- BAPTIZE
- SCRIBES
- RANSOMED
- HIGHWAYMAN
- TRUE'S
- FRASER
- THRIVE
- CHOPS
- RASPBERRIES
- PELLUCID
- GLEN
- SOJOURNED
- OLIVIA
- SCOFFED
- FURTIVE
- CHIMES
- NETTLES
- DASHWOOD
- INCONSIDERATELY
- HENCEFORWARD
- MILSOM
- INCIVILITY
- RIGOURS
- RANKED
- ARTLESS
- CONSEQUENTIAL
- TRANSPORTS
- EMBELLISH'D
- ACCOUTRED
- PROCLAIM'D
- ATCHIEVEMENTS
- PERSIA
- ITABOD
- SYCOPHANTS
- REMOUNTED
- CATCH'D
- ESQUIRES
- CONVEY'D
- PLAY'D
- REBUFFS
- EXCEPTED
- STRATAGEM
- ARTFULLY
- SABRE
- CHIEFTAINS
- PUSH'D
- MUTES
- SWEATED
- SLUGGARD
- AGGRAVATION
- OPPRESS
- JOT
- BENNET'S
- PIES
- LUCASES
- NOURISHES
- SONNET
- GRACIOUSNESS
- MERYTON
- SOLWAY
- BLINDFOLD
- FORDS
- DECEMBER'S
- MOONLESS
- MATIN
- SALUTES
- PARTICIPATED
- GESTICULATIONS
- LOQUACIOUS
- REMOUNTING
- HAUGHTILY
- WHARTONS
- APPREHEND
- SUSPENSE
- BASKING
- METEOR
- DELL
- ROOTED
- INFINITY
- SHRINKS
- TENURE
- INSECURE
- IMPEDIMENTS
- CONTAGIOUS
- PREVENTION
- SUBSCRIPTIONS
- INTERCHANGE
- CARGOES
- ABODES
- CINNAMON
- SPURNS
- DELLS
- POLLUTED
- WEEPS
- EXPORTS
- OPULENCE
- BEGGARY
- GLORIED
- CONCILIATE
- ABANDONING
- ALLEVIATION
- HURRIES
- REMITTANCES
- AFFORDING
- PENSIONERS
- OLDEN
- INDIGENT
- HOE
- LAVISH
- BREEDS
- VISITATION
- SIGNORS
- TRIMMINGS
- BODED
- LUDOVICO
- JEERINGLY
- ARCHLY
- SEBASTIAN
- SLASHING
- CASEMENT
- EXCELLENZA
- EMILY'S
- APPEASE
- IMPLORE
- FOOL'S
- RAMPARTS
- BASEMENT
- FITZGERALD
- MOY'S
- DRINKER
- MONEYED
- SILVERWARE
- AUGMENTED
- GLASSWARE
- MANAGERIAL
- CASHIER
- TAILORED
- VEST
- DRESSY
- COMMENTARY
- BASK
- SEQUESTERED
- EARED
- SENSORY
- PALAVER
- PUFFED
- JULES
- MAYST
- BLYTH
- WRESTLING
- RANGERS
- SHERWOOD
- JUBILEE
- COWHIDE
- MAYHAP
- DARLINGS
- SCURVY
- FUME
- TAN
- METHINKS
- TWAIN
- TESTILY
- MANCHA
- OWNER'S
- MONTERA
- CARDENIO'S
- COMB
- RELIEVES
- DEPICTED
- FERNANDO'S
- PROTECTORS
- PROSTRATION
- CHERISHING
- KLETKE
- MAIMED
- SIGHTLESS
- DIABOLICAL
- QUAKED
- UNINHABITED
- MAIDSERVANT
- GASCONY
- GERMAIN
- BRAWL
- D'ARTAGNAN'S
- DEVIATED
- SUCCOR
- SORTIE
- DISSOLVE
- INCARCERATION
- DUNGEONS
- GRATINGS
- PRETENSE
- MYSTIFIER
- GLITTERS
- TENACIOUS
- LACKEYS
- TRAITOROUS
- REVERIES
- HOAR
- WHIPPER
- BALUSTRADE
- PEDESTAL
- REOPENED
- EXPOSTULATION
- FOUNDER
- KEYHOLE
- COURTESIED
- SMIRK
- INTANGIBLE
- WIXTED
- PRECIPITATELY
- LAUDANUM
- INCENSED
- DISTORTION
- BIZARRE
- VRAIMENT
- WAT
- BRYERLY
- FEIGN
- PETITE
- WONTED
- ENQUIRIES
- MILLINER
- GARRULOUS
- HOUSEMAID
- LES
- WINCED
- HEIGHTENING
- IDIOM
- SMACK
- ODDITY
- SCALED
- SLUMBERING
- BROOMSTICK
- SEMICIRCLE
- UNCLASP
- VOMIT
- FLINT'S
- CHAFFED
- PURCHASER
- GLAZED
- DIFFIDENT
- BOLDER
- IMPRINTED
- RESOUNDED
- EXTINGUISH
- EVENING'S
- HARANGUED
- SURGED
- MAGNETIZER
- PRICKING
- WAGNER'S
- OPERAS
- SUPERSEDE
- RESPIRATION
- REALISTIC
- INCONSISTENCY
- CHARLATAN
- CHARLATANISM
- OPERATORS
- CLAIRVOYANT
- EATER
- LETHARGY
- ACCOUNTABLE
- LOURDES
- PERSONATED
- STRIKINGLY
- MYLES'S
- ARMORY
- BOYHOOD'S
- GRACED
- COARSER
- CHEERLESS
- MARROW
- PUDDINGS
- SWEETENED
- MINSTRELS
- LOATHE
- MUSTARD
- CROCUSES
- GASCOYNE
- COOK'S
- ARBOR
- SPLINTERING
- PROPRIETRESS
- KINDNESSES
- JAKE
- SOMETIME
- FELLERS
- ORTER
- OLE
- CUM
- MORNIN
- WITHERING
- PICTUR
- TIPTON
- SPEEDING
- JOGGINS
- SYSTEMATIZED
- PATRONIZING
- CHEMISTRY
- VOGUE
- DISTILLATION
- SULPHURIC
- PERPETUATION
- ATTESTED
- SUPPLEMENTED
- PERPETUATED
- UNCHARITABLE
- INEXTRICABLY
- ENSUES
- LOOM
- MESMER
- IMBUED
- KROGER
- HYPNOTHERAPY
- AUTHOR'S
- EXPERIENTIAL
- ATAVISTIC
- KLINE
- CONSTITUTES
- INHERENTLY
- CEREBRUM
- CONDITIONED
- REFLEX
- FOURTHLY
- UNITS
- GLADSTONE
- CATO
- DECORATING
- DRAWINGS
- FIG
- BEAUTIFY
- FULFILLING
- ENLARGE
- UNREALITY
- LOBE
- EMBLEM
- WAGING
- SUREST
- PROGRESSING
- DOLING
- DIVED
- CONTRABAND
- SLANDER
- UNWAVERING
- IRRITATING
- GOSSIPS
- SAUNTERED
- EQUESTRIAN
- EDGING
- DUPLICITY
- PLODDING
- QUARRELLING
- REPROVE
- INNUENDOES
- DERISIVELY
- PASTORAL
- DISPLEASING
- DEFEATING
- SCOURGE
- EUDOXIA
- BEGS
- INVITES
- MAJORIAN
- CARTHAGENA
- HOSPITABLY
- BASILICUS
- CLOVIS
- BULGARIA
- GOTHS
- JUSTIN
- GELIMER
- VITIGES
- GRANDEST
- MANUFACTURES
- MOHAMMEDANS
- ISLAM
- INTENDS
- KHADIJAH
- IDOLS
- GABRIEL
- MOHAMMEDAN
- DIETH
- QUADRANGLE
- BA
- MOSLEM
- NORTHWESTERN
- CEYLON'S
- BENGAL
- LUCRATIVE
- OARSMEN
- APOPLEXY
- WHIMS
- EMPLOYERS
- CAREFREE
- SWISS
- JUNGLES
- NOOSE
- SIRR'S
- TERRAIN
- ORIENTALS
- SOLIDIFIED
- PROTEIN
- TESTACEA
- SAXONY
- SECRETING
- SHELLFISH
- CREATURE'S
- ROTTED
- IMMERSED
- SPHERICAL
- EARRINGS
- ERRATICALLY
- VESTMENTS
- SIEVES
- CLASSIFYING
- HARVESTING
- CARP
- PHILOSOPHICALLY
- UH
- DIDACTIC
- NEMO'S
- HARPOON
- COMPLETENESS
- GRIFFITH'S
- SQUIRMING
- AFIRE
- INTERMINGLED
- MADLY
- STYLISH
- COAXING
- SIDEWALKS
- UNRIGHTEOUS
- UNDRAWN
- PANACEA
- FOURSCORE
- TRIBUNALS
- INCOMPETENT
- NOMINALLY
- MASSACRED
- ABANDONMENT
- BURGLARY
- OBSESSION
- TAMELY
- CONFEDERACIES
- REALIZES
- BIGOTRY
- ZIGZAGS
- DORMITORY
- PULP
- BUNKS
- EYEBOLTS
- METRE
- DEPOTS
- NANSEN
- HICKORY
- TAR
- HUITFELDT
- HOEYER
- ELLEFSEN
- STIFFEST
- ASSORTMENT
- BERGEN
- PENETRATES
- RIME
- SPREADS
- ELABORATELY
- TIRES
- PATENTS
- UPPERS
- MEASUREMENTS
- PRIMUS
- STOCKHOLM
- HORIZONS
- MERCURY
- MAKER'S
- AILMENT
- RUST
- GROCER'S
- NOURISHING
- OATMEAL
- EMBANKMENT
- SPORTSMEN
- UNNUMBERED
- CORNICE
- ERECTING
- COPSE
- RADIATED
- SHRUBBERY
- RENTED
- COOPERS
- PULPITS
- NAMESAKE
- MANSFIELD
- INTUITIVE
- RECOMMENDATIONS
- RETICENCE
- EMBELLISH
- RECEIPTS
- MEAD
- LAMENTABLY
- APPENDAGE
- ENCUMBER
- PERIODICALS
- FASTIDIOUSNESS
- MINUET
- GYRATIONS
- ADDISON
- HORNPIPES
- SUPERINTENDED
- CONCOCTION
- DISTILLING
- LEIGH
- FLAX
- BALLAD
- SPINNING
- SINGSONG
- SPORADIC
- SUBCONSCIOUSLY
- IMPOTENT
- MEDLEY
- STARES
- JOSTLED
- ELBOWED
- ESCORTS
- STRAGGLERS
- LOITERED
- UNOPENED
- CURB
- APPROVING
- NONCHALANTLY
- FASTIDIOUSLY
- MUSINGLY
- UNCERTAINLY
- DIAL
- IMMACULATE
- EMPLOYEES
- HARLEM
- BLUNTLY
- BANKNOTES
- LURCH
- PENITENTIARY
- SPURTING
- HALTINGLY
- VIAL
- ALMONDS
- SWIFTEST
- INSIGNIA
- UNGUARDED
- ORB
- FETISHISM
- HAHN'S
- TROTTER
- SILKY
- AMAZON
- PLAITED
- BALCH
- SECLUDED
- EARSHOT
- MAGICIANS
- MESH
- DEFLECTING
- DUD
- FINGERED
- WHATEVER'S
- CARTER'S
- ANITA'S
- LUNG
- CUBBY
- BUZZER
- CYLINDER
- MATERIALIZED
- MIKO
- IDYLLIC
- FETTERS
- ARCHERY
- JUSTIFYING
- CAPABILITY
- DAVILOW
- TOLERANT
- RAINY
- FLACCID
- EUPHONIOUS
- SHORTNESS
- ANTIQUATED
- FLUFF
- WHIMPERED
- HOLLIS
- GOGOFFS
- ARROWPOINT
- MARQUESS
- LUSH'S
- STINTED
- BLOSSOMED
- CRAMP
- UNINTENTIONAL
- SUBMISSIVELY
- DISCOVERABLE
- ACCOMMODATED
- WOODY
- EQUIP
- BERESFORD
- DEDUCTION
- BYES
- ORBITS
- DRAWBACKS
- WAFTED
- PARISHIONERS
- CLASSICS
- MIDDLETON'S
- NEWEST
- HALE'S
- BELLES
- LESSENING
- WOODLANDS
- THORNTON'S
- CAPTIVATED
- IMPERTINENTLY
- TELEGRAPHIC
- LANCASTER
- SUPERNATURALLY
- IMPUTED
- DENOUNCING
- BRIBES
- GLOSS
- BRUMMAGEM
- IMPOTENCE
- PARADE
- IMPECCABLE
- RELEGATED
- RAKED
- EXPANSIVE
- INSISTENTLY
- LOWDER'S
- FLASHLIGHT
- WINCING
- INVOKED
- INHALING
- BENEVOLENTLY
- APPRECIATING
- PROPOUNDED
- CELEBRITIES
- EXPONENT
- ARENA
- CARESSINGLY
- MARTYRED
- NOSING
- AMERICAN'S
- GRIST
- EXPEDITIOUS
- SPANGLES
- CIVILISED
- ENUNCIATED
- SHIRK
- CEREBRAL
- HANDICAPPED
- CHOP
- SPLICE
- KERCHIEF
- WELLED
- OUTWIT
- HEARTEDNESS
- UNTRAMMELLED
- SCUFFLE
- QUALM
- NARROWEST
- CRANNY
- CHIDING
- BESOTTED
- MASKEW'S
- GIDDINESS
- HUMMOCKY
- APACE
- FRESHENED
- SCARING
- ROOKS
- CLOTTED
- FIRELOCK
- OWLS
- EXCAVATIONS
- STEEPLY
- VESTMENT
- GOOSEBERRY
- GASES
- STRANGLE
- OVERGROWN
- TINDER
- ENIGMATICAL
- GARGANTUA
- DEPRAVED
- MERLIN
- ALLEGORIES
- GOETH
- SAITH
- BATTALION
- REVENGING
- JOAQUIN
- PENNED
- SMOLDERING
- MOORE
- HUTCHINGS
- MALL
- UNDERGRADUATE
- COMPETE
- UNGENEROUS
- COSMOPOLITAN
- INSENSITIVENESS
- JUDICIOUSLY
- ICARUS
- ACCLAIMED
- CRUMPLED
- HUGGING
- HERRING
- GRUEL
- BRAT
- TAILED
- PELAGUEYA
- LEVANT
- CYPRUS
- BURIALS
- ABATED
- WAGGONS
- FRIGHTED
- FOOLHARDY
- CRIPPLEGATE
- CLARKENWELL
- WEALTHIEST
- ROTHERHITHE
- COMPUTED
- ACCUSERS
- DONNER
- TORTURING
- STONED
- BREWING
- PRIVATIONS
- FREMONT
- LATHERED
- CLEANSED
- POPPIES
- AIMLESSLY
- BROWNING
- BLIGHT
- SUTTER'S
- KETTLES
- MASON'S
- WATCHWORDS
- PARCELS
- OBSTRUCTIONS
- GRANDMA'S
- SOLDIERLY
- HOMELIKE
- HONEYS
- PUNCTIONS
- TAKIN
- JAKIE'S
- SONOMA
- HARDWOOD
- MODELLED
- DUCKLINGS
- SOCKETS
- MESHES
- EXUBERANT
- CENTREPIECE
- WIRTHIN
- LADLE
- EMBELLISHED
- HARROWING
- JAKIE
- CASTILIAN
- HORNED
- SUPERINTEND
- EVOLUTIONS
- MONMOUTH
- SANGUINARY
- MURRAY
- CONDOLENCE
- UNQUESTIONABLE
- HAGUE
- UNPLEASING
- TARDILY
- FRUSTRATED
- EVASIONS
- SKELTON
- ORKNEYS
- KIRKWALL
- MISADVENTURE
- REPEL
- DUNSTAFFNAGE
- MANIFESTO
- JOACHIM
- HEREIN
- MUSTERED
- ASCENTS
- VOUCHSAFE
- INIQUITY
- ACHIOR
- BETHULIA
- ISRAELITES
- ADORING
- SYNAGOGUE
- WRECKAGE
- SOLUTIONS
- BATTERING
- TRANSOCEANIC
- QUARTO
- HERALD
- FORMULATE
- REFUTED
- ADMISSIBLE
- GENERA
- CETACEAN
- IRONCLAD
- PROFESSORIAL
- LOOPHOLE
- GAZETTE
- INSURANCE
- ARSENALS
- ARMING
- WAYLAID
- VOCATION
- BOTANICAL
- MANSERVANT
- FLEMISH
- UNSOLICITED
- ENTHUSIAST
- BALEEN
- EMOTIONLESS
- SOCKS
- SKELETONS
- UNPREDICTABLE
- MAMMALS
- CONTAINERS
- FUNNELS
- ACCOMMODATIONS
- SKEPTICISM
- CHURNED
- GAFF
- WHALERS
- MANEUVERED
- SPYGLASSES
- POPULATED
- UNCONCERN
- MOLDY
- TACKLED
- CONTINENTS
- TROPIC
- OPTICAL
- GEARS
- IMMENSENESS
- UNSOLVED
- DISK
- SHROUDS
- PROBING
- MURKY
- SATCHELL
- CHURCHYARD
- AGGRAVATED
- DAB
- BETTERS
- CATECHISM
- PINAFORE
- JAM
- STAN
- IN'T
- SATCHELL'S
- FOLKS'S
- SEATING
- THURLE
- ON'T
- ULL
- CHURN
- INS
- OUTS
- INT
- GALLONS
- SCOURING
- EXPIRES
- HANNA
- CAUSEWAY
- NAME'S
- SIGHTEDNESS
- SOLO
- QUARTET
- CORKED
- ANYBODY'S
- ISOLATE
- EGREGIOUS
- FAVORING
- CIRCUMLOCUTION
- GUTTERS
- PRECLUDED
- ASPIRE
- HECTIC
- BARONETCY
- CORRECTING
- CUTLETS
- SNOWING
- SNOWED
- ACCLAMATION
- NEGLECTS
- DISPERSING
- UNPLEASANTNESS
- BEACONS
- THREATENINGLY
- LANDMARKS
- DIARY
- HELMER
- ROPED
- BATTLEFIELD
- HELL'S
- BOTTOMLESS
- RISKING
- QUILLS
- PORCUPINE
- TENACIOUSLY
- DITTIES
- SHELLEY
- DISPROVE
- PERRAULT
- DISTAFF
- VERGOOSE
- VERTIGOOSE
- GRANDCHILD
- GOOSE'S
- WM
- WHITMORE
- MONOGRAPH
- SUSSEX
- CHARLEMAGNE
- CREATIONS
- JUICY
- WRINKLES
- DEARIE
- BLEATING
- VE
- HOBBLING
- RABBITS
- INTERMITTENTLY
- ARIGHT
- OVERLAPPING
- DOMINATED
- CONTINGENT
- DOMINANT
- CLAMOURING
- INTRUDERS
- DEAFENED
- UPBORNE
- ARCHWAYS
- TOOTHLESS
- SHRIVELLED
- CADENCES
- UNISON
- SPACIOUSNESS
- DISTINCTIVE
- FELSPAR
- FEEDER
- RESTRAINTS
- ELIMINATING
- SPLENDOURS
- INDUCEMENTS
- NOURISHED
- LABOURING
- DIFFERENTIATING
- SLITS
- DISFIGUREMENT
- BARBARIC
- ARCHINGS
- UNLOADED
- POWDERED
- INKY
- THUNDERED
- SHRUG
- LOOMS
- REEKS
- TITANS
- SWARTHY
- HUNCHBACK
- RIDICULOUSLY
- STREWED
- VASES
- ADMONITIONS
- VOCAL
- UNSEEMLY
- CERES
- HOUSEWIFERY
- MILLET
- LENTILS
- FLEECES
- NOONTIDE
- EREBUS
- GROVELLING
- BLISSFUL
- FABLES
- WORSHIPPER
- OLYMPUS
- CENSOR
- MOORE'S
- SALES
- SNACK
- GRUYERE
- ANJOU
- FORTISSIMO
- TAWNY
- BRITTLE
- NIPS
- DOCTORING
- DANZIG
- CARAWAY
- SIP
- TRIBAL
- TILLED
- MAIZE
- MOHAWK
- GOADED
- PROLOGUE
- ALGONQUINS
- VOYAGER
- WANDERERS
- CURBED
- GARRISONS
- DEPLETED
- LOYOLA
- ABSOLUTISM
- HEARKEN
- HEARKENING
- SUPPOSITIONS
- TATTOO
- VEX
- KYUSHU
- WITHSTOOD
- MUGEN
- VERB
- MIMETIC
- BUDDHA
- VALUABLES
- SOLES
- IMAGINATIVELY
- JIKININKI
- DEVOURS
- OBLIGES
- AGREES
- ANJITSU
- ROADSIDE
- SKIRTING
- ATONEMENT
- AQUEDUCT
- GNASHED
- SQUATTING
- FOULED
- MURDERER'S
- COLLEAGUES
- KAI
- JOCOSELY
- TOLERATED
- PARLORS
- PEDDLER
- JEANETTE
- INDISCRIMINATE
- DOWER
- EMPIRIC
- ARRESTING
- SIMPLES
- CHECKING
- DISRESPECT
- PERSEVERED
- LAWTON'S
- PRESUMPTUOUS
- BANDAGES
- COMPLACENT
- EXCEEDS
- VIRGINIANS
- VALE
- DESPONDENCY
- RENOVATED
- EXERTING
- PROFFER
- SOMERSAULT
- RESTORING
- SLIPPER
- PORTIONED
- NICETIES
- WIELD
- LORDSHIPS
- ACCOMPANIMENTS
- GOALS
- CARRASCO
- UNHINGE
- APPERTAINING
- ENAMOURED
- ASCERTAINING
- SLACKEN
- SURNAME
- CAMACHO'S
- TOLEDANS
- CORCHUELO
- DISMOUNT
- DESPISING
- ONSET
- DEVOUTLY
- STRIPS
- CUTTLEFISH
- HILT
- AURORA
- FERVENT
- COUNTERPOISE
- ENLIVEN
- SPICES
- CAPTIVATE
- BASHFUL
- GALA
- LATERALLY
- FANGS
- TRANSCENDING
- VISE
- GLIMPSED
- FROTHING
- OVERWHELMINGLY
- FIGURED
- ERSTWHILE
- BATTLED
- TUSK
- PURGATORY
- GEHENNA
- STRAPPED
- UNMANAGEABLE
- THOAT
- SKULLS
- WARHOONS
- TRANSCENDS
- INSUBORDINATE
- RIPPED
- HERCULEAN
- JAILER
- VICTIM'S
- THOATS
- THRONES
- ENCRUSTED
- DIGNITARIES
- PYGMIES
- JAILERS
- LABYRINTHINE
- LOOT
- HELIUM
- ALARMS
- REAPING
- DUSKILY
- TORCHLIGHT
- LUBBER
- ROPE'S
- BUOY
- RUINATION
- HOSTAGE
- HITCH
- TAIN'T
- RACER
- COOLEST
- BANDAGE
- HAWKINS
- WRIGGLING
- AXES
- SILVER'S
- SMOLLETT
- GAYER
- ONSLAUGHT
- RAVING
- BULWARKS
- SOJOURN
- DEDUCED
- CONDUCE
- EXPOUNDED
- CONSUME
- CONFORMATION
- CORRESPOND
- VENA
- INAPPROPRIATELY
- CANALS
- PELLICLES
- PRECLUDE
- LIGATURE
- SMALLNESS
- PERFORATED
- DISTRIBUTING
- IRRATIONAL
- EMITS
- IDIOTS
- BRAINED
- POSTSCRIPT
- LANDLEAGUERS
- KEPPEL
- RUSSELL
- WYKAMIST
- BARRISTER
- SUICIDAL
- BOARDERS
- SUNBURY
- UNFURNISHED
- FOES
- UNATTRACTIVE
- WEALD
- BOARDER
- COWSHEDS
- JOCUND
- FOLIO
- CANTERBURY
- PRODIGAL
- CEASELESS
- BEDSTEAD
- TUGGING
- ADJUTANT'S
- RHYTHMICALLY
- CHILDLIKE
- CALECHE
- DRESSINGS
- DELIRIOUS
- COCKROACHES
- RUSTLED
- SEQUENCE
- ENJOIN
- SPLINTERS
- BOLKONSKI
- BROWNIE'S
- REFRIGERATOR
- BEAVER'S
- FATTY
- COON
- WOODCHUCK
- FELLING
- SWUM
- SPECULATOR
- CURRER
- ALTHESA
- CHATTEL
- DEM
- TOBIAS
- LOUISVILLE
- SLAVE'S
- WASHER
- BATON
- ROUGE
- FETTERED
- E'ER
- PEERLESS
- REND
- FLAY
- BEGRUDGED
- LAVRUSHKA
- BELTS
- LIBERATED
- MATTING
- BROADSHEETS
- COUSINE
- IVANOVNA
- MISINFORMED
- IRRESOLUTION
- JULIE
- VORONTSOVO
- LEPPICH
- COURIER
- FLOGGING
- OUTPOST
- STEADFASTLY
- REJECTING
- INTERFERING
- EFFUSION
- REQUESTING
- DISMISSION
- ASSIDUOUS
- SHORTEN
- WICKHAM
- INSPIRES
- MISLEADING
- ADMIRES
- DISOBLIGING
- CAROLINE'S
- LAMENTING
- IRKSOME
- PRESERVATIVE
- OFFENDING
- CIVILITIES
- SYNCOPATION
- TEMPO
- QUADRUPLE
- TEMPI
- QUINTUPLE
- SPICK
- CARPETS
- SERGE
- DIGESTIVE
- RECITATIONS
- DANCER
- ANTIDOTE
- SWISHING
- TRUTHFULLY
- ASLANT
- BEHRING
- COMIN
- COMMONEST
- SHRUB
- OBSTRUCTED
- BIRTHPLACE
- KEECHAWIK
- LETTUCE
- YUKON
- YAHKUK
- APENNINES
- METEOROLOGICAL
- BIRMINGHAM
- HURRICANES
- ASSERTING
- NIXON
- STICKLEBACKS
- GRAY'S
- AARON
- ACCEPTANCES
- ATTRIBUTABLE
- MORADABAD
- FERIDPOOR
- DISREGARDING
- TORRENTIAL
- PUTREFY
- ATMOSPHERIC
- BUIST
- HELTER
- SKELTER
- HINDON
- ACCURSED
- TORNADO
- SPRINKLE
- SPECIFY
- THEORETICALLY
- ACCEPTS
- CORRELATION
- EQUIVALENCE
- IRRESPECTIVE
- NEWTONIAN
- HARMONIZES
- SARGASSO
- IMMATURE
- STAGNATION
- HATFUL
- APPROXIMATION
- INVESTIGATOR
- WETTER
- CONCEIVABLY
- SNATCHING
- WINGLESS
- FRILLS
- SAUSAGE
- GANGS
- CHECKER
- REGISTERING
- COPYING
- PLUGGING
- WRITER'S
- OVERTIME
- WALLOWED
- SALARIES
- PHILANTHROPIST
- REFRESHMENTS
- TRANCE
- SCALPS
- PONE
- PUYA
- REFILL
- UNGRATEFULLY
- COMBINES
- ORGANIZATIONS
- HUB
- RAIDERS
- TERRORISM
- MANAGEABLE
- PERSONNEL
- COHEN
- WHITCOMB
- ELLA
- FINDEISEN
- KENT
- GRAM
- QUAY
- ROBERTSON
- COLORADO
- INDIANAPOLIS
- STAFFORD
- EMORY
- JACKSONVILLE
- ILLOGICALLY
- SUFFRAGISTS
- HINTING
- SUFFRAGER
- WHITTAKER'S
- COMMITMENT
- OUTSTRIP
- BLACKEST
- DOROTHY
- BRUTALLY
- BANGING
- CLANGING
- SKIMMED
- COGNIZANT
- MAINMAST
- CHEAPER
- DISTEMPERED
- STRAITENED
- PALLET
- ABATING
- INVIOLABLE
- PRACTITIONERS
- BRAWNY
- PUG
- QUELLED
- QUELL
- SESSIONS
- BLOODED
- ALLOY
- AMUCK
- BROWED
- MARINER
- BEDRAGGLED
- BUCCANEERING
- PROVOKING
- OVERT
- RIGGING
- PILLAGE
- UNSUSPECTING
- LIFELIKE
- FANNED
- SENSUAL
- TORMENTING
- HOVER
- DALES
- MISUNDERSTANDINGS
- MANIFOLD
- ABOLISHING
- INFLEXIBLY
- INSERTING
- DITHYRAMBIC
- EVANESCENT
- INNERMOST
- HARSHEST
- WEDLOCK
- PROFOUNDEST
- WITTY
- COMPRISING
- SORCERER
- REASSUMED
- ENTICE
- ACME
- UNENDING
- UNATTAINABLE
- MADONNA
- YEARNED
- POETICALLY
- BRADEMAGUS
- SMITTEN
- RESCUING
- GAWAIN
- CONSIGNED
- TURRETS
- PLEASANTRY
- ENFEEBLED
- DISGRACEFULLY
- SKILFUL
- JEER
- FOOTNOTE
- MIEN
- OVERTHROWN
- UNFASTENED
- VASSAL
- PECHEUR
- SOLICIT
- KINSMEN
- DINSMORE'S
- ENCOURAGINGLY
- FORGAVE
- ROSELANDS
- TEMPERS
- SLOVENLY
- UNFAVORABLE
- STEVENS
- SAFEGUARD
- TAPERS
- EVASION
- PORTAL
- WHIRLPOOL
- GLINT
- DULLY
- MING'S
- DIKE
- DRILLING
- PHOSPHORESCENT
- SENSED
- EERY
- VALLEY'S
- VOMITED
- CUBES
- GEOMETRIC
- PRODIGY
- FLEXING
- SICKENED
- FLAIL
- TOLL
- TWOS
- TRIPOD
- APEX
- TENTACLE
- PLAYFULNESS
- DRAKE'S
- CLUSTERED
- TOTTERED
- DAZEDLY
- HIMALAYAS
- SURGES
- NUDITY
- PITEOUSNESS
- BLOSSOMING
- RECOILED
- CLEFT
- RIFT
- PURGED
- PORTRESS
- SOFAS
- PENDENT
- INVENTORY
- SHRUBBERIES
- WARILY
- ANNUM
- EVINCED
- SELFSAME
- BLUEBERRY
- LOAM
- UNTIE
- BURROWING
- KETCH
- DRAM
- GENTLER
- SPIRAL
- HAW
- WHITTLED
- SCUDDING
- GROOVE
- CAREERING
- KINGSTOWN
- MOTORING
- COVERTLY
- PIANIST
- DEFT
- JIMMY'S
- NUDGES
- INHERITOR
- FREAK
- LORDLY
- MOTORISTS
- SNORTING
- SEGOUIN'S
- GRAFTON
- TWINED
- ENGLISHMAN'S
- SIGNIFICANTLY
- DEVISING
- BUNDLED
- HOPPERSON
- DAFT
- LEVITY
- CURMUDGEON
- GAUGES
- BOOKSTORE
- EVERLASTINGLY
- PRAISING
- SELLERS
- HICKS
- STAMPS
- PANTALOONS
- WARDROBES
- INJUNS
- SINFUL
- QUACK
- ABSOLUTION
- FRANCESCO
- FEVERED
- VIGILS
- ALTERING
- RENEWING
- FESTIVALS
- FLATTERERS
- UNALTERED
- SPRINGFIELD
- FORTE
- ACCOMMODATING
- BACKERS
- FASTEST
- SUPERINTENDENTS
- ORD
- PLATTE
- PHERSON
- STAGER
- HECKSHER
- GORDON
- BREVOORT
- BELMONT
- BACKWOODS
- EMPHATIC
- LOOK'D
- TENET
- WHIMSICALITY
- DIDIUS
- TRIBONIUS
- WARMEST
- NEPHEW'S
- JOSTLING
- SKIRMISHING
- CONTRARIWISE
- INTERROGATIVELY
- FURBISH'D
- POPISH
- UNGRACIOUS
- AIDING
- TARNISH'D
- COCKADE
- THONG
- TASSEL
- MARCH'D
- CELIBACY
- DECLAMATION
- PHIPPS
- MANUFACTORY
- LANCERS
- ANGLING
- CONSIDERS
- PIMPLES
- SATIRICAL
- CLERICAL
- PHERORAS
- COHORTS
- FORTRESSES
- SAMARIA
- OVERRAN
- CONQUERORS
- MIDLAND
- ROBBERIES
- EXHORTATIONS
- THREATENINGS
- SPOILING
- FRIGID
- ARS
- BIRTHRIGHT
- TRIUMVIRATE
- FOULNESS
- GIVEST
- CONTRITION
- UNDERSTANDINGS
- IDOLATORS
- BAPTIZED
- SACRAMENT
- LESCAUT
- DISTRACTIONS
- FRASCATI
- ISOLATING
- GAMBLER
- MARLY
- CLOSES
- COURTESAN
- SEMICIRCULAR
- JOUR
- OSPREY
- NIGHTMARE
- ERST
- THWART
- SULKILY
- GASHED
- GRIMES
- YE'LL
- OFFENCES
- LEVELLED
- MAINLAND
- COMMANDANT
- SCOOPED
- ABYSMAL
- GIRDING
- GORGED
- RETAKEN
- JEM
- SHORTENED
- DUDS
- FORGING
- BUILDER
- DOZE
- OLIVER'S
- CONTENDING
- RIP
- TROLLING
- FLEDGED
- STALWART
- SLACKENING
- BUSHEL
- BREAD'S
- ARID
- INVIGORATED
- SURLY
- EXPENDING
- ENSIGN
- CONNECTICUT
- SUCCESSORS
- TESTIFIES
- CLEAREST
- WOODBURY
- CONGREGATIONAL
- SALISBURY
- GATHERINGS
- HOUSATONIC
- OUTCASTS
- KINSHIP
- VERMIN
- BROADLY
- UNDREAMED
- DYNASTIES
- SIMIANS
- UNLOVELY
- FLIGHTY
- OPERATIVE
- CONVERSELY
- INITIATIVE
- SPEECHLESS
- BANQUETS
- EXTERMINATE
- PUSHES
- APPALACHE
- WEDGES
- HATCHET
- CANAVERAL
- REEFS
- OTTIGNY
- OUTINA'S
- CONJURER
- WELLNIGH
- HOWLINGS
- OUTINA
- TRIBESMEN
- EXULTED
- HOMESICK
- CLEAVE
- COLIGNY
- RIPEN
- YELLS
- BOROUGH
- JOURNEYMAN
- CHILTERN
- NEEDY
- LENDER
- INSERT
- EXPECTANT
- BUCKLED
- DRESDEN
- ASCENDANT
- UNITE
- ILLNESSES
- UNRESERVE
- FRIENDLINESS
- FORETELL
- CLEVERER
- BOASTS
- RANDALLS
- WOODHOUSE'S
- PREDICAMENT
- COMMUNICATIONS
- FIRESIDE
- LOREEN'S
- DISTRUSTED
- ROLLIN'S
- ARNOLD
- REDEEM
- CONSTITUENCY
- DISPLAYS
- INCREDULOUSLY
- DESPAIRINGLY
- RUEFULLY
- CHEAPENED
- MAUSOLEUM
- GRAFTER
- STEALS
- GROCERS
- PLUTORIA
- SLUGGISHNESS
- LAWLESSNESS
- O'HOOLIGAN
- GRATH
- INGRATIATING
- CENTRES
- DAVIDSON
- CLERK'S
- DISCERNED
- UNWELL
- PURPORT
- CLOSETED
- AGHAST
- ESCUTCHEON
- KICKS
- DEMONIAC
- BUFFETED
- JUVENILE
- PEA
- RUEFUL
- HARE'S
- SCORNING
- VASTLY
- CONVULSIONS
- AFY
- JOYCE'S
- YEARN
- DERANGED
- LEGIBLE
- PENS
- PENKNIFE
- CURIOSITIES
- SULPHUR
- COMPRESSION
- MERCEDES
- TRANSPIRE
- SPOTLESS
- DISPOSITIONS
- GUILELESS
- SMOKESTACK
- RAPPING
- JACKKNIFE
- SANCTUM
- RELAPSED
- SIGNALING
- KNOCKOUT
- STOUTNESS
- LAGGED
- BRIGHTON
- WHIN
- POSITIVENESS
- EVERETT
- SHRAPNEL
- POISONOUS
- BOCHES
- FATEFUL
- TRUCKS
- IRREGULARLY
- BRIGADES
- SPASMODIC
- AIRCRAFT
- RIGHTED
- ENCIRCLE
- MOMENTUM
- JUBILATION
- SUBMARINE
- AMIABLY
- PERMEATED
- DEBUTANTES
- ELABORATED
- MORNINGS
- NATALIE
- EVADED
- BARRACK
- CLUSTERING
- HIVES
- WISTFULLY
- BATE
- COPS
- COP
- FINES
- YARNS
- WEAL
- SCRUPULOUSLY
- TRAFFORDS
- EGREMONT
- SCANNING
- INCONVENIENT
- ABSTRACTED
- DUPES
- ASSEMBLIES
- CLAMOROUS
- DEGRADE
- TERRIFY
- OPPRESSORS
- ERMINTRUDE
- JOLLIGINKI
- THICKEST
- POLYNESIA
- DOLITTLE
- CONCIERGERIE
- UNCONNECTED
- ACCEPTATION
- LUCIE
- CITIZEN'S
- THOUGHTLESSNESS
- ASPHYXIATED
- BORINGS
- ICEBERG
- WATERLINE
- STAKED
- SUPERVISED
- WIELDED
- SUPERVISING
- INJECTED
- SCARCER
- SUSTAINING
- PRAISEWORTHY
- NAVIGATORS
- PREDICTS
- NAVIGATED
- BULB
- MIXING
- SPECIFICALLY
- DECIMETERS
- MARVELED
- JELLYFISH
- FESTOONS
- DANGLE
- FERRETING
- APPALLED
- PHRIXUS
- DETHRONED
- CHIRON
- CADMUS
- COXCOMB
- ENCHANTRESS
- SHRIVELS
- GRIPE
- ASSAILS
- GREENSWARD
- BROADCAST
- RIPENED
- CLASHING
- HEWING
- SIMPLETONS
- KINGLY
- ENCHANTRESSES
- FORBIDS
- LOWING
- SNOUTS
- ANTELOPE
- SHATTERING
- BEGONE
- EMBARK
- GRENADES
- CONCENTRATING
- WEAKEST
- UNTENABLE
- INSCRIPTIONS
- VIRGIL
- AMPLIFICATION
- PROGRESSIVES
- PENDING
- CHESS
- CONSERVATION
- AMEND
- RHODE
- MARSHALING
- LETTERED
- DENT
- LIBERATE
- APPOINTEE
- REPORTERS
- FLAUNTED
- BULLETIN
- CONCLUDES
- POLITICALLY
- GRAZED
- DETECTIVES
- SUMMED
- DELEGATION
- FLAMBEAU
- STATUARY
- BROODS
- COSY
- REPENTED
- RITUAL
- FANTASTICALLY
- ADJURATION
- UNLOADING
- BAZAAR
- UNWRAPPED
- COMPANIONABLE
- FINANCIER
- DISARMING
- SPURTED
- FRAYED
- FLORIAN
- GODCHILD
- ANONYMOUS
- SMASHING
- BELVANE'S
- CORONEL'S
- MAIDENLY
- ARMOURER
- ARMOURER'S
- ARCHITECT
- HYACINTH'S
- ELVIRA
- HUMOUREDLY
- COMPUNCTION
- DUGALD
- PRISCILLA'S
- AFGHAN
- BUTTONING
- GASLIGHT
- BRUNWALDE
- SERENADING
- TALENTED
- HANSOM
- JERVIS
- DEPRECIATION
- PREMISE
- DUPLICATE
- CARROZZA
- CABMAN
- UNIFORMED
- WHEREAT
- UNABASHED
- RHAPSODIES
- SPANKING
- STEEDS
- PITIFULLY
- OPPORTUNELY
- TOURIST
- CANVASS
- BRADLEY
- POPPY
- NICKLE
- REEVES'S
- GLEEFULLY
- REEVES
- WAILS
- STOW
- CURING
- SIPPING
- COUNTRYFIED
- REVELED
- MESSY
- BEDROOMS
- CROCK
- SNAPS
- PRIMLY
- GILLIS
- WHITE'S
- SAM'S
- TASSELS
- MAMIE
- TUMBLERFULS
- WATERED
- DETERMINEDLY
- DIZZILY
- DIANA'S
- THOMAS'S
- SERE
- INTOXICATE
- IRRITATE
- UNHOLY
- PESTERING
- OUTSPOKEN
- WILDFIRE
- WHEELBARROWS
- VEERED
- GERANIUM
- PRETTIER
- CHUM
- CANONIZED
- CLAUDE
- BICYCLES
- UNKLE
- CLAIR'S
- REPRODUCE
- WILLIE
- IRVING
- DONNELL
- OFTENEST
- MOONGLADE
- SCUTTLE
- HIRAM
- PRILLIE
- ANGERED
- INSOLENTLY
- ANTHONY'S
- REPENTANT
- HUMILIATIONS
- COMRADESHIP
- CONTINGENCY
- QUIETNESS
- CAMPFIRE
- HENDRY'S
- KNOCKER
- SWARMS
- ANTICIPATING
- MARM
- TANTRUMS
- GRAFTIN
- AIRTH
- ETARNAL
- ABED
- GRAINED
- GRENADIERS
- WHOPPER
- KNOWLES
- WHARTON'S
- REVERSAL
- PROFESSING
- ATTACHES
- MANFULLY
- RATTLERS
- ROBYS
- UNBLUSHING
- INSEPARABLE
- SECURES
- ABSOLVE
- OMNIUM
- AUSPICES
- DROUGHT
- SELECTIONS
- SILVERBRIDGE
- LUCKIEST
- RECEPTIONS
- PICNICS
- FEATHERLESS
- ROBINS
- CATCHER
- RESENTING
- ENTICING
- TUMBLERS
- WINKS
- TORMENTOR
- PERCHING
- TAGGING
- FLAPPING
- LAPPED
- PLUCKY
- SCRUBBED
- GOLDENRODS
- RAVINES
- LIMESTONE
- ADVISERS
- MOUNTAINEERING
- CONE
- ARMPITS
- FATIGUING
- AVALANCHES
- WALLOWING
- HOLLOWED
- DIVERSIFIED
- SIFTING
- NOTEBOOK
- DEPOSITION
- BOSSY
- MATTED
- FLUFFY
- LASSEN'S
- BOSSES
- ENCIRCLING
- DISSOLVING
- FABRICS
- FUMAROLES
- SCALDING
- FINENESS
- COMPELS
- WREATHING
- NORTHEASTERLY
- ENABLING
- VAUNTED
- MORMON
- ZION
- TAINTED
- SAUNTERING
- SAGEY
- COMPARABLE
- LARKS
- SEDIMENTS
- ERYTHRONIUMS
- FRITILLARIAS
- BATTLEMENTS
- ERYTHRONIUM
- SHOWY
- AGLOW
- BULBS
- ATROPURPUREA
- TULIPS
- MORMONS
- GRASSHOPPERS
- SUBSISTED
- GRANDDAUGHTER
- TRANSPLANTED
- COMPOSITORS
- UNWARRANTED
- LOGGING
- BREAKAGE
- PROSPECTOR
- ASPIRING
- BOULEVARDS
- LOGGERS
- ATTRACTIONS
- CORDUROY
- PLUNGES
- HUNT'S
- PARTICLE
- HALLOWED
- INFIDELS
- HAULING
- DAUNTED
- NARRAGANSETT
- HOOPING
- FILTHY
- PSALMIST
- PARCHING
- BEAR'S
- UNSATISFIED
- SAVORY
- TRUMPERY
- PORTUGUEZ
- IRONS
- FORD
- ALLIGATORS
- RESURRECTED
- IMPECUNIOUS
- PLYING
- FURROW
- INSTANT'S
- QUOTATION
- DREARILY
- OMENS
- WA'N'T
- DERNED
- SNAPPY
- JADED
- JACKASSES
- FALK'S
- HURTING
- DOWRY
- DORCAS
- CRESTFALLEN
- SHOEMAKERS
- PRINTERS
- LEVIN'S
- COQUETTISH
- FALANDER'S
- REHNHJELM'S
- RIPPLED
- DISCONCERTED
- COUP
- HJALMAR
- STIPEND
- RENTING
- LEAKING
- SKETCHING
- DIRECTORY
- ADJUNCT
- PROUDER
- EVE'S
- LASCIVIOUS
- BEAUTY'S
- RETENTION
- HERETIC
- ARIZONA
- QUARTZ
- SNUFFED
- INCARNATION
- RADIUS
- IMBUE
- ARMLET
- WITHDRAWING
- ROSTRUM
- BULKS
- DENIZENS
- CAPTOR
- PERFUNCTORY
- PEALS
- JUMPS
- SENSATIONALISM
- LAIR
- BRIGANDAGE
- ENVIRONMENTS
- RECLINED
- BEAUT
- DIVERSIONS
- BANKING
- SOR
- CUDDLE
- MAVOURNEEN
- BREAKIN
- LIVELIHOOD
- SUPREMELY
- HOBOKEN
- FLYAWAY
- FONT
- CHRISTENING
- THREES
- SANNA
- RAVEN'S
- SHOVELS
- SENTENCED
- SALARIED
- ESSAYS
- NOTHINGS
- DRONE
- PEPPERLEIGH
- HAREM
- DROOPED
- MOP
- LENDS
- NEWSPACKET
- JEFFERSON'S
- STROPPED
- EARNINGS
- SCRIP
- JAMMED
- NIPPEWA
- TULIP
- PROSPECTUS
- NETLEY'S
- CAPITALISTS
- CORONA
- INCOMPETENCE
- INSURRECTOS
- RECLAIMED
- DIRECTORS
- FRAUDS
- RECONSTRUCTED
- MABEUF
- TREMBLES
- REVERTED
- SHAKSPEARE
- HIVE
- NUMBERING
- SUICIDES
- FASTING
- VOLUPTUOUSNESS
- EXPIRATION
- PLENITUDE
- THINKERS
- HYDRA
- FICTIONS
- AMPHICTYONS
- SOVEREIGNTIES
- 'NULL'
- CONSCIENCES
- DISMEMBERMENT
- DISPLACE
- EVOKES
- BRUJON
- EPONINE'S
- SPECTRE
- PLUMET
- PARDI
- ICES
- PROUVAIRES
- WHISPERINGS
- VIBRATION
- PROFILES
- ENGULFED
- PONTMERCY
- WIDENING
- UNANSWERED
- LAGGING
- INIQUITOUS
- DEFENDER
- AMBIORIX
- ARTEVELDE
- VIOLATES
- SUFFICES
- PHILIPPE
- REPLACES
- SUPPRESSES
- REASSURING
- SUNSHINY
- GLEESON
- GOODBYE
- UNTRUTH
- MENDER
- EMANUEL
- DIME
- STORIED
- POINTSMAN
- CAUSATION
- WELD
- WELLINGTON
- INFERENCE
- INDUCTION
- HAMILTON'S
- REVIEWING
- RELATIVITY
- REQUISITES
- NOMINAL
- COLLECTIVE
- LOCALLY
- SUCCESSIVELY
- APPETISING
- MANFRED
- STOREY
- SNUB
- SERVILE
- APPLAUDED
- REFINEMENTS
- CURRICULUM
- SUBJUGATE
- TENTHS
- DISDAINFULLY
- PAROXYSM
- DOMINATING
- SWIMS
- LEVELED
- CRESTS
- SIGNIFYING
- DISTINGUISHES
- NEPONSET
- WENDELL
- SUFFOLK
- RESOLVES
- VICISSITUDES
- NURTURED
- RUSKIN'S
- CURSORY
- SOMERVILLE
- LYNDEBORO
- ROLLICKING
- DOMINATE
- MISCELLANEOUS
- TOILSOME
- TOLLING
- DELINQUENTS
- EXACTNESS
- TITHING
- IDOLATRY
- HYMNS
- STIRS
- DEPRESSING
- PEOPLED
- SEVERITIES
- STAMINA
- COCKADOODLE
- PECKED
- BANTAM
- RIPPLING
- BIDDY
- CROAK
- POUNCED
- MATERNAL
- MINT
- DISOBEYING
- SNOWFLAKES
- BLOT'S
- LEAFLESS
- HAWTHORN
- TEASED
- BUN
- VILLAINOUS
- PROMENADE
- KNOTTED
- PAGODA
- CURLING
- EATABLE
- SOBERED
- LAZARETTE
- CHAFING
- PINION
- WEIGHTY
- STAVE
- MANGLED
- SWAMPED
- SABLE
- FOREMAST
- STAVES
- FENDERS
- RANSACKED
- DAZZLE
- WHIRLPOOLS
- INFERRED
- CELEBRATING
- CAMEO
- BIOGRAPHICAL
- HISTORY'S
- GERVINUS
- INTERACTIONS
- EXECUTIONS
- DECORATION
- FOREHEADS
- PARISHIONER
- GOERS
- TAKU
- SOUTHWEST
- NAVIGABLE
- HONEYSUCKLE
- DELTA
- FORESTED
- ABOUNDING
- NORTHEASTERN
- TERRACES
- HEMLOCK
- SPIRES
- HUCKLEBERRIES
- NOTCHES
- MUIR
- FORDWITCH
- DRAWBACK
- INFLICTION
- TOLLER'S
- FOREWARNED
- DEVILISH
- SIXES
- SEVENS
- FAVOURING
- SUPERBLY
- TURBID
- ASSOCIATING
- REMBRANDT
- BLOCKING
- RECONNOITRED
- TROOPERS
- CAPRON'S
- GAUDY
- KEENEST
- KRAG
- DISTILLERY
- ROWLAND
- UPHILL
- CANTEEN
- GAUZE
- STORMING
- DRIFTWOOD
- HEARTSICK
- OLAF
- HARALD
- GREENLANDERS
- GUDRID
- VARIEGATED
- AFFABLY
- FOREARMED
- CROPPERS
- SLATES
- STEELY
- SAPLINGS
- DUCE
- PRIESTLY
- STIPULATION
- PULL'D
- PUZZLE
- NOAH'S
- HARM'S
- POO
- ACCOUTREMENTS
- CHRONOLOGY
- PLOUGHS
- MINERALS
- MARLBOROUGH
- BELBURG
- KERPENORD
- KALSAKEN
- NEWDORF
- LANDENBOURG
- MILDENHEIM
- ELCHINGEN
- GINGEN
- BALMERCHOFFEN
- DEFENCES
- SCHWARTZ
- HAPPEN'D
- LANDEN
- INFUSING
- CLAMBERING
- STAG'S
- DMITRI
- BUZZED
- STEPPE
- TRUMP
- SEDATE
- GODSON
- GROZNOE
- DEALERS
- HAYTERSBANK
- HAMMERS
- WHALING
- COULSON'S
- NEWCASSEL
- PRENTICE
- FAILINGS
- URANIA
- CONJURE
- REID
- ABACK
- YO'RE
- NOAN
- CHARITABLY
- FOSTER'S
- INSOLVENCY
- FORESTALLED
- CULMINATING
- MAR
- LADDIE
- LUCK'S
- THOU'RT
- MOLLIFIED
- BRONZED
- MAK
- THOU'S
- BARABBAS
- JOSEPHUS
- TALMUD
- CORRECTIONS
- SCRIBE
- EGYPTIAN
- LENDING
- WASTEFUL
- DISCLOSURE
- TOL
- WHOA
- ETHEL'S
- CHERUBIM
- CUPIDS
- PELL
- MELL
- UNCHAINED
- SHAMELESSNESS
- SATURNALIA
- CUCKOO
- LANDAU
- APOTHEOSIS
- TRIUMPHAL
- ENTICED
- CONFRONTS
- TINSEL
- PREFECTURE
- CANDOR
- MIRTHFUL
- SUPPOSES
- POTTERY
- BASQUE
- DESSERT
- CAJOLE
- WEIGHS
- ROBESPIERRE
- POMUM
- IMPASSIONED
- MANHATTAN
- RATAPLAN
- MUSCOVADO
- GRANDISSIMO
- BASTINADO
- RENTAL
- EMERALDS
- CREME
- COMMONERS
- FACADE
- SATINS
- HAPLY
- SHEDDING
- PHRONY
- TURNT
- HISSE'F
- INTER
- F'UM
- MO'NFUL
- GOO
- SWOOP
- BLUFFS
- COPPERAS
- CAPTING
- PADDLED
- HEADWAY
- SOWERBY'S
- PREBENDARY
- RECURRING
- PERQUISITES
- JAUNTY
- ENTRAPPED
- APPERTAIN
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- CHARIOTEER
- GRISELDA
- CORNISH
- CLERGYMEN
- RAIMENT
- DEANERY
- DELECTUS
- SAVOURED
- GUAVA
- UNLOAD
- INCUMBENT
- DISPENSED
- PLATOON
- INSURGENT
- LYNCH
- SIMONIANS
- MEMOIRS
- UNAIDED
- AUDACIOUS
- ABOUND
- INTERMITTENCES
- MISCARRIED
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- FATIGUES
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- NETTED
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- BALLET
- MOUNTEBANK
- SYLPHS
- SWANS
- CAREFULNESS
- EXAMINES
- URINE
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- ANALYZING
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- RECOGNIZABLE
- APPENDIX
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- UNDERMINE
- OVERBUSY
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- ENUMERATED
- REFORMS
- PLACARDS
- TRANQUILLY
- INCALCULABLE
- COMMUNISM
- MABEL'S
- FELSENBURGH'S
- BENNINSCHEIN
- UNIMAGINABLE
- PRECEDENTS
- BEGAT
- GOADING
- PROGRAMME
- GULLY
- LICENCES
- BULLOCK
- GROG
- ALLSORTS
- MISCONCEPTION
- SEMITES
- DEFILED
- MENTIONS
- CHILDBIRTH
- SECLUDE
- COMPARES
- SECLUDING
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- ZEALAND
- TABOOS
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- PUBERTY
- REAPPEARS
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- SURVIVES
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- ORCHESTRAL
- TUNED
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- PRIESTESS
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- ROMANINOV
- BUSIER
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- ADORNMENT
- MOOR
- SHADOWING
- BOATMEN
- GINTLEMAN
- ILLUSTRATIONS
- QUALIFICATION
- PICOTTE
- CURTIS
- UNEQUALLED
- COOLIDGE
- HELPERS
- SAC
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- INTERCOLLEGIATE
- JOURNALISTS
- KENZIE
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- BOHEMUS
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- DEITY
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- DISAGREEING
- MEDICORUM
- GUSTY
- MOCCASIN
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- ROCKETS
- HOGARTH
- UNRESTRICTED
- INQUEST
- FEMININITY
- CURSING
- IBRAHIM
- GOODWILL
- SEEKETH
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- SCONES
- BLEMISH
- ASKER
- MAKETH
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- TRENCHER
- HADDEST
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- LIABILITIES
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- DISHONOUR
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- PU
- MAUDIE
- MENACINGLY
- SERPENTS
- REALISATION
- WERMIN
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- EAMES'S
- PRECAUTIONARY
- BESEECHINGLY
- VERIFY
- DIVIDES
- EMPHASIZING
- VIBRATED
- GIGGLE
- REHEARSAL
- TYRRELL
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- SPLEEN
- AGROUND
- STRAYING
- PULSATING
- COWERING
- IMMEASURABLY
- VILLEFORT'S
- TIPTOE
- AFFABILITY
- LAMPREYS
- IMPUDENTLY
- CICERONE
- HONORABLY
- AISY
- WHAT'LL
- GI
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- DREAMER'S
- PARTAKES
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- VOLUPTUOUS
- INTERCEPTED
- WEISHAUPT'S
- WEISHAUPT
- PRUDES
- GUESSES
- SCRUTINIZING
- SERVER
- COGITATIONS
- TWINKLED
- LOANED
- GARAGE
- GREENE
- RODDY
- CALLER
- DISINCLINATION
- SCHOOLMATE
- OUTSIDER
- JACK'S
- HAZEL
- SWASH
- FANNING
- FAUSTUS
- CHEMIST
- ASTRONOMER
- ABLAZE
- SUP
- BABA
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- HEY
- PICARD
- HUSSIES
- CIVILLY
- PAPILLON
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- DISGUSTING
- IDENTIFICATION
- ILLEGIBLE
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- MAGISTRATE'S
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- FASCINATIONS
- UNAPPROACHABLE
- BASTARDY
- PRETENSION
- CREATES
- CIRCUMVALLATION
- BOLEYN'S
- ORTHOGRAPHY
- DICTATION
- IMPOSSIBILITIES
- DIRRY
- MOIR
- BREASTPLATES
- TOASTED
- THATCH
- SCRAPED
- REDOUBTABLE
- UNSHORN
- THRASH
- TRAINER
- HERCULES
- CIRCUSES
- FREQUENTED
- FLESHY
- FELONY
- PROSTITUTE
- ANNUITIES
- SUBSCRIBE
- INCIPIENT
- LEGITIMATELY
- DETRIMENTAL
- TACITUS
- MONOPOLY
- JUS
- AGREEMENTS
- ACQUIRES
- DISINHERITED
- OVERSTEP
- LEGISTS
- EQUITABLE
- BIRTHS
- USUFRUCT
- DEATHLESS
- THENCEFORWARD
- STY
- SLEEPERS
- PHANTOMS
- DISSOLVES
- WANDERER
- BRACKETS
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- GRANARY
- RECONSTRUCTION
- HOEING
- FERTILIZERS
- OAKEY
- PLANTERS
- FENCED
- INFLEXIBLE
- BOLTON
- INCLUDES
- GRABBING
- STURDILY
- PREACHER'S
- NEWBORN
- IDEALIZED
- COAX
- UNCLOTHED
- DUBBED
- BUILDED
- VINEYARD
- BARMOUTH
- GRAND'THER'S
- BANNOCK
- PETTISHLY
- FEATHERY
- MORGESONS
- DISTANTLY
- BOMBAZINE
- FLACON
- SALTS
- UNSWERVING
- HOMELAND
- DISILLUSION
- RAMBLES
- DIMNESS
- GIST
- FRAMPTON
- VACATIONS
- MAILED
- COMPREHENSIBLE
- PHOTOGRAPHY
- ILLOGICAL
- RECEPTACLES
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- MONALDESCHI
- SKEIN
- SCALY
- HOPE'S
- VOGRAAT
- ROTTERDAM
- PARALYTIC
- MOULDY
- REGISTRATION
- FRAEULEIN
- SCHERIN
- DUES
- DICTIONARY
- FACILE
- RUMPLED
- REGISTRAR
- FERRIS'S
- COMPOSING
- UNTAMED
- MUSINGS
- CYNIC
- COLYUMIST'S
- WILDE
- COLYUMISTS
- JEWELLERY
- SCRAWLED
- CONRAD
- PARAGRAPHS
- FOHRENSEE
- DIETRICH'S
- EXPOSTULATED
- SANGUINE
- SUPPORTER
- RIVERMOUTH
- ASPIRED
- CENTIPEDE
- LANGDON
- VESTIGES
- INITIATIONS
- TEARFULLY
- CLAPHAM
- MEEKS'S
- UNPROTECTED
- PROHIBITED
- SHIELDED
- UNCEREMONIOUSLY
- PATRICK
- CLASSIFY
- JAMIESON
- INFORMANT
- HOBGOBLINS
- CHIMBLEY
- GERAGHTY
- NYMPHS
- DA
- HALIBURTON
- CHRYSOSTOM
- ADJOURNMENT
- MADGE
- RETAINER
- KAJI
- YEARNS
- VIE
- THUNDERSTRUCK
- DIRK
- DANZAYEMON
- TANNERS
- ETAS
- BANJO
- YOSHITOMO
- TIPHYS
- NAUPLIUS
- ARCAS
- ADMETUS
- THESEUS
- AFFRIGHT
- REPORTER'S
- TABOR
- PENCROFT'S
- EMBRASURES
- FABRICATION
- INCONTESTABLY
- RESISTS
- ACCOMPLICES
- CONTUSED
- STRANGULATION
- PERFORATION
- SUPPURATION
- COMPRESSES
- SUBSIDE
- DOZED
- HAMATH
- NAIRI
- ARMENIA
- INAUGURATED
- TIGRIS
- PRETENDER
- FLAYED
- PAL'S
- SUKHI
- CHALDAEA
- NINEVEH
- PHOENICIAN
- EXCAVATED
- LAYARD
- HEBREWS
- MOLTEN
- BETHEL
- REBELLIONS
- ZIMRI
- TIRZAH
- GIBBETHON
- CONSPIRED
- QARQAR
- CLAIMANT
- HADAD
- SHALMANESER'S
- INAUGURATING
- THWARTED
- WHOSO
- INVENTOR'S
- WURTEMBERG
- LOTTERY
- BALLOONS
- ELIMINATED
- PRUSSIAN
- AWARD
- MEASURABLE
- GALL
- DESCENTS
- SYMPATHISED
- FORGE
- TEUTONIC
- RAIDING
- PERTAINING
- ILLUSTRATIVE
- CAXTON
- PAMPHLET
- FURNISHING
- ENLARGEMENT
- DOCKETED
- CRAVAT
- BECKY'S
- URN
- REGENT
- GRUDGED
- COUNSELLOR
- CURZON
- TOILETTE
- AMELIA
- JOS
- HUMBLEST
- BOOMERANG
- BADGE
- WARES
- OPPORTUNE
- OPPONENT'S
- SUBSIDIZED
- POLICIES
- SOUTHERNERS
- DISOWNED
- MUTILATION
- CONFEDERATES
- DAMNING
- COWED
- CRAVEN
- CALHOUN'S
- CREOLE
- DIAZ
- RUFFIAN
- DESTROYER
- RESTORER
- TABOOED
- HUCKLEBERRY
- GOSHEN
- HUMBOLDT
- BUCKEYE
- DELIVERS
- POPULARLY
- FREDONIA
- GRITTY
- CATTARAUGUS
- DEIGNING
- PEASANTRY
- DISPLACING
- VISCOUNT
- PEEVISHLY
- LANCET
- RESPIRATORY
- OUNCES
- CRAYTURE
- GALLON
- UTTERMOST
- TAY
- SHEFFIELD
- JANIUS
- DISCOORSIN
- ARISTOPHANES
- TAXED
- LOVELIER
- CELTS
- GLASTONBURY
- TITULARY
- METAPHYSICS
- PROGRAMMES
- HEEDS
- APPLAUD
- SOPHIST
- ANATHEMAS
- ECONOMISTS
- SPARING
- KNEAD
- POETICAL
- FANATICS
- PRECEPT
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- COLLATERAL
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- WITCHLAND
- WASHERWOMAN'S
- MARTYRDOM
- NORWICH
- TYRANNICAL
- PROCLAMATIONS
- BELGIANS
- BISSING
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- LEGATION
- THEREABOUTS
- ANNIE'S
- REDS
- LEATHERN
- NORTHWICK
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- BANGS
- EXPLICIT
- GROUNDED
- HAWTHORNE
- REVEL
- BIGNESS
- ROSELEAVES
- SENTRIES
- BELINDA
- OVERDOING
- GRATIFIES
- SWEETS
- CONTROLLING
- REVOLUTIONARIES
- MASCULINIZATION
- UTOPIA
- CIRCE
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- MILKY
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- BASENESS
- TILT
- BUTTONHOLE
- DIO
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- COMMISSIONS
- OXTED
- ECHOING
- SWEETMEATS
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- BENI
- PUPPETS
- UNALTERABLE
- MINX
- BELITTLE
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- VERSION
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- HYPERION
- EMERGES
- ANALYTIC
- BOLEYN
- PATE
- OFFENDER
- WICKET
- THREADING
- DURABILITY
- LINNAEUS
- HUNKERVILLE
- IMMUNITIES
- UNDERSIGNED
- LAUSANNE
- VEVEY
- PROHIBITIONS
- GAIZDORRA
- CAPGAROUPE
- GOURD
- WICKER
- ADHERED
- UNSEALED
- EXECUTIONER'S
- SERJEANT
- WAIF
- TALBOT
- INSCRIPTION
- EFFRONTERY
- CHRISTINA
- ASSASSINATIONS
- POISONS
- DEDUCTIONS
- COUNTRYSIDE
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- TANTALUS
- SIDEBOARD
- UNDERESTIMATE
- SUBVERT
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- COUNTERACTING
- LYNDON
- PENT
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- BOWLDERS
- HULK
- FERRYMAN'S
- EJACULATIONS
- ORGIN
- PEDALS
- NECESSITATING
- CROONING
- DAMPER
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- SAVANNAS
- RETRACING
- ARMFUL
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- DAILIES
- JOURNALS
- PLUTOCRATIC
- CLIQUE
- UNMAKE
- ENTHUSIASMS
- DISABILITIES
- ICONOCLASTS
- DISPARATE
- DISJOINTED
- MOUTHPIECE
- STIFLE
- METTLE
- LOUDEST
- HEEDING
- RECONCILE
- COLERIDGE
- GODLIKE
- UNPRACTISED
- GENDARMES
- IMPORTUNATE
- SMACKED
- ALIENATE
- DEADLIEST
- VOCIFERATED
- PALERMO
- STRIPLINGS
- INMATES
- STUPEFACTION
- BRAVO
- SACRA
- DECESARIS
- BLUNDERBUSSES
- PARISIAN
- PAMPINARA
- BORGO
- FRIENDSHIPS
- DIAVOLACCIO
- PORTRAITURE
- CHARACTERIZATION
- DEPICTS
- EFFECTIVELY
- MOLIERE'S
- SATIRIST
- DISPARAGE
- REIN
- SEASHORE
- LEVERS
- CLAVICHORD
- VIRGINALS
- ELIZABETHAN
- IMMORTALIZED
- BYRD
- SCARLATTI
- SONATAS
- MODULATIONS
- SOULLESS
- PROGRESSIONS
- CHROMATIC
- EXPLOITATION
- CONSECRATE
- CONFIDENCES
- ELEVATING
- ZWYNY
- HARKENED
- GOSSAMER
- NUANCES
- STACCATO
- INVALUABLE
- FLUCTUATION
- REGULATE
- AUDIENCES
- REPLACING
- GRATIFIERS
- RAILWAYS
- FRANCHISES
- UNEARNED
- INCREMENT
- ALGONKIN
- UNIFORMITY
- SYNONYMOUS
- CONEY
- HOLTS
- BUCKBOARD
- ASPENS
- SUPPOSIN
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- LIZ'BETH
- QUEEREST
- SPIKE
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- RUMBLING
- GROS
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- TRIANGLE
- MURRY'S
- ACCORDION
- MURRY
- ALDENHAM'S
- SAMUEL
- FINANCIALLY
- BULGER
- DESGAS
- BRANDED
- GAGGED
- BLOODLESS
- JEW'S
- SOUNDEST
- MEDDLESOME
- BLAKENEY
- SECTS
- CREDULITY
- FRUGALITY
- ADHERE
- MONASTERIES
- ACQUISITIONS
- AUTHENTICITY
- LUTHERANS
- SECONDING
- BLANDEST
- FREAKS
- ANCESTOR
- BIAS
- INGLORIOUS
- RIGIDLY
- INSULATED
- SUSCEPTIBILITY
- STYLES
- DETECTING
- FERVID
- ELEVATE
- CHRYSALIS
- BU
- CUB'S
- GOODWIFE
- SCORPIONS
- RENAL
- CLINGS
- ABHORRED
- CLERVAL
- ERNEST
- ADVERSITY
- KNEEL
- GIBE
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- DARREL
- CLENCHING
- RAVAGED
- VEHEMENT
- SEER
- HERETICAL
- CYCLE
- OMAR
- ABOMINATION
- JUSTINIAN'S
- ABBOT
- EXPEDIENTS
- NORTHERNMOST
- KHAN
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- BOSPHORUS
- ICONOCLASTIC
- CONTROVERSIES
- CONSTANTINE
- GRADES
- BERRY
- FILLETS
- RAZORS
- COMBS
- LEGGINGS
- KILT
- CLUSIUM
- ETRURIANS
- LARTIUS
- HERMINIUS
- SCHRINER
- BREADS
- BAKERIES
- PATRIOTICALLY
- RATIONING
- BRISTLES
- CHANTS
- PAMPERED
- REARS
- TINNEKONK
- INFLATED
- MIGHTINESSES
- TWANGING
- COMMODITIES
- FESTIVITY
- NEDERLANDS
- MOSQUITOS
- LOMBARDS
- MANEUVER
- MILLSTONES
- UNHESITATINGLY
- CELESTIN
- DIGGER
- MENDICANT
- LITTERED
- JETSAM
- GULP
- ELECTRODES
- VISUALIZE
- SEAWEEDS
- LOOMING
- DERELICT
- PROCURING
- RUPTURE
- INSATIABLE
- COKE
- PHILIPS
- SEYMOUR
- WENTWORTH
- PREPOSTEROUSLY
- ENCROACHMENTS
- CHARLES'S
- EXTANT
- BUCKINGHAM'S
- PREROGATIVES
- SEDITIOUS
- CONCERTED
- SELECTING
- GARFIELD
- CUYAHOGA
- CANTON
- GREYS
- PODINA
- EQUATOR
- PLATTER
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- TOMATO
- POOREST
- ALCHEMY
- CYMBALS
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- MANHEIM
- SIGUNA
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- LOKI'S
- PORCINE
- PENNANT
- CATERPILLARS
- OVERSHADOW
- OAK'S
- KIMBOLTON
- APPRENTICES
- SKIRMISHES
- ASCRIBE
- DIGBY
- CIRCUMSCRIBED
- LITURGY
- INFUSION
- INGREDIENT
- CONSUMMATE
- REFINEDLY
- DUELLI
- LEX
- SCRIPTA
- PERUSED
- SE
- HERMANN'S
- TENOR
- MEDIEVAL
- REVENANT
- SPECTER
- MAGNANIMITY
- HELVOETSLUYS
- BRAZIER
- VENTRILOQUIST'S
- EMPORIUM
- CRETONNE
- NETHER
- SKIMPY
- CONTUSION
- SHERLOCK
- ABRASIONS
- DRAPER
- REVEALS
- HOWE
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- VICUNA
- YESSIR
- UNDERFOOT
- GODDAUGHTER
- FLORINA'S
- ENCHANTER
- BURROWS
- WEASELS
- BACHELOR'S
- SCORPIONFISH
- CONTOURS
- SEAFLOOR
- ELECTRICIANS
- KILOMETERS
- CAKED
- AVENGER
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- SPRAWL
- BOTHERED
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- UNREAL
- INFUSED
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- UNENDURABLE
- FLEES
- MINISTRATIONS
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- REMISS
- INDUBITABLY
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- MONICA'S
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- AMPHITHEATRE
- BEFRIEND
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- IMITATOR
- RETHUMB
- INCARNATE
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- FEES
- Y'ERE
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- YOONG
- MONTGOMERY'S
- OUTED
- FAWCETT
- CANTAB
- SAYIN
- WITHERIN
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- HITTING
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- ICELANDER
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- TEMPESTS
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- PALAEONTOLOGICAL
- JAWBONE
- GENUINENESS
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- DIFFERENTIATE
- BANAL
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- ABSTENTION
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- APPOINTING
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- COINCIDE
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- DUALISTIC
- CONTEXT
- THESIS
- DENTS
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- AFFECTIVE
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- SHYEST
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- SOCIABLY
- RUTTED
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- OVERBRIMMING
- SMARTLY
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- UNMYSTERIOUS
- DISSIMILARITY
- INDICATIVE
- UNDERSTANDINGLY
- JAUNTING
- UNACCOUNTABLY
- UNSUPPOSABLE
- JOLT
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- STRYCHNINE
- ORPHAN'S
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- BALSAMY
- GRAYNESS
- SIDLED
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- GARBED
- DISCERNING
- LUDICROUSLY
- KNACK
- LACY
- MIGHTN'T
- DONATED
- BLOOMIEST
- CHARLOTTETOWN
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- GOBBLE
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- INSINUATIONS
- INFLICTS
- PARTICIPATE
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- ANNOYS
- JESTING
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- FORGERY
- CORSICAN
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- GRUDGING
- FALTERINGLY
- MAUVE
- SILLIES
- DARLING'S
- INTERRUPTS
- REDSKINS
- MERMAID'S
- LAGOON
- NIGHTGOWN
- DIFFIDENTLY
- NIGHTY
- MISINTERPRET
- ORGANISMS
- UNSHRINKING
- ADVENTURESS
- ADVENTURESSES
- NIECE'S
- RESTRICT
- DETESTS
- STACKPOLE'S
- CARAVANSARY
- CHAMBERMAID
- TENUE
- SURVIVAL
- FEUDALISM
- ELAPSE
- GOODWOOD'S
- SIMPLIFYING
- HENRIETTA'S
- OWNERSHIP
- INCONSTANT
- POSTMARK
- ERECTS
- JUDGEMENTS
- UNDEMONSTRABLE
- ACCRETIONS
- SIGNORINO
- REMO
- FLORID
- COARSELY
- STRUMMED
- INCOME'S
- REFLEXION
- ADMIRATIONS
- FIFE
- PREDILECTIONS
- SENTIMENTALLY
- BREAKWATER
- CANDIDLY
- DERIVING
- HONOURABLY
- SITTINGS
- PERSUASIVENESS
- FRIENDLIER
- JOACHIMI
- DESPATCHING
- REGICIDES
- MERGE
- NETHERLANDERS
- DEADLOCK
- EXPULSION
- PARLEYING
- IMPORTATION
- ALIENS
- WITHDRAWAL
- MAGNUS
- INTERCURSUS
- MARQUE
- RAKING
- EVENTUALITIES
- AGGRESSORS
- PAUW
- PAUW'S
- LEANINGS
- STUARTS
- RUPERT
- CRUISES
- ROUTES
- FIRESHIPS
- SUSPEND
- ORANGIST
- CORNELISZ
- WITTE
- DESERTION
- DESERTERS
- CONVOYING
- MANOEUVRING
- REPLENISHMENT
- SUBORDINATES
- EVERTSEN
- FLORISZOON
- SEAMANSHIP
- INITIATE
- DISSOLUTION
- DICTATORIAL
- CONVOYS
- GABBARD
- ADMIRALS
- POLITICO
- RESPUBLICA
- NEGOTIATORS
- REINFORCE
- MAAS
- KATWIJK
- MANOEUVRED
- SCHEVENINGEN
- MORTALLY
- REORGANISING
- PROTECTOR'S
- FORMULATED
- AMBOINA
- STIPULATED
- RESUMPTION
- PASSPORTS
- CLANDESTINE
- STADHOLDER
- PROPRIO
- MOTU
- COGNISANCE
- ADJOURNED
- PERSUASIVE
- DILATORY
- CLAUSE
- PRINCIPALS
- BURGOMASTERS
- ADVOCACY
- ILLEGALITY
- OVERRULED
- BEVERNINGH
- NIEUWPOORT
- UNDELIVERED
- FAIT
- ACCOMPLI
- ACIDULATED
- L'ALLEMANDE
- TARRAGON
- HORSERADISH
- PARBOILED
- FULFILMENTS
- RIPENS
- UNCONTAINABLE
- FORELOOKING
- ANTICIPATES
- PLEDGES
- ENHANCES
- HEYDAY
- PEDANTRY
- STOICISM
- FORSAKES
- ENLARGES
- WARMS
- DEFACED
- SINCEREST
- COMPUNCTIONS
- EMBITTER
- CLEAVES
- USURPS
- FASTENS
- TEASES
- SATCHEL
- COLDEST
- UNSAY
- TREASONABLE
- OUTLASTS
- REVISING
- WITCHCRAFT
- PLUTARCH
- ENAMELLED
- WHERE'ER
- SYMPATHIZES
- DILATES
- AKIMBO
- SOLILOQUIZES
- ACCOSTS
- WETS
- APOLOGIES
- MONOPOLIZING
- DESPOT
- BLACKMAILERS
- ORACULARLY
- URBANE
- DONATIONS
- PINCE
- NEZ
- CROOKEDLY
- CREASED
- TYPED
- HUMP
- EXPECTIN
- MOROCCO
- DURAZZO
- ALBANIAN
- SPLUTTERING
- CANDLEMAS
- PEZARA
- UNOFFENDING
- JOVIALLY
- COMINGS
- ACRIMONY
- SHARPNESS
- SHOCKINGLY
- ANTE
- DANCING'S
- WALTZES
- FIDDLING
- CRUDELY
- DISBURDENED
- SUBTERFUGE
- ELDER'S
- FALLACY
- SOPHISTICAL
- ABATEMENTS
- HARBOURING
- REFLEXIONS
- CONTEMPLATIVE
- EXECUTES
- PASTIME
- BYWAYS
- SANDED
- DESULTORY
- EXTRANEOUS
- LURIDITY
- POSTIK
- HOUARN'S
- FREES
- WIZARDS
- AVEN
- BOATMAN
- GROAC'H
- COCKCHAFERS
- RACKS
- CHURNS
- MORLAIX
- TRAITRESS
- DREAMINGS
- HUMANLY
- OPTIMISTIC
- LUMPED
- MANNERISMS
- GUIDEPOSTS
- REQUIREMENT
- SIDETRACKED
- OUTLAST
- SPOUSE'S
- STAGING
- CRAVES
- SWADDLE
- CHANGEABLENESS
- FORGES
- TOPHEAVY
- SUPERSTRUCTURE
- CHAMELEON
- BEING'S
- NAGGING
- STAGNATES
- ENGROSSMENT
- OVERCLOSE
- OVERCONCENTRATION
- UNDERLIES
- SPECTACULAR
- STAKING
- MELODRAMA
- SETTINGS
- FLOP
- INTERLUDES
- UNCOUTHNESS
- VEXES
- TERRIFIES
- NARROWING
- WINS
- PRIDES
- BABYING
- OVERSENSITIVE
- WIFELY
- STIMULATIVE
- UNADAPTED
- UNGUESSED
- SATISFACTIONS
- BACULUS
- BACKBITER
- CONVERSATIONALIST
- SHAVES
- MONDAYS
- SCRIMMAGE
- SAVERS
- BENEDICTINE
- BIGAMY
- BILLIOUSNESS
- EATABLES
- BIOGRAPH
- STEREOPTICON
- BIRDIE
- ERYTHEMA
- CALORIFIC
- AETEOLOGIZED
- PERCEPTIVENESS
- FACIAL
- CAPILLARIES
- PRAECORDIA
- BOODLE
- BRACE
- BRACER
- BRUM
- BELUM
- MEDULLA
- OBLONGATA
- BUNCO
- STANDER
- ABSENTEE
- SUBSTANTIALLY
- CROPPING
- CRANBERRY
- HARRINGTON'S
- UNBUCKLED
- BESMIRCHED
- SEARCHERS
- CONSTABULARY
- BESTIRRED
- TOWNSHIPS
- PLACATE
- ABNORMALLY
- EARTHWARD
- MARIA'S
- ACCUSER
- DISARRANGEMENT
- BANKNOTE
- BAIL
- BONDSMAN
- PUFFIER
- IDLED
- SATANIC
- INTAKE
- PROPHYLACTIC
- EXPULSIONS
- DUMBNESS
- INTERMENT
- KATHERINE'S
- OBLITERATING
- DRAPINGS
- FORECASTED
- HARTLEY
- HOWELLS
- BLACKBURN'S
- CATALOGUED
- MISERLY
- CONNED
- FOOLING
- ASSEVERATION
- VARIANT
- SKATE
- UNTOOTHSOME
- AVERT
- TUMORS
- ABSCESSES
- GOITRE
- CATARRH
- RHEUM
- ECZEMA
- MIXER'S
- SYRUP
- INSURES
- STRONG'S
- SUNBURN
- CHAPPING
- COLLAPSIBLE
- PREPAID
- COUGHS
- COLDS
- BALSAM
- CATHARTIC
- CAPSULES
- GILL'S
- SUPPOSITORIES
- GRUBE'S
- ERADICATION
- BUNIONS
- CALLOUSES
- REMOVER
- ERASER
- DEBILITATION
- BOWEL
- INSOMNIA
- SLEEPLESSNESS
- ASSIMILATION
- CATARRHAL
- KELLOGG'S
- ORDWAY
- PLASTERS
- BACKACHE
- LUMBAGO
- BRONCHITIS
- STRENGTHENING
- NICKEL
- PLATED
- DETROIT
- AUDITORIUM
- PHARMACY
- IMPAIRS
- TONING
- REJUVENATING
- DENTRIFICE
- ROSSMAN'S
- HEMORRHOIDS
- KIDNEYS
- STOMACHIC
- CLEANS
- POLISHES
- DAMPENED
- BLUED
- ALCOHOL
- TURPENTINE
- BENZINE
- PARAFFINE
- MOOSULMAUN
- WERT
- SUPPLICATED
- CALAMITOUS
- AFFLICTING
- RESOUND
- KINSMAN
- BAIRAM
- REDOUBLING
- MALLET
- METAMORPHOSES
- APPEAREST
- DELIVERER
- YUNAUN
- SYRIAC
- CEREMONIALS
- BETIMES
- ENVIOUS
- EXECUTING
- FANATICALLY
- SWEATER
- BRONCOS
- BUYER
- SLED
- UNIMAGINATIVE
- UNCARED
- STOWBODY
- PENSIONS
- BERT
- TYBEE
- LYMAN
- CASS
- GASSED
- HAMLETS
- MILLERS
- BOOSTING
- HUSTLER
- LIL
- WIFEY
- DOC
- IMPRIMATUR
- ERIK
- ROUGHNECK
- CHUCKED
- SNUBBED
- MENUS
- INJUDICIOUSLY
- WHITEFISH
- FILLET
- PEP
- AGITATORS
- KNUTE
- AMERICANISM
- BOOSTER
- BELCHING
- NORTHWESTLAND
- BURGS
- SNOBS
- ZOB
- YAHOOVILLE
- MINNESOTA'S
- BURG
- BLOOMIN
- BOOSTERS
- REORGANIZED
- GLORIES
- MIDDLEWEST
- POWERED
- HON
- BOOKLET
- PLOVER
- BEAUTEOUS
- GAMEY
- RESIDENCES
- EGOMANIAC
- NEIGHBORLINESS
- ARMISTICE
- CLIQUES
- SCANDALS
- BANGKOK
- PUTATIVE
- TWISTY
- MURDERESS
- MOORS
- DEADENED
- KREISLER
- KINDLIER
- RITE
- MIGNONETTE
- BUTLERS
- LIMOUSINES
- FICTIONAL
- SHERWIN
- COLIC
- SCALLOPED
- DULLNESS
- CONGRESSMAN
- MAJORS
- GEOGRAPHERS
- FISCAL
- ADDRESSER
- MOBBED
- PICNICKING
- PERSISTENCE
- ELFISH
- LOFTS
- IMPRACTICAL
- THEORISTS
- CLARKS
- VILLAGER'S
- ORGANIZERS
- TRAGICALLY
- INTELLECTUALITY
- HOUSEMATE
- MIDDLEWESTERN
- SCABBED
- BLAINE
- TUMOR
- PIANISTS
- LECTURERS
- SCRAWLS
- UNIONS
- TOUCHILY
- DYER
- MARMOT
- CHUCK'S
- WOODCHUCKS
- JUMPER
- WEASEL
- UNDERTAKERS
- PROCUREUR'S
- STUN
- MORCERFS
- MITIGATES
- MOULDINGS
- HAITIAN
- CROESUS
- ILLUMINED
- DEPUTE
- CHECKS
- BOVILLE
- CREDITS
- ROTHSCHILD
- LAFITTE
- RETIRES
- ROTHSCHILD'S
- LAFITTE'S
- IRREPROACHABLE
- ENCLOSING
- CRISTO'S
- LIFEWARD
- GREYING
- ASSERTIVE
- SHEENS
- PERVERSELY
- SENSUOUSNESS
- SWARM
- STRIDENT
- OILSKIN
- QUERULOUS
- BACKGROUNDS
- STUFFINESS
- VILELY
- DIRTIER
- LOATHSOME
- FILTH
- UNCHANGEABLE
- UNMORAL
- DELMONICO'S
- BUS
- ANSWERER
- INGENUOUSNESS
- UNSOPHISTICATED
- GIRLHOOD
- WETNESS
- BEATRICE'S
- INFLUENZA
- BRAINIER
- UNIVEE
- BAYNE
- FAYNE
- SAYNE
- ALEC
- ISABELLE
- SOUTHPAW
- OUTFIELD
- HITTER
- HUMBIRD'S
- ALUMINUM
- SCREWS
- LENTICULAR
- MOLES
- BURNER
- CARBONIZED
- MAHOMET'S
- SPHEROID
- GUARD'S
- COUCHES
- DONKEYS
- REIMBURSE
- TICKING
- CORRESPONDINGLY
- HARVESTED
- CONFRONT
- VERIFIES
- JOURNALISM
- OPALS
- CORSET
- TOBOGGAN
- VAPORY
- LOCOMOTIVE
- QUADRUPEDS
- DRAYAGE
- HEATHENISH
- PRESCRIBES
- LAG
- AFFLUENCE
- DISBELIEVED
- AXIOMATIC
- AMBASSADORSHIP
- GLOBULES
- COHERENTLY
- JAWS'S
- CAMPSITE
- SPALLS
- BALLED
- BLOB
- BOLE
- SURVIVOR'S
- FOREARMS
- IRIS
- CORNEA
- HEARTENED
- WORM'S
- SKYWARD
- RAMP
- PRONG
- CUBS
- WORLDER
- QUICKSAND
- LOOSEN
- SINGED
- DISPASSIONATELY
- TRUCULENT
- CREDENTIALS
- GUIENNE
- SCOTCHMEN
- BLENAN
- ANTOINE
- INTENDANT'S
- SCHEVELING
- CONYNGHAME
- FACTOTUM
- UNSHACKLED
- INSTALLMENTS
- WOOED
- RATCLIFFE
- PABLO
- HOUSEFUL
- GIPSIES
- PHOEBE
- STANDPOINTS
- MISUSED
- HYPNOTIST'S
- PRESUPPOSITIONS
- MESMERISM
- PARANOIA
- INSANITIES
- INTERPRETS
- MISINTERPRETATIONS
- ARGUMENTATION
- INTERPLAY
- SUGGESTIBLE
- INTERDICT
- DRUNKENNESS
- COCAINE
- UNDERMINED
- PERVERSITIES
- WRONGDOER
- NEGATES
- DEPRIVES
- INVENTS
- ANOMALOUS
- PATIENT'S
- PSYCHOTHERAPISTS
- PRESUPPOSITION
- TREATMENTS
- MORPHINIST
- ALKALOIDS
- INJECTION
- SANITARIUM
- HYPODERMIC
- REDUCTIONS
- TABLETS
- INHIBIT
- TRUEST
- AMATEURS
- EXPERIMENT'S
- DEVASTATES
- EARMARKS
- BARBARISM
- ACCREDITED
- EDDY'S
- CULTURAL
- PHILOSOPHICAL
- IMPLICATIONS
- REENFORCES
- PSEUDOPHILOSOPHY
- SAPS
- FRUCTIFIED
- DEMONSTRATES
- RESHAPING
- PATHOLOGICAL
- INTEMPERATE
- CRIMINOLOGISTS
- PERSPECTIVES
- REENFORCE
- ABNORMALITIES
- INHIBITIONS
- CRIMINOLOGICAL
- SUPPLANT
- RECKONS
- OPOSSUM
- POUCHED
- OPOSSUMS
- BUSTER
- CROTCH
- HANDIEST
- DEADEST
- REINTEGRATION
- INTERWEAVING
- STIRRINGS
- DREARINESS
- CONFLUENT
- PATTERING
- BENEFITED
- INSECURITY
- GREYLY
- DISTENDED
- BLADDER
- AROMA
- INTERMEDIATION
- VEINED
- ULTRAMARINE
- UNDEVIATING
- DRONING
- ALERTLY
- BOSCASTLE
- UNSTAINED
- DRUNKARD
- COLOURINGS
- REITERATION
- COERCIVE
- SOPHISTRIES
- STIMULATING
- EVINCES
- EXCRUCIATING
- DAMAGING
- ADVENTITIOUS
- FLOUTED
- UNPRACTICAL
- EFFECTIVENESS
- TUNEFUL
- VITALS
- UNALLOYED
- SLEDGEHAMMER
- DIRECTNESS
- DISHEVELED
- INEXHAUSTIBLY
- RESONANCE
- LISP
- UNDISCOURAGED
- FLAGGING
- IMPERILED
- COMMENDABLE
- TERSE
- IMPERISHABLE
- MORALIZES
- FLASHY
- PENETRATIVENESS
- NAUSEATING
- INEFFABLY
- UNPICTURESQUE
- INSATIABLY
- DOGMATISM
- ENSLAVES
- STIGMATIZED
- PLATITUDE
- NOTORIOUSLY
- PRACTISES
- COPIOUSNESS
- RATIONALLY
- PALLIATIVES
- MITIGATIONS
- QUIXOTIC
- OVERWEENING
- INSUPERABLE
- PROMISCUOUSLY
- PALPABLY
- PARADING
- PATENTLY
- INIMICAL
- COMPROMISES
- CATCHWORDS
- EXEMPLIFIED
- GLOOMIEST
- PORTENTOUS
- MOUSTACHES
- TROUP
- ISCHIA
- CAPRI
- SCORIA
- CALLOW
- VESUVE
- ERUPTION
- VESUVIUS
- FS
- LEONARDI
- MILANO
- ITALIA
- POSING
- TITLED
- DULLARD
- BERATING
- KIDNAPPED
- CARETAKER
- ENDEARING
- HOLDINGS
- BATTLEGROUND
- INDUSTRIALISM
- SHERLEY
- WITTED
- GIGGLED
- STRADER
- NEANDER
- NECTAR
- CRATER
- CONGEALED
- NASMYTH
- INHABITABLE
- PROPITIOUS
- ANATOMICALLY
- ORGANIZING
- ROTARY
- INSOLUBLE
- PRIMORDIAL
- RESPIRABLE
- UNINHABITABLE
- UNDERGOES
- HAZARDOUS
- FANTASTICAL
- TANGIBILITY
- INCUBATION
- PUNNING
- SHOEBLACK
- BLUNTNESS
- WAVELET
- GAPED
- BUBBLES
- ENTRAPPING
- JESUIT
- UNSUPPORTABLE
- CUNEGONDE
- CADIS
- EFFENDIS
- LETHARGIC
- SQUANDERED
- PAQUETTE
- PHILOSOPHISING
- MEDDLEST
- VIZIERS
- SHERBET
- KAIMAK
- CANDIED
- MOCHA
- BATAVIA
- EGLON
- MOAB
- EHUD
- ABSALOM
- NADAB
- BAASA
- ATHALIAH
- JEHOIAKIM
- JECONIAH
- ZEDEKIAH
- DIONYSIUS
- PYRRHUS
- PERSEUS
- HANNIBAL
- JUGURTHA
- ARIOVISTUS
- OTHO
- VITELLIUS
- DOMITIAN
- UT
- OPERARETUR
- EUM
- JOINER
- CONCATENATION
- PROMPTER
- MUMMERY
- EXECRATING
- VAPORS
- IMPURITY
- CHIPPEWAS
- RECREANT
- FIGURATIVE
- BOASTFUL
- VEILING
- NOISELESS
- PROFITABLY
- CANADAS
- BEAVERS
- AMITY
- MAISE
- EMPHATICALLY
- MOCCASINS
- BAUBLES
- DONOR
- OBDURACY
- DIPLOMATIST
- PREJUDICIAL
- ANNUNCIATION
- VENERATE
- LONGUE
- CARABINE
- UNGUARDEDLY
- INJUDICIOUS
- DELIBERATIVE
- ARMLETS
- CINCTURES
- TUTELAR
- TAMENAY
- SQUASHES
- GARNERED
- INEXPENSIVE
- HANDCART
- CREPE
- THOAP
- BRANDS
- COMPANY'S
- VENDER'S
- IMMERSE
- ELIJAH
- ELISHA
- RIGMAROLE
- RO
- COSM
- SIMPSON'S
- RUSTLY
- NOKOMIS
- APOSTROPHIZED
- DREST
- UNSTOPPING
- UNGLUING
- HUSKING
- FATS
- JOYLESS
- CAPSIZING
- UNBUSINESSLIKE
- REMINISCENCE
- ELERGANT
- DRAB
- COMFORTINGLY
- READINGS
- OUTGROW
- MANSIONS
- BEECHER
- STOWE
- HOME'PATH
- GAB
- MAKIN
- INTEMPERANCE
- YOUS
- PERSPIRED
- EXCUSING
- PAINSTAKINGLY
- REWROTE
- DEARBORN'S
- FONDLES
- WASHES
- SMELLIE'S
- SMELLIE
- PRAYIN
- SWEARIN
- MINNIE'S
- BEATIN'EST
- SAWYERS
- IMPROVIN
- ABNER
- SWAPPIN
- MERCIES
- ACCOUNTIN
- STACKED
- KEROSENE
- WICKS
- CRANBERRIES
- DIGNIFYING
- PIANOLA
- BEIN
- TUBBS
- RAPTUROUS
- GOIN'S
- UNCONVINCED
- TENEMENTS
- LADDS
- MAYFLOWER
- COCHERE
- WAGONETTE
- URSULA'S
- POSTILIONS
- YOUSE
- EQUESTRIENNES
- SOUVENIR
- JELLINGS
- CHINS
- WHERE'D
- LAPS
- MAM'SELLE
- NURSEMAID
- DISAPPOINTEDLY
- COOMSDALE
- FEELINGLY
- PORTENTOUSLY
- INTERSTICES
- PASTED
- TOBACCOEY
- RUMMISH
- PATTON
- HISSOP
- DARINGLY
- SQUEALED
- HARTWELL
- PAJAMAS
- UNKINDLY
- WYATT
- RELOCKED
- BANISTERS
- GLADDEN'S
- COUNTRYWOMEN
- EXCULPATE
- RESPELL
- DISPLEASE
- AVAILING
- NOURON
- NIHAR
- MINIONS
- IMPUTE
- UNBOUND
- OBJETS
- FORGIVES
- DISPELLED
- DEFER
- AFRICANS
- KOOLLOOB'S
- CAUZEE
- HISTORIOGRAPHER
- GROSBEAKS
- BROADWING
- FUSSED
- CONTORT
- OGRESS
- ENGRAFTED
- GENDARME
- HANGMAN
- COQUETRY
- CARTERS
- SWINDLER
- LILLE
- ENTAILS
- SUTLERS
- SUTLER
- RECALLS
- SEMINARY
- ORTHOGRAPHICAL
- SLOTHFUL
- GIANTESS
- BANKRUPTCIES
- MAMMIFEROUS
- MATERNITY
- BROWSE
- HITCHED
- POACHER
- APHORISMS
- TRUSS
- EXHORTATION
- BESEEMETH
- CONSTRAINETH
- NIHIL
- GAINSAID
- REASONINGS
- CONCISELY
- PLAINWARD
- WEND
- TORRENT'S
- GRANDFATHERS
- FIRSTLINGS
- MULTIFARIOUSNESS
- LABYRINTHS
- STELLAR
- MORALITIES
- SUBLIMER
- VOLTAIREAN
- FREETHINKER
- LITANIES
- GOODY
- FLAUBERT
- ASTUTENESS
- MEDIOCRITY
- SPIRITUALISES
- SPIRITUALISING
- DESINTERESSE
- DISINTERESTEDLY
- SACRIFICER
- RARER
- MORALISTIC
- BONHOMME
- EXHORTED
- OVERSHADOWING
- UGLIFYING
- DOCUMENTARILY
- SUITING
- BAROCCO
- MORIBUS
- ARTIBUS
- ARISTOPHANIC
- PARODISTS
- CULTURES
- SUPERIMPOSED
- ESPRIT
- VASTE
- DETERMINES
- UNFAVOURABLY
- TRUCKLING
- ATHENIAN
- SHAKESPEARE'S
- CHIAJA
- GOLDENNESS
- GODSENDS
- GLORIFICATIONS
- HEDONISM
- UTILITARIANISM
- EUDAEMONISM
- MISFORTUNED
- HEREDITARILY
- INVENTIVENESS
- ANNEALED
- CAPTIOUS
- DISENGAGE
- HONESTY'
- DEVILRY
- TEDIOUSNESS
- PHILOSOPHIZING
- UTILITARIANS
- RESPECTABLY
- HOMERIC
- BENTHAM
- SENATEUR
- POCOCURANTE
- MORALISTS
- MORALIST
- AUTHORITATIVE
- BIFURCATION
- MAIRE
- STATENLAND
- SPOKESMAN
- PATAGONIANS
- FREISCHUTZ
- MIMICS
- SQUINT
- MIMICRY
- CAFFRES
- AUSTRALIANS
- HOSTAGES
- JEOPARDY
- MATTHEWS
- AQUATIC
- DIRTIED
- SKYLARK
- LANDSMAN
- UNTRIMMED
- OURANGOUTANG
- THRIVING
- BETULOIDES
- SOLANDER
- GUANACOS
- GLOOMINESS
- BARNEVELTS
- ANCHORS
- GREENSTONE
- HAYCOCK
- SOLSTICE
- WOLLASTON
- OTTER
- BAITED
- FUNGI
- CANNIBALS
- CONCURRENT
- DOGGIES
- FIRESIDES
- LIMPET
- CORDILLERA
- CHILE
- CALIBAN
- SETEBOS
- FREAKISH
- VAGARIES
- BLEND
- HALED
- ELF
- PORTENTS
- STUFFILY
- UNBLENCHING
- TOMAHAWKED
- IDIOSYNCRASY
- PITILESSLY
- HAZELS
- PHANTASMS
- IMPORTANCES
- ILLUMINATI
- CONSPIRACIES
- TROW
- NEIGHBOUR'S
- FRIENDLIEST
- PORKER
- CULPRIT
- OLYMPIAN
- SADDENING
- ARCADIA
- AWAKENINGS
- BLUEST
- GERMINATING
- KINDLE
- PRIMROSE
- CRAZE
- STAUNCHLY
- FAD
- WAYFARERS
- COS
- MISPLACED
- GROWLINGS
- UNRECORDED
- HEROISMS
- EFFLUENCE
- SQUELCHING
- SPLASHED
- SKYWARDS
- UNRHYTHMIC
- COMPANIONABLY
- EXPRESSIONLESS
- TRICKSTER
- BLUSTER
- MISRULE
- SHEERED
- THWARTWISE
- CHAINLESS
- TOMFOOLERY
- KINDLINESS
- GYRATING
- LARCENY
- PLUMMET
- PLAYBILLS
- CHAFFINCH
- HEDGEHOG
- DECADENT
- JACKETED
- CANCELLED
- NIBBLED
- EARTHBOUND
- SLUNK
- NOTHINGNESS
- SHACKLES
- TETHERED
- STERNE
- OUTPUT
- COVETING
- DUCKWEED
- BEDABBLED
- FRITZ
- SHIPOWNERS
- BHAERS
- LEIPZIG
- BONS
- SWEETEN
- JOSIE'S
- NAN
- DOSED
- DAISY'S
- BESS'S
- MEG
- PLUMFIELD
- STUDIOUS
- TEDDY'S
- OCTOO
- SNOWDROPS
- DUSTING
- EMIL
- CASABLANCA
- FIDDLED
- HEIMWEH
- BEARABLE
- HERR
- LINDENS
- MINNA
- NAT'S
- STEADFASTNESS
- CADDIS
- BROOKSIDE
- CONFIDINGLY
- THROATED
- FLYCATCHERS
- PEWEE'S
- CRESTY'S
- BLUEBIRD
- TIT
- WOODCOCK
- TUSSOCK
- LONGBILL'S
- FREEZES
- SNIPE
- FUNNIEST
- TEETERED
- SANDPIPER
- TULES
- OBSIDIAN
- GRANITIC
- TOTEM
- MYTHOLOGICAL
- QUAVERING
- UNSOUGHT
- SCURRYING
- CRUSTS
- RETRACE
- FIERCER
- SNAKELIKE
- MONOPOLIZED
- UNTRODDEN
- INCONGRUITY
- PALPABLE
- ROUNDING
- SOAKS
- SYMBOLIZES
- INEXORABLENESS
- INVERTED
- JUTS
- CONSTRUCTS
- SOLOMON'S
- MATING
- PULSELESS
- BENDINGS
- ENCHANTS
- LIMBLESS
- UNRESTFUL
- FULFILS
- WAVY
- SHAMBLED
- CANTER
- UPGRADES
- SURENESS
- DOWNGRADE
- ASKIN
- Y
- LEARNIN
- REJOINDER
- OVERHEARING
- BUNKING
- GIMME
- MA'S
- CUSSING
- CHOW
- SQUATTERS
- LEASTWAYS
- STRAIGHTER
- MIMICKED
- WONDERIN
- NOTHIN'
- JOSIAH
- EDDICATED
- BUCKER
- FIGGERS
- LYIN
- AW
- POLO
- OFF'N
- FLOPS
- OUT'N
- B'LIEVE
- RAISIN
- PERFORMIN
- BUCKIN
- TWISTIN
- DRAWIN
- S'LONG
- ROOMING
- THUMPS
- IDLING
- UNPRESSED
- MODELED
- CHISELING
- HOOKED
- ERECTNESS
- CLUMPED
- SOURLY
- SKUNK
- HARRY'S
- TRYST
- UNEVENLY
- RONICKY'S
- ABDUCT
- MERRYMAKING
- RELIGHT
- ADIEUS
- LAGREE'S
- PERSECUTOR
- TIRING
- MELODIOUSLY
- SYREN
- RAINBOW'S
- BRAVES
- POUCH
- UNHURT
- BOOKSELLER
- DORMOUSE
- CANARY
- PREFERRING
- BEGGAR'S
- SHIRKED
- PROFLIGACY
- PLUMPNESS
- BEETLE
- SUFFERANCE
- WILTSHIRE
- LISTLESSNESS
- UNCANDID
- SARAH'S
- HEROINE'S
- OVERRATED
- DOUBLING
- TREBLING
- LEGACIED
- OPENNESS
- VOUCHERS
- WEAKENING
- RECONCILIATION
- RHODOMONTADE
- OVERTURE
- RELATOR
- DEVOLVE
- READER'S
- RETRACTION
- INTERSTICE
- FORESTALL
- LUDLOWS
- WONDERMENTS
- LUDLOW'S
- OSMOND
- INHERITING
- NEPHEWS
- EUSTON
- BANTLING
- CANDOUR
- CEASES
- ACROPOLIS
- SALAMIS
- HARLINGS
- SKATED
- BONFIRES
- CHOPPY
- HARLING
- IMMOBILITY
- FIELD'S
- BOOTH
- BARRETT
- PAPERY
- KENTUCKY
- BUXOM
- LAUNDRESS
- D'ARNAULTS
- FIDGETS
- MARTHA'S
- LILACS
- PICKANINNY
- HOLLYHOCK
- STRADDLED
- MASTIFF
- MASTIFF'S
- PRESENCES
- VITALIZED
- SPECT
- TRANSOM
- TONY
- SODERBALL
- RESOURCEFUL
- USURY
- HATRACK
- POULTICING
- HANDBAG
- AVOUCHED
- HORSELIKE
- CUTTER'S
- EXACTIONS
- PROSAIC
- ELEMENTAL
- PAIRFECT
- MEERACLE
- OVERVALUED
- BATTENED
- DEVASTATION
- TAMIL
- SMUGGLE
- UNWEARIED
- PYJAMAS
- FRONDS
- THOROUGHFARE
- GARLANDED
- ISLETS
- UNDEFACED
- SEVERER
- UNSUBSTANTIAL
- RENEGADE
- BISMARCK'S
- BRUTALISED
- HATCHWAYS
- CISTERN
- CRANNIES
- COASTING
- PRAUS
- CAMPONGS
- FULGOR
- STAGNANT
- PATNA
- AMIDSHIPS
- SMOULDERING
- SPELLBOUND
- PRESIDING
- IMPASSIBLE
- ASSESSORS
- LOGGED
- FOREHOLD
- BULKHEAD'LL
- MALEVOLENT
- SERRIED
- PITH
- PEONS
- WAYFARER
- VERANDAH
- UNRUFFLED
- MARLOW'S
- REKINDLED
- INDEFINITENESS
- DISENCHANTMENT
- IMPRECATION
- PROD
- PLACATED
- INTERJECTED
- NAUSEOUS
- HAIR'S
- INCISIVELY
- CONFOUNDEDLY
- THROVE
- EXCELLENTLY
- DWINDLE
- DILUTED
- DISCOURAGE
- HUNGERING
- BURGE'S
- GREYSTONE
- INTERPRETERS
- BLASPHEMOUS
- REJOICES
- EVIL'S
- SORROW'S
- CRUDER
- WOODLESS
- OVERARCHING
- WEBLIKE
- UNSCREENED
- OVERSTARTLED
- MONITIONS
- CUMIN
- PEPPERCORNS
- SPAWNING
- OPPIAN
- STARFISH
- WIDEN'D
- PRAWN
- PINTS
- TOMATOES
- VERMICELLI
- SHRIMP
- STUPIFIED
- WHITESIDE
- LYNE
- SMIRKING
- EXTRADITION
- COMPLICITY
- UNLOCK
- MILBURGH'S
- PROVIDENTIALLY
- YONKERS
- UNRELIEVED
- POCK
- UNCHALLENGED
- OVERRIDE
- MOTORCYCLE
- SCHEDULE
- HAB
- OHDAHD
- DARKY
- BRUNG
- POWFUL
- TELEPHOME
- AST
- AFTAH
- HONGRY
- DRAT
- LIAH
- DOLLAH
- CAIN'T
- GROGAN
- FELDERSON'S
- PARALLELING
- SYNAGOGUES
- SOMEDAY
- CRAFTIEST
- AFRITES
- JINNS
- WORKADAY
- CORANTO
- BUSKIRK'S
- IMPERSONATION
- IMPERSONATED
- CHANCING
- ABJECTLY
- INORDINATE
- THEREON
- JANET'S
- JUGS
- BASKETFUL
- FOREGATHERED
- TELLERS
- MASTERFULLY
- FINGERTIPS
- MILESTONE
- DAFFODIL
- SUNSETS
- SKEERED
- EERIE
- ALEC'S
- HEN'S
- VISITANT
- EYEHOLES
- WOEFULLY
- REEKING
- SAUCERFUL
- RESERVEDNESS
- ENSURED
- PROMONTORIES
- THISTLES
- INTERNALLY
- CENSURED
- FERRARS
- DASHWOODS
- CAREYS
- WHITAKERS
- EDGAR'S
- UNFORGIVINGNESS
- AMENABLE
- TILNEYS
- ADMITTANCE
- BEDFORD
- CLEANEST
- RECEIV'D
- EXPRESS'D
- HYRCANIA
- DETERMIN'D
- PROCUR'D
- ACKNOWLEDG'D
- AMPHITHEATRICAL
- COMPLEATLY
- MAGI
- PERPLEXT
- TOURNAMENTS
- CONTINU'D
- WATCH'D
- ALLOW'D
- COVER'D
- VAIL
- INDULG'D
- ABOVEMENTION'D
- MARTIAL
- INSCRIB'D
- CADOR
- COMPLEAT
- ENRICH'D
- AMPHITHEATRES
- ACQUIR'D
- HOVER'D
- FLATTER'D
- ENAMELL'D
- RIBBANDS
- ITOBAD'S
- HEAV'N
- PITCH'D
- BABYLONISH
- SCEPTER
- CRUPPER
- QUIV'RING
- ITOBAD
- DISDAINING
- UNHORS'D
- CONQUER'D
- VANQUISH'D
- GAIN'D
- MIXT
- PALPITATION
- VOLTA'S
- WISH'D
- BUTTOCKS
- GRASP'D
- JUMP'D
- WHEEL'D
- INCENS'D
- OFFER'D
- ADVANC'D
- CLOS'D
- DISARM'D
- DESTIN'D
- RECONDUCTED
- PRESCRIB'D
- ORDER'D
- FATIGU'D
- ZADIG'S
- REPAIR'D
- OVERWHELM'D
- OBLIG'D
- HISS'D
- RIVAL'S
- RECTIFY
- PLUNG'D
- IMPROPITIOUS
- BALEFUL
- IRRETRIEVABLE
- COQUET
- PROV'D
- DISPENSATIONS
- GOVERN'D
- FAIL'D
- CROWN'D
- FRANTICK
- INDOLENT
- RAGOUT
- ABUSING
- CHEAPSIDE
- LOO
- DESPISES
- DERBYSHIRE
- INATTENTION
- CAPTIVATION
- DESPICABLE
- SOLACED
- DUETS
- TRESPASS
- ESTIMABLE
- STUDIER
- NEIGHBOURHOODS
- GARDINER'S
- FORSTER
- TAROSS
- PERCY'S
- ESKE
- LIDDEL
- JULY'S
- UNREFRESHED
- UNTASTED
- PEYTON'S
- AEOLIAN
- MASSA
- VISITOR'S
- SUBALTERNS
- BENIGNANT
- PLAGUES
- NAVIES
- OCEAN'S
- WHELMS
- LIGHTSOME
- UNGENIAL
- IMBIBE
- SUPERABUNDANT
- UNVEILED
- THRONED
- COMEST
- SHORELESS
- SWEEPEST
- VIEWLESS
- VALLIES
- HOLDEST
- GOVERNANCE
- ROARINGS
- DESPOILED
- WRENCH
- MEREST
- WIELDERS
- INTEGRAL
- DOMESTICATE
- VISAGED
- DISQUISITION
- HUNDREDTH
- IMMEDICABLE
- GULLIVER
- BROBDIGNAGIANS
- ERUPTIONS
- RIFE
- QUITO
- PARTIZANS
- HINDOSTAN
- EMPOISONED
- INHALES
- UNINFECTED
- UNCULTIVATED
- DESTROYERS
- CLIMES
- CELT
- UNCOMMUNICATED
- INNOXIOUS
- SPICY
- CIRCASSIA
- ENCREASED
- EQUALIZATION
- PROTECTORATE
- TWELVEMONTHS
- UNLAMENTED
- QUAILING
- UNERASEABLE
- REVULSIVE
- SPECIE
- UNPAID
- NURSLINGS
- PENURY
- PARSIMONIOUSLY
- IMPORTS
- ANTLERED
- PROTEGES
- OFFCASTS
- PLEADINGS
- AGRICULTURIST
- INDULGENCIES
- PALANQUINS
- SECEDE
- PARTERRES
- EMIGRATION
- CONCOMITANTS
- LUDOVICO'S
- CARLO'S
- CARLO
- ROBERTO
- RAMPART
- UDOLPHO
- SIGNORA
- LIVONA
- CONJURED
- SIGNOR'S
- RANSOMS
- ANIMATE
- RETRACTING
- NIGHT'
- DETER
- MONTONI'S
- ACQUAINTANCESHIP
- BROKERS
- ROUNDERS
- DROUET'S
- STUCCO
- STEWARDSHIP
- SOLITAIRE
- ENGRAVING
- INFORMALITY
- FREQUENTING
- TACTFUL
- BARKEEPER
- EVANS
- MILWAUKEE
- BAREST
- MODIFYING
- SELTZER
- PACER
- SCHEMERS
- AUGUR
- GAINSAY
- INFESTED
- SPIRITUALIST
- THINNING
- ALARUM
- LIKINGS
- BIDED
- PIKESTAFF
- BOUTS
- LONGBOW
- BLAND'S
- APLENTY
- HALFSCORE
- COWHIDES
- TANNER'S
- CLOVEN
- BROADSWORD
- WOODCRAFT
- VARLET
- CRAFTSMAN
- BACKTALK
- SOVEREIGN'S
- TALKEST
- CALF'S
- ANCASTER
- FORFEND
- WINDOWPANE
- BELIKE
- MURRAIN
- CUDGELED
- NOTTINGHAMSHIRE
- ELY
- JOCK
- SCATHELOCK
- BELABORED
- COWSKIN
- TWANG
- BOWSTRING
- EPISODES
- AUBURN
- BEHOLDERS
- COMPLYING
- GENTLEFOLK
- BEEHIVES
- OSTENTATION
- COYNESS
- PERJURED
- UNLACE
- BEFOOLED
- SLIGHTED
- ABHOR
- CRIER
- HERDSMAN
- FASTED
- BATHES
- DEWDROPS
- LIMPING
- KNOCKERS
- HYACINTHS
- LOVEABLE
- WARRANTY
- SCABBARDS
- VIEUVILLE
- CARMES
- DESCHAUX
- CAHUSAC
- EDICTS
- SWORDSMEN
- HILTS
- LUNGE
- EJECT
- REPRIMAND
- ADJURE
- EMINENCE'S
- VILLE
- REREAD
- REKISSED
- MUSKETOON
- ENTERTAINS
- EXPATIATED
- WICKETS
- EXEMPLARY
- TINE
- RECOMPENSED
- DELAYING
- SWORDSMAN
- DOMICILE
- EXPLICATIVE
- SAVORS
- PARDIEU
- COINING
- GARDES
- MOONLIGHTED
- SOLITARILY
- MOVELESS
- UNDULATIONS
- SADDENED
- CAEN
- STEMS
- GLADES
- SYLVAN
- PILLARED
- FRONTED
- LICHEN
- HAGAR'S
- QUINCE
- SWEDENBORGIANS
- RUSK'S
- INACCURATE
- SUPERSEDING
- GOGGLED
- UNDERCHAMBERMAID
- IMPOUNDED
- ROUGIERRE
- JUSTER
- ORDAINED
- GRIMACING
- RUTHYN
- GOUT
- AV
- SWEDENBORGIAN
- DURE
- SPITEFULLY
- HOITY
- TOITY
- ELICIT
- FRENCHWOMAN
- OAKLEY
- QUAITE
- APPY
- VOUS
- SAVEZ
- MALADES
- TARTNESS
- PARFOIS
- SHAMMING
- TRANSITIONS
- TESSELLATION
- DETESTABLY
- AUSTIN
- IRREVERENT
- VIALS
- COURTING
- REPUGNANT
- UPROARIOUSLY
- COURSED
- IMPERSONATING
- BLISTER
- MALVOLIO
- WOOING
- HUG
- FAKIR
- SUPPOSITIOUS
- MARTHIEU'S
- BELIEVER
- NUMB
- UNCONSCIOUSNESS
- DEVOTEES
- O'SHANTER
- WARTHIN
- WOTAN
- SIGMUND
- VALHALLA
- CLAIRVOYANCE
- HEALERS
- PROFESSIONALS
- PROSTITUTED
- COCKE'S
- QUINCEY'S
- PROFUNDIS
- MESMER'S
- PROLONGING
- ZOLA'S
- CRUSADES
- POET'S
- DEMONIACAL
- DERVISHES
- FAKIRS
- IMPERSONATIONS
- SUMNER
- CUSHMAN
- COOKE
- PELS
- BUTTS
- MACKWORTH'S
- BLURRING
- JOAN
- FISHIEST
- GRIMNESS
- SCORCHING
- CLOAKING
- MINSTREL
- GROSSER
- SLUSHY
- SNOWBALLS
- MOAT
- SKATES
- GROAT
- JACKDAWS
- YULE
- SUET
- CALDRONS
- MALMSEY
- BROACHED
- DAFFODILS
- FARAWAY
- FALWORTH'S
- BAREHANDED
- BAREARMED
- OUTFIELDERS
- DICCON
- DEVLEN
- SHINNED
- CLEMATIS
- YEW
- GREENNESS
- SUNSTROKE
- TRADE'LL
- THANKY
- BRINGIN
- YE'VE
- THORT
- AN'
- ARRYSTOCRACY
- UNCOILING
- FURLED
- WHARFMEN
- TRADE'S
- TRYIN
- HANKERCHER
- GYPSY
- DERE
- FREND
- THEVES
- WIL
- PARDNERS
- ILE
- CUMS
- ENNY
- FELER
- TRISE
- THATS
- THERES
- YURE
- PERVIDED
- FUST
- RISTOCRAT
- MINNIT
- HARRISON'S
- DUBIOUSNESS
- DORINCOURT'S
- OFTTIMES
- GIRDERS
- HYDROGRAPHIC
- SHIPMASTERS
- VENTURESOME
- COLLATED
- TABULATED
- BASER
- SUBLIMATION
- FILTRATION
- CRYSTALLIZATION
- ANTIMONY
- PHOSPHORUS
- OILS
- REDMAN
- GENOESE
- NAVIGATOR
- LIVINGSTONE
- EVANGEL
- UNIFIED
- PURGING
- DROSS
- TRIVIALITY
- PIVOT
- SWINGS
- DEPRIVATION
- TRUER
- RESURRECTION
- SHOWINGS
- EVOLVING
- COMMENTATORS
- DISHEARTEN
- UNSTRAINED
- FLUIDS
- LODESTONE
- CHARCOT
- METALOGY
- PSYCHIATRICALLY
- ORIENTED
- OBSTETRICIAN
- GYNECOLOGIST
- CATALYZED
- VISUAL
- IMAGERY
- SEMANTIC
- HYPERACUTE
- INTERPERSONAL
- CRUX
- INHIBITED
- ASIANS
- JANET
- WOLBERG
- PSYCHOANALYST
- THEORETICAL
- RETROGRESSIVE
- UNCOVER
- TRAUMATIC
- DUPLICATING
- SCHNECK
- CLINICAL
- EQUATED
- IMMOBILIZATION
- EQUATE
- ANALOGY
- DISSOCIATION
- DREDGED
- AUTOMATICISM
- NEGATE
- OPERATIONAL
- THERAPIST
- THERAPIST'S
- CONNOTATES
- VERNAL
- DONALD
- DAVIS
- SELFLESSNESS
- WITHERS
- COMPLAININGS
- INERTIA
- GLORIFY
- FUNLESS
- PITTSBURGH
- REHEARSING
- INSTITUTIONALIZED
- UNDIVIDED
- STAINLESS
- BRINDISI
- GARTH
- OCULIST
- DALMAIN
- CURT
- PENCE
- APENNY
- TEARLESS
- STEAMED
- CHARING
- STANDSTILL
- CHILDHOOD'S
- DUCHESS'S
- PILOTED
- BROUGHAM
- TRAFALGAR
- HOMICIDAL
- MONGERS
- UNSIGHTLY
- UNCORDIAL
- MEDIATOR
- ACCOSTING
- INCLINING
- PERFIDIOUS
- UNCLOSING
- UNCIVIL
- WILSONS
- MAMMA'S
- LATISH
- FATHERLY
- OFFICIOUSLY
- AFFIRMATORY
- IRATE
- BREWED
- AUDITOR
- SLAMMING
- AT'TI
- ATTILA'S
- TIEW
- THOR'IS
- MOND
- THEODORIC
- THORISMOND
- AETIUS
- HIPPO
- VALENTINIAN
- ALARIC
- JO'RI
- PLUNDERING
- LEO'S
- VANDAL
- TOWING
- JUSTINUS
- STEAMSHIPS
- OSTROGOTHS
- VIT'I
- GES
- RAVENNA
- OSTROGOTH
- TRIB
- O'NI
- HAM'ME
- MUS'SUL
- MANS
- AMIN
- WORSHIPED
- HIRA
- DI'NA
- MOHAMMED'S
- HEJ'I
- RA
- MUSSULMAN
- GIVETH
- KA'A
- SULTANS
- WHIMSICALLY
- SCULPTED
- SINGHALESE
- REENTERING
- REAPS
- INDUSTRIALIZED
- PERCIVAL
- KAFFIR
- WORKDAY
- NOW'S
- CRESPO
- ANDAMAN
- FANTASIZING
- ENVISIONING
- PELVIC
- OFFHAND
- PERENNIAL
- LEAFED
- GLEEFUL
- NATURALIST
- INSINUATING
- OYSTERBANK
- OCCUPATIONAL
- PHOSPHATE
- CARBONATE
- GELATIN
- FESTERING
- SECRETION
- MOLLUSCA
- ACEPHALA
- ABALONE
- TURBO
- SALTWATER
- SCALLOPS
- MOLLUSK
- MELEAGRINA
- MARGARITIFERA
- GLOBULAR
- EMBEDDED
- NUCLEUS
- CONCENTRIC
- YELPED
- PLIERS
- SORTERS
- MEATY
- DAPPLED
- PARAGONS
- MOLLUSK'S
- FARMED
- FIANCEE
- WOW
- AMMONIA
- REWARDING
- SWALLOWING
- GULPS
- HARPOONER
- SWIVEL
- UNDERBODY
- UGLIEST
- GIRTH
- DEGRADEMENT
- BLEARY
- BITCHES
- BITCH
- TRIMMER
- MILLINERY
- EXPECTANTLY
- SICKENS
- DENTIST
- DISPATCHER
- INSTALLMENT
- EXALT
- OPPRESSING
- HOMOLOGY
- INFAMIES
- TURKS
- OUTRAGES
- MEDDLED
- ARBITRATION
- DISBAND
- INTENTIONED
- WRONGING
- OUTSIDERS
- DISARM
- IMMUNITY
- SUDANESE
- INTOLERANCE
- SONNETEER
- VANTAGE
- DISARMAMENT
- ARBITRATE
- VEGETARIANISM
- VACCINATION
- ILLS
- REPUDIATED
- CLAMORS
- BUNDEFJORD
- JOERGEN
- STUBBERUD
- LINDSTROEM'S
- INSULATION
- CELLULOSE
- OBLIQUE
- ROOFING
- LINOLEUM
- LUX
- VENTILATING
- ROENNE
- SEWED
- OUTFITTERS
- CLAMPS
- SHOEING
- EBONITE
- NARRATIVES
- PALSGAARD
- JUTLAND
- ALUMINIUM
- HANDIER
- WINTERED
- FRAM
- LAPPS
- NETCHELLI
- ANORAKS
- BRANDT
- FURRIER
- BURBERRY
- ANORAK
- ROOMY
- WINDPROOF
- FRAMHEIM
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- SERO
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- PLATINUM
- ENTWINED
- MYSTICS
- CONISTON
- ACKNOWLEDGEMENT
- HALSEY
- INTERSTELLAR
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- SPINDLY
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- RASPED
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- UNSEAL
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- CIRCUITED
- NUMBED
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- PENNICOTE
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- GASCOIGNE'S
- WITCHING
- UNARGUMENTATIVELY
- FULFILLMENT
- SUED
- IRREVOCABLY
- HUMDRUM
- PROMOTIONS
- SYLPH
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- LIMPNESS
- KNOWABLE
- IMPRESSES
- LAUNDRESS'S
- HARLETH
- CRISPEST
- DECAMPMENT
- UNUSUALNESS
- UNHESITATING
- CREASES
- DREGS
- COMPUNCTIOUS
- IRIDESCENCE
- MACBETH'S
- BASELY
- DIAL'S
- HENLEIGH
- MALLINGER
- STILLER
- VACILLATING
- SPANIEL
- FOREPAWS
- MALTESE
- UNIMPASSIONED
- INTERRUPTEDLY
- RELIGHTING
- CUSHATS
- DRAWL
- KLESMER
- ORATORY
- RUMPUS
- MARKEDLY
- INSPECTING
- HANDINESS
- GRANDCOURT'S
- CONFIDANT
- RELISHING
- MARCUS
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- HINDERING
- VITIATION
- DERONDA'S
- DOOMS
- FULFILLS
- HUGO
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- LEUBRONN
- INAUGURAL
- LECTURESHIP
- UPTON
- IMPLICITS
- UNDERLIE
- KEMPIS
- TRANCES
- AUTISTIC
- CULTS
- EDUCATIONISTS
- CORPORATE
- UNFAILING
- OLDNESS
- DESCANT
- FIGARO
- COMELINESS
- UNDECIDED
- AN'T
- BROODINGLY
- MESSES
- KEENNESS
- CHANGEABLE
- UNHEALTHINESS
- REPINED
- HUME
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- TRAMMELS
- SENSUOUS
- OVERBALANCED
- STANSFIELDS
- SOUTHAMPTON
- SHOPPY
- SEVERALLY
- WEEDED
- THOMSON'S
- HAYLEY'S
- WARDS
- BERESFORDS
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- BAFFLE
- WITNESSING
- ARBUTUS
- CRAMPTON
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- KNICK
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- SHRINKAGE
- COHERENT
- BLAMELESS
- OSTENSIBLY
- OSTENSIBLE
- CONSUMMATELY
- PONDERABLE
- MAUD'S
- PERFORMER'S
- NOTATION
- PREPONDERANTLY
- MERTON
- SPECTATORSHIP
- AMPLIFIED
- SHARERS
- CONTRACTILE
- INNOCUOUS
- PACIFIED
- PARAGRAPHED
- THEALE'S
- WEARERS
- EXPERTNESS
- UNMISTAKEABLY
- SAMPLED
- CHALKED
- BRITON
- BRITON'S
- FORMULATION
- DENSHER'S
- THEALE
- SEASON'S
- GREGARIOUS
- SCRIBBLING
- JOURNALISING
- BOOMABLE
- PREDICATED
- TWADDLE
- KATE'S
- OVERTLY
- DISCLAIMER
- CHARMER
- PLEASANTRIES
- CROYS
- SUSIE
- CROY
- MANNINGHAM
- WAIVING
- UNOBJECTIONABLE
- MATCHLOCKS
- NUMBNESS
- BLEEDS
- DORCHESTER
- SHERLOCK'S
- FEAR'ST
- CLIMBS
- SLANTS
- LANDER
- DOT
- FAUGH
- REBUKING
- ELZEVIR'S
- FRESHER
- PICKABACK
- FRESHENING
- GUILLEMOTS
- BUDGE
- ZOUNDS
- SUNPATH
- SPANGLED
- MACKEREL'S
- BRASHY
- BUTTRESSED
- PRYING
- THIMBLEFUL
- SOUND'
- BUTTED
- THEREABOUT
- POWDERY
- NUMERATION
- BLUNDERBUSS
- TOPP
- BUZZARD
- TEMPTER
- FOWLING
- WESTERING
- ANVIL
- ASKEW
- QUARRYMEN
- WINCHES
- MANDRIVE
- HEWED
- OVERGREW
- LOTH
- SUBORNING
- JARRINGS
- GARBOILS
- VERIEST
- LOUT
- INNOCENTEST
- EXHORT
- GAOL
- INARIME
- TYPHEUS
- DISPERST
- SHRINKETH
- GODERAN
- GLOSSES
- DOTE
- SUBORNERS
- CHASES
- RACKETS
- GUTS
- FRESNO
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- CAPITAN
- POHONO
- BOLING'S
- MONO
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- TOURIST'S
- MANN
- HITE
- NEAL
- CUNNINGHAM
- RECESSION
- INTIMATES
- SEASICKNESS
- OVERSEAS
- THIRTIES
- TERRIFYINGLY
- MONOLOGUE
- CONNOISSEUR
- TATTOOED
- DEPLIS
- PINCINI'S
- PACKLETIDE'S
- SHOEMAKER
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- EMPLOYER'S
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- LARDER
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- FEDYA
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- QUADRILLE
- ALIONA
- TEGOR
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- CLERKENWELL
- EXTENUATIONS
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- WHITECHAPPEL
- RUMOURED
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- CONFUTED
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- COMPLEXLY
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- SNARE
- FOWLER
- BUCKINGHAMSHIRE
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- LEVITIES
- DESOLATING
- ABSTRACTLY
- INTERMITTED
- UNENCUMBERED
- WAPPING
- RATCLIFF
- STEPNEY
- PREFERMENTS
- CONFLUX
- CONDOLE
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- CANNIBAL
- ORPHANED
- WANTONLY
- CURDLING
- CAIN
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- DIEM
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- CACHE
- WINNERS
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- ELITHA
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- SHIPMENT
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- METE
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- PROVOKINGLY
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- OFFICERED
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- HOED
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- STINGING
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- VEERING
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- ROOSTING
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- HUP
- HIN
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- BUGLE
- MIKE
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- LOAMSNIRE
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- TENANTRY
- ALLAYS
- MISBEHAVE
- RICK
- HETTY'S
- CURTSY
- TOTTY
- CHISELLED
- SUGARY
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- UNROLLING
- BLANKNESS
- SPEARING
- WORRET
- OVERWORK
- DINGALL
- MEASLES
- WELLY
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- GELLS
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- ON'Y
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- SPINNIN
- UNDERHAND
- ISNA
- PORRIDGE
- WAGGONER
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- PONY'S
- UNSPEARING
- THEE'ST
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- KNOW'ST
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- PROMISINGLY
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- SUSPENDING
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- BEHOLDER
- NECTAREOUS
- UNGRATIFIED
- INHABITS
- SHEAVES
- RAKES
- REAPERS
- ALLAY
- PROPITIATE
- STOREHOUSE
- VETCHES
- GODDESSES
- CARRIER
- STYGIAN
- AMBROSIA
- ALLEGORICAL
- PURIFIED
- COMUS
- UNSPOTTED
- FANCY'S
- GROTTO'S
- TRACERY
- SPARS
- NEVERMORE
- APULEIUS
- PHOEBE'S
- REGIONED
- VESPER
- EPICUREAN
- TIPPLE
- SHEEP'S
- FETA
- MIXER
- CELLARED
- VINTAGES
- TASTERS
- PALATES
- NARY
- BURGUNDY
- COTES
- BAUNE
- BURGUNDIAN
- SALUT
- MARGAUX
- NOTABLES
- SAINTE
- MAURE
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- VOUVRAY
- CHABLIS
- CLARET
- PROVOLONE
- CHIANTI
- NEUFCHATEL
- UNIQUELY
- BRINZAS
- TOKAY
- OLOROSO
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- AMONTILLADO
- APPETIZER
- STILTON
- CHEDDARS
- FRAUDULENT
- COUNTERFEIT
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- LIQUEUR
- SEEDED
- SAUCER
- JUNIPER
- FROMAGE
- HOMEMADE
- REDUNDANT
- BUTTERMILK
- MUTABLE
- CARTIER
- QUEBEC
- EXTERMINATED
- CAROLINAS
- SOUTHEASTERN
- ERIES
- CHAMPLAIN
- HARVESTS
- VERMONT
- PROLIFIC
- MICMACS
- PAPINACHOIS
- BERSIAMITES
- TADOUSSAC
- MUSTERING
- CANNIBALISM
- ATTICAMEGUES
- MARAUDERS
- OTTAWA
- UBIQUITOUS
- NIPISSINGS
- SAGUENAY
- CATHOLICITY
- INSENSATE
- POPULATIONS
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- DISSIMULATION
- UNDID
- STIMULATES
- DISMEMBERED
- SCANTLINGS
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- DEFINITENESS
- HYPOCRITICAL
- HARK
- JODO
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- SHO
- CHIKU
- BAI
- PLUMFLOWER
- MYSTICALLY
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- KUNSHI
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- NI
- CHIKAYORAZU
- DAIMYO
- REESTABLISH
- KARMA
- UNBARRING
- TOPKNOT
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- SHAMELESSLY
- NAPE
- LEVERANCE
- DETACHES
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- HEIDAZAEMON
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- MIYAGE
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- GOBLIN'S
- TOMBSTONE
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- SKINNERS
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- TRITE
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- BESPOKE
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- CHINKING
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- PHIALS
- APPLICANT
- PREDICARY
- SECUNDEM
- ARTEM
- INDISPOSITION
- DOCTORED
- YARBS
- UNLETTERED
- RIG'LARS
- RIG'LAR
- COUNSELORS
- BEDRIDDEN
- CONDEMNABLE
- FEBRILE
- HOARFROST
- PHLEBOTOMY
- CONSTITUTIONS
- MASON
- DEBILITATED
- COGENT
- WELLMERE'S
- WELLMERE
- CLINTON
- LEECH
- SINGLETON'S
- FORBEARING
- CONSANGUINITY
- AFFIXED
- IMPROBABLY
- UNIFORMLY
- VARIABILITY
- GAZER
- SOLILOQUY
- SHAMBLING
- QUALIFY
- FATNESS
- MANDATE
- CONVALESCENT
- FORBORE
- UNTOWARD
- YANKEES
- INCLEMENCIES
- VANQUISH
- TRENCHANT
- SPIKES
- BLASPHEMY
- BOBBINS
- PINCHBECK
- CABALLERO
- CENSORIOUS
- MARAVEDIS
- DISTRIBUTES
- ORDAINS
- UNREACHED
- FALTER
- KNICKKNACK
- APOLLONIA
- FOCILE
- BENE
- QUIDEM
- IMPEDE
- DEFRAUDING
- ERRANT'S
- SHATTER
- PANZAS
- HOOPS
- CASK
- REVOKED
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- ALFORJAS
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- ASSES
- GIBBERISH
- PAR
- PEDIGREES
- SOLDER
- FLAWS
- UNGRUDGINGLY
- THROWER
- WRESTLER
- BOWLS
- EWE
- GAZES
- GRAVELLING
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- MAJALAHONDA
- CANON
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- SWORDSMANSHIP
- LICENTIATE'S
- CASSOCK
- WRESTLE
- FENCERS
- FLUTES
- PSALTERIES
- GAMBOLLING
- PHOEBUS
- SLEEPEST
- UNSTINTING
- WORSHIP'S
- STEWPOTS
- WHITEST
- CAULDRONS
- SEWN
- SKIM
- SKIMMINGS
- ARCADE
- JABBERED
- VOUCHSAFED
- EGGSHELL
- APE'S
- DENIZEN
- RESCUER
- ELICITING
- ZEALOUSLY
- UNSCATHED
- HOLSTER
- CASING
- BOLTING
- BREASTPLATE
- MAUDLIN
- WEAKLING
- DEFAMER
- CLINCH
- KOVA'S
- SINUOUS
- INTENTNESS
- BOMBARDED
- JIBBERING
- LOOTING
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- KAN'S
- ZODANGANS
- BARSOOM
- DEPOSED'
- BUNGLING
- DUFF
- HORNPIPE
- VEHEMENCE
- GUM
- NECK'S
- JANGLE
- CLOVE
- TOON
- CUR'OSITY
- DEPPOSED
- OVERTURN
- MERRY'S
- CRUTCH
- TEETOTUM
- HAFT
- MALARIA
- DIAGONAL
- LIVESEY
- GIGS
- INLET
- ANCHORAGE
- GIG
- DERELICTION
- QUADRILATERALS
- AMASSING
- SCUTTLED
- ME'LL
- ABSENCES
- WISPS
- SLIGHTS
- UNWEARYING
- CONNIVED
- CHEAPLY
- SMIT
- RIGGED
- SEAFARING
- DEMONSTRATING
- GEOMETERS
- COMPENSE
- REFUTE
- AGITATE
- DIGRESSION
- PERTAINS
- REDUCES
- HARDENS
- TRANSMUTATION
- VEGETATIVE
- ANNEXED
- VENTRICLES
- CAVA
- ARTERIOSA
- ARTERIA
- VENOSA
- WINDPIPE
- VENTRICLE
- REFLUX
- ORIFICE
- AURICLES
- VERISIMILITUDES
- COUNTERWEIGHTS
- STRAITNESS
- COPIOUSLY
- COVERINGS
- COMPRESS
- EVINCE
- RAREFIES
- REPASSING
- SENSUS
- COMMUNIS
- AUTOMATA
- FABRICATED
- INCOMPARABLY
- VOCABLES
- APPOSITELY
- EDUCED
- EDITED
- INTRODUCTORY
- CONFORMABLY
- HEADINGS
- MASTIFFS
- THACKERAY
- AYALA'S
- WORTLE'S
- FROHMANN
- BLACKWOOD
- HARTING
- GARRULITY
- DIRECTOR
- CHANCERY
- JULIANS
- LIVERED
- CURS
- CINCINNATI
- SUBMITS
- TUITION
- DRACO
- THRASHINGS
- BI
- COLUMNED
- SUPERVEILLANCE
- PARIAH
- STOPPAGE
- DEFALCATION
- APPURTENANCES
- BAILIFF'S
- SIZAR
- ACERBITIES
- ADOLPHUS
- DENOMINATIONS
- SUBDIVISIONS
- UNFLAGGING
- ALPHABET
- LEXICON
- GRADUS
- HOOKHAM'S
- ORLEY
- EXTRAS
- FERULE
- FERULES
- SCOURGINGS
- PROSE
- PETYA
- TROITSA
- DISBELIEVING
- TAPES
- REPLAITING
- UNPLAITED
- REPLAITED
- MAMONOV'S
- SONYA'S
- FLEXIBLE
- ORDERLIES
- BOLSTER
- WRY
- MORTIFYING
- CHARRED
- SEMIDARKNESS
- COLLAPSING
- COMPASSIONATELY
- DAMMING
- SQUIRREL'S
- MISSPELLED
- SPELLED
- CLOTEL
- TRADER'S
- COUNTEFIT
- GENEWINE
- ARTEKIL
- HEARTRENDING
- GENTMAN
- GIB
- SHINEY
- DUS
- FIREMEN
- VALVE
- BOILERS
- SCALDED
- REDEEMING
- STEAMBOAT'S
- BOWIE
- NECKCLOTH
- WALKER'S
- TRAFFICKER
- AFRIC'S
- DEPLORE
- SUFFERER'S
- PIRATE'S
- HAVOC
- PROTECTS
- PRESUMES
- SCAN
- PROTEUS
- UNROLL
- MANTLED
- ASSAIL
- DISSIMULATION'S
- ILYIN
- OVERRESIST
- UNCONCIOUSLY
- ROSTOV'S
- FATTENING
- COMMUNE
- CONSCRIPTED
- WRATHFUL
- UNMEANINGLY
- BICKERING
- YAKOV
- PROPRIETOR'S
- BAST
- DICTIONARIES
- OBTRUDE
- YANKOVO
- BLUSHINGLY
- DUNYASHA
- HEIRESSES
- ROSTOPCHIN'S
- TRADESMEN'S
- VYAZMA
- WITTGENSTEIN
- MUTINOUS
- BONAPARTE'S
- MALEVOLENTLY
- LOCKUP
- CAPTURES
- CAJOLERY
- RELEASING
- FRENCHMAN'S
- PELISSES
- PUCKERED
- LUBYANKA
- LOBNOE
- EVSTAFEY
- PERKHUSHKOVO
- CAISSONS
- IRRATIONALLY
- ACCUSTOM
- MILORADOVICH
- VOYNA
- SEMENOVSK
- GRIDNEVA
- PONIATOWSKI'S
- UVAROV'S
- FELICITATIONS
- PERSISTS
- HERTFORDSHIRE
- GEORGIANA
- INTERMARRIAGE
- BOURGH
- WILFULLY
- DESPONDING
- BENNETS
- LUCAS'S
- SLYNESS
- PATRONESS
- PLURAL
- SPACED
- NORMALLY
- SUBJECTIVELY
- ACCENTING
- CONSTITUENT
- CLASSIFIES
- SPECIFYING
- SEXTUPLE
- TRIPLET
- SEPTUPLE
- STEWARDESS
- BERTHA
- BRAID
- COY
- STRINGED
- RISIBLES
- MAL
- MER
- LIQUIDS
- GOONIES
- ALEUTIAN
- UNIMAK
- GREENS
- SUPPOSEDLY
- TUNDRA
- MISCREANTS
- GORGES
- GULLIED
- ALASKAN
- FLOES
- SCHOONERS
- ELMORE
- EPISODE
- HEARTFUL
- NEUKLUK
- PROSPECTING
- AGEETUK
- SALADS
- MEATS
- DIETING
- DAWSON
- OASIS
- FROGEITY
- MALADJUSTMENT
- TOULOUSE
- PONTUS
- ARAGO
- RAINFALL
- INDISTINGUISHABLES
- EJECTAMENTA
- TAHITI
- HOMOGENEITY
- EXCLUSIONIST
- IDENTIFIES
- SYMONS
- ACCELERATIVE
- ANCESTRALLY
- EXCLUSIONISTS
- ABEDARE
- EXCLUSIONISM
- PUBLISHES
- APOLOGIZES
- PAILFULS
- AXIALLY
- TANGENTIALLY
- THINKABLE
- CARMATHON
- GRIFFITH
- LEIRUS
- GASTEROSTEUS
- BIFURCATE
- FIFESHIRE
- SHIRE
- SERIAL
- MAGNET'S
- DISLODGMENT
- REPULSIONS
- CONVENTIONALISTS
- CONCEPT
- HAYSTACKS
- FERREL
- CALCUTTA
- CUBIT
- FUTTEPOOR
- CUTTERCOATS
- SUNDERLAND
- DIGESTIONS
- UNASSIMILABLE
- BOVINA
- HAILSTORM
- PRESBYTERIANISM
- HAILSTORMS
- INCREDIBILITIES
- POUNCING
- DIVERGENCE
- WAFTING
- HAILSTONES
- UNACCEPTABLE
- INOPERATIVE
- DODOES
- MOAS
- PTERODACTYLS
- CARBONIFEROUS
- DISINTEGRATE
- MUDS
- TROVES
- PALAEONTOLOGISTS
- CYCLONES
- OMNIPRESENCE
- HETEROGENEITY
- TENTATIVELY
- PROVISIONALLY
- LARVAL
- STATIONARINESS
- REDRUTH
- FABULOUS
- RUSTICS
- SHOVELED
- DAS
- PADERBORN
- SIGNIFICANCES
- SEGREGATIONS
- LOCALIZED
- SEGREGATE
- GRADATION
- SELECTIVENESS
- HIBERNATING
- INFINITUDE
- ENORMOUSNESS
- MIGRATORY
- MIGRATE
- FLUTTERINGS
- STRASBOURG
- SAVOY
- ADS
- CONDEMNS
- FRACTIONS
- ASTRONOMY
- LOCATES
- DECIMAL
- THERE'RE
- LOADER
- KANKAKEE
- HAMMERLESS
- HOUND'S
- JAG
- FOOTERS
- TIERCES
- STOREROOMS
- TIMEKEEPER
- TIMEKEEPERS
- EFFECTING
- TYPEWRITERS
- SICKING
- TYPEWRITING
- JIM'S
- CONSUMER
- STEERS
- REPRIEVE
- GAUGED
- FERTILIZER
- ELABORATING
- GUNNING
- OBSEQUIES
- SHORTS
- WORKINGMAN'S
- SYRUPY
- HEIFER
- PAVILION
- DIVIDENDS
- REFINERY
- CUCUMBER
- SUNBEAM
- DRAGS
- CATFISH
- CLYTEMNESTRA
- VOODOO
- SNIFTY
- JINKS
- STRINGING
- AMAZONS
- MEMORIAL
- SLAT
- CLIP
- TRIGGER'S
- SUBSPACE
- MANTELISH'S
- SPECIALTIES
- CORRELATED
- GALACTICS
- BASIC
- BEASTIES
- PLANETARY
- SUBPLANETARY
- UMPTEEN
- LORDY
- BIOLOGIST
- MACCADON
- CUBICLE
- FIDGETED
- NOSY
- INDUCES
- MODIFIES
- FAYLE'S
- FEDS
- SPILLED
- REFLECTIVELY
- SHEINBERG
- HORNSBY
- CYN
- THIA
- TILDEN
- BURRITT
- BROOKLINE
- CAMILLA
- DANIELS
- MONTCLAIR
- SHANE
- KENTFIELD
- DECKER
- GENEVIEVE
- MANITOU
- BARNES
- MARTINETTE
- WILEY
- EASTON
- CONSECRATION
- MULLOWNY
- ADVISEMENT
- SUMMARILY
- HOUSING
- ILLEGALLY
- MALONE
- DISPENSARY
- MATILDA
- MARINES
- QUANTICO
- RIVALED
- BURN'S
- SMUGGLED
- SARDINE
- HANDCUFFED
- MORALE
- STRIKERS
- INTERROGATED
- INDICTED
- TUMULTY'S
- INTERCEDE
- TUMULTY
- INADEQUATELY
- DANA
- COUNSELOR
- LIBERTARIAN
- NARRATING
- TERRORIZE
- O'BRIEN'S
- WADDILL
- MOOT
- JURISTS
- HAVEN
- RECRUIT
- REDRIFF
- SLUR
- NARROWNESS
- MISMANAGING
- ENGROSS
- DISTASTED
- LANDLADY'S
- SHEPTON
- BED'S
- IMMODEST
- RAVISHER
- BUNTING
- BULLION
- DESPERADO
- MUNDANE
- IMPROPERLY
- THOROUGHBRED
- FORBEARANCE
- BOSWELL
- ASSURES
- JAWED
- MARINERS
- SCRAPES
- DISQUIETED
- RECREATIONS
- DISPORTED
- YUCATAN
- TRIBUTOR
- CHRONICLER
- TRUCULENTLY
- CHRONICLES
- DRAPES
- POESY
- TROPE
- BLENDINGS
- INTERTWININGS
- IMPAIR
- LUCINDA
- UNDULATES
- CRADLED
- FRIVOLITIES
- REFASHION
- INCONTESTABLE
- BECKONS
- PROGENY
- THIRSTS
- WOMANLINESS
- SEPARATIONS
- WANTONNESS
- ANTITHESIS
- FRIVOLITY
- MUTUALITY
- CONSTRAINING
- DESECRATES
- SANCTITIES
- CLARITY
- FUSE
- ILLUMINATES
- GERMINATED
- DOUBLES
- WILHELMINA
- FATHOMLESS
- DISSONANCES
- WALKERS
- SICKLINESS
- SUNDERED
- IRRADIATED
- IDOLIZED
- GODLINESS
- REASSURANCE
- MOCK'D
- AGRIVAINE
- BRANDILES
- SAGRAMOUR
- DESIRUS
- DODYNAS
- SAUVAGE
- OZANNA
- LADYNAS
- PERSANT
- INDE
- IRONSIDE
- PELLEAS
- MAYED
- LASHED
- PRIVILY
- LOVETH
- LAMBETH
- DITCHES
- CUMBERED
- OFFAL
- VORACIOUSLY
- CARTED
- MISREPRESENTATION
- LORDES
- FAIRE
- GERE
- SAWEST
- SWOONED
- DESIREST
- PELENORE
- PATER
- NOSTER
- FLASKS
- BOAR'S
- STOMACHER
- BAWBLE
- ORACULAR
- URIEN
- FOREBORE
- SANGREAL
- HERMIT'S
- SURNAMED
- UNADVISEDLY
- UNCOURTEOUSLY
- THEREAT
- UNGENTLE
- UPRIGHTNESS
- HANDMAIDENS
- PRAYS
- RENEW
- SINNER'S
- ASKETH
- MEEKNESS
- TRAVILLA
- HEALTHIER
- CARRINGTONS
- FLATLY
- WEEPER
- CROSSEST
- BANISHMENT
- UNLADYLIKE
- PENITENT
- PLEADINGLY
- DOLL'S
- FLORA'S
- GLUE
- UNTRUTHFUL
- FLATTERETH
- SPREADETH
- ARNOTT
- LESLIE'S
- LORA
- IMMENSITIES
- RAYED
- POPPIED
- UNBELIEVING
- CAROLING
- WARBLERS
- AMBERING
- CORSELETS
- BATTLECRIES
- MILLING
- MART
- TUNNEL'S
- OUTFLANKED
- WOLFLIKE
- DYNAMITE
- WHINED
- LACQUERED
- CLUBBED
- AWESOME
- RELAXING
- PENNONED
- LUCENT
- SPEARSMEN
- PIKEMEN
- AUTOMATONS
- VIBRANT
- WAILINGS
- DRAGONED
- COLUMN'S
- IMMOBILE
- UNLEASHED
- BOXER
- SPIKED
- GLADIATORS
- PREENING
- GAPS
- JAVELINED
- HUDDLING
- DISINTEGRATED
- SCAMPERING
- FLAILING
- PRONGS
- TRIDENT
- TEETERING
- MADDENED
- SCYTHE
- FROTH
- JE
- LAMMERGEIERS
- SCAVENGERS
- LINGERINGLY
- SATED
- SCANTINESS
- OLDSTER
- RIMMING
- CINCTURE
- CHIMINGS
- VASTNESSES
- THRUMMING
- EFFORTLESS
- STRIDING
- TAPS
- DRUMMING
- ENIGMATICALLY
- ETERNITIES
- CRESCENDO
- FACETS
- AIRLESS
- WATERLESS
- SUNLESS
- SMARTER
- REUTER
- CURSORILY
- PLAT
- PENDULE
- MANTELPIECE
- CONSOLES
- CHIFFONNIERE
- TENDRILS
- DEMESNE
- PARTERRE
- ENGLISHWOMAN
- RESTE
- STEEPS
- FURRINERS
- RHODODENDRON
- COVERT
- SLOUCHED
- HOODED
- UNDERMIST
- FOOTFALLS
- DRIP
- TINKLED
- LIMBER
- GILLS
- HOWDYE
- HAIN'T
- AFFIRMATIVELY
- HIT'S
- SHET
- PURTY
- NONCHALANT
- HUMOUROUS
- RUMBLED
- HYEH
- TWUSN'T
- GITTIN
- DEBATING
- UNAVAILINGLY
- SPECTACLED
- NAIVE
- THAR'S
- REMONSTRATING
- KINDLIEST
- UNJOINTED
- CRAWFISH
- CHINKED
- GOAD
- SPURNED
- PELLETS
- NAAS
- VIRTUAL
- TRIMLY
- ELECTRICIAN
- OPTIMIST
- NATIONALIST
- REMONSTRATIVE
- VILLONA'S
- CONFUSE
- ELATES
- CONTINENTALS
- GONGS
- TRAM
- GAZERS
- EQUATION
- UNPURCHASEABLE
- SUPPED
- VOLUBLY
- MADRIGAL
- INGENUOUSLY
- MECHANICIANS
- LUTES
- SHEPHERDED
- TORPID
- NOISIEST
- ROUSSEL
- HOHE
- FORM'S
- HUNGARY
- VOLUNTARIES
- OBSCURELY
- LOSERS
- COMMITTEES
- CONCEALMENTS
- ENNOBLED
- DROLLERY
- PUB
- DOCS
- CONGRESSMEN
- ERASING
- HOPPERSON'S
- SANCTIMONIOUS
- COMMITTEEMEN
- COMMINGLING
- PERSIFLAGE
- FRANKING
- FACETIOUS
- OFFICIO
- ENNOBLE
- BRIBERY
- LEONI
- SPOLETO
- HANGINGS
- GIRALAMO
- USURPER
- COMMANDANTE
- GIOVANNI
- ORGY
- FERRARA
- NOVICE
- PRONUNCIATION
- ABSTINENCE
- PENANCES
- REVERENTIAL
- LUTHER'S
- THEOLOGIAN
- DEATHLIKE
- VANITIES
- ADULTEROUS
- OBSCENE
- FRESCOBALDI
- MONK'S
- PIERO
- CONSTITUTIONALLY
- ENERVATED
- KILLER
- NOISED
- ARTA
- BREECH
- WINDWARD
- SCALPED
- GROVER
- FLEETNESS
- CENTERS
- HAYES
- AVOCATION
- RAFFLE
- CONTESTANTS
- BONHAM
- POKY
- THOROUGHBREDS
- WILCOX'S
- ALEXIS
- SHERIDAN'S
- RUCKER
- LEONARD
- INFORMAL
- OVERTON
- BUNTLINE'S
- BENNETT'S
- CODY
- BELMONT'S
- LIEDERKRANZ
- INSPIRITING
- NIBLO'S
- JARRETT
- PALMER
- MAEDER
- DRAMATIZED
- STUDLEY
- FRELEIGH
- GUSS
- S'DEATH
- PANTAGRUEL
- ENNASIN
- UNTWISTED
- JOINTURE
- ENTER'D
- CAWL
- SUCK'D
- PICK'D
- CHEW'D
- PEEL'D
- DIGESTED
- LAWYER'S
- EXSUDATIONS
- BACKSIDE
- DIGESTING
- INTRENCH
- CIRCUMVALLATIONS
- TRANSGRESSION
- DEVOUREDST
- AMBLE
- WEEDER
- DISPUTATIONS
- CIRCUMVENTION
- PASS'D
- PROWESSE
- BEGUINE
- BEGUINES
- FUSTY
- BAGGAGES
- NEGLECTEST
- ENTERPRIZE
- OFTNER
- WEAVES
- FACETIOUSNESS
- ENTEREST
- RESOLV'D
- FAG
- VENTED
- CONJUGALLY
- CALL'D
- SENSIBILITIES
- CHRYSTAL
- MOTE
- ABETTING
- THEREUNTO
- TRAGICOMICAL
- REVERENCES
- RAMALLIE
- CURL'D
- ASSIMILATED
- SOVEREIGNLY
- TAFFETA
- METALLICK
- TUCK'D
- PUFF'D
- CLIPP'D
- VAPOURINGLY
- BEGUILED
- STEDFASTLY
- DRAGG'D
- SYLLOGISMS
- NULLIFIED
- PREMISS
- LANDOR
- COMPENSATING
- PHIPPS'S
- STUMPY
- AUREOLE
- CZERLASKI'S
- MISDEMEANOURS
- UNIMPEACHED
- QUADRAGENARIAN
- MATHEMATICIAN
- PACHYDERMS
- BRIDMAIN'S
- DEBATABLE
- COMMONALTY
- PAROCHIAL
- INITIATED
- PAS
- QUADRILLES
- PROBLEMATIC
- COUSINSHIP
- BLEMISHES
- DISQUALIFICATIONS
- SEVEREST
- UNDENIABLY
- PAUCITY
- CONGREGATIONS
- WEDNESDAYS
- SHUNS
- GORGON
- SOPHOCLES
- ABSORBINGLY
- NEE
- UNSKILLFUL
- IDUMEA
- GITTHA
- DAPHNE
- ANTIOCH
- FOREBODED
- IRRUPTION
- FOREFRONT
- CANA
- HINDERMOST
- PAPPUS'S
- ARISTOBULUS
- MARIAMNE
- PHOENICIA
- AMBUSHES
- SOSIUS'S
- CENTURIONS
- PROPORTIONABLY
- CALUMNIATED
- MALICHUS
- PARTHIANS
- APAMIA
- PACIFY
- PARTHIA
- MINISTRANT
- NATURA
- INSTILLANT
- HOWSOEVER
- PRIMOGENITURE
- DEPENDANCE
- DICTATES
- MEUM
- TUUM
- OVERALL
- INTERMIT
- SAKES
- PESTILENTIAL
- EXALTS
- FILIATION
- SOUGHTEST
- FOUNDEST
- JOYFULNESS
- DISEASEFUL
- MALIGN
- PESTILENT
- PASSETH
- FILLEST
- SACRAMENTS
- DISLOYAL
- HYPOCRITES
- INFECTIONS
- BEDCHAMBER
- RELUCTATION
- VIPERS
- CATECHISED
- RECEVEUR
- DIPLOMA
- NAIVETE
- PAYMENTS
- D'ANTIN
- SUBSTITUTING
- HYGIENIC
- CHAMPS
- ELYSEES
- GAMING
- NANINE
- STEWED
- ARNOULD'S
- LAMARTINE
- SCUDO
- EFFACE
- EMBODY
- PRUDENCE'S
- PROFESSEDLY
- UNSUSPECTINGLY
- GAINER
- SERMONIZED
- GAUTIER
- DUPRAT
- BOUQUETS
- USELESSNESS
- SHIRKING
- BOMBAST
- PRISTINE
- BATES'S
- GENERIC
- TAUNTER
- BARKED
- BLOODTHIRSTY
- UNHEEDING
- STERNWAY
- SPLINTERED
- BRIG'S
- CHESHIRE'S
- TWITCH
- BEGOTTEN
- UNRISEN
- GABBETT
- NAB
- TROUGHS
- LEVIATHAN
- WALLOW
- CREAMED
- BANDAGING
- VILLAINY
- BEACHED
- LAGGARD
- HUMOROUSLY
- UNBUCKLING
- HELMSMAN
- BISECTED
- VETCH'S
- BLUNT'S
- IMPENITENTLY
- SYDNEY
- STUMPED
- CHARTS
- KILN
- PURFOY'S
- WAXING
- HOYSTERIN
- CANARIES
- NOTION'S
- MADAM'S
- EAGLEHAWK
- WIND'S
- ISTHMUS
- LAMBENT
- VULCAN'S
- SMITHY
- DEMIGOD
- ROUSTABOUT
- DICKERING
- MELVILLE
- BARSTON
- SWERVED
- HOMESITE
- LAZIEST
- SIGHTSEEING
- HIGHWATER
- CALDRON
- RAINIER
- WINTHROP
- PUYALLUP
- TOWNSEND
- SHACKS
- RASCALLY
- DEBAUCH
- BUFFETING
- MILESTONES
- SLOUCH
- VENTILATION
- PIONEERS
- BLOOMER
- STUMPER
- HARDTACK
- PROVENDER
- BIGHT
- YAKIMA
- TETHERING
- ROUSING
- HOBBLES
- NATCHESS
- WHEELMEN
- OBSTRUCTION
- OBTAINABLE
- NISQUALLY
- UNEVENTFUL
- BACCALAUREATE
- NATHANIEL
- AUGUSTUS
- FENN
- VAILL
- HAZZARD
- MISSOURI
- BERKELEY
- BRIDGEPORT
- GLAZIER
- HOPSON
- GARWOOD
- MERWIN
- REUNIONS
- REGIMENT'S
- ARLINGTON
- NAUGATUCK
- UPRISINGS
- ANCESTRY
- AMPHIOXUS
- DINOSAURS
- BARBELLION
- DISGUSTEDLY
- VAUDEVILLE
- MONKEYISH
- GREATGRANDSONS
- WRYLY
- COMPETING
- HEADSHIP
- COSMOS
- LEMURS
- AESTHETICALLY
- UNEMPLOYED
- EUGENICS
- DARWIN
- REPRESSIVELY
- HYPER
- RUCK
- PLAUSIBLY
- TRUSTFUL
- EXPLOITABLE
- PARASITIC
- GROSSNESS
- STUPORS
- COSMOPOLITANS
- TOLERANCE
- OUTLAWS
- TERRORIZED
- SLINK
- URBANELY
- PARVENUS
- PLUCKS
- SWISHES
- FLANKS
- UNLEARNED
- ATROPHIES
- QUENCHLESS
- SLOGS
- CONFORM
- ENERGIZED
- PERPETUATE
- ITCH
- MANTLES
- GROTAUT
- HOSTAQUA
- HUMBLER
- GAMBIE
- EDELANO
- VERGED
- SUPERNAL
- CABOOSA
- OATHCAQUA
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- SILLIEST
- SPOONEY
- FLATFISH
- IDIOTICALLY
- SIMILE
- QUR'AN
- FECUNDATION
- PAIRING
- UNACHIEVABLE
- PHONOGRAPHY
- GILDING
- WAINSCOTING
- AZULEJOS
- LEBEL
- ABDUCTIONS
- DISAPPEARANCES
- CAVERNS
- CHAROLAIS
- COURCHAMP
- PENNYWELL
- VI
- CLAM
- PRECARIO
- OUBLIETTES
- SAVOURING
- STAIRCASES
- EXITS
- CONTRIVANCES
- NICHES
- ELTZ
- RIZZIO
- CROSSWAY
- QUAINTER
- FITTINGS
- ENAMELS
- MINIATURES
- HYPOCHONDRIASIS
- BOUDOIRS
- LACEWORK
- QUEENS
- TRITONS
- TERMINATIONS
- PRISMATIC
- SPARKLES
- PIGEON'S
- BERYLS
- MAB
- GEO
- RETARD
- ENCOMPASSED
- MINOTAUR
- CLEMENCY
- OMNISCIENT
- SURMOUNT
- BALLASTED
- PORTHOLES
- STOWING
- FLAPS
- SLOOPS
- TILLER
- POINTI'S
- CARNERO
- AXLETREES
- HOMO'S
- GWYNPLAINE'S
- HESITATES
- REOPEN
- SEMESTER
- UNPREMEDITATED
- ABSTRACTEDLY
- GRAMMATICAL
- REINSCRIBED
- REENROLLED
- OVERDUE
- HOSTESSES
- REENTERTAINING
- HALLUCINATION
- INCLOSE
- OUTGROWS
- PALTRINESS
- ADDLED
- SLUTTISH
- TINGLED
- ENGENDERS
- LINEAL
- DESCENDENT
- FRANCOIS
- RABELAIS
- EDITORIAL
- PARAGRAPHERS
- LASTLINE
- SPOOFING
- ISAIAH
- IMPRECATING
- CHRONICLING
- MUDDLED
- JESTER
- TICKS
- PESSIMIST
- BIDDLE
- CAPTION
- ODDITIES
- PLUMPLY
- ABAFT
- PENCILLED
- PINKISH
- RACKING
- HURLY
- INTELLIGIBLY
- FRONTAGE
- SCRIBBLERS
- TRAVAIL
- TETCHY
- SCHOOLED
- STEWING
- LAUREATE
- NICOTINE
- SAINTED
- BLASI'S
- REHBOCK
- EXPLAINS
- ARRANGES
- BOUNCED
- DEALER'S
- FATHOMED
- ONEROUS
- INITIATION
- LANGDON'S
- PRONGED
- PITCHFORK
- COMPUTE
- DIRGE
- ASPIRANT
- CENTIPEDAL
- TRICKED
- DIABOLUS
- BLAKE'S
- MYSTIFY
- RIVERMOUTHIANS
- TOWNSFOLK
- SIGNBOARDS
- TRUSTFULLY
- PEANUT
- CLAPBAM
- DISCLAIMED
- HAYMOW
- COOPS
- EXHUMED
- SELECTMAN
- MUDGE
- CRONIES
- STAGECOACH
- PETTINGIL'S
- ICECREAMS
- MARDEN'S
- ABLY
- SYRUPS
- WENDED
- PESTLE
- PLACARD
- LIVELIER
- ANDREWS'S
- LUSTILY
- WINDMILL
- TEMPLARS
- STAGGERER
- MULATTER
- GALLOWSES
- BRACES
- PHILISTINE
- UNJUSTIFIABLE
- CASTE
- TRADITIONARY
- POMMELLED
- DUMPLING
- COCOANUT
- NUTTER
- CONSISTENTLY
- DRO
- PUGILISM
- BLINDERS
- UNPREPARED
- VERSUS
- GRIMSHAW'S
- MALTREATS
- TAMEST
- MUMBLED
- MOLESTATION
- ABIGAIL'S
- SANITARY
- OPODELDOC
- REQUISITION
- FLIRTATIONS
- SWEENEYS
- CASSIDYS
- PRESBYTERY
- INERNEYS
- SCRATCHINGS
- BACKSTAIRS
- FRIEN'S
- INCRIMINATING
- SHAMED
- SECRET'S
- TURGID
- MISSHAPEN
- CLANE
- CHILDER
- STINKIN
- BUSTED
- PINSIONS
- HAN'S
- SWEEPERS
- WESKIT
- CONNIVANCE
- FOULLY
- SOMEONE'S
- MISLAID
- BRICKS
- SOOTY
- LACERATED
- WHILES
- SCREECHING
- EGGED
- DOMNED
- SCREECHIN
- DEASEY'S
- HANKERING
- MORBIDITY
- CAROLLINGS
- MERCIANS
- PLOD
- THAW
- SPREE
- BOLGIE
- LYCIDAS
- NATIVITY
- CAPO
- FAIRFIELD
- HALIBURTON'S
- CLEMENT'S
- PINCKNEY
- WALTER'S
- EPHREM
- STITCHES
- HANOVER
- GERRY'S
- UNCTION
- CONFECTIONERS
- MILLIONNAIRES
- FROTHINGHAM'S
- MANGER
- CAMP'S
- SISERA
- RETORTING
- APPROPRIATELY
- BLUFFED
- GRABBED
- BRIDLES
- LOPE
- AGENT'S
- BATCH
- REVOLVERS
- LIABILITY
- OUTWITTING
- STATE'S
- SIDING
- CULLEN
- RALLES
- KANDA
- ADZUMA
- OJI
- PROFLIGATE
- AMAZE
- GULLED
- SMARTENED
- CONSENTING
- RONIN
- CHOKICHI'S
- HATAMOTO
- ASAKUSA
- YEDO
- PEDIGREE
- MENDICANTS
- SORCERERS
- DIVINERS
- HERMITS
- DISOBEDIENT
- COBBLERS
- TORIOI
- SHAMISEN
- BURYING
- CONCUBINE
- MUNEMORI
- LEDA
- CENTAUR
- PELION
- POSEIDON
- WINGING
- AGAMEMNON
- PELEUS
- TELAMON
- OREITHYIA
- ERECHTHEUS
- BOREAS
- THESEUS'S
- ANAURUS
- METALWORKERS
- SMITHS
- ZEUS'S
- PELIAS
- ENCIRCLES
- PECCARY
- DUGONG
- NEB'S
- HAYMAKING
- HOUSED
- PULLEYS
- TRANSIT
- GREASING
- POLISHING
- JUP
- CYLINDROCONIC
- RICOCHETED
- MANDIBLE
- JAGUARS
- THOUGHTLESSLY
- MAYN'T
- HUMANITY'S
- HARDING'S
- PROPS
- AYRTON'S
- STANCHED
- HERBERTS
- INFLAMMATORY
- STYPTICS
- ANTIPHLOGISTICS
- TEPID
- COMPRESSING
- SUPPURATE
- LINT
- CICATRIZATION
- COAPTATION
- TOP'S
- SHAMASH
- SUBBILULIUMA
- CARCHEMISH
- TABAL
- CILICIA
- KHILAKKU
- TARSUS
- TIANA
- COMANA
- KAMMANU
- THRACO
- PHRYGIAN
- MOBILITY
- RAIDED
- TUKULTI
- NINIP
- REVOLTING
- BUBU
- NISHTUN
- AKHIABABA
- KHABAR
- BALIKH
- ADINI
- KIRKHI
- REPRISALS
- METED
- KINABU
- UNFAITHFUL
- DAMDAMUSA
- TELA
- TELLO
- DEVASTATED
- STRATEGICAL
- SUPPLANTED
- ZABDANU
- KASHSHI
- KALDU
- REDECORATED
- JAIF
- KESHAF
- INDISTINCTLY
- KURDISH
- SUMERIAN
- AKKADIAN
- ASSYRIA'S
- DELINEATING
- CANAANITES
- AHIJAH
- ABIJAH
- JESHANAH
- RAMAH
- MESOPOTAMIAN
- TABRIMON
- HEZION
- ARZA
- ZIMRI'S
- SHORTLIVED
- PHILISTINES
- MICAH
- BACKSLIDERS
- PUTTETH
- AKHUNI
- HITTITE
- ORONTES
- AKHABBU
- REPULSING
- OVERLORD
- BORSIPPA
- CUTHAH
- UNCONQUERED
- CONSPIRE
- DEPOSE
- JEZREEL
- NIMSHI
- DRIVETH
- HERMON
- TYRIANS
- SIDONIANS
- YAUA
- UNPRONOUNCED
- ALEPH
- JEHUA
- BAAL
- BACKSLIDER
- CULT
- ISRAELITISH
- MUSICK
- FOLLOWETH
- JEHOAHAZ
- DANIN
- APLI
- IMGURBEL
- BALAT
- DEPENDENCIES
- RECONQUEST
- BABYLONIAN
- DABAN
- BLASTING
- MORTGAGING
- PROPELLERS
- STRUCTURAL
- DIRIGIBLE
- DEFLATION
- GAMELY
- REMEDIED
- KAISER
- LIQUIDATE
- CRUCIAL
- COLLIDING
- CLIMATIC
- CONFLAGRATION
- UNPREJUDICED
- SYNONYM
- LEVELHEADED
- ZEPPELINS
- FRIEDRICHSHAFEN
- DOCKS
- AIRMEN
- CONVINCINGLY
- RATED
- TANTAMOUNT
- PARSEVAL
- AERONAUTICAL
- RUTHEMBERG
- SIEMENS
- SCHUKERT
- SERVICEABILITY
- CONCLUSIVELY
- COLONELS
- ROUGHEST
- RESPLENDENTLY
- SUMPTUOSITY
- ELIOT'S
- MATHER
- ATLASES
- PRINTER'S
- UNPRIZED
- MOULDERED
- SURPLUS
- UNWRITTEN
- COMPILER
- LIBRARIAN
- SPOFFORD
- SUPERINTENDING
- TOILETTES
- BARONET'S
- SYMMETRICAL
- PITT'S
- BUTTERED
- RAWDING
- CRAWLEYS
- DAMPED
- SMUGLY
- STARCHED
- RAGLAND
- FONDER
- VILLAIN'S
- SURMISED
- BAILIFFS
- ENSUE
- RAWDON'S
- AUGURING
- LIVERIES
- SILENUS
- GREENGROCER
- LOLLING
- JOKED
- KNIGHTSBRIDGE
- CONSORTED
- FANCIERS
- BRUISERS
- BRISTLY
- MARKER
- CAMPAIGNER
- QUOD
- INCOHERENTLY
- KEP
- MACMURDO'S
- LORDSHIP'S
- SHINTY
- LANDLORD'S
- DRINKIN
- DRAWINGROOM
- MUSTACHIOS
- INEXPRESSIBLY
- TAPEWORM
- GLUM
- GEORGY
- AMELIA'S
- CONCLAVES
- DISTRAITE
- WEBER'S
- RUMMAGING
- GEORGY'S
- MONOGRAM
- EDIBLES
- RUSSE
- JELLIES
- MOUSSES
- CAVIARS
- DAUBED
- SOAPS
- TOBACCOS
- PROPOSES
- CIRCUMVENT
- BETH'S
- ERASTUS
- CHALLENGES
- FAIRVIEW
- VOTER
- NOMINATIONS
- DISTRIBUTORS
- TIRADE
- FRIZZLE
- MISSES
- EDUCATING
- SOUTHERNER
- GALLING
- UNTENANTED
- VERGING
- CRISPLY
- COLLINGWOODS
- INTRUDING
- UNEXPLAINABLE
- CYNTHIA'S
- UNUTTERABLE
- CANOPIED
- PORTENT
- SYMBOLISES
- CYNOSURE
- CRIMINALITY
- CANTONMENT
- POINDEXTER'S
- PAYED
- SYMPATHISER
- BULLYISM
- UNBURDEN
- CICADAS
- FORMALISED
- INTERROGATORY
- CONJECTURING
- CULMINATES
- GLINTS
- RICOCHET
- CORPOREAL
- VULTURES
- PLANTER
- MUSTANGER
- CASA
- CORVO
- WOODLEY
- HACIENDA
- COVARUBIO
- LLANOS
- SADDLING
- JOYED
- LAZO
- REGULATORS
- SUMMARY
- SWARTH
- TEXANS
- ISIDORA
- PATHLESS
- WITHAL
- CORRALES
- PHELIM
- O'NEAL
- YCLEPT
- FLORINDA
- GOBBLER
- EXCITES
- BENIGHTED
- CRAM'S
- LINGERS
- CRUM'S
- MISHAWAKA
- GOSHENITES
- HOOSIER
- JUMBO
- DELIGHTFULLY
- JAUNDICE
- PHLEGMATIC
- PUMPKIN
- YOUNGSTER'S
- RELINQUISHING
- UNBIDDEN
- SAGEBRUSH
- REBEAUTIFIED
- OHIOANS
- RESOUNDS
- PERRYSBURG
- BELLEVUE
- GARFLELD'S
- MENTOR
- GARFIELD'S
- FRUCTIFEROUS
- GLISTEN
- INTERSPACES
- GIRARD
- TAHOE
- NEVADA
- WHEELMAN
- TORTUOUS
- ANGOLA
- SWASHING
- MISFIT
- AUDACIOUSLY
- SWIPES
- JOGS
- ISHAM'S
- TRAYNOR
- NELLY
- CONGESTION
- MEMBRANES
- ACCENTUATION
- PROVINCIALISM
- AFEAR'D
- COWLD
- SLUICE
- INTERCOSTIALS
- SANGUINEOUS
- WEATHERIN
- CHAPS
- HEXAMETERS
- GRIDDLE
- RAYSON
- MEDICATRIX
- CONTIGIT
- HYDRAULIC
- THEM'S
- DISAZE
- YE'RE
- TEMPORIAL
- SALTPETRE
- DIAPHORESIS
- PHENOMENONS
- INORGANIC
- SHINDY
- PUER
- INGENUUS
- FRONTAL
- RELISHED
- TANKARD
- BAYCON
- TROTH
- SORRA
- ILLIGANT
- PRETENTIOUSLY
- OBSTRUCKT
- IMPADE
- CORRESPONDIENCE
- SARVE
- SINTRY
- YAWL
- GRENADIER
- CARBINEER
- FRINCH
- BRIGADED
- ARENTSCHILD'S
- HANOVERIANS
- SCREWING
- SCRAPING
- SURROUNDERS
- CHANSONS
- RHINE
- RHONE
- SATIS
- HEARTIEST
- FLATTERIES
- IRRITABILE
- TUPPENCE
- DANCIN
- HANDIN
- EMPRESSES
- SACRET
- PENKNIVES
- BODKINS
- BEGINNIN
- FORNINT
- FASTIN
- THRIVES
- DROPPIN
- FILTERIN
- GLITTERIN
- REFULGENT
- DISTURBS
- O'ERCASTS
- GILD
- YELLOWER
- MOUNTAIN'S
- VALES
- SWAINS
- ACCUSES
- DOABLE
- THRAVELS
- HOLLANDIA
- UNDEFENDED
- MEGANTIC
- INEXPRESSIVENESS
- INEXPRESSIVE
- DELUSIONS
- LAKE'S
- SUCHLIKE
- CASTON
- SHELVED
- PROTECTIVE
- SWEETHEARTS
- FALMOUTH
- LINER
- SOMERSET
- STONEHENGE
- ROMANIZED
- BRITONS
- DYKES
- GLOUCESTERS
- TEWKESBURY
- NORTHMEN
- CORINTHIAN
- TRIENNIAL
- SUCCINCT
- AMELIORATING
- CANDIDACY
- PHILOLOGY
- LINGUISTICS
- ENUMERATION
- INSEPARABLY
- INCLUSIVE
- REDOUND
- PARDONS
- ANACHRONISM
- BEHOOVES
- CONFOUNDING
- INDISCRIMINATELY
- PROPAGATORS
- DISAVOW
- COMPATRIOTS
- FULMINATING
- BROCHURE
- PUBLICIST
- DISGRACING
- FORWARDING
- VIOLATING
- RECONSTRUCT
- EQUALIZE
- IMPROVISATIONS
- UTOPIST
- ROUSSEAU
- INCENDIARY
- GRAVEST
- PERORATION
- PRUNING
- CHARACTERIZE
- MANIFESTOES
- IMPARTIALITY
- BLANQUI
- TERMINATES
- WEARIES
- RECAPITULATING
- ADMINISTRATIVE
- PELLET
- GALAIL
- EDDY
- EVILDOER
- OGRESSES
- CLAMBER
- NIMBLY
- PARROT'S
- GEW
- GAWS
- MAIDEN'S
- CRIMEAN
- SWARDESTON
- CONFORMANCE
- HOXTON
- PANCRAS
- COMMENTING
- FOOTSORE
- OFFENSES
- FABRICATIONS
- EXPOSTULATE
- LEVAL
- HEROICALLY
- ENLISTMENT
- SILVERED
- PEARLED
- BUTTRESS
- TRANSGRESS
- REFECTORY
- MOULDING
- WRING
- DEVOTIONAL
- TEENS
- PROVISIONAL
- PUP
- WRONGER
- ILLUMINATIVE
- STETHOSCOPE
- PALPITATING
- CONVENTIONALLY
- UNDOING
- WILMINGTON'S
- FLAGMAN
- DEAFENINGLY
- JOUNCING
- GRASSLESS
- WINDOWY
- CINDERS
- ASPHALT
- WATTEAU
- RUSSETS
- VURRY
- CORNERWISE
- AUNTY
- MORRELL
- JULIET'S
- CARVEN
- NORAH
- AFFLUENT
- NORTHWICK'S
- HAUTEUR
- NORTHWICKS
- IMPROBABILITY
- OPPOSITES
- FILIAL
- IMPOSINGLY
- FEINTS
- SASHES
- SPLICED
- FROWSED
- LYRA'S
- SUPERFLUITY
- ROMEO
- REHEARSALS
- INCARNATIONS
- MEDIOCRE
- DOMINATES
- IRONIC
- CUSSEDNESS
- ROMANTICISTS
- SALEM
- LONGFELLOW
- AMOS
- BRONSON
- ALCOTT
- EUCALYPTUS
- POMEGRANATE
- SORDIDNESS
- CATHAY
- BROBDINGNAGIAN
- DAMPENS
- ALKALI
- PHOTOPLAYS
- THRILLINGLY
- THINNESS
- ENUMERATE
- ROCKIES
- EXISTENT
- SHOUTER
- HURRAHING
- CONFETTI
- RELEGATES
- EPIC
- OVERWHELMS
- PHOTO
- PLAYWRIGHT
- MOBS
- ROMANESQUE
- REDWOODS
- REPROVED
- MELODRAMATICS
- ASHTON
- TREADER
- SCENARIOS
- INCONSIDERATE
- ULTIMATUM
- UNCONGENIAL
- COUPLING
- FLIRTATIOUS
- KENELM
- BOURNEMOUTH
- HEARTHRUG
- ADORES
- BUTCHERED
- WOMENKIND
- HARMFUL
- CONDESCENSIONS
- UNEXACTING
- FIXITY
- HA'PORTH
- INTERPENETRATING
- UPBRINGING
- PLEASINGLY
- BEHINDS
- RESHAPE
- PIGGY
- WIGGYS
- SEIZES
- REALIZATIONS
- MEDITATIVELY
- LASSO
- REVIEWED
- WHO'D
- SPENDERS
- WASTERS
- SKYLINE
- JUDGMENTS
- GRAMMONT'S
- REGRETTABLE
- COMPLETEST
- PRACTICABILITY
- REFT
- HARVESTER
- MENSERVANTS
- PIECED
- ASCRIBING
- COMPLETER
- SCIENTIFICALLY
- COMMONSENSE
- REPLACEABLE
- UNWONTED
- OUTRUNS
- STIFLES
- DAIS
- PERSEIS
- STRAYS
- CUPIDON
- CRATE
- APROPOS
- ILLUSTRATING
- DRIVELLED
- GRANDILOQUENT
- INGENUE
- BOHEMIANS
- DEBUT
- ARSENIC
- FENDER
- PALETTE
- BOHEMIENNE
- REVILING
- POUTING
- CHIMED
- UNLOCKING
- SEALSKIN
- SOURED
- TYPHOID
- FATALLY
- CRAYON
- INFATUATED
- SOPRANO
- TROTERE'S
- GARLANDS
- WISHERS
- REVIEWERS
- MINGLES
- ENDORSED
- GUSHING
- INANE
- JEUNESSE
- DOREE
- RECOGNISING
- ZELIE
- SPRAWLY
- DISFIGURING
- VALUELESS
- OBLITERATION
- APERTURES
- DIABLERIE
- ENHANCE
- LAUDATORY
- VOSGES
- METHODICAL
- ENMESHED
- CHANTREY
- ADIEUX
- RESUSCITATE
- GLEAN
- OVERDOSE
- MORPHIA
- HOUSEHOLDER
- EVILDOERS
- NOTS
- RINGED
- SPRITE
- NIGHTINGALES
- DISPEL
- PLAYFELLOW
- BRUNHILDA
- CRONES
- TRELLIS
- CARROT
- RADISH
- SPROUTING
- FORSOOK
- GNOME'S
- BRAIDED
- BRIDLED
- SUBMISSIVE
- MOSSY
- ENCUMBERED
- THUNDERCLOUDS
- CORNISHMAN
- ARNOLD'S
- CELTIC
- FINSBURY
- CENSORIOUSNESS
- GUY'S
- COWDEN
- CLARKE
- INTENSIFIER
- QUALIFIES
- VULGARLY
- YARMOUTH
- MERCI
- INDICATOR
- UNWARRANTABLE
- DEPRECIATING
- COLVIN'S
- MORBIDNESS
- MAWKISHNESS
- VIRILITY
- RECASTING
- PAGANS
- REFORMER
- FELICITATE
- UNSUSCEPTIBLE
- PLEASURABLE
- WOFUL
- COLERIDGE'S
- IRREMEDIABLE
- INEXPUGNABLE
- UNREMITTINGLY
- AVERSIONS
- UNDIMINISHED
- ANALYSING
- COMPLEMENTS
- CORRECTIVES
- SEQUENCES
- COHERE
- HAL
- BOWNTANCE
- ARRAGON
- BLUFFLY
- ADDLE
- DOFF
- BESHREW
- MINION
- MARK'S
- GRAPPLED
- HERNE
- FORTIFICATION
- EIGHTH'S
- OCTANGULAR
- LOOPHOLES
- ARQUEBUSIER
- CONVEYS
- NINETIETH
- ANN
- BRADSHAW
- TOLERATIONS
- BUCCA
- FISSA
- COMPRACHICOS
- CHEYLAS
- HARDQUANONNE'S
- INDICTMENTS
- FULMINATIONS
- HANDWRITINGS
- HEBRIDES
- QUARTOURZE
- NARBONNAIS
- LUC
- GALDEAZUN
- INTERSPERSING
- MATUTINA
- BISCAY
- TILE
- GERNARDUS
- OAKUM
- MALEFACTOR
- PLAGIARY
- WRETCH'S
- CRUCIFIED
- DENZILL
- VERIFICATION
- ATTESTATIONS
- PURSY
- ERMINE
- WAIFS
- UNCORKED
- ANATOMIST
- BIDLOO
- YELVERTON
- LONGUEVILLE
- ROTS
- LINNAEUS'S
- CHANCELLORSHIP
- PATHOLOGICALLY
- LACENAIRE
- COMMITS
- DISSIMULATE
- SIMANCAS
- PAYMASTER
- ENDORSEMENT
- JUSSU
- UNFEMININE
- MONARCHIES
- AULIC
- CARLOVINGIAN
- AURICULARIUS
- PALATINE
- LAPWING
- HUDBUD
- SENIORATU
- ERIPIMUS
- ROTURAGIO
- CADAT
- READJUSTING
- TARNISH
- BLAZON
- ABDOLMUMEN
- REINSTATEMENT
- MISCALLED
- ZENA'S
- UNRAVELED
- ACUMEN
- THREADBARE
- DODDERING
- KNOLL
- TRESPASSERS
- HILLY
- CLIMBABLE
- BRACKEN
- NOTCHED
- UNERRING
- SCRUB
- TAINT
- MANIA
- BORNEO
- DETECTION
- LUNATIC
- ELUCIDATED
- BURGLARIES
- AMATEURISHNESS
- BURGLE
- BURGLED
- FACEACHE
- REPETITIONS
- FLOATERS
- MUDBANKS
- BLACKBIRDS
- HERONS
- PLOVERS
- FORAGING
- HAWK'S
- PROPHET'S
- CLOUD'S
- SNAGS
- SPRING'S
- TRAUT
- BOGGY
- EDDYING
- BREAKNECK
- AWNING
- MUDBANK
- HALLOO
- ODOROUS
- FERRYING
- AVOWEDLY
- FERRYMEN
- GUARANTIED
- FERRYMAN
- ERIE'S
- MILE'S
- PARLEY
- DEVIOUS
- HINGELESS
- SHIFTLESSNESS
- UNTASTEFUL
- PRETENTIOUSNESS
- WHEEZY
- DIAGONALLY
- SIDELONG
- JOCKEY
- OSTRICH
- PINEAPPLES
- SANKEY
- WHEEZES
- MORNIN'S
- MILKIN
- HUL
- BLOWIN
- FASHI'N
- BUSTLES
- HOOPSKIRTS
- UNDERWEAR
- CREAKY
- CLEAT
- HI
- DUMPY
- SHOVELLED
- COB
- CUSPADORE
- VINEGARY
- SLANGY
- GLIB
- COTERIE
- USIN
- SOME'N
- JINED
- KNOWIN
- KIDS
- NOUGH
- KEM
- LOWED
- AGINT
- WORKIN
- DUCK'S
- HAMPTIN
- KEER
- DRUV
- GOV'MENT
- AMMERNITION
- WAGIN
- VETERAN'S
- QUONDAM
- CANOEISTS
- TENS
- ASTONISHINGLY
- INEDIBLE
- DOUGHY
- PASTEY
- GRAVY
- LEATHERY
- ADULTERATED
- SALERATUS
- ATKINSON
- HYGIENISTS
- WIDEN
- MORASSES
- DIREFUL
- CAPSIZED
- INTERVENE
- WORT
- LOBELIA
- SUNBAKED
- ASHY
- BURLINGTON
- QUINCY
- SPURT
- VOYAGED
- PORTAGE
- CHURLISH
- CANOEIST
- CHAINING
- PAYMASTERS
- SWINDLING
- JEOPARDIZING
- EXCHANGERS
- DYNAMICS
- ADVERSELY
- SHAREHOLDER
- PILLS
- GAMBLED
- INTERMIXED
- ECONOMICALLY
- SUBSIDY
- NOMINATING
- USURPING
- DEPOSED
- CONTROLLERS
- HARMSWORTH
- ADJECTIVE
- MISDEMEANOUR
- CLAMOURS
- ELIMINATION
- AUDITING
- GOVERNS
- CRITICIZE
- DUPED
- WARPS
- DEPLETES
- SNIPES
- PARVENU
- CONNOTE
- VAGUEST
- PLATITUDES
- INVERTEBRATE
- FORMATIVE
- AGNOSTIC
- NATIONALIZING
- OPPRESSOR
- COGNATE
- BREEDERS
- DEBATS
- ECCENTRICITY
- CONNOTES
- PENALTIES
- CENTRICS
- PLAINEST
- SUPPRESSIONS
- SPECIALITY
- CENTRIC
- TEETOTAL
- DIABOLISTS
- RATIONALIST
- ATHEIST
- PARTICULARISM
- CANCEL
- PARTICULARIST
- REVOCABLE
- DUAL
- EDITOR'S
- VULNERABILITY
- VULGARIAN
- DISTORT
- CONTRASTS
- NOBODIES
- LIMELIGHT
- NOMINATE
- NUMEROUSLY
- MULTIPLYING
- HUMANITARIAN
- OVERAWE
- DISPIRIT
- VINDICTIVENESS
- STRATUM
- SYMPATHIZING
- MISSILES
- GLASCOCK
- JARVIS
- BANTERED
- INNOVATORS
- IRREDEEMABLE
- HIRELING
- EXTREMISTS
- FEASIBILITY
- DISQUALIFY
- ASSORTING
- FILING
- ABOLITIONIST
- INDEPENDENCE'
- INDWELLING
- BLESSEDNESS
- OLASTON
- WITHSTANDING
- STILLING
- GLASTON
- HEAVINGS
- REPASSED
- APOSTOLIC
- MISREPRESENTATIONS
- TASTEFUL
- UNHOMELIKE
- ALMSHOUSE
- PAUPER'S
- CLIME
- CARVING
- WARWICKSHIRE
- RITTENHOUSE
- PLANETARIUM
- POTTS
- IRONSIDES
- ALLIBONE
- ALMANAC
- SUBSCRIBER
- BOOKSHELVES
- CHAILLU
- LOWELL'S
- IDYL
- FENIMORE
- EDGAR
- ALLAN
- POE'S
- POE
- ERASURES
- DANTE'S
- DIVINA
- COMMEDIA
- WORDSWORTH
- PRESIDENTS
- PEABODY
- WOOTTON
- FLINTSTONE
- MILKROOM
- DUFFERIN
- NORTHCOTE
- WALLER
- HUGHES
- EVARTS
- CENTENNIAL
- GRANT'S
- NEWSBOYS
- INGOLSTADT
- SLAKED
- ALLURED
- INCLEMENCY
- PURLOINED
- THRUSH
- COTTAGER
- INTERCHANGING
- POIGNANTLY
- ENDEARED
- SADDEST
- PERIODICALLY
- RECOMMENCING
- DISPELLING
- ENTRANCINGLY
- ENRAPTURED
- PEACEABLY
- VOLNEY'S
- DECLAMATORY
- DEGENERATING
- NARRATIONS
- DOTED
- DEIGNS
- SHARPENING
- PLUMING
- NIGGER'S
- QUELLING
- CADEROUSSE
- HEELED
- ALLIANCES
- PINCETTE
- BAILED
- RESIDES
- MENACES
- COACHMEN
- DISSIPATING
- TARQUIN
- LINDEN
- HELIOTROPES
- FLICKERINGS
- NOIRTIER'S
- DENTED
- IMPASSIBILITY
- POISONERS
- BEVERAGES
- UNBLEMISHED
- WHIRLS
- SUCCINCTLY
- RALLYING
- FELONS
- PERVADED
- PIAZZI
- BARBERINI
- MAZZINI
- BELGIOJOSO
- QUIRINAL
- ENDEARMENT
- PINCIAN
- PORTER'S
- RIETI
- CORSO
- DORIA
- SEQUINS
- GONDOLAS
- STILTS
- PULCHINELLOS
- AFFETTATORE
- CONFESSES
- CONVEYANCE
- LAUDATION
- ANTONINUS
- FAUSTINA
- PIQUED
- BLOCKHEADS
- HELDER
- GAND
- COLOSSEO
- GASPARONES
- FOREWARN
- CREDENCE
- PORTA
- POPOLO
- ALBERT'S
- PRESERVERS
- PASTRINI'S
- TERRACINA
- CORNEILLE
- LACRYMA
- CHRISTI
- BUGABOO
- LARA
- FERENTINO
- ALATRI
- PESTE
- PRECOCITY
- STYLUS
- FELICE
- IMITATIVE
- GIOTTO
- PINELLI
- VALMONTONE
- FELICE'S
- LIVERIED
- BRESCHIA
- OLIVETREE
- SABINE
- CONTADINO
- SABINES
- EXTIRPATED
- GARIGLIANO
- AMASINE
- SONNINO
- JUPERNO
- GASPERONE
- DISQUIETUDE
- SURVEYOR
- BANDIT'S
- LASCIVIOUSNESS
- ENTREATIES'
- CLINCHED
- DIOVOLACCIO
- DIOVALACCIO
- PICKAXES
- PICKPOCKET
- UNFEELINGLY
- UNPOPULARITY
- VACUOUS
- BILLINGSGATE
- EXASPERATION
- POSTER
- PEARS'S
- ARGUABLE
- TRAMPS
- IRISHMEN
- BROADBENT
- REFRACTING
- DOCTRINAIRE
- JUSTIFIES
- FIZZING
- ANGELO
- VELASQUEZ
- ADMIXTURE
- FALSIFICATION
- DUBEDAT'S
- FARCICAL
- COMEDIES
- FALSIFICATIONS
- DOCTRINAL
- COMICALLY
- PARAMORE
- PHILANDERER
- MISANTHROPE
- EPIGRAMS
- OUTRAGING
- BELLIED
- CONTROVERSIALISTS
- DISILLUSIONIST
- SCEPTIC
- CHALLENGING
- SCOLDINGS
- BEATINGS
- CRAW'S
- BLUEBELLS
- OVERSLEPT
- MELON
- ZIZZ
- WARDER
- SMOOTHER
- PEEK
- SIMPLETON'S
- ZENZA'S
- CHARGER
- TILDA'S
- HARNESSES
- CARTWHEEL
- WHIRRED
- SHABBILY
- TANGENTS
- OCTAVES
- VOLATILE
- WORKBOX
- BURNEY
- EXECUTANT
- CLAVICEMBALO
- CLAVECIN
- SPINET
- PROGENITOR
- TRANSCRIBED
- BAPTISTE
- LULLY
- EXTENSIVELY
- ORCHESTRATION
- ADVISES
- IMPLICIT
- BEGINNERS
- PRACTICING
- RAMEAU
- COUPERIN'S
- VIRTUOSITY
- DOMENICO
- JOHANN
- MIRRORING
- HARMONIES
- EXPRESSIVENESS
- SPONTANEITY
- DEFACE
- PIANIST'S
- EXEMPLIFY
- TUNING
- TUNER
- FORKEL
- SUITES
- FANTASIA
- RHAPSODIST
- RUBINSTEIN
- SOULFUL
- POLAND'S
- COMPLEMENTING
- LANGUOROUS
- SCINTILLATING
- HOMESICKNESS
- DIVINEST
- UNFETTERED
- MELODIC
- SPIRITUALIZED
- TONAL
- UNBARED
- JARRING
- POLAND
- TONALITY
- UHLAND
- PREDOMINATES
- SOFTENS
- UPLIFTS
- STRENGTHENS
- TRAVAILING
- ORGANIST
- THEORIST
- DUNCE
- REVERING
- LISZT
- RAPHAEL
- INDIVIDUALIZED
- PIANISSIMO
- GRADED
- FLUCTUATIONS
- BALZAC
- CHOPIN'S
- KLECZYNSKI
- REPRODUCTION
- ANDANTE
- PRESTISSIMO
- ARPEGGIO
- INDIVIDUALIZATION
- HARMONICS
- PEDAL
- ADVOCATED
- MISAPPREHENSIONS
- MISINTERPRETED
- ASPIRANTS
- SENTIMENTALISM
- DISFIGURES
- RUBARE
- PLIABLE
- INTONING
- GREGORIAN
- BEETHOVEN
- UNSYMPATHETIC
- SPIRITUALIZING
- TIMBRE
- LIBERATING
- CHORAL
- EXTENSIONS
- ARABESQUES
- CANTILENA
- NOCTURNES
- FAERY
- POLONAISES
- VALSES
- MAZURKAS
- IMPROMPTUS
- RECAPTURING
- SCHUMANN
- PROUDEST
- BIE
- VESTURE
- ALGEBRAIC
- UTILITIES
- DEDUCTING
- EXPENDITURES
- STORING
- INDIRECT
- TRANSPORTING
- CONTRIBUTING
- UNFUNDED
- REDUCIBLE
- PARADOXES
- INCONSISTENTLY
- CAMPUS
- MULTIPLIES
- PRODUCER
- DREDGING
- COLLECTIVELY
- DITCHED
- DIKED
- HILLSIDES
- IMPROVES
- GROUPINGS
- UTILIZATION
- FELDSPAR
- HECKLING
- MINERAL
- COLLAPSES
- LATHE
- DYNAMOS
- CONVERTING
- GUSHED
- IOSKEHA
- SARAMA
- VEDA
- SANSCRIT
- RESCUES
- PACHACAMA
- MERITING
- PACHAYACHACHIC
- VIRACOCHA
- REBUS
- YOLCUAT
- RATTLESNAKE
- TOHIL
- RUMBLER
- HUEMAC
- DUALISM
- ANALOGUES
- TULA
- TLAPALLAN
- FIGURATIVELY
- PRECURSORS
- INDELIBLE
- NATTY
- HOLT
- FORESTRY
- DARLINT
- INSPECT
- WOODSMEN
- ILK
- TABLEAU
- GAWKING
- CHUB
- ADMONISHING
- GIRUL
- YOWLING
- SCURRY
- STOVES
- PINON
- STRAWBERRIES
- GROUSE
- GLENHOLDT
- BRONTOSAURUS
- TESCHALL
- HARKRUDDER
- BEL
- TINWARE
- GUY
- LULLS
- CUPLIKE
- GRUMBLINGLY
- HUNTER'S
- HAYNES'S
- WEAKLY
- CHIRK
- WINDFALL
- BREWSTER
- SECRETED
- BRISTLE
- GOLIAR
- BIFF
- BLACKTHORN
- SHILALEY
- CLYDE
- FRAID
- CAYUSE
- TEARIN
- RIBALD
- ZOO
- DESIGNATION
- UNPRETENTIOUS
- PEDANTIC
- LAMELY
- ALDENHAM
- CASTERBRIDGE
- HABAKKUK
- UNNATURALLY
- LYRICAL
- QUOTES
- BRAWN
- APRONED
- SHORTBREADS
- PILCHARDS
- GETHER
- GOLFING
- ATHENAEUM
- HOMER'S
- OOZE
- SIXPENNY
- ROBINSON'S
- WHIPS
- RACONTEUR
- INDIARUBBER
- HASTENS
- BUMPS
- ICH
- DIEN
- ARMAND'S
- SULLENLY
- SCHOONER'S
- CREEKS
- DIPLOMATIST'S
- APERTURE
- STOOLS
- FISHERMAN'S
- CITOYEN'S
- ENDANGERING
- PROBLEMATICAL
- PIMPERNEL
- PARALYZES
- VENTING
- MALICIOUSLY
- BELABOUR
- TRUMPS
- ENROLLED
- ANATHEMA
- OVERBURDENED
- STOLID
- ROSENBAUM
- FINANCES
- PERVERT
- INCULCATED
- DISORDERLY
- CONVENTICLE
- OBLATIONS
- LIBERALITIES
- INDULGENCES
- JUSTIFICATION
- UNEXHAUSTED
- RETAIL
- SCHISMATICS
- FALSITY
- IMPOSTURES
- LUTHERAN
- SCRUPLED
- VALID
- CHARTER
- DAMNABLE
- ANTICHRIST
- WHORE
- PROPAGATION
- SOVEREIGNS
- INVEIGHED
- ENCROACHING
- MONASTIC
- CONVENTS
- LIBERTINISM
- INVADER
- CONCUR
- WOLSEY
- BRUGES
- SEMBLED
- FLOW'RS
- LUCE
- SPARKLE
- EV'NING
- WORDLY
- FORESHADOWING
- TURGOT
- PRIESTLEY
- CONDORCET
- EXEMPLIFYING
- PERFECTIONISTS
- UNWROUGHT
- FORTUITOUS
- PRECONCERT
- VORTEX
- PREEMINENT
- ATTAINABLE
- TILLERS
- PROVERBIALLY
- INSTANCED
- INTUITION
- RADICALLY
- ELLISON'S
- AMASSED
- FACTO
- ABORTIVE
- SEABRIGHT
- EXTRAVAGANCES
- BUSYING
- MINISTERIAL
- VIRTU
- ENDOWING
- SUPERABUNDANCE
- ENAMORED
- EXPATIATING
- MULTIFORM
- MULTICOLOR
- SOLVING
- ARTISTICAL
- SCULPTURAL
- GLADDENS
- GENERALIZATION
- COMPEERS
- COLLOCATIONS
- ADAPTING
- RETIREMENTS
- CAPABILITIES
- INCONGRUITIES
- APPERTAINS
- ODYSSEY
- INFERNO
- PROMETHEUS
- SOPHISTS
- INCONTROVERTIBLE
- TECHNICALITY
- HARMONIZED
- DEFINITIVENESS
- INTELLIGENCES
- EMANATION
- EXEMPTION
- SCRUFF
- SAGES
- SHINTO
- YOSHIDA
- FUSHIMI
- KANJUJI
- PERQUISITE
- DAIMIOS
- PHARMACOPOEIA
- PEKING
- DECOCTION
- DYSENTERY
- ACUPUNCTURE
- VESICAL
- CALCULI
- CORROSIVE
- DECOCTIONS
- MUMMIES
- COMMENDED
- PULVERIZED
- CALCULUS
- PETRI
- ANDREAE
- MATTHIOLI
- PROFUSE
- BADGER'S
- POSTHUMOUS
- REPENTING
- FUSED
- THROTTLES
- PEST
- ERELONG
- FIEND'S
- JUSTINE
- MADDENING
- WREAK
- DEVIATING
- INVECTIVE
- HOVERS
- CHAMOIS
- INTIMIDATED
- PROPORTIONATE
- FLIT
- PRESIDE
- IMPASSIVE
- HOVELS
- WILDNESS
- RUGGEDNESS
- ADVERSARY'S
- INSTIGATED
- PROTRACTION
- UNFULFILLED
- SASSY
- EXTRICATING
- MISHAPS
- SCAPEGOAT
- DECLINES
- MEASLY
- IMPETUOUSLY
- OBJECTING
- STUBBORNLY
- ROWDY
- REENTER
- SCAPEGOATS
- DODGED
- TRAPPING
- MILTON'S
- AFTERTHOUGHT
- BULL'S
- WILL'S
- LUMBERMAN
- LUMBERJACKS
- PORTENDED
- HOOT
- LIMPED
- PALS
- DRUDGERY
- DASTARDLY
- TOTE
- TRE
- MENDOUS
- ANTLERS
- SHRILLED
- CAMERA
- WHIFFING
- FRISK
- FORERUNNER
- BUCKSHOT
- WOMANISH
- REDEEMED
- SWEATERS
- OUTDOORS
- EYETEETH
- MARCHLAND
- HARRIED
- AVARS
- THRACE
- PORE
- SHEAR
- DISUNITED
- AUSTERITY
- POLYTHEISTS
- SCOFF
- GIBBON
- CHEAPEST
- PROVINCIALS
- MONOPHYSITES
- JACOBITES
- GOVERNMENTAL
- RECONQUERED
- RECORDING
- GABATHA
- ITURAEA
- LEGIONS
- BOSTRA
- HIEROMAX
- KHALED
- EMESA
- HELIOPOLIS
- SOPHRONIUS
- SEBASTOPOLIS
- ACCOUNTANT
- EUNUCH
- FLAMBARD
- RUFUS
- FLAGRANT
- PAYERS
- UNPOPULAR
- IMPRISON
- DECIMATE
- DICTATORSHIP
- HELLAS
- SOPHIA
- ABDALMALIK
- BEFEL
- CRIMEA
- SEBASTOPOL
- SUZERAINTY
- AZOF
- INGRATIATED
- KHAN'S
- EUXINE
- WEATHERED
- TERBEL
- BULGARIAN
- UNREADY
- RELENTLESS
- CATHISMA
- HIPPODROME
- TRAMPLE
- APSIMARUS
- WREAKING
- MEANER
- ANARCHICAL
- DEMORALIZATION
- ADRAMMYTIUM
- PHRYGIA
- ANATOLIC
- CAPPADOCIA
- LYCAONIA
- AMORIUM
- BUREAUCRACY
- MONOTHELITE
- MONOPHYSITE
- SLAVS
- PERSECUTING
- PARDONED
- ACQUIESCING
- IMMURED
- ONUS
- KARL
- HONORIUS
- ROMULUS
- AUGUSTULUS
- EQUIPOISE
- ANTIQUARIES
- LONGMANS
- GIBBS
- PERVADING
- CIVILISING
- HEPTARCHY
- SPENSER
- GOODNESSE
- ABRIDGED
- CONDENSATION
- READABLE
- GRENA
- CORMAC'S
- GLOSSARY
- ALDER
- SPRIGS
- FENA
- ERIN
- CONALL
- KERNACH'S
- DERGA
- PLAITS
- SWORDLETS
- KELLS
- HAIRDRESSERS
- BARBERS
- MUSEUMS
- MANUFACTURING
- BADGERS
- EDGINGS
- EXPORTED
- DYEING
- CHEQUERED
- DOMNALL
- CONGAL
- SLEEVED
- LOOPS
- UNTANNED
- STITCHED
- THONGS
- TORQUES
- CRESCENTS
- GORGETS
- LARS
- TUSCANS
- ETRURIAN
- ECONOMIZE
- BEAUFORT
- FREEMAN
- FRISLEY
- SALTER
- MASSEY'S
- DORSEY
- COMMITTEE'S
- SAIRSVILLE
- CHUNKY
- GRUM
- OATEN
- CEREAL
- UNBREADLIKE
- DOUGH
- RUBBERY
- STIFFENS
- POROUS
- WHEATLESS
- CRUMBLY
- VITALLY
- LICENSED
- CRACKER
- RETAILER
- FIRMS
- SUBSTITUTES
- FLOURS
- DIETARY
- HOUSEHOLDS
- MACARONI
- NOODLES
- CONFORMING
- DELECTABLE
- PASTRIES
- BAKESHOPS
- SELFISHLY
- CONSERVE
- TRUSTS
- RUMBLES
- TRUMPET
- BRAYS
- LIVELONG
- MADRIGALS
- DOFFS
- ENERVATE
- CASQUE
- GRASPS
- EQUIPPING
- BUCKLERS
- HELMS
- ARRAYS
- STUYVESANTS
- REGIMENTAL
- CHIVALRIC
- PUMMEL
- SPIRITEDLY
- BELIE
- SWEDE
- PIETERZEN
- VRIE
- BOUSER
- POTATIONS
- POTTLE
- GUZZLING
- SWASHBUCKLERS
- CAROUSALS
- ROBUSTIOUS
- DISCOVERERS
- MANHATTOES
- MEASURER
- BROECK
- TESTY
- OUTSTRUT
- OUTSWELL
- COCKS
- BROADEST
- TRENCHERMAN
- SKIPPERS
- JACOBUS
- LARDERS
- JOLLIFICATION
- GORMANDISERS
- JAN
- NIEUW
- CRAVINGS
- PILON
- D'OR
- OBTUSE
- HARICOTS
- OLIVES
- ACUTELY
- GUSTO
- EMBALM
- BORING
- MINDFUL
- TENDEREST
- FIGS
- FILBERTS
- TOURS
- MUSCATEL
- HEATING
- OGRE
- ANISEED
- RIND
- WARRENS
- DELICIOUSLY
- SNORTED
- MOUSQUETON
- PAON
- PIERREFONDS
- BROWSED
- FELINE
- VALLON
- ANTWERP
- ARTESIAN
- ARTOIS
- FLORINS
- MENDS
- KNITS
- TUNNY
- SHOPFUL
- COATING
- BEDSTEADS
- CASTORS
- UPROARIOUS
- BIBBERS
- TRUCHEN'S
- MORDIOUX
- PROMINENTLY
- SLAUGHTERING
- STICKLER
- BRICKED
- OFFICIATING
- BEADLE
- KNEELS
- CHEVREUSE
- FRANCISCAN
- EXHAUST
- SLAPPING
- BOOTERS
- UNGRACIOUSLY
- RAGGING
- GRAVELLED
- ACCELERATOR
- WINDSHIELD
- ROADSTER
- SHACK
- STUCCOED
- CORRODED
- MUNITIONS
- FLAMBOYANT
- HOLLOWLY
- GRILLED
- BATHROBE
- TENSELY
- JADE
- INDESCRIBABLY
- GULL
- STARTLINGLY
- DEATHLY
- LACERATIONS
- EXPERIMENTALLY
- INSULATORS
- POTENTIOMETERS
- RHEOSTATS
- SWITCHES
- ELECTRODE
- SWITCHBOARD
- WIZARDRY
- PLUGGED
- PROPEL
- DEVILISHLY
- GADGETS
- GRIDLEY
- GESTURING
- VISUALIZED
- FILTERED
- IGLOOS
- ESKIMOS
- COLORFUL
- DALLIED
- TORPEDOES
- CINCTURED
- VISUALIZING
- KELP
- ARROWED
- WHETTED
- HAZILY
- FUSING
- TUG
- TERRA
- FIRMA
- HESITANTLY
- TIMER
- SHAKENLY
- SITTEN
- PROROGATION
- WRITS
- UNEXPERIENCED
- IMPOLITIC
- POTENTATES
- PRINCIPALITY
- ENDUED
- SUBSIDIES
- DEPENDANTS
- DISPROPORTIONED
- TENETS
- CABALS
- PROSECUTION
- SANDYS
- DIGGES
- ELLIOT
- SELDEN
- PYM
- METHODISED
- PROSECUTED
- GENIUSES
- EXTORTING
- ALLOWABLE
- COMPLIANT
- PURITANISM
- PETITIONED
- APPELLATIONS
- CADIZ
- UNDISCIPLINED
- REEMBARKED
- GALLEONS
- INTRUSTING
- IMPRUDENCE
- SHERIFFS
- INCAPACITATED
- ENOW
- PATRIOTS
- UNDISGUISED
- REDRESSING
- REINSTATING
- ABSENTING
- COVENTRY
- CONTUMACY
- RECRIMINATION
- GRANTS
- ACQUAINTING
- RIVETTED
- ENRAGE
- EXTOLLING
- CARLETON
- ANCIENTLY
- OVERTHREW
- BRINGETH
- TURBULENCY
- EXASPERATE
- PRECIPITANCY
- ARUNDEL
- DISQUALIFYING
- IMPOSITIONS
- CANVASSING
- NINTHS
- WREST
- PERFIDY
- UNDISPOSED
- COUNTERWORK
- RELIGIONISTS
- MEDIATION
- SOLICITATIONS
- BARKS
- REYNARD
- ROOSTED
- CENSUS
- TWOMBLY
- NEFARIOUS
- WADS
- RAISERS
- ROOSTS
- ALGEBRA
- CRISES
- WAD
- QUART
- MOUSEY
- BRIARS
- SQUEAKS
- HOLLER
- YELP
- WHACKS
- PHOTOS
- SKITTLES
- LANDLORDS
- ROYALLY
- KINLEY
- MASSILLON
- PHOTOGRAPHS
- KINLEY'S
- INAUGURATION
- HURRAHED
- DALTON
- HEZEKIAH
- BRIMLEY
- LOUNGERS
- PATRONIZED
- MACKEREL
- KITS
- OVERSHOES
- CHEWED
- HEZ'S
- CORNED
- CASTOR
- DIPPERSFUL
- CREMATED
- HORUM
- FYE
- MULGARRY
- BAPTISMAL
- SPONSORS
- TABLESPOON
- MUMPS
- PORTERHOUSE
- SLIVER
- POD'S
- TOUGHER
- SCARCITY
- SAWS
- IMBEDDED
- ORNITHOLOGISTS
- BOTANISTS
- ENTOMOLOGISTS
- WOODMAN
- SMELT
- ERUDITE
- WOODSMAN
- HUSTLE
- PASTURAGE
- HAIRBREADTH
- UNCALCULATED
- DUPLICATED
- CANDLELIGHT
- MEOWED
- DRAPING
- SEDUCTIVELY
- LAMPING
- VISAGES
- DISCARNATE
- DIABOLICALLY
- UNOPPOSED
- VICARIOUSLY
- RADIATIONS
- MAGICALLY
- PENDER
- FILTER
- UNSELFISHNESS
- MODULATED
- SIGILS
- COLLIE'S
- PURRINGS
- PAWED
- KNEADING
- LICKS
- HYMEN
- TAENARUM
- LACONIA
- CERBERUS
- PROSERPINA
- FLOCKING
- BEGUILE
- ORPHEUS'S
- MOCKS
- UNWEPT
- NAIADS
- REVELERS
- SINGER'S
- HEBRUS
- LESBOS
- SAPPHO
- STILLEST
- THOR'S
- THUNDERER
- BALDUR
- ASA
- GUNBOATS
- COMMODORE
- GOLDSBOROUGH
- RACERS
- BOATSWAIN
- SQUEALS
- INTONATIONS
- EMISSION
- STROKER
- HOSPITALITIES
- TOOTHSOME
- PIGGISH
- DIGESTION
- CUTICLE
- FRISKINESS
- DISGRUNTLED
- GATTY
- CATERPILLAR'S
- DISBELIEVE
- NETTLED
- LARK'S
- FEN
- SQUANDER
- CLOUT
- OVERTOPPED
- DEMOCRATICAL
- COMMONER
- MALIGNANTS
- ROUNDHEADS
- SIGNALIZE
- DISTINGUISHABLE
- ENSUING
- INDECENT
- INSTIGATING
- INVIDIOUS
- FONDEST
- SEDITION
- CALUMNIES
- MACHINATIONS
- TRAITOROUSLY
- ASPERSIONS
- IMPEACHED
- SCOTS
- INDEPENDENCY
- BREACHES
- HALBERTS
- GUILDHALL
- RESOUNDING
- PROJECTORS
- PANICS
- KINGSTON
- INFRINGE
- UNKNOWINGLY
- EXCEPTIONABLE
- PRELACY
- VIRULENT
- MOBBISH
- JUDICATURE
- RAVISH
- CONSULTATIONS
- LOANS
- TREASURERS
- MOIETY
- ABSENTED
- MACES
- GROTESQUERIE
- TIECK
- ACCOUNTING
- UNIQUITY
- UNMITIGATED
- CUE
- HERACLITUS
- EMERITUS
- UNPARDONABLE
- WHIMSICALITIES
- BUFFOONERIES
- SUFFUSE
- LINEAMENT
- SKEPTICAL
- ABSURDITIES
- MYSTIFIC
- INCUBUS
- ENGROSSING
- IMPRESSIVENESS
- AFFECTIONATENESS
- ELICITED
- RIDICULER
- FANFARONADE
- DUELLING
- BARON'S
- SERMONIC
- DUELLIST
- MOMENTLY
- DISCREDITABLE
- QUIZZICAL
- UNBENT
- MISCONCEIVED
- CADAVEROUSLY
- SPECIFICATION
- OBVIATED
- FAVYN
- BRANTOME'S
- DEROME
- DROLLEST
- CONSTRUCTIONEM
- CARTEL
- REFERRING
- EPISTLE
- AMICABLE
- QUIZZES
- REMISSNESS
- WRAITHED
- CHIVALROUSLY
- SENILE
- TIMELESS
- INDIVIDUALISM
- LIBERALISM
- LATCHKEYS
- COMPENSATIONS
- CONSOLATIONS
- MALARIAL
- VAPOROUS
- COWERED
- CLO'ES
- INSIDIOUSLY
- WELCOMES
- SATIRIZES
- MONKISH
- ENERY
- STUART'S
- DOTES
- ACY
- VERSATILE
- PHANTASMAL
- BLACKWOOD'S
- BRANDER
- MATTHEWS'S
- CANTERVILLE
- TRAVESTY
- ZANGWILL
- ENGAGINGLY
- KENDRICK
- LLOYD
- O'NEILL'S
- SOPHISTICATED
- MERCIFULLY
- SCURVILEGS
- HIGHNESS'S
- ASTRAKHAN
- UNLOVABLE
- WHISKER
- STROKABLE
- UNTAMABLE
- SCOLLOPS
- PERIWINKLE
- TWINES
- FARINA
- CUMBERSOME
- PEGASUS
- ROSINANTE
- HAWES
- EXECRATIONS
- URSULE
- FIACRE
- ABDUCTING
- SHINERS
- REVERY
- BRANDISHED
- BENVENUTO
- CELLINIS
- ASPIRES
- COINAGE
- UNSCREWED
- ENJOINING
- UNINTERRUPTEDLY
- COMMODE
- PARDIE
- PONINE
- BOOBIES
- PRESUMING
- HUCKABACK
- DIMITY
- GREYISH
- INDETERMINATE
- LAPPEL
- REFOLDED
- TINFOIL
- FIXTURE
- SAYN
- INDECOROUS
- DISPASSIONATE
- EXPLICITNESS
- REALIST
- DIAGRAM
- UNEMOTIONAL
- ABRASION
- GRADUATING
- BLOTCHY
- TUMID
- REDNESS
- STIPPLED
- SHOPMAN
- CONTUSIONS
- IMPACT
- BEGINNER
- BICYCLING
- CONCUSSIONS
- TREADLE
- BLISTERS
- ROEHAMPTON
- APPRENTICED
- DRAPER'S
- TAILLESS
- REFOLDING
- KNIGHTLY
- WASHABILITY
- UNFADING
- SPASMS
- STRAIGHTENS
- PRITCHARD
- ISAACS
- CLAMOURED
- BETTWS
- TRAMLINE
- VOCIFERATING
- GOV'NOR
- AWESTRICKEN
- COMATOSE
- DININGROOM
- SSSH
- CREAK
- CONVERSATIONAL
- BECHAMEL'S
- MONOMANIAC
- KNUCKLE
- FAIRISH
- EIGH
- MARVELLING
- KNICKERBOCKERS
- BLASPHEMIES
- SULLIED
- MIDHURST
- HASLEMERE
- GUILDFORD
- RIPLEY
- RAPIERS
- AGINCOURT
- ELOPEMENT
- VILLAS
- LAMPLIT
- SPIRE
- FOOTFALL
- SUBTILE
- TREADLES
- SHIMMER
- STARLIKE
- SPIRITUALISED
- TRANSFIGURING
- TURNINGS
- PROMPTITUDE
- SOTTO
- VOCE
- PATRONYMIC
- HUTCH
- CHRIS
- CONVERGE
- MOONLIGHT'S
- DEE
- THENKS
- SIS
- EYELASHES
- INCONSOLABLE
- TROUT'S
- MILLINERS
- DRESSMAKERS
- OSTRICH'S
- FINGER'S
- GODMOTHER'S
- SULKINESS
- BEADY
- RENOUNCE
- THWARTS
- GAOLER
- ADORNING
- BEMOANING
- AMETHYSTS
- LYNX
- WARBLED
- MINETTA
- GLOWERED
- LINNET
- POSTILION
- VALETS
- SOUSSIO'S
- JIGGLING
- SLEEKER
- TWINE
- RUMPLING
- HINDFOOT
- BUSHIEST
- SEDGES
- PARSNIPS
- FROLICKING
- MUSKRAT'S
- IRONWORK
- UNDERSIDES
- ESTUARY
- OBITUARY
- INMAN
- ISIS
- PARAMATTA
- LYONNAIS
- SEDIMENTATION
- STREAM'S
- COUNTERCURRENT
- BREAKUP
- BONEYARD
- BILLIONS
- LUMPFISH
- MONOGAMY
- EELPOUT
- MORAY
- WOLFFISH
- VIVIPAROUS
- GOBIO
- PRICKLES
- SCORPION
- BULLHEAD
- NODULES
- BANDED
- SNOUTED
- MULLET
- NORWEGIANS
- DEPOPULATED
- CODFISH
- GRAPPLING
- SPIDERWEB
- ALERTED
- GEARING
- TRANSMITTING
- METRIC
- RETRIEVED
- RESUBMERGED
- PADDING
- TEXTILE
- SHEATH
- SADOVA
- SEASHELL
- SEA'S
- BLANC
- FASTNET
- GRAVITATING
- LOWERMOST
- SCILLY
- GLOOMIER
- RELIVING
- HUNCH
- ZENITH
- FACILITATED
- VERTICAL
- BEACON'S
- BULGE
- ENSHROUDED
- SEASHELLS
- POYPE
- VERTRIEUX
- PRESTON
- D'ESTAING
- GRENADA
- GRASSE
- BREST
- STABEL
- PREENED
- GRUMPY
- BELOSTOMA'S
- FORELEG
- DUCKLING
- KATYDIDS
- WIGGLY
- LITTLER
- KATYDID
- UNHOOKED
- FROLICKED
- TEDDER
- CHANGEFUL
- INDISCRIBABLE
- FITTEST
- UMBRAGE
- HUSK
- AVAILETH
- UNRECEPTIVE
- INHARMONIOUS
- GLOOMED
- CURATE'S
- UNLOVED
- THEREFROM
- DUCO
- STAAL
- BABUINO
- MERCATO
- FIORI
- WORKROOM
- RUMMAGE
- HEADGEAR
- SUNLIT
- BAEDEKERS
- SATURN
- FLUTED
- DORIC
- IONIC
- BASILICAS
- PREEXISTENCE
- TOGA'D
- ARCHITRAVE
- UNWITTING
- FORTUITOUSNESS
- TITUS
- PREDESTINATION
- MISTILY
- GOLDS
- BAEDEKERED
- BUSTS
- TORSOS
- FRAILNESS
- ACTUALITY
- PRERAPHAELITE
- LIRE
- PETRARCH'S
- CORNELIE
- RETZ
- SAYINGS
- PIECEMEAL
- WOUTER
- TWILLER
- ACKNOWLEDGMENTS
- SALLUST
- WORTHIES
- LIVY
- WAYFARING
- COMPILED
- WINNOWING
- DISCARDING
- PITHY
- ENTHRALLED
- PIOUSLY
- REUNITE
- SHRILLING
- FANFARE
- LILTING
- DINNING
- FLARE'S
- RAUCOUS
- LIGHT'S
- BAND'S
- DIMMING
- GARDEN'S
- OVERLAID
- DEARTH
- BUCKWHEAT
- SHEEN
- MARGINAL
- JANGLED
- STARRING
- FIREFLIES
- ASPEN
- RAM'S
- STEAMS
- VOTARIES
- NUMBS
- ETCHES
- PATTERNED
- WHAN
- APRILLE
- MARSHES
- DOORYARDS
- RHYTHMED
- COUPLINGS
- SUNRISES
- LAPPING
- INQUISITORIAL
- GENERALITIES
- POLWARTH
- REFRACTION
- EXECRATION
- ACE
- DIVISIONS
- SCATTERINGS
- COUNSELLING
- SEVEIRITY
- HEARTLESSNESS
- BASCOMBE
- NOWISE
- TRUISMS
- RESTORATIVES
- DISMAYFULLY
- JILT
- EXECUTIONERS
- LINGARD
- SPECTRES
- LOATHES
- WINGFOLD'S
- GAWKY
- UNPOLISHED
- UNORNAMENTAL
- EMMELINE
- RAINDROPS
- SUNNING
- MEA
- LAGS
- WADING
- CUDDLED
- FIREBRANDS
- DIFFUSING
- BRIMFUL
- EMANCIPATOR
- INDOCTRINATE
- AMISTAD
- FREEDMEN
- BEFRIENDING
- OSTRACISED
- TAPPANS
- INVOICE
- FREEDMAN
- ODIUM
- OBLOQUY
- APPRIZED
- DICTATING
- AMANUENSIS
- NARRATE
- GA
- DOUBTFULNESS
- BIDDER
- JOURNEYMEN
- PURSUER
- EVERYONE'S
- GRATIS
- ORIOLE'S
- ORIOLES
- KITE
- KITE'S
- TITMOUSE
- CHECKERBERRY
- SASSAFRAS
- VARLETS
- JEERING
- SCANDALOUSLY
- OPERATIC
- TYROLEAN
- APING
- ADMONISH
- UNBEFITTING
- INTERLOCKING
- GIZZARD
- VOL
- COMPARTMENTS
- CAPERING
- FEEDERS
- SPEEDED
- RUDDERS
- STATEROOMS
- WHIZZER
- TRANSMITTERS
- WORRIMENT
- SWIFT'S
- BOWLED
- HATBAND
- YORKE'S
- FROCK'S
- FANNY'S
- CHAPERON
- GALLOWAY'S
- REV
- AFFLICTS
- CHANNINGS
- CONTRIBUTOR
- PRESCRIPTIVE
- UNCHECKED
- LEGALITIES
- CHURCHWARDEN'S
- ERUDITION
- EPISCOPACY
- HEARSAY
- ARROGATE
- SHELLINESS
- BROWNRIGG'S
- ADDICEHEAD
- DISTRAINT
- TEMPLETON'S
- RUBICUND
- QUENCH
- UNMINGLED
- PERPLEXES
- AUTHORIZES
- ORDINANCES
- DISSENTERS
- REPOSITORY
- TABERNACLES
- COMPULSORY
- PROCEEDETH
- PROSELYTIZE
- INTERLOPER
- COMMEMORATE
- CHURCHMAN
- MARSHMALLOWS
- COURTNEY
- KNAPP
- REMOTEST
- PHILEMON
- SUTHERLAND'S
- CALUMNY
- GAVEL
- CREDIBLE
- JURY'S
- PUSILLANIMITY
- BATSY'S
- PAVE
- REALISING
- RIGHTFULLY
- UNCALLED
- IMPROPRIETY
- PROVISO
- ZABEL
- PARTOOK
- COMMUNICABLE
- INGRATITUDES
- EMPHASISED
- INFERENCES
- MISDOINGS
- LOATHED
- TRICKING
- ESCAPADES
- CATECHUMEN
- RIVALLED
- GLADIATORIAL
- SMASHERS
- UPSETTERS
- OUTSTRIPPED
- GERMS
- TRUSTFULNESS
- STAINS
- SOLACING
- FRIENDLESS
- MOTHERLESS
- TOMBS
- GEOMETRY
- STUDENT'S
- EMPTINESS
- DISDAINED
- VAINGLORY
- PASSWORDS
- HONORATUS
- CONTENTIOUS
- THIRSTING
- MANICHEISM
- GYRE
- TRANSCEND
- CLARIONS
- HANDMAID
- VAGRANT
- THITHERWARD
- CALAHORRA
- PARAMOUR
- ESPOUSALS
- DOWERED
- POSSESSIVE
- DOMINIC
- JOANNA
- OSTIENSE
- TADDEO
- MANNA
- FADETH
- DRESSER
- DECIMAS
- QUAE
- SUNT
- PAUPERUM
- APOSTOLICAL
- RUNNELS
- ORBIT
- TWILL
- CASAL
- ACQUASPARTA
- AVOIDS
- ILLUMINATO
- AGOSTINO
- MANGIADOR
- RABANUS
- CALABRIAN
- PALADIN
- DISCOURSES
- BEGINNETH
- CHIANA
- MOVETH
- OUTSPEEDS
- CONCORDANT
- OPE
- FOUNT
- DISUNITES
- INTRINED
- POTENCIES
- IMMUTABLE
- SUPREMEST
- NOTEST
- TURNEST
- AFFIRMS
- DENIES
- RETURNETH
- SABELLIUS
- ARIUS
- HARBOUR'S
- ORISON
- LAMENTETH
- LIVETH
- CIRCUMSCRIBING
- BESTOWS
- SUBSISTENCES
- CIRCUMFERENCES
- ENKINDLED
- HOLOCAUST
- BESEEMED
- HELIOS
- GLIMMERS
- GALAXY
- CONSTELLATED
- QUADRANTS
- ENSAMPLE
- UPGATHERED
- RAPT
- LAUD
- POSTPONING
- BETHINKS
- RIGHTEOUSLY
- TIGHTEN
- DESPOILS
- CHANGETH
- ENDURETH
- CROSS'S
- ANCHISES
- ELYSIUM
- BENEDIGHT
- TRINE
- THINKEST
- GLADSOME
- SHOWEST
- SIMILITUDES
- DIVERSELY
- PINIONS
- E'EN
- GRANDSIRE
- BEHOVES
- SHOULDST
- TAKETH
- TIERCE
- NONES
- CORONAL
- SANDAL
- SHOON
- O'ERRUN
- SARDANAPALUS
- NERLI
- VECCHIO
- TROJANS
- LAPO
- SALTERELLO
- CINCINNATUS
- BAPTISTERY
- CACCIAGUIDA
- MORONTO
- ELISEO
- VAL
- PADO
- BEGIRT
- LAW'S
- PASTOR'S
- EXECRABLE
- FALLACIOUS
- LANGUISHES
- SHORTENS
- SHEARS
- PERSEVERES
- HARDIHOOD
- SHEEPFOLD
- QUICKENS
- REINFLAME
- RUNNETH
- SIGNA
- SIMIFONTE
- GRANDSIRES
- MONTEMURLO
- CERCHI
- ACONE
- VALDIGRIEVE
- BUONDELMONTI
- INTERMINGLING
- SURFEITS
- LUNI
- URBISAGLIA
- CHIUSI
- SINIGAGLIA
- BARES
- UGHI
- SANNELLA
- ARCA
- SOLDANIER
- ARDINGHI
- BOSTICHI
- RAVIGNANI
- GUIDO
- WHOE'ER
- BELLINCIONE
- PRESSA
- GALIGAJO
- POMMEL
- VAIR
- SACCHETTI
- GIUOCHI
- FIFANT
- BARUCCI
- GALLI
- CALFUCCI
- CURULE
- SIZII
- ARRIGUCCI
- ENFLOWERED
- CONSISTORY
- UBERTIN
- DONATO
- CAPONSACCO
- GIUDA
- INFANGATO
- DELLA
- PERA
- KEEPETH
- GUALTEROTTI
- IMPORTUNI
- UNFED
- EMA
- CAMEST
- BEHOVED
- MASQUERADED
- MEEKER
- HURSETON
- PLANTAGENET'S
- MALLESON'S
- PUPPET
- TABERLEY
- SNEERINGLY
- PITTANCE
- BLACKMAILING
- SPITFIRE
- UNDERSIZED
- CHERUB
- NIP
- CHRYSANTHEMUMS
- TOBACCONIST'S
- OSTENTATIOUSLY
- SNUFFLED
- TROWEL
- STEPSISTER
- DRUNKARD'S
- WASHINGTONIAN
- EULOGY
- SONNY
- IMBECILE
- GORILLA
- TURNCOAT
- DELEGATIONS
- COMET
- ANTIETAM
- FLATBOAT
- BUFFOONERY
- GENTRYVILLE
- CONSTABLE'S
- SEWARDS
- FREDERICKSBURG
- MEADE
- GUNBOAT
- BISECT
- DEDICATORY
- NOAH
- GARDNER'S
- EVERETT'S
- LISPING
- FLOWERTH
- PRETHIDENT
- COBBLER
- TANTRUM
- PIGEONHOLE
- ASSASSINATION
- INBRED
- NOTHER
- EVACUATION
- APPOMATTOX
- PROMULGATED
- OUTLINING
- PENDEL
- GURGLE
- ECSTACY
- ROMPING
- TADDIE
- SURGICAL
- INEQUITIES
- ARBITER
- WHILENESS
- FASTNESSES
- DEERLIKE
- GARMENTED
- KOREANS
- CEASELESSLY
- SPRINGY
- WEEDING
- RIVERBED
- PIGTAILS
- KUELIAN
- CHING
- SOWERS
- PEKIN
- PEDLARS
- CONDENSED
- MUGS
- HORSESHOERS
- SHAMPOOED
- TINS
- COGNAC
- INEFFICIENCY
- WORTHLESSNESS
- EPITOMIZES
- INTERMINABLY
- PREPARES
- ENDURES
- SQUADS
- SLIVERS
- BULLIED
- SEN
- ETHICS
- ADAPTABLE
- CHANG
- MANCHURIAN
- INSCRIBES
- DIVERGED
- MONGOL
- DIFFERENTIATIONS
- INFUSIONS
- SAMENESS
- MALAY
- AMBITIOUSLY
- NAPOLEONIC
- NIPPON
- BANZAI
- UNANIMITY
- DESPOIL
- SUBSTANTIALITY
- PLANET'S
- ORGANIZER
- BALKED
- INCOMPREHENSION
- TWISTS
- PRESTO
- DOZING
- AFRIKANDER
- UNAFRAID
- DUPLICATES
- RIVALLING
- INTERCHANGEABLE
- HIEROGLYPHICS
- THUMBED
- LUSTS
- VIOLENCES
- LOGARITHMS
- BETOKENS
- SEERS
- HARKED
- CONSTABLE
- REICHSSTAAT
- KULTURSTAAT
- INDIVIDUALIST
- PROMPTING
- CONQUEST'S
- POSTULATE
- ADVENTURING
- CHENG
- CYCLOPEAN
- SPOUTED
- BANKED
- POLLUTING
- DESPONDENT
- RASPING
- STOCKY
- OVERBEARING
- UNPROVOKED
- WHIZZING
- UPPERCUT
- DOMN
- MOPPING
- FLAPPIN
- LOOKY
- SORLEY
- NONPAREIL
- COALPIT
- MAGDALENE
- BOXCLOTH
- NOWT
- STANDIN
- RESPEC
- ASSISTANT'S
- BICEPS
- WILLIN
- SLAUGHTERER
- SHILLIN
- NIEF
- QUEERED
- QUEENSBERRY
- WRITIN
- BRADFORD
- SPARRING
- OGILVY
- MEDAL
- PROMOTERS
- EFFEMINACY
- LOR
- RUMMAGED
- THYSEL
- PUGILIST'S
- OWD
- LOITERERS
- MIDLANDER
- DUNN
- FERNIE
- WILLOX
- UNDISMAYED
- NORTON
- LEVI
- CONCEDING
- QUIRE
- BETTING
- UNTRIED
- UNDERRATE
- GUTTA
- PERCHA
- PUNCHED
- SLOGGER
- THOU'LT
- DEPRECATED
- FACER
- RUFFIANLY
- BLACKGUARDS
- PUND
- SCRATTED
- POTMAN
- CHEQUERS
- SPARRIN
- SPYIN
- DOAN'T
- BRAY
- BRAYED
- MAISTER'S
- FINISHER
- TURBLE
- DENOTES
- GRAEUBEN
- ANEROID
- THERMOMETERS
- SPADES
- HEADLAND
- BREAKFASTING
- GEYSER
- COMPUTATIONS
- OSCILLATIONS
- PERTINACIOUSLY
- DEFYING
- UNMEASURED
- SURTURBRAND
- REFITTED
- FEUDALISED
- CARAPACES
- RIDGED
- RESERVOIR
- SEDIMENTARY
- CONTORTED
- PREHISTORIC
- FOSSILS
- CUVIERS
- LEPTOTHERIA
- MERICOTHERIA
- LOPHIODIA
- ANOPLOTHERIA
- MEGATHERIA
- PROTOPITHECAE
- PTERODACTYLES
- BIBLIOMANIAC
- ALEXANDRIAN
- APOSTROPHE
- SAVANTS
- ABBEVILLE
- DEFENDANTS
- CUVIER
- MAXILLARIES
- GROTTOES
- GOLGOTHA
- BORDEAUX
- DESICCATED
- THOMASES
- PALAEONTOLOGY
- BARNUM
- KNEEPAN
- AJAX
- ORESTES
- SPARTANS
- ASTERIUS
- CUBITS
- TRAPANI
- POLYPHEMUS
- LUCERNE
- PLATER
- SCHEUCHZER'S
- ADAMITE
- CAMPET
- GIGANTOSTEOLOGIE
- MAMMOTH
- INCRUSTED
- SOLVENT
- OVOID
- CHEEKBONES
- PROGNATHISM
- JAPHETIC
- ECCENTRICITIES
- CLEFTS
- CATACOMB
- SCEPTICS
- COMMINGLED
- SLIDED
- PAPERED
- UNCHEERFUL
- UNPIN
- TARNISHED
- HASP
- UNFOUNDED
- PREPONDERATED
- FAGGOT
- AWFULNESS
- INSTANTS
- CELERITY
- UNSEARCHED
- LININGS
- EXEMPLIFICATION
- REKINDLING
- MAID'S
- UNDISCOVERED
- DOOR'S
- TEACHABLENESS
- LUCIDLY
- STAFFORDSHIRE
- DISENGAGED
- CONVERTS
- KNOLLS
- UNFIXED
- ENDEARS
- ENDEAR
- OVERDRAWN
- LASSITUDE
- REVERT
- TRANSFORMS
- REGRESSIVE
- FIRSTLY
- RETOLD
- FORMATIVELY
- SURROUNDS
- SELECTIVELY
- DISCARDS
- LICENTIOUS
- SUBSTANTIATED
- STIMULATOR
- DERIVATION
- PREDISPOSED
- SUPERSEDED
- GLOSSED
- ACCOMMODATES
- PROPINQUITY
- NURSERIES
- AROUSES
- HEMS
- APPROXIMATING
- RECEDES
- ISOLATES
- SACHS
- OVERESTIMATE
- OUTWORN
- PSYCHOANALYTIC
- PROSCRIBED
- INTERPRETATIONS
- DERIVATIONS
- CASTRATION
- INTIMIDATION
- SEXUALITY
- COMMENCES
- GENITALS
- DIFFERS
- HOMOSEXUALITY
- UNBRIDGABLE
- EXCREMENT
- ACCREDITS
- GENITAL
- POLYMORPHUS
- MISREPRESENTING
- ANSWERABLE
- FURTHERANCES
- REDISCOVER
- EVOLUTIONARY
- PSYCHICALLY
- INBREEDING
- DETERIORATE
- INCESTUOUS
- SLIGHTNESS
- REGRESSES
- DECEPTIVE
- UNDISTORTED
- UNINTERPRETED
- TRANSLATES
- REAWAKENS
- PREDOMINANCE
- ORIGINATES
- COMPLETES
- PROPOUND
- OUTGROWN
- CONVULSE
- FERMENTS
- CABAL
- PROVIDENT
- PREEXISTING
- CORRUPTING
- UNSAFE
- PRECARIOUSNESS
- CONSPIRING
- INTRUST
- UNQUALIFIED
- ADULATOR
- ARTIFICES
- COMPORTS
- DEPARTMENTS
- INDUED
- COEQUAL
- ASSIGNS
- DISBURSEMENT
- APPROPRIATIONS
- DEPUTIES
- NOMINATION
- ATTACHMENTS
- PERMANENCY
- EXCLUDING
- PECULATION
- EMOLUMENTS
- PROPENSITY
- ADAGE
- BAN
- FELLOWCITIZENS
- BANISHING
- ESSENTIALITY
- INTERDICTION
- NECESSITATE
- OPTION
- OBVIATE
- READMISSION
- COUNTERBALANCE
- RESENTMENTS
- DISABLING
- MATERIA
- PRIMA
- ANGERS
- COMICALITY
- PRECIOUSNESS
- INVALIDATING
- ANALYZED
- SUFFUSES
- INTERPENETRATES
- INTERRELATION
- IMPENETRABILITY
- RETICULATIONS
- TIGRESS
- INACTIVE
- ILLUSTRATES
- SUBJECTIVITY
- OBJECTIVITY
- CLASSIFICATIONS
- AMBIGUOUSLY
- CLASSING
- OBJECTORS
- CITING
- ADJECTIVES
- SANTAYANA
- OBJECTIFIED
- MASTERLY
- ESTHETIC
- RHETORICAL
- CONNOTING
- VERTIGO
- SIDIS
- GOODHART
- EQUIVOCALITY
- CONVENIENCES
- COEFFICIENTS
- DISPLACES
- ENGENDERING
- TRANSLOCATING
- GALILEO
- DESCARTES
- ATOMIC
- KANTIANS
- ILLUSORY
- TRANSLOCATION
- RATIONALISM
- MIND'
- ANTIPATHETIC
- PLOTTED
- AFFINITIES
- TENSIONS
- ANTHROPOMORPHIC
- DANGEROUSNESS
- VASCULAR
- DISCRETE
- EXTRACORPOREALLY
- SUBSERVE
- CONSECUTION
- FIXES
- INFALLIBLY
- SORTED
- WOOES
- PALMARY
- DETERMINATIONS
- SENSORIAL
- PERTURBATIONS
- INTROSPECTION
- HUTIBUDI
- CRUNCHING
- HEADMAN
- SAL
- MERAL
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_word_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
shibli/wav2vec2-large-xls-r-300m-pun-colab
|
shibli
| 2022-02-22T18:51:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pun-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pun-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert
|
espnet
| 2022-02-22T18:44:52Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- iemocap
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert`
This model was trained by Yushi Ueda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout dfa2868243a897c2a6c34b7407eaea5e4b5508a5
pip install -e .
cd egs2/iemocap/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sat Feb 12 23:11:32 EST 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `f6cde1c419c814a14ccd40abe557a780508cbcdf`
- Commit date: `Fri Feb 11 12:25:33 2022 -0500`
## Using Conformer based encoder, Transformer based decoder, and self-supervised learning features with spectral augmentation and predicting transcript along with sentiment
- ASR config: [conf/tuning/train_asr_conformer_hubert.yaml](conf/tuning/train_asr_conformer_hubert.yaml)
- token_type: word
- Sentiment Labels: Positive, Neutral, Negative
|dataset|Snt|Intent Classification Macro F1 (%)| Weighted F1 (%)| Micro F1 (%)|
|---|---|---|---|---|
|decode_asr_model_valid.acc.ave_10best/valid|754|66.5|76.4|75.7|
|decode_asr_model_valid.acc.ave_10best/test|1650|62.0|65.5|65.8|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_hubert.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_hubert_sentiment
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- i
- you
- Negative
- to
- it
- '''s'
- the
- '''t'
- that
- and
- Neutral
- Positive
- a
- know
- what
- of
- like
- we
- don
- just
- is
- do
- this
- '''m'
- me
- have
- can
- in
- for
- 'no'
- so
- not
- '''re'
- my
- but
- mean
- be
- going
- all
- was
- they
- well
- want
- yeah
- right
- get
- 'on'
- there
- he
- oh
- here
- go
- out
- with
- your
- if
- okay
- are
- she
- at
- '''ll'
- '''ve'
- got
- think
- about
- up
- see
- then
- why
- how
- time
- really
- one
- now
- or
- as
- back
- look
- her
- him
- been
- because
- 'yes'
- would
- didn
- little
- did
- good
- some
- them
- something
- need
- maybe
- never
- um
- come
- take
- god
- had
- could
- will
- uh
- am
- people
- thing
- when
- very
- let
- much
- sorry
- from
- again
- long
- give
- anything
- too
- make
- fish
- years
- where
- isn
- three
- said
- things
- nothing
- help
- work
- tell
- guess
- over
- 'off'
- business
- even
- sir
- any
- his
- around
- were
- way
- who
- new
- kind
- '''d'
- our
- everything
- more
- came
- an
- should
- down
- understand
- only
- great
- else
- man
- line
- us
- ask
- last
- doing
- say
- waiting
- other
- lot
- job
- feel
- yourself
- point
- thought
- day
- whole
- away
- coming
- better
- marry
- always
- these
- still
- wrong
- two
- sure
- care
- phone
- probably
- remember
- annie
- life
- year
- believe
- gonna
- supposed
- went
- first
- talk
- listen
- alright
- before
- thinking
- after
- stuff
- happy
- ever
- turn
- thank
- home
- fine
- into
- than
- call
- money
- stay
- actually
- every
- hope
- love
- huh
- married
- wait
- somewhere
- has
- being
- father
- larry
- hell
- wanted
- trying
- getting
- guys
- name
- saying
- bag
- hear
- girl
- hey
- flashlight
- beach
- put
- leave
- dollars
- mind
- augie
- does
- won
- fifty
- excited
- hate
- four
- done
- through
- their
- keep
- car
- lost
- doesn
- happen
- wouldn
- school
- big
- calm
- night
- '''cause'
- id
- another
- though
- myself
- nobody
- somebody
- best
- might
- same
- form
- mom
- nice
- matter
- spot
- stop
- told
- by
- shut
- enough
- five
- joe
- hard
- find
- course
- chris
- drunk
- snap
- luggage
- rather
- standing
- someone
- laugh
- took
- those
- please
- live
- six
- ridiculous
- minute
- looking
- bring
- show
- start
- brought
- days
- must
- pretty
- sort
- talking
- sand
- child
- working
- send
- next
- hundred
- whatever
- many
- moon
- moment
- champagne
- s
- problem
- end
- real
- dear
- happened
- person
- place
- fill
- awesome
- house
- such
- cool
- c
- haven
- knew
- die
- finally
- glasses
- stupid
- least
- dad
- supervisor
- totally
- each
- try
- waited
- idea
- u
- party
- asked
- anymore
- sick
- evening
- license
- kid
- wow
- flight
- felt
- pay
- since
- single
- miss
- without
- different
- mmhmm
- free
- sometimes
- yet
- couldn
- view
- hour
- knows
- drive
- themselves
- swim
- ah
- brandy
- fact
- ma
- '''am'
- already
- part
- sit
- thanks
- comes
- check
- everyone
- started
- kiss
- weren
- hotel
- own
- beast
- bad
- above
- run
- worst
- grunions
- darling
- seem
- baby
- turned
- gone
- shouldn
- exactly
- reason
- full
- both
- crazy
- pack
- bit
- swimming
- liquor
- seemed
- serious
- cause
- peter
- burden
- gosh
- forgot
- happens
- alone
- pass
- letters
- heard
- manager
- hours
- baggage
- card
- number
- argue
- seen
- walk
- forget
- kids
- family
- blanket
- honey
- open
- quite
- gotta
- forms
- mother
- old
- needs
- times
- airline
- which
- once
- service
- week
- together
- twenty
- stand
- made
- fun
- dead
- sake
- men
- kate
- today
- plane
- most
- carla
- driving
- deal
- information
- wanna
- definitely
- while
- yea
- certificate
- particular
- lots
- calling
- fortune
- write
- entire
- found
- trouble
- use
- forever
- woman
- enjoy
- room
- damn
- war
- meaning
- longer
- jacket
- ticket
- twice
- sent
- wonder
- small
- amanda
- cannot
- able
- half
- ha
- saw
- bus
- ago
- hmm
- hi
- kidding
- giving
- gave
- move
- women
- ahead
- york
- guy
- suppose
- company
- incredible
- either
- minutes
- tonight
- shoes
- utterly
- wasn
- filled
- gets
- amazing
- beautiful
- hello
- birth
- prove
- choice
- friend
- expect
- says
- blue
- anywhere
- died
- weird
- umm
- blood
- d
- face
- body
- alive
- diagram
- goes
- read
- far
- race
- wind
- fly
- interested
- california
- coast
- news
- past
- charles
- floor
- idiotic
- indeed
- absolutely
- softball
- answer
- somehow
- having
- campus
- completely
- file
- everybody
- given
- fair
- front
- telling
- tried
- sign
- helping
- dollar
- used
- takes
- hair
- behind
- head
- also
- question
- pull
- brother
- nonsense
- kill
- pocket
- cold
- mine
- watching
- shall
- divorce
- driver
- m
- makes
- cried
- security
- suitcase
- seems
- control
- set
- letter
- realized
- paper
- weeks
- address
- sweet
- lose
- huge
- death
- ones
- living
- glad
- bed
- until
- thinks
- wedding
- pieces
- parents
- ready
- almost
- forgive
- kissed
- silver
- during
- forty
- lives
- grow
- arrive
- eyes
- putting
- quiet
- poor
- presents
- sting
- tired
- row
- anyhow
- window
- v
- thousand
- watch
- ashamed
- figure
- vacation
- application
- left
- certainly
- calls
- months
- student
- close
- helpful
- called
- welcome
- major
- match
- morning
- fit
- reach
- door
- wife
- faith
- noticed
- several
- killed
- accident
- rat
- flop
- hands
- ear
- dancing
- hairs
- bugging
- dinner
- bills
- worked
- bored
- conversation
- tunis
- overbearing
- grand
- nine
- amusing
- vile
- tempered
- obviously
- tomorrow
- taken
- eight
- venice
- worth
- boy
- realize
- midnight
- evil
- sixteen
- gotten
- paying
- bottle
- smart
- cindy
- excuse
- along
- seven
- children
- figured
- jobs
- joke
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- suck
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- crush
- throughout
- test
- waters
- movies
- vermont
- cruiser
- abused
- frat
- boys
- dorms
- dell
- requests
- fixed
- dealt
- worries
- refunded
- situa
- relevant
- ordered
- orders
- others
- incorrectly
- tomatoes
- del
- cents
- attached
- cuz
- hoped
- opportunity
- rushing
- goods
- skipped
- breath
- kleenex
- alaska
- bearing
- hated
- holes
- calf
- witch
- whore
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned
|
oguzhanolm
| 2022-02-22T18:22:01Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: tr
tags:
- question-answering
- loodos-bert-base
- TQuAD
- tr
datasets:
- TQuAD
model-index:
- name: loodos-bert-base-uncased-QA-fine-tuned
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: TQuAD
type: question-answering
args: tr
metrics:
- name: Accuracy
type: acc
value: 0.91
---
# Turkish SQuAD Model : Question Answering
I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the "loodos/bert-base-turkish-uncased" model gave the best results for the Turkish language in classification in the "Auto-tagging of Short Conversational Sentences using Transformer Methods" research we conducted with my teammates, I used this model because I thought that the success rate could be high in the question-answering.
* Loodos-BERT-base-uncased: https://huggingface.co/loodos/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
```
!python3 Turkish-QA.py \
--model_type bert \
--model_name_or_path loodos/bert-base-turkish-uncased
--do_train \
--do_eval \
--train_file trainQ.json \
--predict_file dev1.json \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 6 \
--max_seq_length 384 \
--output_dir "./model"
```
# Example Usage
> Load Model
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned")
model = AutoModelForQuestionAnswering.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned")
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
```
> Apply the model
```
def ask(question,context):
temp = nlp(question=question, context=context)
start_idx = temp["start"]
end_idx = temp["end"]
return context[start_idx:end_idx]
istanbul="İstanbul, Türkiye'de Marmara Bölgesi'nde yer alan şehir ve Türkiye Cumhuriyeti Devletinin 81 ilinden biridir. Ülkenin nüfus bakımından en çok göç alan ve en kalabalık ilidir. Ekonomik, tarihî ve sosyo-kültürel açıdan önde gelen şehirlerden biridir. Şehir, iktisadi büyüklük açısından dünyada 34. sırada yer alır. Nüfuslarına göre şehirler listesinde belediye sınırları göz önüne alınarak yapılan sıralamaya göre Avrupa'da birinci, dünyada ise altıncı sırada yer almaktadır."
soru1 = "İstanbul büyüklük açısından kaçıncı sıradadır?"
print(ask(soru1,istanbul))
soru2 = "İstanbul nerede bulunur?"
print(ask(soru2,istanbul))
```
|
ronanki/ml_mpnet_768_MNR_10
|
ronanki
| 2022-02-22T18:14:36Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ronanki/ml_mpnet_768_MNR_10
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/ml_mpnet_768_MNR_10')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ronanki/ml_mpnet_768_MNR_10')
model = AutoModel.from_pretrained('ronanki/ml_mpnet_768_MNR_10')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_mpnet_768_MNR_10)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
ronanki/ml_use_512_MNR_10
|
ronanki
| 2022-02-22T18:12:25Z | 125 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ronanki/ml_use_512_MNR_10
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/ml_use_512_MNR_10')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_use_512_MNR_10)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
NeonBohdan/stt-polyglot-pl
|
NeonBohdan
| 2022-02-22T17:27:31Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
keras-io/convmixer
|
keras-io
| 2022-02-22T16:42:59Z | 4 | 0 |
tf-keras
|
[
"tf-keras",
"ConvMixer",
"keras-io",
"en",
"dataset:cifar10",
"arxiv:2201.09792",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- ConvMixer
- keras-io
license: apache-2.0
datasets:
- cifar10
---
# ConvMixer model
The ConvMixer model is trained on Cifar10 dataset and is based on [the paper](https://arxiv.org/abs/2201.09792v1), [github](https://github.com/locuslab/convmixer).
Disclaimer : This is a demo model for Sayak Paul's keras [example](https://keras.io/examples/vision/convmixer/). Please refrain from using this model for any other purpose.
## Description
The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like [ViT](https://arxiv.org/abs/2010.11929v2). One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost.
### Intended Use
This model is intended to be used as a demo model for keras-io.
|
sanchit-gandhi/wav2vec2-2-bart-large-frozen-enc
|
sanchit-gandhi
| 2022-02-22T15:43:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3123
- Wer: 0.0908
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4937 | 0.28 | 500 | 5.2312 | 0.9660 |
| 3.821 | 0.56 | 1000 | 4.5810 | 0.9066 |
| 1.2129 | 0.84 | 1500 | 1.3723 | 0.3928 |
| 0.6575 | 1.12 | 2000 | 0.6645 | 0.1810 |
| 0.489 | 1.4 | 2500 | 0.5523 | 0.1479 |
| 0.3541 | 1.68 | 3000 | 0.4585 | 0.1195 |
| 0.3573 | 1.96 | 3500 | 0.3859 | 0.1066 |
| 0.2437 | 2.24 | 4000 | 0.3747 | 0.1015 |
| 0.1406 | 2.52 | 4500 | 0.3346 | 0.0952 |
| 0.1468 | 2.8 | 5000 | 0.3123 | 0.0908 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
spasis/bert-finetuned-ner
|
spasis
| 2022-02-22T13:23:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9214944042132982
- name: Recall
type: recall
value: 0.9422753281723325
- name: F1
type: f1
value: 0.9317690131469462
- name: Accuracy
type: accuracy
value: 0.9849738034967916
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0569
- Precision: 0.9215
- Recall: 0.9423
- F1: 0.9318
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 439 | 0.0702 | 0.8847 | 0.9170 | 0.9006 | 0.9795 |
| 0.183 | 2.0 | 878 | 0.0599 | 0.9161 | 0.9391 | 0.9274 | 0.9842 |
| 0.0484 | 3.0 | 1317 | 0.0569 | 0.9215 | 0.9423 | 0.9318 | 0.9850 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
|
mbeukman
| 2022-02-22T11:42:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- am
tags:
- NER
- token-classification
datasets:
- masakhaner
metrics:
- f1
- precision
- recall
widget:
- text: "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
---
# xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amharic part.
More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
## About
This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Contact & More information
For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
### Training Resources
In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1.
## Data
The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
The motivation for the use of this data is that it is the "first large, publicly available, high quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
## Intended Use
This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
## Limitations
This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer.
Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data).
As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often.
Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to.
### Privacy & Ethical Considerations
The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
No explicit ethical considerations or adjustments were made during fine-tuning of this model.
## Metrics
The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
## Caveats and Recommendations
In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data.
## Model Structure
Here are some performance details on this specific model, compared to others we trained.
All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
| Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) |
| -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- |
| [xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic) (This model) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | amh | 70.34 | 69.72 | 70.97 | 72.00 | 75.00 | 51.00 | 73.00 |
| [xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic) | [amh](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) | amh | 79.55 | 76.71 | 82.62 | 70.00 | 84.00 | 62.00 | 91.00 |
| [xlm-roberta-base-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-amharic) | [base](https://huggingface.co/xlm-roberta-base) | amh | 72.63 | 70.49 | 74.91 | 76.00 | 75.00 | 52.00 | 78.00 |
## Usage
To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = 'mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
ner_results = nlp(example)
print(ner_results)
```
|
mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
|
mbeukman
| 2022-02-22T11:30:02Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- am
tags:
- NER
- token-classification
datasets:
- masakhaner
metrics:
- f1
- precision
- recall
widget:
- text: "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
---
# xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-amharic](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amharic part.
More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
## About
This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Contact & More information
For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
### Training Resources
In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1.
## Data
The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
The motivation for the use of this data is that it is the "first large, publicly available, high quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
## Intended Use
This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
## Limitations
This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer.
Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data).
As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often.
Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to.
### Privacy & Ethical Considerations
The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
No explicit ethical considerations or adjustments were made during fine-tuning of this model.
## Metrics
The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
## Caveats and Recommendations
In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data.
## Model Structure
Here are some performance details on this specific model, compared to others we trained.
All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
| Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) |
| -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- |
| [xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic) (This model) | [amh](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) | amh | 79.55 | 76.71 | 82.62 | 70.00 | 84.00 | 62.00 | 91.00 |
| [xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | amh | 70.34 | 69.72 | 70.97 | 72.00 | 75.00 | 51.00 | 73.00 |
| [xlm-roberta-base-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-amharic) | [base](https://huggingface.co/xlm-roberta-base) | amh | 72.63 | 70.49 | 74.91 | 76.00 | 75.00 | 52.00 | 78.00 |
## Usage
To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = 'mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
ner_results = nlp(example)
print(ner_results)
```
|
cammy/distilbart-cnn-12-6-finetuned-weaksup-1000
|
cammy
| 2022-02-22T08:49:00Z | 40 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-weaksup-1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-12-6-finetuned-weaksup-1000
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6818
- Rouge1: 25.9199
- Rouge2: 11.2697
- Rougel: 20.3598
- Rougelsum: 22.8242
- Gen Len: 66.44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.644 | 1.0 | 1000 | 1.6818 | 25.9199 | 11.2697 | 20.3598 | 22.8242 | 66.44 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k
|
vocab-transformers
| 2022-02-22T08:25:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 230k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embedding matrix was frozen.
|
z3c1f4/distilbert-base-uncased-finetuned-cola
|
z3c1f4
| 2022-02-22T07:48:31Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5320879841803337
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7400
- Matthews Correlation: 0.5321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5298 | 1.0 | 535 | 0.5168 | 0.4092 |
| 0.349 | 2.0 | 1070 | 0.4993 | 0.5099 |
| 0.2345 | 3.0 | 1605 | 0.6194 | 0.5046 |
| 0.1731 | 4.0 | 2140 | 0.7400 | 0.5321 |
| 0.1282 | 5.0 | 2675 | 0.8724 | 0.5078 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Santiagot1105/wav2vec2-lar-xlsr-finetune-es-col
|
Santiagot1105
| 2022-02-22T06:32:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-lar-xlsr-finetune-es-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-lar-xlsr-finetune-es-col
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1669
- Wer: 0.2595
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1108 | 8.51 | 400 | 0.5936 | 0.6085 |
| 0.3015 | 17.02 | 800 | 0.2071 | 0.2941 |
| 0.0989 | 25.53 | 1200 | 0.1669 | 0.2595 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Fan-s/reddit-tc-bert
|
Fan-s
| 2022-02-22T05:25:39Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-uncased-base
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-uncased-base
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset.
This model can be used for Text Classification: Given two sentences, see if they are related.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Accuracy: 0.9267
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 320
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.11.0
## Usage (HuggingFace Transformers)
You can use the model like this:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# label_list
label_list = ['matched', 'unmatched']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert")
# Set the input
post = "don't make gravy with asbestos."
response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."
# Predict whether the two sentences are matched
def predict(post, response, max_seq_length=128):
with torch.no_grad():
args = (post, response)
input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt")
output = model(**input)
logits = output.logits
item = torch.argmax(logits, dim=1)
predict_label = label_list[item]
return predict_label, logits
predict_label, logits = predict(post, response)
# Matched
print("predict_label:", predict_label)
```
|
yancong/distilbert-base-uncased-finetuned-quantifier
|
yancong
| 2022-02-22T02:57:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-quantifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-quantifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7478
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2007 | 1.0 | 94 | 2.3496 |
| 2.2332 | 2.0 | 188 | 1.8656 |
| 2.0141 | 3.0 | 282 | 1.8479 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000
|
ASCCCCCCCC
| 2022-02-22T02:51:29Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-chinese-finetuned-amazon_zh_20000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1683
- Accuracy: 0.5224
- F1: 0.5194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.2051 | 1.0 | 2500 | 1.1717 | 0.506 | 0.4847 |
| 1.0035 | 2.0 | 5000 | 1.1683 | 0.5224 | 0.5194 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T23:04:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 12.573320719016083, 'f1': 22.855895753681814}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T22:04:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 4.541154210028382, 'f1': 10.04181288563879}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Anthos23/distilbert-base-uncased-finetuned-sst2
|
Anthos23
| 2022-02-21T21:50:56Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Anthos23/distilbert-base-uncased-finetuned-sst2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Anthos23/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0662
- Validation Loss: 0.2623
- Train Accuracy: 0.9083
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21045, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2101 | 0.2373 | 0.9083 | 0 |
| 0.1065 | 0.2645 | 0.9060 | 1 |
| 0.0662 | 0.2623 | 0.9083 | 2 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.5.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ajrae/bert-base-uncased-finetuned-cola
|
ajrae
| 2022-02-21T21:40:59Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5864941797290588
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8385
- Matthews Correlation: 0.5865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4887 | 1.0 | 535 | 0.5016 | 0.5107 |
| 0.286 | 2.0 | 1070 | 0.5473 | 0.5399 |
| 0.1864 | 3.0 | 1605 | 0.7114 | 0.5706 |
| 0.1163 | 4.0 | 2140 | 0.8385 | 0.5865 |
| 0.0834 | 5.0 | 2675 | 0.9610 | 0.5786 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T21:29:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 39.04446546830653, 'f1': 49.90230650794353}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Santiagot1105/wav2vec2-large-xlsr-finetune-es-col
|
Santiagot1105
| 2022-02-21T21:19:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-finetune-es-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-finetune-es-col
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6514
- Wer: 0.9874
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9709 | 3.25 | 400 | 2.9673 | 1.0 |
| 2.9488 | 6.5 | 800 | 2.9075 | 0.9973 |
| 2.907 | 9.76 | 1200 | 2.8772 | 0.9688 |
| 2.886 | 13.01 | 1600 | 2.8245 | 0.9484 |
| 2.8043 | 16.26 | 2000 | 2.7134 | 0.9874 |
| 2.7288 | 19.51 | 2400 | 2.6750 | 0.9874 |
| 2.7072 | 22.76 | 2800 | 2.6651 | 0.9874 |
| 2.6892 | 26.02 | 3200 | 2.6573 | 0.9874 |
| 2.683 | 29.27 | 3600 | 2.6514 | 0.9874 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-16-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T20:54:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-16-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 8.618732261116367, 'f1': 14.074017518582023}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-1024-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T20:36:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-1024-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
{'exact_match': 66.90633869441817, 'f1': 77.54482247690522}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k_emb_updated
|
vocab-transformers
| 2022-02-21T20:13:11Z | 95 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dense_encoder-msmarco-distilbert-word2vec256k
**Note: Token embeddings where updated!**
This model is based on [msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository.
Performance:
- MS MARCO dev: 34.51 (MRR@10)
- TREC-DL 2019: 66.12 (nDCG@10)
- TREC-DL 2020: 68.62 (nDCG@10)
## Usage (Sentence-Transformers)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7858 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
|
vocab-transformers
| 2022-02-21T20:12:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 785k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
**Note: Token embeddings where updated!**
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k_emb_updated
|
vocab-transformers
| 2022-02-21T20:12:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 445k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
**Note: Token embeddings where updated!**
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k_emb_updated
|
vocab-transformers
| 2022-02-21T20:12:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 210k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
**Note: Token embeddings where updated!**
|
anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T18:55:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 12.93282876064333, 'f1': 21.98821604201723}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
joe5campbell/ROBERTA_Tweet_Sentiment_50k_2eps
|
joe5campbell
| 2022-02-21T18:43:54Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: ROBERTA_Tweet_Sentiment_50k_2eps
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ROBERTA_Tweet_Sentiment_50k_2eps
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3553
- Train Accuracy: 0.8504
- Validation Loss: 0.5272
- Validation Accuracy: 0.7652
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5213 | 0.7298 | 0.4817 | 0.7715 | 0 |
| 0.3553 | 0.8504 | 0.5272 | 0.7652 | 1 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Tokenizers 0.11.0
|
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T18:31:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 3.207190160832545, 'f1': 6.680463956037787}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
kSaluja/autonlp-tele_red_data_model-585716433
|
kSaluja
| 2022-02-21T12:46:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"en",
"dataset:kSaluja/autonlp-data-tele_red_data_model",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- kSaluja/autonlp-data-tele_red_data_model
co2_eq_emissions: 2.379476355147211
---
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 585716433
- CO2 Emissions (in grams): 2.379476355147211
## Validation Metrics
- Loss: 0.15210922062397003
- Accuracy: 0.9724770642201835
- Precision: 0.950836820083682
- Recall: 0.9625838333921638
- F1: 0.9566742676723382
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/kSaluja/autonlp-tele_red_data_model-585716433
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
codeceejay/HIYACCENT_Wav2Vec2
|
codeceejay
| 2022-02-21T12:39:51Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T
Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: https://github.com/amceejay/HIYACCENT-NE-Speech-Recognition-System
##Usage: The model can be used directly (without a language model) as follows...
#Using the ASRecognition library:
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
##Writing your own inference speech:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2"
SAMPLES = 10
#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: https://openslr.org/70/
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
|
Kayvane/distilbert-undersampled-noweights
|
Kayvane
| 2022-02-21T11:54:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: distilbert-undersampled-noweights
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-undersampled-noweights
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/bert_bert_summarization_cnn_dailymail
|
Ayham
| 2022-02-21T08:57:52Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bert_bert_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Muennighoff/SBERT-base-nli-v2
|
Muennighoff
| 2022-02-21T06:21:24Z | 8 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2202.08904",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# SBERT-base-nli-v2
This model is used in "SGPT: GPT Sentence Embeddings for Semantic Search" and "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning".
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-mean-nli
|
Muennighoff
| 2022-02-21T06:20:14Z | 2 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# SGPT-125M-mean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-scratchmean-nli
|
Muennighoff
| 2022-02-21T06:18:07Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-scratchmean-nli
** Trained from scratch only on NLI with reinitialized GPT-Neo weights **
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-mean-nli-linear5
|
Muennighoff
| 2022-02-21T06:11:37Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-mean-nli-linear5
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
(3): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
(4): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
(5): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
(6): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-mean-nli-linearthenpool5
|
Muennighoff
| 2022-02-21T06:10:51Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-mean-nli-linearthenpool5
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU', 'key_name': 'token_embeddings'})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU', 'key_name': 'token_embeddings'})
(3): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU', 'key_name': 'token_embeddings'})
(4): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU', 'key_name': 'token_embeddings'})
(5): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU', 'key_name': 'token_embeddings'})
(6): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SBERT-base-nli-v2-bitfit
|
Muennighoff
| 2022-02-21T06:10:03Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2202.08904",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# SBERT-base-nli-v2-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact
|
Muennighoff
| 2022-02-21T06:09:13Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Dense({'in_features': 768, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'key_name': 'token_embeddings'})
(2): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-mean-nli-bitfit
|
Muennighoff
| 2022-02-21T06:08:15Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# SGPT-125M-mean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-learntmean-nli
|
Muennighoff
| 2022-02-21T06:07:22Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-learntmean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): WeightedMeanPooling()
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-weightedmean-msmarco
|
Muennighoff
| 2022-02-21T06:05:32Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-msmarco
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-weightedmean-msmarco-specb
|
Muennighoff
| 2022-02-21T06:04:34Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-msmarco-specb
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-weightedmean-msmarco-asym
|
Muennighoff
| 2022-02-21T06:03:46Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-msmarco-asym
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Asym(
(QRY-0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(DOCPOS-0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(DOCNEG-0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
)
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-125M-lasttoken-msmarco-specb
|
Muennighoff
| 2022-02-21T06:03:03Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-lasttoken-msmarco-specb
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SBERT-base-msmarco-bitfit
|
Muennighoff
| 2022-02-21T05:58:04Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2202.08904",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# SBERT-base-msmarco-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
swcrazyfan/KingJamesify-T5-base-lm-adapt
|
swcrazyfan
| 2022-02-21T04:33:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
202015004/wav2vec2-base-timit-demo-colab
|
202015004
| 2022-02-21T03:49:39Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6259
- Wer: 0.3544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.6744 | 0.5 | 500 | 2.9473 | 1.0 |
| 1.4535 | 1.01 | 1000 | 0.7774 | 0.6254 |
| 0.7376 | 1.51 | 1500 | 0.6923 | 0.5712 |
| 0.5848 | 2.01 | 2000 | 0.5445 | 0.5023 |
| 0.4492 | 2.51 | 2500 | 0.5148 | 0.4958 |
| 0.4006 | 3.02 | 3000 | 0.5283 | 0.4781 |
| 0.3319 | 3.52 | 3500 | 0.5196 | 0.4628 |
| 0.3424 | 4.02 | 4000 | 0.5285 | 0.4551 |
| 0.2772 | 4.52 | 4500 | 0.5060 | 0.4532 |
| 0.2724 | 5.03 | 5000 | 0.5216 | 0.4422 |
| 0.2375 | 5.53 | 5500 | 0.5376 | 0.4443 |
| 0.2279 | 6.03 | 6000 | 0.6051 | 0.4308 |
| 0.2091 | 6.53 | 6500 | 0.5084 | 0.4423 |
| 0.2029 | 7.04 | 7000 | 0.5083 | 0.4242 |
| 0.1784 | 7.54 | 7500 | 0.6123 | 0.4297 |
| 0.1774 | 8.04 | 8000 | 0.5749 | 0.4339 |
| 0.1542 | 8.54 | 8500 | 0.5110 | 0.4033 |
| 0.1638 | 9.05 | 9000 | 0.6324 | 0.4318 |
| 0.1493 | 9.55 | 9500 | 0.6100 | 0.4152 |
| 0.1591 | 10.05 | 10000 | 0.5508 | 0.4022 |
| 0.1304 | 10.55 | 10500 | 0.5090 | 0.4054 |
| 0.1234 | 11.06 | 11000 | 0.6282 | 0.4093 |
| 0.1218 | 11.56 | 11500 | 0.5817 | 0.3941 |
| 0.121 | 12.06 | 12000 | 0.5741 | 0.3999 |
| 0.1073 | 12.56 | 12500 | 0.5818 | 0.4149 |
| 0.104 | 13.07 | 13000 | 0.6492 | 0.3953 |
| 0.0934 | 13.57 | 13500 | 0.5393 | 0.4083 |
| 0.0961 | 14.07 | 14000 | 0.5510 | 0.3919 |
| 0.0965 | 14.57 | 14500 | 0.5896 | 0.3992 |
| 0.0921 | 15.08 | 15000 | 0.5554 | 0.3947 |
| 0.0751 | 15.58 | 15500 | 0.6312 | 0.3934 |
| 0.0805 | 16.08 | 16000 | 0.6732 | 0.3948 |
| 0.0742 | 16.58 | 16500 | 0.5990 | 0.3884 |
| 0.0708 | 17.09 | 17000 | 0.6186 | 0.3869 |
| 0.0679 | 17.59 | 17500 | 0.5837 | 0.3848 |
| 0.072 | 18.09 | 18000 | 0.5831 | 0.3775 |
| 0.0597 | 18.59 | 18500 | 0.6562 | 0.3843 |
| 0.0612 | 19.1 | 19000 | 0.6298 | 0.3756 |
| 0.0514 | 19.6 | 19500 | 0.6746 | 0.3720 |
| 0.061 | 20.1 | 20000 | 0.6236 | 0.3788 |
| 0.054 | 20.6 | 20500 | 0.6012 | 0.3718 |
| 0.0521 | 21.11 | 21000 | 0.6053 | 0.3778 |
| 0.0494 | 21.61 | 21500 | 0.6154 | 0.3772 |
| 0.0468 | 22.11 | 22000 | 0.6052 | 0.3747 |
| 0.0413 | 22.61 | 22500 | 0.5877 | 0.3716 |
| 0.0424 | 23.12 | 23000 | 0.5786 | 0.3658 |
| 0.0403 | 23.62 | 23500 | 0.5828 | 0.3658 |
| 0.0391 | 24.12 | 24000 | 0.5913 | 0.3685 |
| 0.0312 | 24.62 | 24500 | 0.5850 | 0.3625 |
| 0.0316 | 25.13 | 25000 | 0.6029 | 0.3611 |
| 0.0282 | 25.63 | 25500 | 0.6312 | 0.3624 |
| 0.0328 | 26.13 | 26000 | 0.6312 | 0.3621 |
| 0.0258 | 26.63 | 26500 | 0.5891 | 0.3581 |
| 0.0256 | 27.14 | 27000 | 0.6259 | 0.3546 |
| 0.0255 | 27.64 | 27500 | 0.6315 | 0.3587 |
| 0.0249 | 28.14 | 28000 | 0.6547 | 0.3579 |
| 0.025 | 28.64 | 28500 | 0.6237 | 0.3565 |
| 0.0228 | 29.15 | 29000 | 0.6187 | 0.3559 |
| 0.0209 | 29.65 | 29500 | 0.6259 | 0.3544 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
SteveC/sdc_bot_15K
|
SteveC
| 2022-02-21T02:04:26Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
It's just a dialog bot trained on my Tweets. Unfortunately as tweets aren\'t very conversational it comes off pretty random.
|
hugsao123/XLM-R-fine-tuned-for-ner
|
hugsao123
| 2022-02-21T01:09:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: XLM-R-fine-tuned-for-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.8377982238973259
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-R-fine-tuned-for-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- F1: 0.8378
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4202 | 1.0 | 2500 | 0.3449 | 0.7963 |
| 0.2887 | 2.0 | 5000 | 0.2756 | 0.8057 |
| 0.2309 | 3.0 | 7500 | 0.2971 | 0.8040 |
| 0.1832 | 4.0 | 10000 | 0.3319 | 0.8167 |
| 0.1461 | 5.0 | 12500 | 0.3958 | 0.8350 |
| 0.114 | 6.0 | 15000 | 0.4087 | 0.8316 |
| 0.0833 | 7.0 | 17500 | 0.4320 | 0.8361 |
| 0.0614 | 8.0 | 20000 | 0.4885 | 0.8353 |
| 0.039 | 9.0 | 22500 | 0.5408 | 0.8390 |
| 0.0251 | 10.0 | 25000 | 0.5679 | 0.8378 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2
|
BigSalmon
| 2022-02-21T00:14:01Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
|
Kayvane/distilbert-undersampled
|
Kayvane
| 2022-02-20T22:37:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-undersampled
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-undersampled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0826
- Accuracy: 0.9811
- F1: 0.9810
- Recall: 0.9811
- Precision: 0.9812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0959 | 0.2 | 2000 | 0.0999 | 0.9651 | 0.9628 | 0.9651 | 0.9655 |
| 0.0618 | 0.41 | 4000 | 0.0886 | 0.9717 | 0.9717 | 0.9717 | 0.9731 |
| 0.159 | 0.61 | 6000 | 0.0884 | 0.9719 | 0.9720 | 0.9719 | 0.9728 |
| 0.0513 | 0.81 | 8000 | 0.0785 | 0.9782 | 0.9782 | 0.9782 | 0.9788 |
| 0.0219 | 1.01 | 10000 | 0.0680 | 0.9779 | 0.9779 | 0.9779 | 0.9783 |
| 0.036 | 1.22 | 12000 | 0.0745 | 0.9787 | 0.9787 | 0.9787 | 0.9792 |
| 0.0892 | 1.42 | 14000 | 0.0675 | 0.9786 | 0.9786 | 0.9786 | 0.9789 |
| 0.0214 | 1.62 | 16000 | 0.0760 | 0.9799 | 0.9798 | 0.9799 | 0.9801 |
| 0.0882 | 1.83 | 18000 | 0.0800 | 0.9800 | 0.9800 | 0.9800 | 0.9802 |
| 0.0234 | 2.03 | 20000 | 0.0720 | 0.9813 | 0.9813 | 0.9813 | 0.9815 |
| 0.0132 | 2.23 | 22000 | 0.0738 | 0.9803 | 0.9803 | 0.9803 | 0.9805 |
| 0.0136 | 2.43 | 24000 | 0.0847 | 0.9804 | 0.9804 | 0.9804 | 0.9806 |
| 0.0119 | 2.64 | 26000 | 0.0826 | 0.9811 | 0.9810 | 0.9811 | 0.9812 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Worldman/distilbert-base-uncased-finetuned-emotion
|
Worldman
| 2022-02-20T21:29:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.9227046184638882
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2162
- Accuracy: 0.9225
- F1: 0.9227
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8437 | 1.0 | 250 | 0.3153 | 0.903 | 0.9005 |
| 0.2467 | 2.0 | 500 | 0.2162 | 0.9225 | 0.9227 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cpu
- Datasets 1.18.3
- Tokenizers 0.11.0
|
hugsao123/xlm-roberta-base-finetuned-panx-de
|
hugsao123
| 2022-02-20T15:03:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7589617892939993
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3903
- F1: 0.7590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0489 | 1.0 | 50 | 0.5561 | 0.6565 |
| 0.4953 | 2.0 | 100 | 0.4385 | 0.7189 |
| 0.35 | 3.0 | 150 | 0.3903 | 0.7590 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.8.2+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
joe5campbell/BERT_Tweet_Sentiment_100k_2eps
|
joe5campbell
| 2022-02-20T14:45:01Z | 8 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: BERT_Tweet_Sentiment_100k_2eps
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_100k_2eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1259
- Train Accuracy: 0.9542
- Validation Loss: 0.6133
- Validation Accuracy: 0.8315
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3330 | 0.8562 | 0.3847 | 0.8415 | 0 |
| 0.1259 | 0.9542 | 0.6133 | 0.8315 | 1 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Tokenizers 0.11.0
|
jordimas/gec-opennmt-english
|
jordimas
| 2022-02-20T13:36:43Z | 0 | 2 |
opennmt
|
[
"opennmt",
"gec",
"en",
"arxiv:2106.03830",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- gec
library_name: opennmt
license: mit
metrics:
- bleu
inference: false
---
### Introduction
This repository contains a description on how to use OpenNMT on the Grammar Error Correction (GEC) task. The idea is to approch GEC as a translation task
### Usage
Install the necessary dependencies:
```bash
pip3 install ctranslate2 pyonmttok
```
Simple tokenization & translation using Python:
```python
import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="jordimas/gec-opennmt-english", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/sp_m.model")
tokenized=tokenizer.tokenize("The water are hot. My friends are going to be late. Today mine mother is in Barcelona.")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]['tokens']))
```
# Model
The model has been training using the [clang8](https://github.com/google-research-datasets/clang8) corpus for English language.
Details:
* Model: TransformerBase
* Tokenizer: SentencePiece
* BLEU = 85.50
# Papers
Relevant papers:
* [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://aclanthology.org/N18-1055.pdf)
* [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf)
# Contact
Email address: Jordi Mas: [email protected]
|
nimrah/wav2vec2-large-xls-r-300m-my_hindi_presentation-colab
|
nimrah
| 2022-02-20T11:04:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-my_hindi_presentation-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-my_hindi_presentation-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ckenlam/nlu_sherlock_model_20220220
|
ckenlam
| 2022-02-20T09:02:06Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nlu_sherlock_model_20220220
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nlu_sherlock_model_20220220
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -955, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ckenlam/nlu_sherlock_model
|
ckenlam
| 2022-02-19T22:40:42Z | 7 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nlu_sherlock_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nlu_sherlock_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -947, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
swcrazyfan/KingJamesify-T5-large-lm-adapt
|
swcrazyfan
| 2022-02-19T20:43:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
keepitreal/vietnamese-sbert
|
keepitreal
| 2022-02-19T08:01:34Z | 9,271 | 47 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"vietnamese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- vietnamese
---
# {vietnamese-sbert}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search on Vietnamese language.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Cô giáo đang ăn kem", "Chị gái đang thử món thịt dê"]
model = SentenceTransformer('keepitreal/vietnamese-sbert')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['Cô giáo đang ăn kem', 'Chị gái đang thử món thịt dê']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained(''keepitreal/vietnamese-sbert')
model = AutoModel.from_pretrained('keepitreal/vietnamese-sbert')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
shahukareem/wav2vec2-xls-r-1b-dv-with-lm
|
shahukareem
| 2022-02-19T04:02:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
# wav2vec2-xls-r-1b-dv-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
|
phongdtd/wavLM-VLSP-vi
|
phongdtd
| 2022-02-19T00:36:24Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- phongdtd/VinDataVLSP
- generated_from_trainer
model-index:
- name: wavLM-VLSP-vi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavLM-VLSP-vi
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 45.8892
- Wer: 0.9999
- Cer: 0.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 3.4482 | 9.41 | 40000 | 3.4480 | 0.9999 | 0.9974 |
| 3.4619 | 18.81 | 80000 | 3.4514 | 0.9999 | 0.9974 |
| 3.7961 | 28.22 | 120000 | 3.8732 | 0.9999 | 0.9974 |
| 24.3843 | 37.62 | 160000 | 22.5457 | 0.9999 | 0.9973 |
| 48.5691 | 47.03 | 200000 | 45.8892 | 0.9999 | 0.9973 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
mofawzy/Bert-hard-balanced
|
mofawzy
| 2022-02-18T23:29:24Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"HARD",
"ar",
"dataset:HARD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ar
datasets:
- HARD
tags:
- HARD
widget:
- text: "جيد. المكان جميل وهاديء. كل شي جيد ونظيف"
- text: "استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق"
---
# BERT-ASTD Balanced
Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from booking.com (HARD) dataset balanced version to identify sentiments opinion in Arabic language.
## Data
The model were fine-tuned on ~93000 book reviews in arabic using bert large arabic
Dataset:
- Train 70%
- Validation: 10%
- Test: 20%
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0 | 0.9733 | 0.9547 | 0.9639 | 10570 |
| 1 | 0.9555 | 0.9738 | 0.9646 | 10570 |
| Accuracy | | | 0.9642 | 21140 |
## How to use
You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name="mofawzy/Bert-hard-balanced"
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
|
pyf98/librispeech_100h_transformer
|
pyf98
| 2022-02-18T21:43:49Z | 1 | 1 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/librispeech_100h_transformer`
This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout f6779876103be2116de158a44757f8979eff0ab0
pip install -e .
cd egs2/librispeech_100/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_100h_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri Feb 18 16:00:45 EST 2022`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `f6779876103be2116de158a44757f8979eff0ab0`
- Commit date: `Fri Feb 18 15:57:13 2022 -0500`
## asr_transformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam20_ctc0.3/dev_clean|2703|54402|93.0|6.4|0.5|1.1|8.1|63.1|
|beam20_ctc0.3/dev_other|2864|50948|82.5|15.9|1.6|2.7|20.2|83.8|
|beam20_ctc0.3/test_clean|2620|52576|92.8|6.5|0.7|1.2|8.4|63.3|
|beam20_ctc0.3/test_other|2939|52343|82.1|16.0|1.9|2.6|20.5|84.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam20_ctc0.3/dev_clean|2703|288456|97.5|1.4|1.1|0.9|3.4|63.1|
|beam20_ctc0.3/dev_other|2864|265951|92.1|4.8|3.1|2.4|10.3|83.8|
|beam20_ctc0.3/test_clean|2620|281530|97.4|1.4|1.2|0.9|3.5|63.3|
|beam20_ctc0.3/test_other|2939|272758|92.0|4.7|3.2|2.3|10.2|84.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam20_ctc0.3/dev_clean|2703|69558|89.9|6.1|4.0|0.8|10.9|63.1|
|beam20_ctc0.3/dev_other|2864|64524|78.5|15.3|6.2|2.8|24.3|83.8|
|beam20_ctc0.3/test_clean|2620|66983|90.0|6.2|3.9|0.8|10.9|63.3|
|beam20_ctc0.3/test_other|2939|66650|77.9|15.2|6.9|2.5|24.6|84.8|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr_transformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_transformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 70
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 400
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_clean_100_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_clean_100_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
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- ▁A
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- ▁I
- ▁HE
- ▁WAS
- ▁THAT
- ING
- ▁IT
- ''''
- ▁HIS
- ▁HAD
- ▁WITH
- ▁YOU
- ▁FOR
- T
- ▁AS
- ▁HER
- LY
- ▁NOT
- ▁BUT
- ▁SHE
- ▁BE
- D
- E
- ▁IS
- ▁AT
- ▁ON
- ▁HIM
- ▁THEY
- ▁BY
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- Y
- ▁MY
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- ▁ALL
- ▁THIS
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- ▁FROM
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- ▁SAID
- ▁WE
- N
- ER
- ▁NO
- ▁THERE
- ▁WHEN
- ▁AN
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- ▁WOULD
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- ▁THEM
- R
- ▁IF
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- ▁VERY
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- ▁YOUR
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- ▁DID
- ▁THAN
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- ▁HAS
- L
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- IN
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- ▁CAN
- ▁WELL
- ▁OTHER
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- ▁TWO
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- EN
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- ▁AFTER
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- ▁DOWN
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- ▁OLD
- ▁DAY
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- ▁KNOW
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- ▁GOOD
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- ▁THESE
- ▁CAME
- ▁UN
- ▁SHOULD
- ▁HOW
- ▁MISTER
- ▁GO
- ▁MUCH
- ▁WHERE
- ▁MUST
- ▁NEVER
- ▁COME
- ▁BACK
- ION
- 'ON'
- ▁LONG
- F
- ▁AGAIN
- ▁FIRST
- LE
- ▁MEN
- ▁EVEN
- NESS
- ▁MIGHT
- ▁OWN
- ▁MAY
- K
- ▁HIMSELF
- ▁SAY
- ▁JUST
- ▁THROUGH
- ▁RE
- ▁AM
- ▁ITS
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- ▁THOUGHT
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- ▁DE
- ▁MAKE
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- ▁THINK
- ▁HOUSE
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- H
- ATION
- ▁LIFE
- IT
- ▁EYES
- ▁MOST
- ▁WITHOUT
- ▁TOO
- ▁THOSE
- ABLE
- ▁EVERY
- ▁DON
- ▁MANY
- ▁AWAY
- ITY
- VE
- W
- ▁STILL
- ▁BEING
- ▁C
- ▁LAST
- ▁NIGHT
- ▁O
- ▁HEAD
- AN
- ▁FOUND
- ▁NOTHING
- ▁YOUNG
- ▁WHILE
- ▁TAKE
- ▁GET
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- RO
- ▁OFF
- ▁THOUGH
- EST
- ▁YET
- ▁THREE
- TH
- ▁RIGHT
- ▁UNDER
- AR
- ▁FACE
- IES
- ▁ROOM
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- ▁SAW
- RA
- V
- ▁ASKED
- ▁TELL
- ERS
- ▁SAME
- MENT
- ▁HEART
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- ▁PLACE
- ▁ANOTHER
- ▁EVER
- ▁LEFT
- ▁SHALL
- ▁FATHER
- ▁PUT
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- ▁TOOK
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- ▁ALWAYS
- ▁SEEMED
- ▁PART
- IL
- UR
- ▁WHY
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- CE
- ▁MIND
- ▁LOOKED
- ▁HEARD
- ▁SOON
- ▁LOOK
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- ▁SIDE
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- ATE
- ▁MOMENT
- ENT
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- ▁THINGS
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- ▁LIGHT
- ▁FIND
- ▁GOING
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- ▁WORLD
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- ▁DOOR
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- ▁KNEW
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- ▁AGAINST
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- ▁WANT
- ▁GIRL
- ▁DONE
- ▁SEEN
- ▁HUNDRED
- ▁CALLED
- ▁BETWEEN
- ▁MORNING
- FUL
- AS
- ▁FELT
- TER
- ▁KIND
- X
- CH
- ▁HERSELF
- ANT
- ▁TOWARD
- ▁HALF
- ▁OH
- ▁AMONG
- ▁HOWEVER
- ▁TURNED
- ▁ALSO
- ▁BOTH
- ▁POOR
- ▁PERHAPS
- ▁REPLIED
- ▁COURSE
- UL
- ▁QUITE
- ▁REST
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- ▁MYSELF
- NG
- LO
- ANCE
- ▁MA
- ▁SET
- ▁SMALL
- ▁B
- ▁SURE
- ▁F
- ▁GAVE
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- ▁HIGH
- ▁ALMO
- ▁R
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- ▁NEAR
- ▁CARE
- ▁WAR
- ▁GOD
- ▁TOGETHER
- ▁SAT
- ▁SHOW
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- ▁BEST
- ▁UNTIL
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- ▁W
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- ▁DEAR
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- ▁WORDS
- ▁SINCE
- ▁LAND
- ▁DIS
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- ▁FEET
- ▁NEXT
- ▁GENERAL
- LING
- ▁LAY
- ▁NOR
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- ▁BLACK
- ▁POWER
- ▁BROUGHT
- Z
- IE
- ▁ROUND
- ▁BELIEVE
- ▁LARGE
- ▁ALONG
- ▁HELP
- ▁DAYS
- ▁FIVE
- ▁K
- ▁HOPE
- AM
- ▁CO
- ▁KEEP
- ▁FULL
- ▁WALK
- ▁MASTER
- ATED
- ▁NATURE
- ▁JOHN
- ▁POINT
- ▁DUR
- ▁MATTER
- ▁MONEY
- ▁CHILD
- ▁LOOKING
- ▁RATHER
- ▁AIR
- IA
- ▁P
- ▁TWENTY
- ▁FIRE
- OL
- ▁LESS
- ▁SHORT
- ▁PASSED
- ▁INDEED
- TY
- ▁CASE
- ▁WORD
- ▁WISH
- ▁COUNTRY
- LED
- ID
- ▁BOY
- ▁SOUND
- ▁FORM
- ▁CRIED
- LA
- ▁FRIEND
- TON
- ▁FACT
- ▁UNCLE
- ▁TAKEN
- ▁AL
- ▁TEN
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- ▁GONE
- ▁SEA
- ▁REASON
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- ▁LI
- ▁DEATH
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- AD
- ▁NEED
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- ▁CALL
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- ▁EUROPE
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- ▁TONGUE
- ▁SPACE
- ▁PATSY
- ▁MISTRESS
- ▁HENRY
- ▁JERRY
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- ▁BOOKS
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- ▁EXCITEMENT
- ▁MIDDLE
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- ▁COP
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- ▁OAK
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- ▁EXTEND
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- ▁DESPAIR
- ▁TRIUMPH
- ▁SQUARE
- ▁THROAT
- ▁BOUGHT
- ▁PERMIT
- ▁SPEND
- ▁TRIP
- ▁THREATEN
- ▁ROME
- INESS
- ▁EXPOS
- GON
- ▁WRITING
- ▁INCREASED
- ▁PORTION
- ▁TENT
- IUS
- ▁YO
- ▁INTENDED
- ▁NAMED
- RATION
- ▁NOTIC
- ▁PIPE
- ▁WILLING
- ▁INSTANTLY
- ▁SERVED
- ▁BAL
- ▁POSSESS
- ▁CRE
- ▁ADMIRATION
- ▁LIBERTY
- ▁OPPORTUNITY
- ▁SELDOM
- ▁BIRTH
- ▁GLOW
- ▁INCLUD
- ▁REQUEST
- ▁TYPE
- ▁SLEPT
- ▁CRIME
- ▁MOTIVE
- ▁ELSIE
- ▁BEGUN
- ▁CONSENT
- ▁ADMITTED
- ▁AVOID
- ▁ADDRESS
- ▁HATE
- ▁DEMANDED
- ▁APPARENTLY
- ▁SUGGESTION
- ▁CONSIDERATION
- ▁BLESS
- ▁PROCEEDED
- NCY
- ▁PRISON
- ▁CONT
- ▁SHOUTED
- ▁FACES
- ▁SPIRITS
- ▁DEVELOP
- ▁ACCIDENT
- ▁ADVICE
- ▁INNOCENT
- ▁INSTINCT
- ▁UNCONSCIOUS
- ▁MYSTERIOUS
- ▁PRETEND
- ▁PEEP
- ▁ANYONE
- ▁DUKE
- ▁PLUM
- VILLE
- ▁SEVERE
- ▁ALAS
- ▁DELIGHTED
- ▁ISSUE
- ▁ASKING
- ▁CROW
- ▁ACCEPTED
- ▁RIDE
- ▁DOORS
- ▁TAR
- ▁PREPAR
- ▁SUGGEST
- WOOD
- ▁CITIZEN
- ▁ENTRANCE
- ▁LINCOLN
- ▁POLITICAL
- ▁PRACTICAL
- ▁STIFF
- ▁WIDOW
- ▁CAPITAL
- ▁CLEVER
- ▁MAMMA
- ▁CREDIT
- ▁OBEY
- ▁STRING
- ▁DAILY
- ▁ARGUMENT
- ▁HEAP
- ▁APARTMENT
- ▁FLIGHT
- ▁ELDER
- ▁PUR
- ▁PAGE
- ▁DUST
- ▁GAZE
- ▁NATIONAL
- ▁BABY
- DDING
- ISTS
- ▁TEACH
- ▁STREETS
- CAL
- ▁GE
- AFF
- ▁GOES
- ▁POSSIBL
- UNG
- ▁LINES
- GUE
- ▁VOTE
- ▁HUNTING
- ▁QUO
- ▁RESEMBL
- ▁BASKET
- ▁CIRCLE
- ▁CONSEQUENCE
- ▁KITCHEN
- ▁TREASURE
- ▁NEVERTHELESS
- ▁FANCI
- ▁ASSEMBL
- ▁GRIEF
- ▁VEIL
- ▁SEASON
- ▁INVENT
- ▁VIRGINIA
- ▁HUT
- ▁GUEST
- ▁ROAR
- ▁BEHOLD
- ▁VICTORY
- ▁CAPABLE
- ▁DULL
- ▁SHOE
- ▁FLOAT
- ▁MERRY
- ▁IMMEDIATE
- ETH
- ▁ELEANOR
- ▁EXPLANATION
- ▁PARLIAMENT
- ▁PRINCIPAL
- ▁PROPORTION
- ▁RESOLUTION
- ▁UNUSUAL
- ▁BLUFF
- ▁NINETEEN
- ▁SENSATION
- ▁VISIBLE
- ▁INCOME
- ▁FATE
- ▁SUPER
- ▁LAUGHTER
- ▁EASE
- ▁LOAD
- ▁JEW
- ▁ZE
- ▁FEVER
- ▁WEDDING
- ▁JOINED
- ▁TRACE
- ▁LEADER
- ▁CLEARLY
- ▁FLOWER
- ▁TERMS
- ▁EMPLOYED
- OCK
- ▁PARTICULARLY
- ▁MEMBERS
- ▁CONFESS
- ▁GRO
- ▁ADDRESSED
- ▁CHRIST
- ▁ACCOMPANI
- ▁AFFORD
- ▁AMOUNT
- ▁BRILLIANT
- ▁COMMUNICAT
- ▁FIERCE
- ▁RECORD
- ▁SACRIFICE
- ▁TEMPT
- ▁CORDIAL
- ▁COLOUR
- ▁PROOF
- ▁ESTATE
- ▁PARDON
- ▁ADVIS
- ▁ATTITUDE
- ▁IMPORTANCE
- ▁BOOT
- ▁SHOCK
- ▁FIR
- ▁PLENT
- ▁HIT
- ▁MEMBER
- ▁SUR
- ▁SEATED
- ▁MAG
- AVING
- ▁FAVOUR
- ▁REMARK
- ▁DIM
- ▁FAITHFUL
- ▁SAVED
- CHI
- ▁SIN
- THE
- ▁CONFIDENCE
- ▁EXTRAORDINARY
- ▁FORTUNATE
- ▁MISFORTUNE
- ▁PATIENCE
- ▁RELIGIOUS
- ▁SATISFACTION
- ▁POSITIVE
- ▁SIMILAR
- ▁EXCHANG
- ▁RETREAT
- ▁FLESH
- ▁ADMIRE
- ▁SPIRITUAL
- ▁DAWN
- ▁BURIED
- ▁URGE
- ▁SUNDAY
- ▁FOX
- ▁EMMA
- ▁NURSE
- ▁SNAPP
- ▁PARK
- ▁OBTAIN
- ▁RECOGNIZED
- ▁SPEED
- ▁MAGIC
- ▁LAWS
- ▁REMOVED
- ▁HAM
- ▁PRESERV
- ▁AID
- HOUSE
- ▁MENTIONED
- ▁CONSCIENCE
- ▁CONTEMPT
- ▁DETAIL
- ▁IMMENSE
- ▁NERVOUS
- ▁PRISCILLA
- ▁UNFORTUNATE
- ▁UNHAPPY
- ▁COMPLAIN
- ▁TWICE
- ▁WHISTL
- ▁SNAKE
- ▁WASHINGTON
- ▁PIRATE
- ▁WICKED
- ▁BODIES
- ▁DESIGN
- ▁JASON
- ▁VAGUE
- ▁CONSIST
- ▁GIFT
- ▁ANGEL
- ▁RODE
- ▁FOLD
- ▁BRIDE
- ▁ANGER
- ▁BASE
- ITUDE
- ▁CONCLUDED
- ▁ALTER
- ▁FRI
- ▁PANT
- ▁BID
- ▁HIGHEST
- ▁SAILOR
- MPLE
- ▁OBSERV
- ▁CHEERFUL
- IFICATION
- RID
- ▁DESCRIBED
- ▁BIN
- ▁JEWEL
- ▁ARTIST
- ▁PEER
- ▁NORA
- ▁SKI
- ▁DIAMOND
- ▁ENCOURAGE
- ▁PRIVILEGE
- ▁PROJECT
- ▁ANYBODY
- ▁ENCOUNTER
- ▁HOLLOW
- ▁YIELD
- ▁BOBBY
- ▁SAVAGE
- ▁SOMEBODY
- ▁OTHERWISE
- ▁PRAISE
- ▁PROBLEM
- ▁DISTRESS
- ▁UGLY
- ▁WARRIOR
- ▁MOURN
- ▁RELIEV
- ▁DESK
- ▁FOOLISH
- ▁STARTLED
- ▁SKILL
- SHONE
- ▁LONE
- ▁OBSERVATION
- ▁DENI
- ▁NEST
- ▁SOLDIER
- ▁RELATION
- ▁TRULY
- ▁VISITOR
- ▁OFFICERS
- ERSON
- ▁YA
- ▁EVIDENT
- ▁DREAMS
- ▁KEEPING
- ▁PLAINLY
- ▁DRUNK
- ▁EMBRAC
- ▁INTELLIGENCE
- ▁LIEUTENANT
- ▁PERSUADE
- ▁SURROUNDING
- ▁UNIVERSAL
- ▁GLEAM
- ▁SUPERIOR
- ▁WHEEL
- ▁JEALOUS
- ▁QUEER
- ▁PIERRE
- ▁MILK
- ▁RAIL
- ▁FLUSH
- ▁STAIRS
- ▁JESUS
- ▁HORN
- ▁REGION
- ▁SAFETY
- ▁KA
- ▁GUIDE
- ▁CAKE
- ▁CUP
- ▁INQUIRED
- ▁DEFI
- ▁LESSON
- ▁WRETCHED
- ▁PACE
- ▁TEST
- ▁READING
- ▁ENTIRE
- ▁NET
- ▁DOGS
- ▁COMMANDER
- ▁PRODUCE
- ▁GAINED
- ▁ARRIVAL
- ▁FAMILIAR
- ▁MEANWHILE
- ▁SUSPICION
- ▁CHOICE
- ▁IMPULSE
- ▁THRUST
- ▁PROCESS
- ▁SUMMON
- ▁SHEPHERD
- ▁HASTILY
- ▁GRASP
- ▁COUNTESS
- ▁STYLE
- ▁DWELL
- ▁MERIT
- ▁PITCH
- ▁HUNGRY
- ▁SPORT
- ▁LOUISE
- ▁STERN
- ▁PROVIDED
- ▁ASSUME
- ▁EARLIE
- ▁RAGE
- ▁U
- ▁RAPIDLY
- PORT
- ▁SUCCESSFUL
- ▁FLED
- ▁AGREE
- ▁CONDITIONS
- ▁RELATIONS
- ▁DREAD
- ▁NATURALLY
- ▁EARL
- ▁GAY
- ▁HYPNOTI
- ▁PUTT
- ▁GAZ
- ▁JIM
- ▁PAUS
- ▁PROPOS
- ▁ADMINISTRATION
- ▁ELEVEN
- ▁HOSPITAL
- ▁MAGISTRATE
- ▁STRIKE
- ▁DIGNITY
- ▁GLORY
- ▁BOTTLE
- ▁THRONE
- ▁RECKON
- ▁COSETTE
- ▁MOREOVER
- ▁APPLI
- ▁HIND
- ▁PRODUCT
- ▁POOL
- ▁TRIAL
- HAN
- ▁ERIC
- ▁CUB
- ▁PIECES
- ▁EXCEPTION
- ▁ENJOYED
- ▁DARED
- ▁TRU
- ▁CLOSELY
- ▁RAPID
- ▁AFFECTED
- ▁REQUIRE
- ▁SOFTLY
- ▁BROW
- UCK
- ▁MARKED
- ▁SEVENT
- ▁ELECT
- ▁FORGOT
- ▁CORRECT
- ▁FRANCS
- ▁MARGUERITE
- ▁SCIENCE
- ▁UNEXPECTED
- ▁FOUGHT
- ▁MILITA
- ▁THUNDER
- ▁VOYAGE
- ▁GANEM
- ▁FREEDOM
- ▁NODDED
- ▁CAPTURE
- ▁MORTAL
- ▁OWNER
- ▁POLITE
- ▁VISION
- ▁EDUCATION
- ▁GOVERNOR
- ▁RAV
- ▁REWARD
- ▁HASTE
- ▁REPEAT
- ▁DETERMIN
- ▁PITI
- ▁KNEE
- LINE
- ▁DEVOTED
- ▁INTERRUPTED
- ▁FOLKS
- ▁EXTREME
- ▁APPROACH
- ▁CONTINUE
- ▁BEARING
- ▁CHAP
- ▁ACQUAINTED
- ▁GLIMPSE
- ▁GRADUALLY
- ▁SUNSHINE
- ▁PRACTICE
- ▁SUPPLI
- ▁DAVID
- ▁DRIFT
- ▁SHOWING
- ▁LEVEL
- ▁PROMPT
- ▁QUARREL
- ▁REPRESENTATIVE
- ▁PLUNG
- ▁GIANT
- FALL
- ▁STOUT
- CHA
- WEPT
- ▁GLANC
- ▁SALT
- ▁CHOSEN
- ▁BUCK
- ▁REALIZED
- ▁REALITY
- ▁TUR
- ▁DRIVEN
- ▁CARD
- ▁PRAYER
- ▁TERM
- AID
- ▁HOLY
- ▁ENDURE
- ▁RANGE
- ▁HANG
- ▁SAM
- LAN
- ▁CAVE
- INA
- ▁GRI
- ▁SIGH
- ▁NEIGHBOUR
- ▁COUNCIL
- ▁EXERCISE
- ▁NAUTILUS
- ▁SOMEWHERE
- ▁SYLVIA
- ▁THOROUGH
- ▁VICTIM
- ▁BRIDGE
- ▁COMPELLED
- ▁INCLINED
- ▁OVERCOME
- ▁RESERVE
- ▁ARREST
- ▁PRECIOUS
- ▁DUTCH
- ▁OCEAN
- ▁ACQUIR
- ▁RECALL
- ▁DESTIN
- ▁ATTACH
- ▁SLIM
- ▁WEEP
- ▁CONSCIOUSNESS
- ▁TIGHT
- ▁WAKE
- ▁COMFORTABLE
- ▁ACTIVE
- ▁WINGS
- ▁GRIN
- ▁AFFECT
- ▁WHIT
- ▁IDEAL
- ▁EASTER
- ▁APPROACHING
- ▁CREATED
- ▁PLANS
- ▁INCREASE
- ▁FLYING
- ▁SHOUT
- OES
- MISSION
- ▁ARMED
- ABILITY
- ▁BLUSH
- ▁CONNECTION
- ▁MATTHEW
- ▁MEDICINE
- ▁REMIND
- ▁EXHIBIT
- ▁BLOCK
- ▁DESERVE
- ▁LISTENING
- ▁TITLE
- ▁FLOUR
- ▁FLAME
- ▁AGENT
- ▁USEFUL
- ▁BRIG
- ▁BOIL
- ▁ASSURED
- ▁REFLECTION
- ▁PINE
- ▁WAG
- ▁YOUNGER
- ▁BEARD
- ▁KINDNESS
- CTUALLY
- ▁ACTUAL
- ▁WEIGHT
- ▁LILY
- ▁IMPRESS
- ▁DESCRIBE
- ▁BEHELD
- ▁COMMUNITY
- ▁DESPERATE
- ▁DISPLAY
- ▁ENEMIES
- ▁MELANCHOLY
- ▁MIRROR
- ▁RECOMMEND
- ▁SPANISH
- ▁BLAME
- ▁VOLUME
- ▁SHOOT
- ▁COMBIN
- ▁SHAKING
- ▁SOUTHERN
- ▁MYSTERY
- ▁EVERYONE
- ▁COMMISSION
- ▁COMPOSED
- ▁UDO
- ▁IMAGE
- ▁DECEIV
- ▁FAILURE
- ▁PATTY
- ▁ALICE
- ▁FRAME
- ▁MODEST
- ▁MAGNIFICENT
- ▁BRANCHES
- ▁REIGN
- ▁RAG
- ▁PARISH
- ▁KATE
- ▁AMID
- ▁SLEEPING
- ▁ANNOUNCED
- ▁EAGERLY
- ▁WIRE
- ▁LAP
- ▁ARAB
- ▁EATING
- ▁RUM
- ▁CAREFUL
- ▁DISCUSS
- WORTH
- ▁DISTRICT
- ▁FOREHEAD
- ▁FRANCIS
- ▁INCIDENT
- ▁APPEAL
- ▁EMBARRASS
- ▁MAINTAIN
- ▁PRONOUNC
- ▁FURNISH
- ▁STRAIN
- ▁ELEMENT
- ▁SILK
- ▁FEAST
- ▁RECENT
- ▁DANCING
- ▁LODGE
- ▁ASHAMED
- ▁TRICK
- ▁BOBO
- ▁STUFF
- ▁ET
- ▁ASSERT
- ▁SANK
- ▁TREATMENT
- ECI
- ▁SWIM
- ▁BECOMING
- ▁SINGING
- ▁PLATE
- ▁SCATTERED
- ▁EXTREMELY
- ▁GRIM
- ▁SANG
- ▁FIGHTING
- ▁FACTOR
- ▁PAINFUL
- ▁HIDE
- ▁FUNN
- ▁AFTERWARD
- ▁FROG
- ▁VENTURE
- ▁DISAPPOINT
- ▁COMRADE
- ▁MONSIEUR
- ▁OBVIOUS
- ▁PASSENGER
- ▁PROFOUND
- ▁PUBLISH
- ▁ACCUSTOM
- ▁BLOOM
- ▁SMITH
- ▁RELATIVE
- ▁ACCUSE
- ▁MANIFEST
- ▁SOLID
- ▁MONSTER
- ▁MARIUS
- ▁CANDLE
- ▁PROCUR
- ▁INTERFERE
- ▁HOUSEHOLD
- ▁DEVELOPMENT
- ▁AGREEABLE
- ▁HALT
- ▁NECESSITY
- FOLD
- ▁CITIES
- ▁REGI
- ▁GLOOMY
- BBL
- ▁SEPARATED
- ▁CHEST
- ▁STRIP
- ▁SPAR
- ▁DUN
- ▁SETTLE
- ▁STARED
- ▁HANGING
- ▁FEATURES
- ▁PILE
- ▁ORIGIN
- ARIES
- ▁LION
- ▁ALI
- ▁ASTONISHMENT
- ▁COMPLIMENT
- ▁DELICATE
- ▁COUNSEL
- ▁FIFTH
- ▁SUPPRESS
- ▁BURDEN
- ▁COMPLEX
- ▁ADDITION
- ▁CRUSH
- ▁TWIST
- ▁PIANO
- ▁BRUSH
- ▁CHECK
- ▁ANNIE
- ▁SHELTER
- ▁IMPROV
- ▁WESTERN
- ▁LOCAL
- ▁APPLE
- ▁GREET
- ▁MASK
- ▁RUSSIAN
- ▁TOWER
- ▁CREW
- ▁TIP
- ▁WANDERING
- ▁READER
- ▁WANDERED
- ▁DESTROY
- ▁OBSERVE
- MORE
- ▁ESCAPED
- ▁PET
- ▁BUILD
- ▁REAR
- ▁DESTROYED
- HIN
- ▁OWE
- ▁RANG
- ▁TEAR
- ▁NED
- ▁OFFICER
- ▁TRAP
- ▁OCCUR
- ▁APPOINTED
- ▁ATMOSPHERE
- ▁CHOOSE
- ▁CONCLUSION
- ▁CULTIVAT
- ▁DESCRIPTION
- ▁ENORMOUS
- ▁EXHAUSTED
- ▁LANDSCAPE
- ▁NATASHA
- ▁PROSPECT
- ▁REFRESH
- ▁SPECIES
- ▁SURROUNDED
- ▁WEAPON
- ▁BLANK
- ▁DEFEND
- ▁EDITH
- ▁HORRIBL
- ▁BETRAY
- ▁FERKO
- ▁LABOUR
- ▁NEGRO
- ▁RESUMED
- ▁LEAF
- ▁MUSKET
- ▁INTENSE
- ▁MERCY
- ▁ADOPT
- ▁SCORE
- ▁DASH
- ▁LAWYER
- ▁SLOPE
- ▁CHUCK
- ▁ASSISTANCE
- ▁BROOK
- ▁BREAKING
- ▁ASSIST
- ▁GROAN
- ▁HELEN
- ▁BEHAV
- ▁MAIDEN
- ▁CRIS
- ▁SHOUTING
- ▁NAY
- ▁PIG
- ▁ACCORDINGLY
- ETTE
- ▁DESIR
- ▁RUB
- ▁GRU
- ▁PIT
- ▁HEAVI
- ▁OBTAINED
- ▁SPARE
- ▁BRANCH
- ▁COUNTER
- ▁APART
- ▁AMBITION
- ▁ASTONISHED
- ▁CORRESPOND
- ▁DRIVING
- ▁ENERGY
- ▁HISTORIAN
- ▁REVOLUTION
- ▁SWEEP
- ▁TREMBLING
- ▁CRAFT
- ▁FAMILIES
- ▁LITERATURE
- SBURG
- ▁FEMALE
- ▁TILNEY
- ▁GENEROUS
- ▁SUBMIT
- ▁INTELLECTUAL
- ▁ORCHARD
- ▁STORIES
- ▁DIANA
- ▁VEIN
- ▁TRIFL
- ▁TWIN
- ▁WORSHIP
- ▁MARBLE
- ▁GALLANT
- ▁SENSIBLE
- ▁NEAT
- ▁BROWNIE
- ▁JUNE
- ▁SHAW
- ▁WORST
- ▁USELESS
- ▁FISHING
- ▁CRYING
- ▁MAYBE
- ▁VARI
- ▁PRESERVE
- ▁VOL
- ▁EMPLOY
- ▁INTERRUPT
- ▁SLIGHTLY
- ▁ACCOMPLISHED
- NEY
- ▁STEAM
- ▁BALANC
- ▁LEANING
- ▁SIGHED
- ▁REFUSE
- ▁IMAGINED
- ▁DATE
- GROUND
- ▁ENTERTAIN
- ▁PERCEIVE
- ▁ABROAD
- ▁CHEESE
- ▁DESTRUCTION
- ▁ESSENTIAL
- ▁EXPEDITION
- ▁GRANDFATHER
- ▁INFINITE
- ▁LIBRARY
- ▁MULTITUDE
- ▁NEGLECT
- ▁SWALLOW
- ▁VILLEFORT
- ▁BELOVED
- ▁COMMITTEE
- ▁CONFIDENT
- ▁PURPLE
- ▁PURCHAS
- ▁SCRAP
- ▁SPOIL
- ▁LIKEWISE
- ▁EXTRA
- ▁STRAW
- ▁SALUT
- ▁SOURCE
- ▁HASTENED
- ▁RESENT
- ▁FLOCK
- ▁LOFT
- ▁FLO
- ▁CLO
- ▁CONVINCED
- ▁GOODNESS
- ▁HYPNOTIZ
- ▁SETTING
- ▁HAIL
- ▁PHI
- ▁GROVE
- ▁DISCOVERY
- ▁DAMP
- ▁WHISPER
- ▁LIFT
- ▁HOP
- ▁SUSPECTED
- ▁SCR
- OLI
- ▁FAC
- ▁BUSH
- ▁FOREVER
- ▁BARRICADE
- ▁CONSTITUTION
- ▁ENDEAVOR
- ▁ENTHUSIASM
- ▁EXECUTION
- ▁HYACINTH
- ▁PERCEVAL
- ▁PSYCHE
- ▁REPROACH
- ▁THIRTEEN
- ▁ABSORB
- ▁GRATITUDE
- ▁MERCER
- ▁REPUTATION
- ▁SCREAM
- ▁PUPIL
- ▁RETIRED
- ▁STEEP
- ▁SUMMIT
- ▁MISERABLE
- ▁STRICT
- ▁MINGLED
- ▁DEFEAT
- ▁REVEAL
- ▁LOVING
- ▁GOOSE
- ▁ECHO
- ▁AWAIT
- ▁MOOD
- ▁CRAWLEY
- ▁CELL
- ▁ENGAGEMENT
- ▁PRECED
- ▁SOMEONE
- ▁ARRANGEMENT
- ▁PICKET
- ▁GASP
- ▁HUMOR
- ▁INVITATION
- ▁JOB
- WITHSTAND
- ▁LAMENT
- ▁CLASSES
- ▁HUNGER
- ▁DISPOSED
- ▁STEAMER
- ▁FEARFUL
- ▁GER
- ▁FINAL
- ▁FLAG
- ▁JULY
- ▁DIG
- WORK
- ▁OPPOS
- ▁ANXIETY
- ▁AUDIENCE
- ▁BACHELOR
- ▁COLUMN
- ▁HANDKERCHIEF
- ▁IMPATIENT
- ▁JUDGMENT
- ▁KNIFE
- ▁SOVEREIGN
- ▁STRIKING
- ▁THOMPSON
- ▁EMPIRE
- ▁FULFIL
- ▁CONSULT
- ▁JENNY
- ▁THENARDIER
- ▁POYSER
- ▁FOURTEEN
- ▁JAPANESE
- ▁INDULG
- ▁MARTIAN
- ▁COUNTRIES
- ▁FETCH
- ▁CRITIC
- ▁ROBBER
- ▁CROOK
- ▁DEPARTURE
- ▁MABEL
- ▁PREACH
- ESCENT
- ▁WHIP
- ▁NAIL
- ▁DELIGHTFUL
- ▁DISCUSSION
- ▁SENTENCE
- ▁LANE
- ▁ENGINEER
- ▁ARRANGED
- MMY
- ▁LEST
- ▁RENT
- MMED
- ▁LIST
- ▁ROBE
- ▁MISSION
- ▁GRACEFUL
- ▁LIGHTN
- STONE
- COURT
- ▁CONCEPTION
- ▁CONTRACT
- ▁DROWN
- ▁EXPERIMENT
- ▁HITHERTO
- ▁PLAGUE
- ▁PORTHOS
- ▁SHRIEK
- ▁DETECT
- ▁ACCENT
- ▁ERECT
- ▁SAZEN
- ▁PROFIT
- ▁VIVID
- ▁SQUIRE
- ▁OPERATION
- ▁SMELL
- ▁SIMON
- ▁EXTENT
- ▁KEEN
- ▁EMERG
- ▁REVIV
- ▁REGIMENT
- ▁DISAPPOINTMENT
- ▁STOLE
- ▁DIVINE
- ▁GUILTY
- ▁COWARD
- ▁EXPECTATION
- ▁SIGNOR
- ▁MODE
- ▁CENTRE
- ▁FIL
- HOW
- ▁WEARI
- ▁TOTAL
- ▁VICTOR
- ▁GOVERN
- ▁RAISE
- ▁ABANDON
- ▁ABSURD
- ▁ASPECT
- ▁CRIMINAL
- ▁DEFINITE
- ▁DELIBERAT
- ▁FEATHER
- ▁FLORINA
- ▁MIDNIGHT
- ▁RICHMOND
- ▁SATISFY
- ▁SINGULAR
- ▁STEADILY
- ▁SUPREME
- ▁TIMBER
- ▁PSYCHOLOG
- ▁GESTURE
- ▁VALUABLE
- ▁INTERVAL
- ▁CONFUSION
- ▁FLUTTER
- ▁SACRED
- ▁DISEASE
- ▁UNDERTAKE
- ▁PENETRAT
- ▁MARVEL
- ▁NORTHERN
- ▁GRIEV
- ▁GENIUS
- ▁SADDLE
- ▁NOVEL
- ▁MISERY
- ▁CONVICTION
- ▁SINK
- ▁WAGON
- ▁ARISE
- ▁COMMENT
- ▁BARN
- UPON
- ▁FENCE
- ▁ASSOCIATION
- ▁BONES
- ▁IDLE
- ▁DOUBTFUL
- ▁PREPARATION
- IZZ
- ▁RAIS
- ▁BITTERLY
- ▁JOE
- ▁RELI
- ADI
- ▁METAL
- ▁EXACT
- ▁GLOOM
- FIELD
- ▁DANGLARS
- ▁DISGRACE
- ▁EXAMINATION
- ▁FASCINAT
- ▁GLITTER
- ▁INCREASING
- ▁MESSENGER
- ▁PATRIOT
- ▁PLATFORM
- ▁PROVISION
- ▁QUALITIES
- ▁SELECT
- ▁STEADY
- ▁POVERTY
- ▁POWDER
- ▁PROPHET
- ▁HOLLAND
- ▁TRUNK
- ▁VARIETY
- ▁PLANCHET
- ▁CONQUER
- ▁CONCEIVE
- ▁COMBAT
- ▁STOOP
- ▁SHIRT
- ▁GENERATION
- ▁COMMITTED
- ▁INSULT
- ▁CONFUSED
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- ▁DEBT
- ▁IMITAT
- ▁DART
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- ▁SWAM
- ▁WREN
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- ▁BRAND
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- ▁DIRT
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- ▁BUSHES
- ▁MINK
- ▁ROUT
- ▁EQUALITY
- ▁HESITATED
- ▁BARK
- ▁ANTI
- ▁STATEMENT
- PHER
- ▁SUNK
- ▁DAT
- ▁BACKWARD
- ▁SUSPECT
- ▁OBJECTION
- ▁RAP
- ▁CHIN
- ▁MATE
- ▁REDUC
- ▁GREGG
- ▁ACCOMPANY
- ▁ANYWHERE
- ▁BENEFIT
- ▁CLERK
- ▁EXPENSE
- ▁FETNAH
- ▁INTERPRET
- ▁LUKASHKA
- ▁NUMEROUS
- ▁SURGEON
- ▁PUZZL
- ▁RESCUE
- ▁GRATEFUL
- ▁APPROV
- ▁RIVAL
- ▁NIECE
- ▁FLOOD
- ▁VANISHED
- ▁ERROR
- ▁BLAZ
- ▁TUMBL
- ▁WENDY
- ▁PERSIST
- ▁CONSOL
- ▁SOAP
- ▁HUMOUR
- ▁FITTED
- ▁HOUSEKEEPER
- ▁ENABL
- ▁OCCASIONALLY
- ▁HATRED
- ▁SWELL
- ▁WORRY
- ▁RUST
- ▁PURSUIT
- ▁INTIMATE
- ▁SEAL
- ▁COLLECTION
- ▁TREMBLED
- ▁DENY
- ▁HUMANITY
- ▁FATAL
- ▁COCK
- ▁DRIVER
- ▁HOPELESS
- ▁MISTAKEN
- ▁LUC
- ▁ACCOMPLISH
- ▁COAL
- ▁ACCORD
- ▁PURSE
- ▁SEPARATE
- ▁ARRIVE
- ▁SMOK
- ▁MADAM
- ▁ASSOCIAT
- ▁INSTRUCT
- ▁CELEBR
- ▁CHANNEL
- ▁CIVILIZATION
- ▁DOCTRINE
- ▁ENDEAVOUR
- ▁GLACIER
- ▁INTELLIGENT
- ▁INVOLVE
- ▁LEATHER
- ▁MUTTERED
- ▁OLENIN
- ▁PENCROFT
- ▁PERPLEX
- ▁SPECTATOR
- ▁UNIVERSITY
- ▁ATTAIN
- ▁INEVITABL
- ▁YONDER
- ▁ENCHANT
- ▁REPAIR
- ▁CURRENT
- ▁ASCEND
- ▁CREEK
- ▁SPARKL
- ▁RUE
- ▁BEAVER
- ▁INFANT
- ▁CONTINUALLY
- ▁CLASP
- ▁IRISH
- ▁ROLLIN
- ▁PUNISHMENT
- ▁LUNCH
- ▁AGONY
- ▁RUDE
- ▁DRAGG
- ▁INQUIRI
- ▁SEX
- ▁TERRIFI
- ▁ROBIN
- ▁PROFESSIONAL
- ▁SPUR
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- ▁VINE
- ▁PENN
- ▁ROC
- ▁CHASE
- ▁INFORM
- ▁WRITER
- ▁AVO
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- ▁CREAT
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- ▁CIRCUMSTANCE
- ▁OIL
- ▁ROUSE
- ▁COLUMB
- ▁CUNNING
- ▁DOMESTIC
- ▁GLORIOUS
- ▁INDIGNATION
- ▁PRECISELY
- ▁PRUDENCE
- ▁RAILROAD
- ▁SATURDAY
- ▁UTMOST
- ▁VIOLENCE
- ▁WHIRL
- ▁CALCULAT
- ▁OVERWHELM
- ▁PERPETUAL
- ▁QUARLES
- ▁SLENDER
- ▁TELEGRAPH
- ▁ALOUD
- ▁OPPRESS
- ▁CROPPER
- ▁CANADIAN
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- ▁SUPPLY
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- ▁DUSK
- ▁EXCESS
- ▁HUMBLE
- ▁FURIOUS
- ▁RIDGE
- ▁BULLET
- ▁PONY
- ▁STATU
- ▁ENJOYMENT
- ▁CONWAY
- ▁DIFFICULTIES
- ▁PATCH
- ▁JOYCE
- ▁CLOCK
- ▁RESTORED
- ▁ARGU
- ▁WIG
- ▁CHATT
- ▁PLAC
- ▁REMOVE
- ▁TORN
- ▁DISAPPEAR
- TIME
- WELL
- ▁RECOGNIZE
- ▁FISHE
- ▁DECLARE
- ISTIC
- ▁AUTHOR
- ▁WHISK
- ▁COFFEE
- ▁COMPREHEND
- ▁DISGUISE
- ▁ELZEVIR
- ▁ENTERPRISE
- ▁HOLIDAY
- ▁HORIZON
- ▁IGNORANT
- ▁INTERVIEW
- ▁OLIVER
- ▁RONICKY
- ▁CAPACITY
- ▁DISPOSITION
- ▁EXTERNAL
- ▁OPPOSITION
- ▁REPUBLIC
- ▁WHEAT
- ▁CORPSE
- ▁DARLING
- ▁THRILL
- ▁INHABITANTS
- ▁ORNAMENT
- ▁SHIFT
- ▁RECOGNISE
- ▁SHIVER
- ▁BOAST
- ▁HINT
- ▁BOSTON
- ▁MULTI
- IFYING
- ▁STEAL
- ▁INSTRUCTIONS
- ▁ELECTRIC
- ▁SWING
- ▁SOOTH
- ▁SCALE
- ▁MORLAND
- ▁DISLIKE
- ▁FLATTER
- ▁COACH
- ▁LEIF
- ▁STAMP
- ▁ANYHOW
- ▁MOTIONLESS
- ▁ANDREA
- ▁LOSING
- ▁PAUL
- ▁CAROL
- ▁ADVANC
- ▁IMAGIN
- ▁CENTER
- ▁JAR
- ▁SUCCEED
- ▁DISMISS
- CTOR
- ▁RECEIV
- ▁DRAG
- ▁INTENT
- ▁BARBAR
- ▁PUNISH
- ▁ABRUPTLY
- ▁BERNARD
- ▁DECISION
- ▁INDEPENDENT
- ▁PROVINCE
- ▁SLEEVE
- ▁TREMENDOUS
- ▁UNPLEASANT
- ▁LEISURE
- ▁THRONG
- ▁THUMB
- ▁BANNER
- ▁CONTRADICT
- ▁RESTRAIN
- ▁DIVIDED
- ▁WRAPPED
- ▁HAUNT
- ▁SNEER
- CHESTER
- ▁JULIA
- ▁MILD
- ▁CONTACT
- ▁MEANTIME
- ▁NEEDLE
- ▁BLOT
- ▁BARREL
- ▁ISABELLA
- ▁THEATRE
- ▁ESTABLISHMENT
- ▁MARKET
- ▁CHINA
- ▁FORBID
- ▁PERISH
- ▁DOORWAY
- ▁CARLING
- ▁PERIL
- ▁PRIZE
- ▁HATCH
- ▁CURL
- ▁REFER
- ▁DEVOT
- EMBER
- MONT
- ▁CANOE
- ▁PROFESSION
- ▁CONVICT
- ▁CRAWL
- ▁ACTIVITY
- ▁BEWILDER
- ▁BREEZE
- ▁CONTEMPLAT
- ▁DISGUST
- ▁FATIGUE
- ▁MERRICK
- ▁PRAIRIE
- ▁REFORM
- ▁SPECTACLE
- ▁STUDENT
- ▁TUMULT
- ▁UNIFORM
- ▁VIGOROUS
- ▁CONDEMN
- ▁GENUINE
- ▁THOMAS
- ▁ARROW
- ▁PILLOW
- ▁FEEBLE
- ▁RALPH
- ▁SCHEME
- ▁COLLAR
- ▁JUSTINIAN
- ▁NERVE
- ▁OYSTER
- ▁BENNET
- ▁DUTIES
- ▁BINGLEY
- ▁CHRISTMAS
- ▁CONVEY
- ▁DESPIS
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- ▁GARMENTS
- ▁GOWN
- ▁BERYL
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- ▁STAFF
- ▁KARA
- ▁MONTE
- ▁READILY
- ▁VENTUR
- ▁HENCE
- ▁ROPE
- ▁CRIES
- ▁ANGLE
- ▁RESPECTABLE
- ▁MOAN
- ▁OUTLINE
- BORN
- ▁FIX
- ▁INTEND
- LIA
- ▁CHILL
- ▁CREP
- ▁CHOSE
- ▁SPECULAT
- ▁ATTRIBUT
- ▁BUFFALO
- ▁ENTREAT
- ▁ENVELOP
- ▁FREDERICK
- ▁IMPATIENCE
- ▁INDIFFERENCE
- ▁INDUSTRY
- ▁INSTITUTION
- ▁LYNDE
- ▁RETAIN
- ▁TROUTINA
- ▁UNCOMFORTABL
- ▁VENGEANCE
- ▁JENKS
- ▁CONGRESS
- ▁SMART
- ▁THITHER
- ▁DISAGREE
- ▁IMPROVEMENT
- ▁PISTOL
- ▁GOSSIP
- ▁ETERNAL
- ▁BELIEF
- ▁SLEDGE
- ▁AROUSED
- ▁ORANGE
- ▁FASTENED
- ▁MONKEY
- ▁WITHDREW
- ▁OFFEND
- ▁PIERC
- ▁MOONLIGHT
- ▁OARS
- ▁GROOM
- ▁FIDDLER
- ▁BARBARA
- SHIRE
- ▁ATTENDANT
- ▁DIVERS
- ▁DUCK
- ▁PROPOSAL
- ▁GROWTH
- ▁CURATE
- ▁STEWAR
- ▁MOCK
- ▁SUCCESSION
- ▁CREATION
- ▁PARTIAL
- ▁SWU
- ▁FROST
- ▁EIGHTH
- ▁AWE
- ▁PERCH
- ▁LACE
- SPOON
- ▁ARRANGE
- SERIES
- ▁FOG
- ▁SCU
- ▁ABRAHAM
- ▁ADMIRAL
- ▁BARBICANE
- ▁CAMPAIGN
- ▁CONSEQUENTLY
- ▁CULTURE
- ▁GRAMMONT
- ▁GWYNPLAINE
- ▁HAPPILY
- ▁HOOPDRIVER
- ▁INDEPENDENCE
- ▁LEOPOLD
- ▁MISCHIEF
- ▁MONTGOMERY
- ▁NECESSARILY
- ▁PSYCHIC
- ▁RABBIT
- ▁REFUGE
- ▁RESPONSIBILIT
- ▁SENATOR
- ▁UNCERTAIN
- ▁MENSTRUA
- ▁FANNY
- ▁SUBSTANCE
- ▁APRIL
- ▁ELBOW
- ▁QUALITY
- ▁BORDER
- ▁BRUTAL
- ▁CARPET
- ▁SOLITAR
- ▁FROWN
- ▁SCENT
- ▁ANNOY
- ▁NAKED
- ▁BOSOM
- ▁CONSUM
- ▁TIGER
- ▁ITALIAN
- ▁PARSON
- ▁DECLIN
- ▁NEIGHBORHOOD
- ▁GREGGORY
- ▁EXCEED
- ▁SILLY
- ▁ICELAND
- ▁HIDEOUS
- ▁STRU
- ▁ALTERNAT
- ▁CABINET
- ▁ABILITY
- ▁BEECH
- ▁SECRETARY
- ▁CONTEST
- ▁MONK
- ▁PADD
- ▁EVA
- ▁CREST
- ▁FINISH
- ▁APPARENT
- ▁MIX
- ▁SLIP
- ▁LUXURI
- ▁AUTUMN
- ▁CIRCULAR
- ▁COMPOSITION
- ▁DISPLEAS
- ▁EXCELLENC
- ▁FURNITURE
- ▁GRADUATE
- ▁INDIFFERENT
- ▁JOSEPH
- ▁OCCUPATION
- ▁POSSIBILITY
- ▁RENEWED
- ▁RESPONDED
- ▁PREVAIL
- ▁HOARSE
- ▁PRACTIS
- ▁FAREWELL
- ▁JULIET
- ▁OVERHEAD
- ▁THREAD
- ▁APPLICATION
- ▁SOLITUDE
- ▁ADAPT
- ▁FALK
- ▁LARK
- ▁COARSE
- ▁MANKIND
- ▁KICK
- ▁BATTER
- ▁SOLICIT
- ▁RESIGN
- ▁MOTOR
- ▁STEEL
- ▁CONTRIV
- ▁AUTHORITIES
- ▁HARSH
- ▁FAVORITE
- ▁TALENT
- ▁FLEECE
- ▁AGITATION
- ▁ABBE
- ▁STUCK
- ▁HEDGE
- ▁BIBLE
- ▁RECOLLECTION
- ▁PARTNER
- ▁DAMON
- ▁SHINE
- ▁HOOK
- ▁CONFESSION
- ▁ASSENT
- ▁ELDE
- ▁BIGGE
- ▁PEACEFUL
- SCRIBED
- ▁WEIGH
- CARLET
- ▁DECIDE
- ▁RECOLLECT
- ▁BOHEMIA
- ▁CALIFORNIA
- ▁CONSTRUCT
- ▁DEMONSTRAT
- ▁DISTRIBUT
- ▁FRIGHTFUL
- ▁GNOME
- ▁IGNORANCE
- ▁JANUARY
- ▁JULIUS
- ▁MEMORIES
- ▁OCCUPY
- ▁PHRASE
- ▁WHIRLWIND
- ▁WILMINGTON
- ▁CARLINI
- ▁CHAUVELIN
- ▁ESTEEM
- ▁GENZABURO
- ▁GLOBE
- ▁LECOQ
- ▁MARGARET
- ▁MONARCH
- ▁NAPOLEON
- ▁SCORN
- ▁STAGGER
- ▁SUSTAIN
- ▁TRADITION
- ▁ADJUST
- ▁FROZEN
- ▁IMPRISON
- ▁LANTERN
- ▁MICHEL
- ▁STOMACH
- ▁TORRENT
- ▁WITHDRAW
- ▁FRANZ
- ▁POISON
- ▁SURVEY
- ▁BRITISH
- ▁ELEVAT
- ▁AWOKE
- ▁ESTHER
- ▁INHERIT
- ▁TRAVERS
- ▁STOPPING
- ▁IRELAND
- ▁COMPARATIVE
- ▁SOBB
- ▁FAVOURITE
- ▁CANVAS
- ▁CLOAK
- ▁GLAR
- ▁ASSISTANT
- ▁DAMAGE
- ▁PEAK
- ▁DISTINCTION
- FARE
- ▁DOLLAR
- ▁BEGGAR
- LUSIVE
- ▁MODEL
- ▁SECUR
- ▁DISPOS
- ▁SLID
- ▁PEA
- ▁SPEEDI
- HOLD
- ▁SNAP
- ▁CIGAR
- ▁AFFLICT
- ▁AMAZEMENT
- ▁LAUNCELOT
- ▁LEAGUE
- ▁MARIPOSA
- ▁POPULATION
- ▁UNEASY
- ▁BLOSSOM
- ▁CATERPILLAR
- ▁INCLINATION
- ▁SUSPEND
- ▁SYNDIC
- ▁TAYLOR
- ▁WILSON
- ▁CONTRAST
- ▁PORTRAIT
- ▁CORONER
- ▁GREEK
- ▁BUNDLE
- ▁BLEW
- ▁THORPE
- ▁ORPHAN
- ▁MUSCLE
- ▁DEAF
- ▁SURVIV
- ▁EXCEEDINGLY
- ▁TENDENC
- ▁ISRAEL
- ▁QUANTIT
- ▁PENSION
- ▁DRIED
- TEXT
- ▁REFERENCE
- ▁REPOSE
- ▁FOLLY
- ▁REPLACE
- ▁TERR
- ▁ANKLE
- ▁SUNLIGHT
- ▁SECURITY
- ▁SHOV
- ▁RAW
- CULAR
- ▁JACKET
- ▁TUNE
- ▁HOBB
- ▁MARTIN
- DUCED
- ▁FIST
- ▁BEGG
- ▁CHOK
- ▁INQUIRE
- ▁INTELLECT
- ▁AMUSEMENT
- ▁APPROPRIATE
- ▁CONGRATULAT
- ▁CONVENTION
- ▁DISCOURAG
- ▁EXQUISITE
- ▁FOUNTAIN
- ▁JUNIOR
- ▁NONSENSE
- ▁OBSTACLE
- ▁SPECIMEN
- ▁SWEAR
- ▁TRANQUIL
- ▁VEHICLE
- ▁WISDOM
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- ▁CAUTIOUS
- ▁CENTURIES
- ▁CORRUPT
- ▁EXPLOR
- ▁TURKEY
- ▁BARGAIN
- ▁CONFOUND
- ▁FUNCTION
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- ▁MONICA
- ▁ILLUSTRAT
- ▁CRUMB
- ▁REMEDY
- ▁REMOTE
- ▁REVENGE
- ▁BABYLON
- ▁CAUTION
- ▁INTERIOR
- ▁CRISTEL
- ▁BRAZ
- ▁THIRST
- ▁PROBABLE
- ▁HARMONY
- ▁CHARITY
- ▁DECAY
- ▁COLONI
- ▁AVAIL
- ▁REPULS
- ▁ABSENT
- ▁PULSE
- ▁PRESUM
- ▁CRANE
- ▁NEIGHBOURHOOD
- ▁SUNSET
- ▁CANNON
- ▁GRAPE
- ▁SOFA
- ▁DRANK
- MINOUS
- ▁DECLARATION
- ▁CLOSING
- ▁MEEK
- ▁STARV
- ▁BUNCH
- ▁PERFORMANCE
- ▁ENTERTAINMENT
- ▁STRIV
- ▁EMILY
- ▁VALET
- MPOSED
- ▁INTIMA
- ▁POLISH
- ▁HIRE
- POST
- ▁TREMBLE
- ▁CEASE
- ▁VIRGIN
- ▁RUSSIA
- COURSE
- ▁EDUCAT
- BOUND
- ▁INHABIT
- ▁SUPERINTEND
- ▁BISCUIT
- ▁CHICAGO
- ▁CHOKICHI
- ▁CONFLICT
- ▁ENCLOS
- ▁EXCLUSION
- ▁EXECUTIVE
- ▁GRANDMOTHER
- ▁HEADQUARTERS
- ▁INFERIOR
- ▁INVISIBLE
- ▁MUTUAL
- ▁OPPONENT
- ▁SENSITIVE
- ▁STUDIED
- ▁TEMPORARY
- ▁UNWILLING
- ▁PERMANENT
- ▁BEDROOM
- ▁NOVEMBER
- ▁COMPLICAT
- ▁DEVOUR
- ▁SCRAMBL
- ▁SECTION
- ▁PROPOSITION
- ▁DEPRIV
- ▁RYNCH
- ▁PLEAD
- ▁TORTURE
- ▁SCOUT
- ▁PILOT
- ▁CHERISH
- ▁SPEAR
- ▁SUGAR
- ▁JASPER
- ▁STRAY
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- ▁NORMAL
- ▁JERK
- ▁HONEY
- ▁AWAKENED
- ▁QUIVER
- ▁PYE
- ▁APPLY
- LICK
- JA
- ▁ANNOUNC
- FORE
- ▁ENGINE
- ▁HESITATE
- ▁PROVIDE
- ▁REALIZE
- ▁SEIZE
- ▁RESTORE
- MOUTH
- FOOT
- ▁DIFFER
- ▁ULTIMATE
- ▁ABUNDANCE
- ▁APPRECIATE
- ▁APPREHENSION
- ▁AVENUE
- ▁AWKWARD
- ▁CETERA
- ▁CHIMNEY
- ▁CLUTCH
- ▁CONVENIENT
- ▁CORRIDOR
- ▁DISTRACT
- ▁ELEGANT
- ▁ELSEWHERE
- ▁ENTHUSIASTIC
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- DIGNIFIED
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- ▁SIGNIFICANT
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- COMING
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- ▁CONNISTON
- ▁CONSTITUTE
- ▁DAZZL
- ▁DEFECT
- ▁DISCHARG
- ▁ESCORT
- ▁EXAGGERAT
- ▁GWENDOLEN
- ▁IRRESISTIBL
- ▁PHILOSOPHY
- ▁PHOTOGRAPH
- ▁PILGRIM
- ▁PLEASING
- ▁QUIXOTE
- ▁RESPONSE
- ▁SCRATCH
- ▁SERGEANT
- ▁SHERIFF
- ▁SHUDDER
- ▁STRUCTURE
- ▁SUFFRAGE
- ▁SURRENDER
- ▁SWORE
- ▁VILLAIN
- ▁HESITATING
- ▁FLORENCE
- ▁IRRITAT
- ▁RIGID
- ▁SINISTER
- ▁STUDIO
- ▁RAFT
- ▁CHAMPION
- ▁PAVEMENT
- ▁WOLF
- ▁DEVICE
- ▁WRECK
- ▁HESITATION
- ▁LAZY
- ▁ADJO
- ▁DECENT
- ▁INTERVEN
- ▁WOOL
- ▁ILLUSION
- ▁HAWK
- ▁IMPART
- ▁LUNGS
- ▁WINNING
- ▁VITAL
- ▁CONSPI
- ▁SUBTLE
- ▁CONSTANC
- ▁HURL
- ▁AMIABL
- ▁FOLK
- GGY
- ▁NECESSIT
- ▁PROFESS
- WASH
- ▁ADMIRING
- ▁AMBITIOUS
- ▁ANTHONY
- ▁CEREMONY
- ▁CONTRIBUTE
- ▁CRAGGS
- ▁DETAIN
- ▁DISCLOS
- ▁DWELT
- ▁EGYPT
- ▁FELIX
- ▁JOURNAL
- ▁KWAIRYO
- ▁LIBERAL
- ▁LUMBER
- ▁OCTOBER
- ▁ORGANIZATION
- ▁POPULACE
- ▁PRECAUTION
- ▁PREJUDICE
- ▁PROCLAIM
- ▁PROPRIETOR
- ▁RESPONSIBLE
- ▁RHYTHM
- ▁RIDICULOUS
- ▁SCHOLAR
- ▁SQUEEZ
- ▁SUBSTITUTE
- ▁SURPASS
- ▁THRESHOLD
- ▁WHARTON
- ▁FLICKER
- ▁AMAZED
- ▁BRONZE
- ▁COSSACK
- ▁SPILETT
- ▁CASUAL
- ▁DARCY
- ▁PARLOUR
- ▁SEXUAL
- ▁INSECT
- ▁NATHAN
- ▁EMINENT
- ▁PENCIL
- ▁PETITION
- ▁ROTTEN
- ▁VIGIL
- ▁CAESAR
- ▁EAGLE
- ▁TREAD
- ▁REACTION
- ▁TACIT
- ▁PARLOR
- ▁SPAIN
- ▁WILDERNESS
- ▁DICTAT
- ▁GRATIFY
- ▁STOVE
- ▁SKIRT
- ▁UTILI
- ▁CONCERT
- ▁GORGE
- ▁DECORAT
- ▁LATIN
- ▁ANCHOR
- ▁KNOT
- ▁MONDAY
- ▁GABLES
- ▁TOLERABL
- ▁ROGER
- BERRIES
- ▁INVAD
- IMMER
- OMETER
- ▁PRODUC
- OBIL
- ▁PERMISSI
- FICIENCY
- ▁WANDER
- RREL
- PIECE
- HORN
- ▁COMMIT
- ▁ACCUMULAT
- ▁JAPAN
- ▁ABUNDANT
- ▁ACADEMY
- ▁ALBERT
- ▁BANQUET
- ▁DELICIOUS
- ▁DOCUMENT
- ▁EXCLAMATION
- ▁FEBRUARY
- ▁GROTESQUE
- ▁HEATHERSTONE
- ▁HUMPHREY
- ▁HURSTWOOD
- ▁MOHAMMED
- ▁MOSCOW
- ▁NICHOLAS
- ▁OBSTINATE
- ▁PHANTOM
- ▁PHILOSOPHER
- ▁RECEPTION
- ▁SPANIARD
- ▁SWOLLEN
- ▁TELEPHONE
- ▁TRIBUTE
- ▁TUNNEL
- ▁UNREASONABL
- ▁WIGWAM
- ▁BUTTERFLY
- ▁COLLINS
- ▁DISPATCH
- ▁EDITOR
- ▁CONTINENT
- ▁DIMINISH
- ▁HORRID
- ▁KEATS
- ▁PROVIDENCE
- ▁BEHALF
- ▁CHARLEY
- ▁DRAKE
- ▁LAUNCH
- ▁SALOON
- ▁GIGANT
- ▁DISPUTE
- ▁HYSTERI
- ▁DEFENCE
- ▁SCREEN
- ▁VAULT
- ▁NINTH
- ▁HARBOR
- ▁FLANK
- ▁SPECK
- ▁UPRIGHT
- ▁KEMP
- ▁CANADA
- ▁STALK
- ▁OWL
- ▁BRUTE
- ▁FERRIS
- ▁DECREE
- ▁HABITUAL
- ▁BRISK
- ▁INSPIRE
- ▁HUSH
- ▁CROUCH
- ▁FRIDAY
- ▁MOUNTAINEER
- ▁HISTORIC
- ▁BATES
- ▁RUSK
- ▁SEMI
- DICTION
- ▁BUSI
- ▁REMOV
- MMI
- ▁SUFFIC
- ▁FLEE
- ▁LOUIS
- NLEA
- ▁IMPORT
- OLOGY
- ▁CLERGY
- ▁ADVERTISEMENT
- ▁BENEVOLEN
- ▁BORODINO
- ▁CATHOLIC
- ▁COMMERCIAL
- ▁CONJECTURE
- ▁CURTAIN
- ▁CUTHBERT
- ▁DEMOCRACY
- ▁GUARANTEE
- ▁HYPNOSIS
- ▁INDEFINITE
- ▁INVESTIGATION
- ▁IRREGULAR
- ▁KOYO
- ▁MERRIWIG
- ▁MIRANDA
- ▁NICHOLL
- ▁ONLOOKER
- ▁PERSECUT
- ▁RECOGNITION
- ▁REJOICE
- ▁REMEMBRANCE
- ▁REVELATION
- ▁SCOLD
- ▁SENIOR
- ▁SQUIRREL
- ▁SYMPATHETIC
- ▁TEMPEST
- ▁TREACHER
- ▁UNDERNEATH
- ▁UNEASINESS
- ▁UNNECESSARY
- ▁UPSTAIRS
- ▁VEXATION
- ▁ACCESS
- ▁CHEAP
- ▁ESTIMATE
- ▁HAZARD
- ▁HORSEBACK
- ▁PLUNDER
- ▁RASCAL
- ▁ROSTOV
- ▁ACCUR
- ▁GRAVITY
- ▁SITUATED
- ▁INVARIABL
- ▁PLENTIFUL
- ▁SPENCER
- ▁WALLACE
- ▁POLICY
- ▁WARRANT
- ▁ENVY
- ▁LAMB
- ▁EXTRACT
- ▁CORRAL
- ▁PANEL
- ▁LINK
- ▁LILIES
- ▁BECKON
- ▁SENOR
- ▁BORG
- ▁DEBATE
- ▁STEER
- COGNI
- COMB
- ▁SETTL
- ▁VENERA
- ▁FEATURE
- ▁TERRIBL
- CAPABLE
- OLOGICAL
- ▁INCESSANT
- ▁RESOLUTE
- SHAUGHNESSY
- ▁ABOLITION
- ▁ASSASSIN
- ▁BEHAVIOUR
- ▁BLUNT
- ▁COMMERCE
- ▁CONSTANTINOPLE
- ▁CRICKET
- ▁DISCIPLINE
- ▁DROUET
- ▁DWARF
- ▁INJUSTICE
- ▁LUXURY
- ▁MANUSCRIPT
- ▁MISUNDERSTAND
- ▁POLITICIAN
- ▁REDOUBT
- ▁SALVATION
- ▁SERMON
- ▁STRUGGLING
- ▁SURPRISING
- ▁TRIGGER
- ▁TUESDAY
- ▁TWILIGHT
- ▁UNDOUBTEDLY
- ▁VEGETABLE
- ▁VULGAR
- ▁WAISTCOAT
- ▁WRINKLE
- ▁ALEXANDER
- ▁CEILING
- ▁ECONOMIC
- ▁EVERLASTING
- ▁INFLICT
- ▁LEVISON
- ▁LOBSTER
- ▁OVERFLOW
- ▁SNATCH
- ▁TRAGEDY
- ▁DEASEY
- ▁ENLIGHTEN
- ▁FRIGATE
- ▁INSPECT
- ▁MARVELLOUS
- ▁ATLANTIC
- ▁LUFTON
- ▁BLADE
- ▁CRASH
- ▁SLAUGHTER
- ▁ANNUAL
- ▁CONFERENCE
- ▁TWIG
- ▁REASSUR
- ▁UNIQUE
- ▁WRATH
- ▁CRADLE
- ▁HULLO
- ▁LIQUID
- ▁MIRTH
- ▁EXPERT
- ▁HARVEY
- ▁RESTORATION
- ▁PRETTI
- ▁APOLOGY
- ▁SLAIN
- ▁BARBER
- ▁UPROAR
- ▁SCANT
- ▁BADGER
- ▁GROCER
- ▁ACRES
- ▁BRIDLE
- ▁SPECIFI
- ▁TANGLE
- ▁FERTIL
- ▁PATRON
- WIXT
- LAMOUR
- ▁DARN
- ▁POPE
- ▁PERCEIV
- ▁CONCLUDE
- ▁SIMPL
- ▁GUILT
- ▁CARRIE
- EFFICIENT
- SGIVING
- ▁APPOINTMENT
- ▁APPRECIATION
- ▁CARTRIDGE
- ▁CHALLENGE
- ▁CRAYFISH
- ▁CRIMSON
- ▁CUCUMETTO
- ▁ENERGETIC
- ▁EPOCH
- ▁EXAMINING
- ▁EXTENSIVE
- ▁EXTINGUISH
- ▁GLOODY
- ▁INSIGNIFICANT
- ▁LANDLORD
- ▁LANGUID
- ▁LEGISLATURE
- ▁MAJESTIC
- ▁PACIFIC
- ▁PASTRINI
- ▁PHRONSIE
- ▁RECONCIL
- ▁SIMULTANEOUS
- ▁SKELETON
- ▁SKETCH
- ▁TRANSFORM
- ▁UNJUST
- ▁VEXED
- ▁ASYLUM
- ▁CLUSTER
- ▁ERRAND
- ▁EXPEND
- ▁NEGATIVE
- ▁NORHALA
- ▁SCANDAL
- ▁STIMULAT
- ▁SWEAT
- ▁COMPOUND
- ▁DECEMBER
- ▁EXPAND
- ▁PROLONG
- ▁PURITAN
- ▁CONQUEST
- ▁MAGUA
- ▁SANCHO
- ▁TRENCH
- ▁ENTITLE
- ▁PEPPER
- ▁DISASTER
- ▁REGAIN
- ▁SHREWD
- ▁SULLEN
- ▁CLAVIER
- ▁COLOSS
- ▁SHILLING
- ▁ETHEL
- ▁MYSTERIES
- ▁BULK
- ▁GRANDEUR
- ▁AGNES
- ▁CONVERT
- ▁WRIST
- ▁GLID
- ▁TERRACE
- ▁SONYA
- ▁DANTES
- ▁MOULD
- ▁MAGNET
- ▁PLOT
- RANK
- ▁CAVIT
- ▁SUBSID
- ▁SLAP
- TURNED
- ▁THREAT
- BREAK
- ▁ANCESTORS
- ▁ANTICIPATED
- ▁APPLAUSE
- ▁ASSAULT
- ▁ATTORNEY
- ▁AUTOMATIC
- ▁CARAVAN
- ▁CATASTROPHE
- ▁CAVALCANTI
- ▁CROMWELL
- ▁ENVOY
- ▁EXHAUSTION
- ▁FIEND
- ▁GENEROSITY
- ▁GIMBLET
- ▁HARDQUANONNE
- ▁HOUARN
- ▁INJURY
- ▁MACKINSON
- ▁OGLETHORPE
- ▁PETTICOAT
- ▁RASPBERR
- ▁REHNHJELM
- ▁REJOICING
- ▁REMNANT
- ▁SCOTLAND
- ▁SHRINK
- ▁STANDPOINT
- ▁TESTIMONY
- ▁THEREAFTER
- ▁THIRTIETH
- ▁TWENTIETH
- ▁TYRANT
- ▁VENTNOR
- ▁VETERAN
- ▁WHITTAKER
- ▁ZVERKOV
- ▁ARCHITECTUR
- ▁BLUNDER
- ▁DENSHER
- ▁FORTNIGHT
- ▁JUDITH
- ▁MARIANNE
- ▁MEMORABLE
- ▁REFINED
- ▁REVOLV
- ▁UNDERTAKING
- ▁CLUMP
- ▁GRUMBLE
- ▁SYMPATHI
- ▁TICKET
- ▁TWITCH
- ▁EDITION
- ▁FALANDER
- ▁CARTHAGE
- ▁ORLEANS
- ▁POSSUM
- ▁SWITCH
- ▁CLUNG
- ▁CARDINAL
- ▁GNAW
- ▁LOCATED
- ▁HARROW
- ▁RASH
- ▁SIEGE
- ▁LOAF
- ▁BRUISE
- ▁REGULAT
- ▁RESORT
- ▁SARAH
- ▁LEVIN
- ▁NAVY
- ▁MOOSE
- ▁STOOL
- ▁CHANCELLOR
- ▁INGENIOUS
- ▁CHALK
- ▁PRETENCE
- ▁REPAY
- ▁ROAST
- ▁PLUTO
- ▁BAFFL
- ▁STUMBL
- ▁SPHERE
- ▁PLEDGE
- ▁SPRAWL
- ▁WRAP
- ▁FRINGE
- ▁DREAR
- ARRINGTON
- ▁FEDERA
- KEEPER
- ▁PHYSIC
- ▁ADVENT
- HUMAN
- OLOGIST
- ▁ALEXANDR
- ▁APPARITION
- ▁BARTHOLEMY
- ▁CITOYEN
- ▁CLIMATE
- ▁CONTEMPORAR
- ▁DESOLATE
- ▁DISCONTENT
- ▁ELEPHANT
- ▁FERNANDO
- ▁FERRALTI
- ▁FOLIAGE
- ▁FUGITIVE
- ▁GAMBLING
- ▁INVOLUNTARILY
- ▁LABYRINTH
- ▁LEGITIMATE
- ▁MILLIONAIRE
- ▁PERCEPTION
- ▁PROPRIETY
- ▁REBELLION
- ▁REFRAIN
- ▁RUGGLES
- ▁SCRIPTURE
- ▁SPLENDOR
- ▁SQUADRON
- ▁STRICKEN
- ▁SWARM
- ▁THEODORA
- ▁TOMORROW
- ▁VELVET
- ▁WOLVES
- ▁DISREGARD
- ▁GLIMMER
- ▁SHROUD
- ▁TWINKLING
- ▁UNEQUAL
- ▁CHANNING
- ▁CLUMS
- ▁ENIGMA
- ▁NAVIGAT
- ▁TARKAS
- ▁TEMPERATURE
- ▁DIVISION
- ▁GRATIFICATION
- ▁MONUMENT
- ▁SQUEAK
- ▁KAVIN
- ▁INTERPOSE
- ▁THORNTON
- ▁SOLUTION
- ▁STREAK
- ▁SHRILL
- ▁APRON
- ▁PITEOUS
- ▁HAUGHTY
- ▁RECKLESS
- ▁EMPTI
- ▁WADMAN
- ▁BONNET
- ▁MARTHA
- ▁DUMB
- ▁SHATTER
- ▁ACUTE
- ▁BRINK
- ▁CAPRICE
- ▁HURON
- ▁INFERN
- ▁FOWL
- ▁ENRAGE
- ▁ADORN
- ▁CRUIS
- ▁PROBABILIT
- ▁EXPIR
- ▁IMPETU
- ▁OVERHEAR
- BURTON
- ▁TRANSLAT
- ▁ENGAGE
- ▁CONVINCE
- ▁ABNORMAL
- ▁GESTICULAT
- ▁ABOMINABL
- ▁ADVERSARY
- ▁ADVERTISER
- ▁ADVERTISING
- ▁ANNIHILAT
- ▁ARTILLERY
- ▁CATHEDRAL
- ▁COMPETITOR
- ▁COULSON
- ▁CREVICE
- ▁CUSHION
- ▁DEBRAY
- ▁DEJECT
- ▁DIETRICH
- ▁DISADVANTAGE
- ▁ELLISON
- ▁EMPHASIS
- ▁EXCURSION
- ▁FANTASTIC
- ▁HYPOTHES
- ▁INCONVENIENCE
- ▁INDESCRIBABLE
- ▁INDUSTRI
- ▁INVALID
- ▁MERCILESS
- ▁MESOPOTAMIA
- ▁MOSQUITO
- ▁NARRATIVE
- ▁NOWADAYS
- ▁OPPORTUNITIES
- ▁PROMISING
- ▁RECTANGLE
- ▁REMONSTRANCE
- ▁RESTAURANT
- ▁RIBBON
- ▁SCIENTIST
- ▁SHALMANESER
- ▁SKULL
- ▁SPRUCE
- ▁SUBSTANTIAL
- ▁SYMBOL
- ▁TEAPOT
- ▁TERRITORY
- ▁TRAFFIC
- ▁TREASON
- ▁TRUMPET
- ▁TYRANN
- ▁UNANIMOUS
- ▁UNAWARE
- ▁VICINITY
- ▁WREATH
- ▁ZADIG
- ▁CHATEAU
- ▁CONFRONT
- ▁DUCHESS
- ▁EMBODI
- ▁FEMININ
- ▁FURNACE
- ▁MONTONI
- ▁RENOWN
- ▁SMASH
- ▁HARVARD
- ▁NEWBERRY
- ▁PERFUME
- ▁SIGNATURE
- ▁SPLASH
- ▁SUPPOSITION
- ▁HARBOUR
- ▁ASSURANCE
- ▁BRISTOL
- ▁BUCKINGHAM
- ▁DUDLEY
- ▁INTENSITY
- ▁CHOPIN
- ▁ENLIST
- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 18
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
calebcsjm/distilgpt2-finetuned-wikitexts
|
calebcsjm
| 2022-02-18T16:01:53Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitexts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitexts
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7608 | 1.0 | 2334 | 3.6655 |
| 3.6335 | 2.0 | 4668 | 3.6455 |
| 3.6066 | 3.0 | 7002 | 3.6424 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Milos/slovak-gpt-j-405M
|
Milos
| 2022-02-18T13:46:50Z | 1,188 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"Slovak GPT-J",
"causal-lm",
"sk",
"arxiv:2104.09864",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- sk
tags:
- Slovak GPT-J
- pytorch
- causal-lm
license: gpl-3.0
---
# Slovak GPT-J-405M
Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M). Since then a larger [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B) was released.
## Model Description
Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 405M trainable parameters.
<figure>
| Hyperparameter | Value |
|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| \\(n_{parameters}\\) | 405,677,136 |
| \\(n_{layers}\\) | 24 |
| \\(d_{model}\\) | 1024 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3†) |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
<p><strong>†</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure>
## Training data
Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model.
The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information.
## Training procedure
This model was trained for a bit more than 36.5 billion tokens over 69,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was `2.821`.
## Intended Use
Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt.
### How to use
This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-405M")
model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-405M")
```
When generating a prompt keep in mind these three things, and you should be good to go:
1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`.
2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`.
3. In case of a new line always enter `\n\n` instead of a single `\n`
To illustrate an example of a basic text generation:
```
>>> prompt = "Tradičné jedlo na Orave sú"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input)
>>> tokenizer.decode(output[0])
'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov'
```
### Capabilities, Limitations, and Biases
The capability of this particular model is somewhat decent despite its small size totalling 405M parameters. With relative ease it can manage to generate interesting and grammatically correct content.
For example, you can try few of the following prompts. (For sake of simplicity, I have omitted all the boilerplate code and swapped `\n` for new lines).
Try generating "How to" articles:
```
PROMPT
Ako napísať pôsobivú esej krok za krokom
OUTPUT
Ako napísať pôsobivú esej krok za krokom
V tomto článku sa dozviete, ako napísať esej, ktorá bude mať v sebe niečo, čo zaujme a bude sa vám páčiť.
V prvom rade si musíte uvedomiť, že esej je vlastne písaný text, ktorý má byť napísaný na počítači.'
```
However, relying on the model to produce factually correct information isn't recommended.
Or try to generate chat conversations:
```
PROMPT
Peter: Ako sa mas?
Lucia: Ale celkom dobre. Co si robil?
Peter:
OUTPUT
Peter: Ako sa mas?
Lucia: Ale celkom dobre. Co si robil?
Peter: No, bol som na chate.
Lucia: A co si tam robil?
Peter: No, bol som tam s kamošmi.
Lucia: A co si tam robil?
Peter: No, bol som tam s kamošmi.
```
Apparently either Peters are more likely to act suspiciously in this particular context or there's a problem with the model. Let's assume that the second explanation may hold some merit. In general, GPT models can (and often will) get into a repeating cycle of generating same content. This is a common problem beyond the scope of this README, however, see [generate's documentation](https://huggingface.co/docs/transformers/master/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate) on how to introduce a frequency/repetition penalty.
Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech:
```
>>> prompt = "Věta nesmí být sprostá a musí být zcela"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input, max_length=16)
>>> tokenizer.decode(output[0])
'Věta nesmí být sprostá a musí být zcela pravdivá.'
```
## Citation and Related Information
This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :)
If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile.
### BibTeX entry
To cite this model:
```bibtex
@misc{slovak-gpt-j-405m,
author = {Kondela, Milos},
title = {{Slovak GPT-J-405M}},
howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-405M}},
year = 2022,
month = February
}
```
To cite the codebase that trained this model:
```bibtex
@misc{mesh-transformer-jax,
author = {Wang, Ben},
title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
## Acknowledgements
This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).
|
AkshatSurolia/BEiT-FaceMask-Finetuned
|
AkshatSurolia
| 2022-02-18T13:40:53Z | 93 | 1 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- image-classification
datasets:
- Face-Mask18K
---
# BEiT for Face Mask Detection
BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei.
## Model description
The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that.
## Training Metrics
epoch = 0.55
total_flos = 576468516GF
train_loss = 0.151
train_runtime = 0:58:16.56
train_samples_per_second = 16.505
train_steps_per_second = 1.032
---
## Evaluation Metrics
epoch = 0.55
eval_accuracy = 0.975
eval_loss = 0.0803
eval_runtime = 0:03:13.02
eval_samples_per_second = 18.629
eval_steps_per_second = 2.331
|
keras-io/ner-with-transformers
|
keras-io
| 2022-02-18T07:47:32Z | 20 | 1 |
tf-keras
|
[
"tf-keras",
"multimodal-entailment",
"generic",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- multimodal-entailment
- generic
---
## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.
This repo contains code using the model. [Named Entity Recognition using Transformers](https://keras.io/examples/nlp/ner_transformers/).
Credits: [Varun Singh](https://www.linkedin.com/in/varunsingh2/) - Original Author
HF Contribution: [Rishav Chandra Varma](https://huggingface.co/reichenbach)
## Background Information
### Introduction
Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.
We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the [dataset website](https://www.clips.uantwerpen.be/conll2003/ner/). However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this [dataset](https://huggingface.co/datasets/conll2003).
|
swcrazyfan/KingJamesify-T5-Base
|
swcrazyfan
| 2022-02-18T03:46:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Bible",
"KJV",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- Bible
- KJV
---
# King Jamesify
This seq2seq model is my first experiment for "translating" modern English to the famous KJV Bible style.
The model is based on Google's "T5 Efficient Base" model. It was fine-tuned for 3 epochs on a NET to KJV dataset.
|
rribeiro/gtp3
|
rribeiro
| 2022-02-17T23:37:39Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
hakurei/lit-125M
|
hakurei
| 2022-02-17T22:52:19Z | 15 | 6 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- pytorch
- causal-lm
license: mit
---
# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling
Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-Neo 125M](https://huggingface.co/EleutherAI/gpt-neo-125M), which is a 125 million parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/)..
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
***
When a traveler in north central Massachusetts takes the wrong fork...
```
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.
## Example Code
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('hakurei/lit-125M')
tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-125M')
prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
```
An example output from this code produces a result that will look similar to:
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler takes a trip through the streets of the world, the traveler feels like a youkai with a whole world inside her mind. It can be very scary for a youkai. When someone goes in the opposite direction and knocks on your door, it is actually the first time you have ever come to investigate something like that.
That's right: everyone has heard stories about youkai, right? If you have heard them, you know what I'm talking about.
It's hard not to say you
```
## Team members and Acknowledgements
- [Anthony Mercurio](https://github.com/harubaru)
- Imperishable_NEET
|
bryan6aero/wav2vec2-base-timit-demo-colab
|
bryan6aero
| 2022-02-17T22:00:53Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4779
- Wer: 0.3453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4307 | 4.0 | 500 | 1.4129 | 0.9980 |
| 0.626 | 8.0 | 1000 | 0.4605 | 0.4499 |
| 0.2199 | 12.0 | 1500 | 0.4457 | 0.3898 |
| 0.1303 | 16.0 | 2000 | 0.4418 | 0.3771 |
| 0.0851 | 20.0 | 2500 | 0.4647 | 0.3548 |
| 0.0604 | 24.0 | 3000 | 0.4603 | 0.3499 |
| 0.0461 | 28.0 | 3500 | 0.4779 | 0.3453 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
oemga38/distilbert-base-uncased-finetuned-cola
|
oemga38
| 2022-02-17T21:51:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5570389007427182
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7475
- Matthews Correlation: 0.5570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5251 | 1.0 | 535 | 0.5304 | 0.4272 |
| 0.3474 | 2.0 | 1070 | 0.4874 | 0.5136 |
| 0.2356 | 3.0 | 1605 | 0.6454 | 0.5314 |
| 0.1699 | 4.0 | 2140 | 0.7475 | 0.5570 |
| 0.1244 | 5.0 | 2675 | 0.8525 | 0.5478 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
BigSalmon/GPTNeo350MInformalToFormalLincoln
|
BigSalmon
| 2022-02-17T21:37:07Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
|
huggingtweets/owljohn
|
huggingtweets
| 2022-02-17T21:30:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/owljohn/1645133439835/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/984175260135231488/eqWrIzlg_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Scott Hutchison</div>
<div style="text-align: center; font-size: 14px;">@owljohn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Scott Hutchison.
| Data | Scott Hutchison |
| --- | --- |
| Tweets downloaded | 2807 |
| Retweets | 454 |
| Short tweets | 239 |
| Tweets kept | 2114 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pbwq7lq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @owljohn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6jgoemgd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6jgoemgd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/owljohn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
JBNLRY/distilbert-base-uncased-finetuned-cola
|
JBNLRY
| 2022-02-17T19:56:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5471613867597194
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8366
- Matthews Correlation: 0.5472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5224 | 1.0 | 535 | 0.5432 | 0.4243 |
| 0.3447 | 2.0 | 1070 | 0.4968 | 0.5187 |
| 0.2347 | 3.0 | 1605 | 0.6540 | 0.5280 |
| 0.1747 | 4.0 | 2140 | 0.7547 | 0.5367 |
| 0.1255 | 5.0 | 2675 | 0.8366 | 0.5472 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
cankeles/DPTNet_WHAMR_enhsingle_16k
|
cankeles
| 2022-02-17T19:53:06Z | 13 | 2 |
asteroid
|
[
"asteroid",
"pytorch",
"audio",
"DPTNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
tags:
- asteroid
- audio
- DPTNet
- audio-to-audio
datasets:
- Libri1Mix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `cankeles/DPTNet_WHAMR_enhsignle_16k`
Description:
This model was trained by M. Can Keleş using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
mode: min
nondefault_nsrc: null
sample_rate: 16000
segment: 2.0
task: enh_single
train_dir: wav16k/min/tr/
valid_dir: wav16k/min/cv/
filterbank:
kernel_size: 16
n_filters: 64
stride: 8
main_args:
exp_dir: exp/tmp
help: null
masknet:
bidirectional: true
chunk_size: 100
dropout: 0
ff_activation: relu
ff_hid: 256
hop_size: 50
in_chan: 64
mask_act: sigmoid
n_repeats: 2
n_src: 1
norm_type: gLN
out_chan: 64
optim:
lr: 0.001
optimizer: adam
weight_decay: 1.0e-05
positional arguments: {}
scheduler:
d_model: 64
steps_per_epoch: 10000
training:
batch_size: 4
early_stop: true
epochs: 60
gradient_clipping: 5
half_lr: true
num_workers: 4
```
Results:
On custom min test set :
```yml
'sar': 12.853384266251018,
'sar_imp': 8.950332361953906,
'sdr': 12.853384266251018,
'sdr_imp': 8.950332361953906,
'si_sdr': 12.247012621312548,
'si_sdr_imp': 8.429646186633407,
'sir': inf,
'sir_imp': nan,
'stoi': 0.9022338865380519,
'stoi_imp': 0.09735707619500522
```
|
huggingtweets/terra_lunatics
|
huggingtweets
| 2022-02-17T18:42:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/terra_lunatics/1645123350159/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1482058200237101070/bffBfLZO_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">SuperTerra🌖</div>
<div style="text-align: center; font-size: 14px;">@terra_lunatics</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from SuperTerra🌖.
| Data | SuperTerra🌖 |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 440 |
| Short tweets | 395 |
| Tweets kept | 2412 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cqexjw8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @terra_lunatics's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q70oo5u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q70oo5u/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/terra_lunatics')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
noharm-ai/anony
|
noharm-ai
| 2022-02-17T17:12:25Z | 4 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"pt",
"license:mit",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
language: "pt"
widget:
- text: "FISIOTERAPIA TRAUMATO - MANHÃ Henrique Dias, 38 anos. Exercícios metabólicos de extremidades inferiores. Realizo mobilização patelar e leve mobilização de flexão de joelho conforme liberado pelo Dr Marcelo Arocha. Oriento cuidados e posicionamentos."
---
## Portuguese Name Identification
The [NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) paper contains Flair-based models for Portuguese Language, initialized with [Flair BBP](https://github.com/jneto04/ner-pt) & trained on clinical notes with names tagged.
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("noharm-ai/anony")
# make example sentence
sentence = Sentence("FISIOTERAPIA TRAUMATO - MANHÃ Henrique Dias, 38 anos. Exercícios metabólicos de extremidades inferiores. Realizo mobilização patelar e leve mobilização de flexão de joelho conforme liberado pelo Dr Marcelo Arocha. Oriento cuidados e posicionamentos.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [5,6]: "Henrique Dias" [− Labels: NOME (0.9735)]
Span [31,32]: "Marcelo Arocha" [− Labels: NOME (0.9803)]
```
So, the entities "*Henrique Dias*" (labeled as a **nome**) and "*Marcelo Arocha*" (labeled as a **nome**) are found in the sentence.
## More Information
Refer to the original paper, [De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) for additional details and performance.
## Acknowledgements
We thank Dr. Ana Helena D. P. S. Ulbrich, who provided the clinical notes dataset from the hospital, for her valuable cooperation. We also thank the volunteers of the Institute of Artificial Intelligence in Healthcare Celso Pereira and Ana Lúcia Dias, for the dataset annotation.
## Citation
```
@inproceedings{santos2021identification,
title={De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier},
author={Santos, Joaquim and dos Santos, Henrique DP and Tabalipa, F{\'a}bio and Vieira, Renata},
booktitle={Brazilian Conference on Intelligent Systems},
pages={33--41},
year={2021},
organization={Springer}
}
```
|
keras-io/CycleGAN
|
keras-io
| 2022-02-17T16:47:55Z | 13 | 10 |
tf-keras
|
[
"tf-keras",
"gan",
"computer vision",
"horse to zebra",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- gan
- computer vision
- horse to zebra
license:
- cc0-1.0
---
## Keras Implementation of CycleGAN model using [Horse to Zebra dataset](https://www.tensorflow.org/datasets/catalog/cycle_gan#cycle_ganhorse2zebra) 🐴 -> 🦓
This repo contains the model and the notebook [to this Keras example on CycleGAN](https://keras.io/examples/generative/cyclegan/).
Full credits to: [Aakash Kumar Nain](https://twitter.com/A_K_Nain)
## Background Information
CycleGAN is a model that aims to solve the image-to-image translation problem. The goal of the image-to-image translation problem is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, obtaining paired examples isn't always feasible. CycleGAN tries to learn this mapping without requiring paired input-output images, using cycle-consistent adversarial networks.
|
Andranik/TestQA2
|
Andranik
| 2022-02-17T16:43:26Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: electra_large_discriminator_squad2_512
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra_large_discriminator_squad2_512
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
figurative-nlp/se4fig-roberta-base
|
figurative-nlp
| 2022-02-17T15:54:01Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model can measure semantic similarity between pairs of texts containing figurative language. As far as we know,
this model works slightly better than sup-simCSE-roberta-base. For example :
**sentence 1**: I have been in seventh heaven since Harry entered my life .
**sentence 2**: I have been in very happy since Harry entered my life.
the cosin score of simcse: 0.897
the cosin score of us: 0.897
-------------------------------------------------------------------
**sentence 1**: I have been in seventh heaven since Harry entered my life .
**sentence 2**: I have been in pain since Harry entered my life .
the cosin score of simcse: 0.846
the cosin score of us: 0.753
--------------------------------------------------
It's still a big challenge for us to measure semantic similarity of figurative language from the sentence embedding perspective.
unsupvised models may useless as the key is to infer the literal meaning of the figurative expression, since the annotated is rare.
|
Milos/slovak-gpt-j-1.4B
|
Milos
| 2022-02-17T14:29:47Z | 415 | 6 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"Slovak GPT-J",
"causal-lm",
"sk",
"arxiv:2104.09864",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- sk
tags:
- Slovak GPT-J
- pytorch
- causal-lm
license: gpl-3.0
---
# Slovak GPT-J-1.4B
Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M), are still available.
## Model Description
Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 1.4B trainable parameters.
<figure>
| Hyperparameter | Value |
|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| \\(n_{parameters}\\) | 1,415,283,792 |
| \\(n_{layers}\\) | 24 |
| \\(d_{model}\\) | 2048 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3†) |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
<p><strong>†</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure>
## Training data
Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model.
The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information.
## Training procedure
This model was trained for a bit more than 26.5 billion tokens over 48,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was `2.657`.
## Intended Use
Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt.
### How to use
This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-1.4B")
model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-1.4B")
```
When generating a prompt keep in mind these three things, and you should be good to go:
1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`.
2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`.
3. In case of a new line always enter `\n\n` instead of a single `\n`
To illustrate an example of a basic text generation:
```
>>> prompt = "Tradičné jedlo na Orave sú"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input)
>>> tokenizer.decode(output[0])
'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov'
```
### Capabilities, Limitations, and Biases
Based on the parameter count and our internal testing this should be by far the most capable model out of all models released in Slovak GPT-J series.
For sake of simplicity, I have omitted all the boilerplate code and swapped `\n` for new lines in the examples that follow. These were generated with both `top_p` and `temperature` set to `0.9`.
A spin on "Mor ho!" by Samo Chalpuka
```
PROMPT
Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu.
Duní Dunaj a luna za lunou sa valí
OUTPUT
Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu.
Duní Dunaj a luna za lunou sa valí: nad ním svieti pevný hrad na vysokom bralí. Pod tým hradom je zem slovenská, na nej je Slovensko naše.
Na tom vrchu leží slovenský národ, pod tým bralom leží krajina slovenská. A pod tým hradom leží i jeho sláva: na tom vrchu, na tom brale leží sláva slovenského národa.```
```
Jokes are a bit hit or miss. Very often they contain vulgarisms and/or are highly politically incorrect. This one is rather exception than the rule.
```
PROMPT
Nasledujú najlepšie vtipy.
Prečo
OUTPUT
Nasledujú najlepšie vtipy.
Prečo má policajt pri sebe vždy dva nože? Jeden je na sebaobranu, druhý na boj zblízka.
```
The infamous unicorn prompt from GPT-2 paper.
```
PROMPT
V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky.
OUTPUT
V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky.
V Andách ich totiž žilo približne 700 jedincov, ktoré sa živili výhradne materským mliekom. Slováci sú tak pravdepodobne prvými Európanmi, ktorí v Andách stretli jednorožca. "Je to dôkaz, že tieto zvieratá sú inteligentné a že žijú v Andách už stovky rokov," povedal pre webový portál televízie JOJ profesor geológie, geografie a zoológie, Milan Kováč.
Podľa profesora Kováča si v Andách zvieratá vytvárajú svoj vlastný jazyk. Je to zároveň dôkaz, že jednorožce žili v minulosti aj v slovenských pohoriach. "Jednorožce sa tam síce vyskytovali, ale neboli tak dobre preskúmané, ako teraz v Andách."
Na Slovensku však ľudia o jednorožcoch donedávna vedeli veľmi málo.<|endoftext|>
```
Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech:
```
>>> prompt = "Věta nesmí být sprostá a musí být zcela"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input, max_length=16)
>>> tokenizer.decode(output[0])
'Věta nesmí být sprostá a musí být zcela pravdivá.'
```
## Citation and Related Information
This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :)
If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile.
### BibTeX entry
To cite this model:
```bibtex
@misc{slovak-gpt-j-1.4B,
author = {Kondela, Milos},
title = {{Slovak GPT-J-1.4B}},
howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-1.4B}},
year = 2022,
month = February
}
```
To cite the codebase that trained this model:
```bibtex
@misc{mesh-transformer-jax,
author = {Wang, Ben},
title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
## Acknowledgements
This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).
|
Jour/m2m100_418M-fr
|
Jour
| 2022-02-17T13:41:07Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: m2m100_418M-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m100_418M-fr
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.0+cpu
- Datasets 1.16.1
- Tokenizers 0.10.3
|
emre/distilbert-tr-q-a
|
emre
| 2022-02-17T13:40:11Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: tr
tags:
- question-answering
- loodos-bert-base
- TQuAD
- tr
datasets:
- TQuAD
---
# Turkish SQuAD Model : Question Answering
Fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
* Loodos-BERT-base: https://huggingface.co/loodos/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
```
!python3 Turkish-QA.py \
--model_type bert \
--model_name_or_path loodos/bert-base-turkish-uncased
--do_train \
--do_eval \
--train_file trainQ.json \
--predict_file dev1.json \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--output_dir "./model"
```
# Example Usage
> Load Model
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("emre/distilbert-tr-q-a")
model = AutoModelForQuestionAnswering.from_pretrained("emre/distilbert-tr-q-a")
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
```
> Apply the model
```
def ask(question,context):
temp = nlp(question=question, context=context)
start_idx = temp["start"]
end_idx = temp["end"]
return context[start_idx:end_idx]
izmir="İzmir, Türkiye'de Ege Bölgesi'nde yer alan şehir ve ülkenin 81 ilinden biridir. Ülkenin nüfus bakımından en kalabalık üçüncü şehridir. Ekonomik, tarihi ve sosyo-kültürel açıdan önde gelen şehirlerden biridir. Nüfusu 2021 itibarıyla 4.425.789 kişidir. Yüzölçümü olarak ülkenin yirmi üçüncü büyük ilidir."
soru1 = "İzmir'in nüfusu kaçtır?"
print(ask(soru1,izmir))
soru2 = "İzmir hangi bölgede bulunur?"
print(ask(soru2,izmir))
```
|
moshew/miny-bert-aug-sst2-distilled
|
moshew
| 2022-02-17T11:48:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:augmented_glue_sst2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- augmented_glue_sst2
metrics:
- accuracy
model-index:
- name: miny-bert-aug-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: augmented_glue_sst2
type: augmented_glue_sst2
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9128440366972477
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# miny-bert-aug-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the augmented_glue_sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2643
- Accuracy: 0.9128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.602 | 1.0 | 6227 | 0.3389 | 0.9186 |
| 0.4195 | 2.0 | 12454 | 0.2989 | 0.9151 |
| 0.3644 | 3.0 | 18681 | 0.2794 | 0.9117 |
| 0.3304 | 4.0 | 24908 | 0.2793 | 0.9106 |
| 0.3066 | 5.0 | 31135 | 0.2659 | 0.9186 |
| 0.2881 | 6.0 | 37362 | 0.2668 | 0.9140 |
| 0.2754 | 7.0 | 43589 | 0.2643 | 0.9128 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
peterhsu/tf-distilbert-base-uncased-finetuned-imdb
|
peterhsu
| 2022-02-17T09:51:52Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: peterhsu/tf-distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# peterhsu/tf-distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5691
- Validation Loss: 2.4661
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8546 | 2.6095 | 0 |
| 2.6594 | 2.5243 | 1 |
| 2.5691 | 2.4661 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
cuongngm/layoutlm-bill
|
cuongngm
| 2022-02-17T09:45:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
Fine tuning LayoutLMv2 model on Vietnamese bill dataset
```python
from transformers import LayoutLMv2ForTokenClassification
model = LayoutLMv2ForTokenClassification.from_pretrained('cuongngm/layoutlm-bill', num_labels=len(labels))
```
labels = ['price',
'storename',
'total_cost',
'phone',
'address',
'unitprice',
'item',
'subitem',
'other',
'time',
'unit',
'total refunds',
'total_qty',
'seller',
'total_received']
|
DATEXIS/CORe-clinical-diagnosis-prediction
|
DATEXIS
| 2022-02-17T09:36:23Z | 636 | 29 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"diagnosis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- bert
- medical
- clinical
- diagnosis
- text-classification
thumbnail: "https://core.app.datexis.com/static/paper.png"
widget:
- text: "Patient with hypertension presents to ICU."
---
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is **fine-tuned on the task of diagnosis prediction**.
The model expects patient admission notes as input and outputs multi-label ICD9-code predictions.
#### Model Predictions
The model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the **3-digit code predictions at inference time**, because only those have been evaluated in our work.
#### How to use CORe Diagnosis Prediction
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
```
The following code shows an inference example:
```
input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."
tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)
import torch
predictions = torch.sigmoid(output.logits)
predicted_labels = [model.config.id2label[_id] for _id in (predictions > 0.3).nonzero()[:, 1].tolist()]
```
Note: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.
### More Information
For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
### Cite
```bibtex
@inproceedings{vanaken21,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
publisher = {Association for Computational Linguistics},
year = {2021},
}
```
|
figurative-nlp/t5-figurative-generation
|
figurative-nlp
| 2022-02-17T09:23:23Z | 17 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
This model can convert the literal expression to figurative/metaphorical expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("figurative-nlp/t5-figurative-generation")
input_ids = tokenizer(
"research is <m> very difficult </m> for me.", return_tensors="pt"
).input_ids # Batch size 1
outputs = model.generate(input_ids,beam_search = 5)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
#result : research is a tough nut to crack for me.
For example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):
**Input**: as of a cloud that softly <m> covers </m> the sun.
**Output**: as of a cloud that softly drapes over the sun.
**Input**: that car coming around the corner <m> surprised me. </m>
**Output**: that car coming around the corner knocked my socks off.
Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model.
|
phongdtd/wavlm-vindata-demo-dist
|
phongdtd
| 2022-02-17T05:00:57Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"dataset:vin_data_vlsp",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- phongdtd/VinDataVLSP
- generated_from_trainer
datasets:
- vin_data_vlsp
model-index:
- name: wavlm-vindata-demo-dist
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-vindata-demo-dist
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4439
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:---:|
| 4.0704 | 0.01 | 100 | 3.8768 | 1.0 |
| 3.6236 | 0.01 | 200 | 3.4611 | 1.0 |
| 6.597 | 0.02 | 300 | 3.4557 | 1.0 |
| 3.4744 | 0.03 | 400 | 3.4567 | 1.0 |
| 5.3992 | 0.04 | 500 | 3.4631 | 1.0 |
| 4.5348 | 0.04 | 600 | 3.4651 | 1.0 |
| 3.2457 | 0.05 | 700 | 3.4917 | 1.0 |
| 3.9245 | 0.06 | 800 | 3.4680 | 1.0 |
| 3.2904 | 0.07 | 900 | 3.4518 | 1.0 |
| 3.4768 | 0.07 | 1000 | 3.4506 | 1.0 |
| 3.2418 | 0.08 | 1100 | 3.4474 | 1.0 |
| 3.3111 | 0.09 | 1200 | 3.4684 | 1.0 |
| 3.986 | 0.09 | 1300 | 3.4465 | 1.0 |
| 4.3206 | 0.1 | 1400 | 3.4723 | 1.0 |
| 4.682 | 0.11 | 1500 | 3.4732 | 1.0 |
| 4.858 | 0.12 | 1600 | 3.4416 | 1.0 |
| 3.2949 | 0.12 | 1700 | 3.4481 | 1.0 |
| 3.4435 | 0.13 | 1800 | 3.4570 | 1.0 |
| 5.0695 | 0.14 | 1900 | 3.4448 | 1.0 |
| 3.4962 | 0.14 | 2000 | 3.4416 | 1.0 |
| 3.4891 | 0.15 | 2100 | 3.4455 | 1.0 |
| 4.1281 | 0.16 | 2200 | 3.4447 | 1.0 |
| 3.5956 | 0.17 | 2300 | 3.4512 | 1.0 |
| 3.6312 | 0.17 | 2400 | 3.4484 | 1.0 |
| 4.5383 | 0.18 | 2500 | 3.4435 | 1.0 |
| 6.1329 | 0.19 | 2600 | 3.4530 | 1.0 |
| 3.709 | 0.2 | 2700 | 3.4466 | 1.0 |
| 3.289 | 0.2 | 2800 | 3.4463 | 1.0 |
| 4.3301 | 0.21 | 2900 | 3.4418 | 1.0 |
| 4.6656 | 0.22 | 3000 | 3.4447 | 1.0 |
| 3.4288 | 0.22 | 3100 | 3.4715 | 1.0 |
| 3.5506 | 0.23 | 3200 | 3.4437 | 1.0 |
| 3.7497 | 0.24 | 3300 | 3.4910 | 1.0 |
| 3.5198 | 0.25 | 3400 | 3.4574 | 1.0 |
| 3.4183 | 0.25 | 3500 | 3.4607 | 1.0 |
| 4.5573 | 0.26 | 3600 | 3.4421 | 1.0 |
| 3.5737 | 0.27 | 3700 | 3.4481 | 1.0 |
| 4.9008 | 0.28 | 3800 | 3.4411 | 1.0 |
| 4.8725 | 0.28 | 3900 | 3.4422 | 1.0 |
| 3.5799 | 0.29 | 4000 | 3.4659 | 1.0 |
| 3.3257 | 0.3 | 4100 | 3.4519 | 1.0 |
| 3.6887 | 0.3 | 4200 | 3.4827 | 1.0 |
| 3.3037 | 0.31 | 4300 | 3.4632 | 1.0 |
| 5.5543 | 0.32 | 4400 | 3.4480 | 1.0 |
| 3.2898 | 0.33 | 4500 | 3.4404 | 1.0 |
| 3.2794 | 0.33 | 4600 | 3.4633 | 1.0 |
| 3.7896 | 0.34 | 4700 | 3.4439 | 1.0 |
| 3.6662 | 0.35 | 4800 | 3.4587 | 1.0 |
| 3.588 | 0.35 | 4900 | 3.4520 | 1.0 |
| 4.0535 | 0.36 | 5000 | 3.4450 | 1.0 |
| 3.4335 | 0.37 | 5100 | 3.4577 | 1.0 |
| 3.6317 | 0.38 | 5200 | 3.4443 | 1.0 |
| 5.2564 | 0.38 | 5300 | 3.4505 | 1.0 |
| 3.8781 | 0.39 | 5400 | 3.4418 | 1.0 |
| 4.6269 | 0.4 | 5500 | 3.4425 | 1.0 |
| 3.6095 | 0.41 | 5600 | 3.4581 | 1.0 |
| 4.6164 | 0.41 | 5700 | 3.4404 | 1.0 |
| 3.117 | 0.42 | 5800 | 3.4596 | 1.0 |
| 4.3939 | 0.43 | 5900 | 3.4401 | 1.0 |
| 3.5856 | 0.43 | 6000 | 3.4413 | 1.0 |
| 3.5187 | 0.44 | 6100 | 3.4452 | 1.0 |
| 4.7991 | 0.45 | 6200 | 3.4481 | 1.0 |
| 3.3905 | 0.46 | 6300 | 3.4420 | 1.0 |
| 3.5086 | 0.46 | 6400 | 3.4494 | 1.0 |
| 4.8217 | 0.47 | 6500 | 3.4477 | 1.0 |
| 3.3193 | 0.48 | 6600 | 3.4382 | 1.0 |
| 5.3482 | 0.49 | 6700 | 3.4580 | 1.0 |
| 3.3947 | 0.49 | 6800 | 3.4767 | 1.0 |
| 6.3352 | 0.5 | 6900 | 3.4476 | 1.0 |
| 3.4448 | 0.51 | 7000 | 3.4557 | 1.0 |
| 3.5358 | 0.51 | 7100 | 3.4438 | 1.0 |
| 3.3499 | 0.52 | 7200 | 3.4445 | 1.0 |
| 3.6932 | 0.53 | 7300 | 3.4463 | 1.0 |
| 6.9058 | 0.54 | 7400 | 3.4482 | 1.0 |
| 4.5514 | 0.54 | 7500 | 3.4422 | 1.0 |
| 3.517 | 0.55 | 7600 | 3.4505 | 1.0 |
| 7.4479 | 0.56 | 7700 | 3.4461 | 1.0 |
| 3.3761 | 0.56 | 7800 | 3.4511 | 1.0 |
| 4.5925 | 0.57 | 7900 | 3.4389 | 1.0 |
| 5.2682 | 0.58 | 8000 | 3.4563 | 1.0 |
| 5.6748 | 0.59 | 8100 | 3.4601 | 1.0 |
| 4.4335 | 0.59 | 8200 | 3.4439 | 1.0 |
| 5.1686 | 0.6 | 8300 | 3.4444 | 1.0 |
| 3.5245 | 0.61 | 8400 | 3.4629 | 1.0 |
| 4.9426 | 0.62 | 8500 | 3.4389 | 1.0 |
| 4.4654 | 0.62 | 8600 | 3.4427 | 1.0 |
| 3.5626 | 0.63 | 8700 | 3.4521 | 1.0 |
| 4.7086 | 0.64 | 8800 | 3.4489 | 1.0 |
| 3.238 | 0.64 | 8900 | 3.4478 | 1.0 |
| 4.2738 | 0.65 | 9000 | 3.4510 | 1.0 |
| 3.4468 | 0.66 | 9100 | 3.4411 | 1.0 |
| 3.2292 | 0.67 | 9200 | 3.4416 | 1.0 |
| 3.4972 | 0.67 | 9300 | 3.4643 | 1.0 |
| 7.3434 | 0.68 | 9400 | 3.4587 | 1.0 |
| 3.708 | 0.69 | 9500 | 3.4799 | 1.0 |
| 4.6466 | 0.69 | 9600 | 3.4490 | 1.0 |
| 3.3347 | 0.7 | 9700 | 3.4532 | 1.0 |
| 5.1486 | 0.71 | 9800 | 3.4427 | 1.0 |
| 3.6456 | 0.72 | 9900 | 3.4492 | 1.0 |
| 5.3904 | 0.72 | 10000 | 3.4497 | 1.0 |
| 4.8832 | 0.73 | 10100 | 3.4476 | 1.0 |
| 3.4482 | 0.74 | 10200 | 3.4539 | 1.0 |
| 3.617 | 0.75 | 10300 | 3.4547 | 1.0 |
| 5.4691 | 0.75 | 10400 | 3.4663 | 1.0 |
| 4.2759 | 0.76 | 10500 | 3.4401 | 1.0 |
| 8.2106 | 0.77 | 10600 | 3.4404 | 1.0 |
| 3.4894 | 0.77 | 10700 | 3.4426 | 1.0 |
| 3.6875 | 0.78 | 10800 | 3.4439 | 1.0 |
| 3.3277 | 0.79 | 10900 | 3.4446 | 1.0 |
| 4.5175 | 0.8 | 11000 | 3.4456 | 1.0 |
| 5.2161 | 0.8 | 11100 | 3.4388 | 1.0 |
| 3.5234 | 0.81 | 11200 | 3.4418 | 1.0 |
| 4.2212 | 0.82 | 11300 | 3.4392 | 1.0 |
| 3.6923 | 0.83 | 11400 | 3.4494 | 1.0 |
| 3.4863 | 0.83 | 11500 | 3.4572 | 1.0 |
| 6.3201 | 0.84 | 11600 | 3.4377 | 1.0 |
| 3.7543 | 0.85 | 11700 | 3.4533 | 1.0 |
| 3.3959 | 0.85 | 11800 | 3.4600 | 1.0 |
| 3.5691 | 0.86 | 11900 | 3.4673 | 1.0 |
| 3.49 | 0.87 | 12000 | 3.4407 | 1.0 |
| 7.1165 | 0.88 | 12100 | 3.4427 | 1.0 |
| 6.731 | 0.88 | 12200 | 3.4394 | 1.0 |
| 4.4682 | 0.89 | 12300 | 3.4407 | 1.0 |
| 3.3696 | 0.9 | 12400 | 3.4415 | 1.0 |
| 4.0241 | 0.9 | 12500 | 3.4454 | 1.0 |
| 3.521 | 0.91 | 12600 | 3.4379 | 1.0 |
| 5.5273 | 0.92 | 12700 | 3.4423 | 1.0 |
| 3.4781 | 0.93 | 12800 | 3.4635 | 1.0 |
| 3.4542 | 0.93 | 12900 | 3.4411 | 1.0 |
| 3.2363 | 0.94 | 13000 | 3.4396 | 1.0 |
| 5.3009 | 0.95 | 13100 | 3.4458 | 1.0 |
| 3.498 | 0.96 | 13200 | 3.4398 | 1.0 |
| 6.3325 | 0.96 | 13300 | 3.4514 | 1.0 |
| 3.5368 | 0.97 | 13400 | 3.4437 | 1.0 |
| 5.1164 | 0.98 | 13500 | 3.4623 | 1.0 |
| 3.6144 | 0.98 | 13600 | 3.4512 | 1.0 |
| 6.6018 | 0.99 | 13700 | 3.4493 | 1.0 |
| 3.7539 | 1.0 | 13800 | 3.4597 | 1.0 |
| 3.2903 | 1.01 | 13900 | 3.4813 | 1.0 |
| 3.3243 | 1.01 | 14000 | 3.4510 | 1.0 |
| 3.3485 | 1.02 | 14100 | 3.4389 | 1.0 |
| 3.6197 | 1.03 | 14200 | 3.4519 | 1.0 |
| 3.322 | 1.04 | 14300 | 3.4399 | 1.0 |
| 3.2897 | 1.04 | 14400 | 3.4378 | 1.0 |
| 3.3969 | 1.05 | 14500 | 3.4476 | 1.0 |
| 3.3289 | 1.06 | 14600 | 3.4646 | 1.0 |
| 3.3556 | 1.06 | 14700 | 3.4520 | 1.0 |
| 3.2527 | 1.07 | 14800 | 3.4575 | 1.0 |
| 3.4003 | 1.08 | 14900 | 3.4443 | 1.0 |
| 3.3171 | 1.09 | 15000 | 3.4434 | 1.0 |
| 3.4034 | 1.09 | 15100 | 3.4448 | 1.0 |
| 3.4363 | 1.1 | 15200 | 3.4560 | 1.0 |
| 3.3969 | 1.11 | 15300 | 3.4405 | 1.0 |
| 3.4134 | 1.11 | 15400 | 3.4408 | 1.0 |
| 3.5059 | 1.12 | 15500 | 3.4395 | 1.0 |
| 3.3963 | 1.13 | 15600 | 3.4488 | 1.0 |
| 3.2937 | 1.14 | 15700 | 3.4482 | 1.0 |
| 3.5635 | 1.14 | 15800 | 3.4621 | 1.0 |
| 3.4463 | 1.15 | 15900 | 3.4433 | 1.0 |
| 3.2588 | 1.16 | 16000 | 3.4434 | 1.0 |
| 3.3617 | 1.17 | 16100 | 3.4542 | 1.0 |
| 3.3721 | 1.17 | 16200 | 3.4388 | 1.0 |
| 3.3867 | 1.18 | 16300 | 3.4577 | 1.0 |
| 3.34 | 1.19 | 16400 | 3.4510 | 1.0 |
| 3.3676 | 1.19 | 16500 | 3.4434 | 1.0 |
| 3.5519 | 1.2 | 16600 | 3.4410 | 1.0 |
| 3.3129 | 1.21 | 16700 | 3.4507 | 1.0 |
| 3.3368 | 1.22 | 16800 | 3.4718 | 1.0 |
| 3.3107 | 1.22 | 16900 | 3.4439 | 1.0 |
| 3.2987 | 1.23 | 17000 | 3.4471 | 1.0 |
| 3.3102 | 1.24 | 17100 | 3.4435 | 1.0 |
| 3.2089 | 1.25 | 17200 | 3.4432 | 1.0 |
| 3.415 | 1.25 | 17300 | 3.4472 | 1.0 |
| 3.2884 | 1.26 | 17400 | 3.4388 | 1.0 |
| 3.3837 | 1.27 | 17500 | 3.4444 | 1.0 |
| 3.3181 | 1.27 | 17600 | 3.4438 | 1.0 |
| 3.3071 | 1.28 | 17700 | 3.4406 | 1.0 |
| 3.389 | 1.29 | 17800 | 3.4573 | 1.0 |
| 3.3246 | 1.3 | 17900 | 3.4580 | 1.0 |
| 3.3122 | 1.3 | 18000 | 3.4455 | 1.0 |
| 3.282 | 1.31 | 18100 | 3.4606 | 1.0 |
| 3.2671 | 1.32 | 18200 | 3.4378 | 1.0 |
| 3.3441 | 1.32 | 18300 | 3.4432 | 1.0 |
| 3.3115 | 1.33 | 18400 | 3.4458 | 1.0 |
| 3.3542 | 1.34 | 18500 | 3.4617 | 1.0 |
| 3.3924 | 1.35 | 18600 | 3.4549 | 1.0 |
| 3.4895 | 1.35 | 18700 | 3.4557 | 1.0 |
| 3.4071 | 1.36 | 18800 | 3.4462 | 1.0 |
| 3.3373 | 1.37 | 18900 | 3.4606 | 1.0 |
| 3.3497 | 1.38 | 19000 | 3.4458 | 1.0 |
| 3.3088 | 1.38 | 19100 | 3.4712 | 1.0 |
| 3.333 | 1.39 | 19200 | 3.4483 | 1.0 |
| 3.3773 | 1.4 | 19300 | 3.4455 | 1.0 |
| 3.357 | 1.4 | 19400 | 3.4379 | 1.0 |
| 3.3506 | 1.41 | 19500 | 3.4477 | 1.0 |
| 3.2944 | 1.42 | 19600 | 3.4478 | 1.0 |
| 3.241 | 1.43 | 19700 | 3.4492 | 1.0 |
| 3.4317 | 1.43 | 19800 | 3.4441 | 1.0 |
| 3.3478 | 1.44 | 19900 | 3.4385 | 1.0 |
| 3.3952 | 1.45 | 20000 | 3.4437 | 1.0 |
| 3.4808 | 1.46 | 20100 | 3.4644 | 1.0 |
| 3.3625 | 1.46 | 20200 | 3.4529 | 1.0 |
| 3.4842 | 1.47 | 20300 | 3.4524 | 1.0 |
| 3.3887 | 1.48 | 20400 | 3.4551 | 1.0 |
| 3.3198 | 1.48 | 20500 | 3.4433 | 1.0 |
| 3.3397 | 1.49 | 20600 | 3.4448 | 1.0 |
| 3.3173 | 1.5 | 20700 | 3.4590 | 1.0 |
| 3.3687 | 1.51 | 20800 | 3.4720 | 1.0 |
| 3.257 | 1.51 | 20900 | 3.4461 | 1.0 |
| 3.4451 | 1.52 | 21000 | 3.4541 | 1.0 |
| 3.2979 | 1.53 | 21100 | 3.4556 | 1.0 |
| 3.3566 | 1.53 | 21200 | 3.4438 | 1.0 |
| 3.3466 | 1.54 | 21300 | 3.4422 | 1.0 |
| 3.308 | 1.55 | 21400 | 3.4637 | 1.0 |
| 3.3952 | 1.56 | 21500 | 3.4435 | 1.0 |
| 3.4009 | 1.56 | 21600 | 3.4434 | 1.0 |
| 3.7952 | 1.57 | 21700 | 3.4675 | 1.0 |
| 3.3891 | 1.58 | 21800 | 3.4565 | 1.0 |
| 3.31 | 1.59 | 21900 | 3.4538 | 1.0 |
| 3.3186 | 1.59 | 22000 | 3.4492 | 1.0 |
| 3.3512 | 1.6 | 22100 | 3.4381 | 1.0 |
| 3.309 | 1.61 | 22200 | 3.4558 | 1.0 |
| 3.597 | 1.61 | 22300 | 3.4484 | 1.0 |
| 3.4474 | 1.62 | 22400 | 3.4574 | 1.0 |
| 3.3316 | 1.63 | 22500 | 3.4498 | 1.0 |
| 3.3909 | 1.64 | 22600 | 3.4384 | 1.0 |
| 3.6999 | 1.64 | 22700 | 3.4503 | 1.0 |
| 3.6071 | 1.65 | 22800 | 3.4578 | 1.0 |
| 3.2812 | 1.66 | 22900 | 3.4563 | 1.0 |
| 3.2921 | 1.67 | 23000 | 3.4564 | 1.0 |
| 3.3291 | 1.67 | 23100 | 3.4490 | 1.0 |
| 3.3454 | 1.68 | 23200 | 3.4403 | 1.0 |
| 3.4212 | 1.69 | 23300 | 3.4409 | 1.0 |
| 3.5481 | 1.69 | 23400 | 3.4534 | 1.0 |
| 3.2784 | 1.7 | 23500 | 3.4486 | 1.0 |
| 3.4625 | 1.71 | 23600 | 3.4413 | 1.0 |
| 3.2427 | 1.72 | 23700 | 3.4694 | 1.0 |
| 3.8438 | 1.72 | 23800 | 3.4444 | 1.0 |
| 3.4009 | 1.73 | 23900 | 3.4505 | 1.0 |
| 3.8029 | 1.74 | 24000 | 3.4712 | 1.0 |
| 3.36 | 1.74 | 24100 | 3.4552 | 1.0 |
| 3.2751 | 1.75 | 24200 | 3.4511 | 1.0 |
| 3.309 | 1.76 | 24300 | 3.4368 | 1.0 |
| 3.4597 | 1.77 | 24400 | 3.4517 | 1.0 |
| 3.2812 | 1.77 | 24500 | 3.4475 | 1.0 |
| 3.4425 | 1.78 | 24600 | 3.4413 | 1.0 |
| 3.3968 | 1.79 | 24700 | 3.4482 | 1.0 |
| 3.35 | 1.8 | 24800 | 3.4473 | 1.0 |
| 3.3156 | 1.8 | 24900 | 3.4435 | 1.0 |
| 3.3008 | 1.81 | 25000 | 3.4439 | 1.0 |
| 3.3365 | 1.82 | 25100 | 3.4382 | 1.0 |
| 3.5473 | 1.82 | 25200 | 3.4396 | 1.0 |
| 3.3568 | 1.83 | 25300 | 3.4577 | 1.0 |
| 3.28 | 1.84 | 25400 | 3.4458 | 1.0 |
| 3.4389 | 1.85 | 25500 | 3.4436 | 1.0 |
| 3.345 | 1.85 | 25600 | 3.4435 | 1.0 |
| 3.3295 | 1.86 | 25700 | 3.4428 | 1.0 |
| 4.4622 | 1.87 | 25800 | 3.4638 | 1.0 |
| 3.3717 | 1.88 | 25900 | 3.4450 | 1.0 |
| 3.3 | 1.88 | 26000 | 3.4616 | 1.0 |
| 3.3399 | 1.89 | 26100 | 3.4391 | 1.0 |
| 3.4243 | 1.9 | 26200 | 3.4375 | 1.0 |
| 3.326 | 1.9 | 26300 | 3.4533 | 1.0 |
| 3.3337 | 1.91 | 26400 | 3.4538 | 1.0 |
| 3.2655 | 1.92 | 26500 | 3.4460 | 1.0 |
| 3.2963 | 1.93 | 26600 | 3.4443 | 1.0 |
| 3.3967 | 1.93 | 26700 | 3.4392 | 1.0 |
| 3.3203 | 1.94 | 26800 | 3.4609 | 1.0 |
| 3.4581 | 1.95 | 26900 | 3.4388 | 1.0 |
| 3.2519 | 1.95 | 27000 | 3.4434 | 1.0 |
| 3.488 | 1.96 | 27100 | 3.4653 | 1.0 |
| 3.3446 | 1.97 | 27200 | 3.4465 | 1.0 |
| 3.4035 | 1.98 | 27300 | 3.4535 | 1.0 |
| 3.2898 | 1.98 | 27400 | 3.4442 | 1.0 |
| 3.3309 | 1.99 | 27500 | 3.4491 | 1.0 |
| 3.2765 | 2.0 | 27600 | 3.4477 | 1.0 |
| 3.3352 | 2.01 | 27700 | 3.4540 | 1.0 |
| 3.4456 | 2.01 | 27800 | 3.4602 | 1.0 |
| 3.6378 | 2.02 | 27900 | 3.4578 | 1.0 |
| 6.4491 | 2.03 | 28000 | 3.4494 | 1.0 |
| 6.1705 | 2.03 | 28100 | 3.4570 | 1.0 |
| 3.4253 | 2.04 | 28200 | 3.4504 | 1.0 |
| 3.4053 | 2.05 | 28300 | 3.4399 | 1.0 |
| 3.6719 | 2.06 | 28400 | 3.4464 | 1.0 |
| 3.2769 | 2.06 | 28500 | 3.4473 | 1.0 |
| 3.3132 | 2.07 | 28600 | 3.4484 | 1.0 |
| 3.3756 | 2.08 | 28700 | 3.4413 | 1.0 |
| 5.5583 | 2.08 | 28800 | 3.4411 | 1.0 |
| 3.6191 | 2.09 | 28900 | 3.4406 | 1.0 |
| 3.4681 | 2.1 | 29000 | 3.4461 | 1.0 |
| 4.463 | 2.11 | 29100 | 3.4409 | 1.0 |
| 3.4645 | 2.11 | 29200 | 3.4556 | 1.0 |
| 3.6549 | 2.12 | 29300 | 3.4545 | 1.0 |
| 3.437 | 2.13 | 29400 | 3.4410 | 1.0 |
| 3.5002 | 2.14 | 29500 | 3.4370 | 1.0 |
| 3.4375 | 2.14 | 29600 | 3.4407 | 1.0 |
| 3.3798 | 2.15 | 29700 | 3.4390 | 1.0 |
| 3.6778 | 2.16 | 29800 | 3.4386 | 1.0 |
| 3.4647 | 2.16 | 29900 | 3.4600 | 1.0 |
| 3.4328 | 2.17 | 30000 | 3.4492 | 1.0 |
| 3.4381 | 2.18 | 30100 | 3.4406 | 1.0 |
| 3.3253 | 2.19 | 30200 | 3.4461 | 1.0 |
| 3.4112 | 2.19 | 30300 | 3.4478 | 1.0 |
| 3.6158 | 2.2 | 30400 | 3.4482 | 1.0 |
| 3.5541 | 2.21 | 30500 | 3.4424 | 1.0 |
| 4.3339 | 2.22 | 30600 | 3.4432 | 1.0 |
| 3.818 | 2.22 | 30700 | 3.4453 | 1.0 |
| 3.8914 | 2.23 | 30800 | 3.4457 | 1.0 |
| 5.5706 | 2.24 | 30900 | 3.4605 | 1.0 |
| 4.3359 | 2.24 | 31000 | 3.4700 | 1.0 |
| 3.6418 | 2.25 | 31100 | 3.4558 | 1.0 |
| 3.4288 | 2.26 | 31200 | 3.4396 | 1.0 |
| 3.4512 | 2.27 | 31300 | 3.4411 | 1.0 |
| 3.3326 | 2.27 | 31400 | 3.4473 | 1.0 |
| 3.5872 | 2.28 | 31500 | 3.4400 | 1.0 |
| 3.5426 | 2.29 | 31600 | 3.4469 | 1.0 |
| 4.2227 | 2.29 | 31700 | 3.4499 | 1.0 |
| 3.5461 | 2.3 | 31800 | 3.4388 | 1.0 |
| 3.5507 | 2.31 | 31900 | 3.4503 | 1.0 |
| 3.5177 | 2.32 | 32000 | 3.4429 | 1.0 |
| 3.7237 | 2.32 | 32100 | 3.4617 | 1.0 |
| 3.3513 | 2.33 | 32200 | 3.4487 | 1.0 |
| 3.3827 | 2.34 | 32300 | 3.4678 | 1.0 |
| 3.3311 | 2.35 | 32400 | 3.4441 | 1.0 |
| 3.2852 | 2.35 | 32500 | 3.4433 | 1.0 |
| 3.5712 | 2.36 | 32600 | 3.4514 | 1.0 |
| 4.6259 | 2.37 | 32700 | 3.4520 | 1.0 |
| 3.8864 | 2.37 | 32800 | 3.4544 | 1.0 |
| 3.3284 | 2.38 | 32900 | 3.4444 | 1.0 |
| 3.6078 | 2.39 | 33000 | 3.4450 | 1.0 |
| 3.4026 | 2.4 | 33100 | 3.4454 | 1.0 |
| 3.7527 | 2.4 | 33200 | 3.4541 | 1.0 |
| 3.3741 | 2.41 | 33300 | 3.4386 | 1.0 |
| 3.4498 | 2.42 | 33400 | 3.4518 | 1.0 |
| 3.3424 | 2.43 | 33500 | 3.4554 | 1.0 |
| 4.8226 | 2.43 | 33600 | 3.4412 | 1.0 |
| 3.3503 | 2.44 | 33700 | 3.4434 | 1.0 |
| 3.509 | 2.45 | 33800 | 3.4393 | 1.0 |
| 3.586 | 2.45 | 33900 | 3.4375 | 1.0 |
| 3.5242 | 2.46 | 34000 | 3.4402 | 1.0 |
| 3.4351 | 2.47 | 34100 | 3.4389 | 1.0 |
| 3.4445 | 2.48 | 34200 | 3.4416 | 1.0 |
| 6.6676 | 2.48 | 34300 | 3.4571 | 1.0 |
| 4.3937 | 2.49 | 34400 | 3.4560 | 1.0 |
| 3.4177 | 2.5 | 34500 | 3.4482 | 1.0 |
| 3.3966 | 2.5 | 34600 | 3.4640 | 1.0 |
| 3.2845 | 2.51 | 34700 | 3.4538 | 1.0 |
| 3.438 | 2.52 | 34800 | 3.4555 | 1.0 |
| 3.3874 | 2.53 | 34900 | 3.4524 | 1.0 |
| 3.5068 | 2.53 | 35000 | 3.4448 | 1.0 |
| 4.2406 | 2.54 | 35100 | 3.4503 | 1.0 |
| 3.2986 | 2.55 | 35200 | 3.4538 | 1.0 |
| 3.4044 | 2.56 | 35300 | 3.4443 | 1.0 |
| 3.3105 | 2.56 | 35400 | 3.4391 | 1.0 |
| 3.4048 | 2.57 | 35500 | 3.4411 | 1.0 |
| 3.5645 | 2.58 | 35600 | 3.4488 | 1.0 |
| 3.4912 | 2.58 | 35700 | 3.4400 | 1.0 |
| 3.4028 | 2.59 | 35800 | 3.4390 | 1.0 |
| 3.4601 | 2.6 | 35900 | 3.4455 | 1.0 |
| 3.6066 | 2.61 | 36000 | 3.4441 | 1.0 |
| 4.5312 | 2.61 | 36100 | 3.4414 | 1.0 |
| 3.6372 | 2.62 | 36200 | 3.4421 | 1.0 |
| 4.1912 | 2.63 | 36300 | 3.4572 | 1.0 |
| 3.4793 | 2.64 | 36400 | 3.4419 | 1.0 |
| 4.5538 | 2.64 | 36500 | 3.4407 | 1.0 |
| 3.3823 | 2.65 | 36600 | 3.4446 | 1.0 |
| 3.3592 | 2.66 | 36700 | 3.4396 | 1.0 |
| 3.4974 | 2.66 | 36800 | 3.4529 | 1.0 |
| 3.4599 | 2.67 | 36900 | 3.4380 | 1.0 |
| 4.7097 | 2.68 | 37000 | 3.4654 | 1.0 |
| 6.7037 | 2.69 | 37100 | 3.4386 | 1.0 |
| 3.3465 | 2.69 | 37200 | 3.4652 | 1.0 |
| 4.9762 | 2.7 | 37300 | 3.4506 | 1.0 |
| 3.9189 | 2.71 | 37400 | 3.4427 | 1.0 |
| 3.4746 | 2.71 | 37500 | 3.4465 | 1.0 |
| 3.3842 | 2.72 | 37600 | 3.4470 | 1.0 |
| 3.2445 | 2.73 | 37700 | 3.4480 | 1.0 |
| 3.382 | 2.74 | 37800 | 3.4456 | 1.0 |
| 3.7279 | 2.74 | 37900 | 3.4431 | 1.0 |
| 3.4329 | 2.75 | 38000 | 3.4374 | 1.0 |
| 3.4607 | 2.76 | 38100 | 3.4447 | 1.0 |
| 3.2394 | 2.77 | 38200 | 3.4476 | 1.0 |
| 3.7795 | 2.77 | 38300 | 3.4380 | 1.0 |
| 3.4419 | 2.78 | 38400 | 3.4526 | 1.0 |
| 3.6452 | 2.79 | 38500 | 3.4428 | 1.0 |
| 3.3474 | 2.79 | 38600 | 3.4424 | 1.0 |
| 3.4645 | 2.8 | 38700 | 3.4479 | 1.0 |
| 4.1143 | 2.81 | 38800 | 3.4580 | 1.0 |
| 4.6453 | 2.82 | 38900 | 3.4585 | 1.0 |
| 4.022 | 2.82 | 39000 | 3.4567 | 1.0 |
| 4.3049 | 2.83 | 39100 | 3.4377 | 1.0 |
| 3.3382 | 2.84 | 39200 | 3.4413 | 1.0 |
| 3.6022 | 2.85 | 39300 | 3.4548 | 1.0 |
| 4.4217 | 2.85 | 39400 | 3.4411 | 1.0 |
| 3.5139 | 2.86 | 39500 | 3.4552 | 1.0 |
| 3.1215 | 2.87 | 39600 | 3.4471 | 1.0 |
| 3.4514 | 2.87 | 39700 | 3.4378 | 1.0 |
| 4.822 | 2.88 | 39800 | 3.4605 | 1.0 |
| 5.6699 | 2.89 | 39900 | 3.4489 | 1.0 |
| 3.4183 | 2.9 | 40000 | 3.4644 | 1.0 |
| 5.7492 | 2.9 | 40100 | 3.4514 | 1.0 |
| 3.2879 | 2.91 | 40200 | 3.4543 | 1.0 |
| 3.3076 | 2.92 | 40300 | 3.4450 | 1.0 |
| 5.2845 | 2.92 | 40400 | 3.4459 | 1.0 |
| 3.7927 | 2.93 | 40500 | 3.4481 | 1.0 |
| 7.1549 | 2.94 | 40600 | 3.4554 | 1.0 |
| 3.4544 | 2.95 | 40700 | 3.4486 | 1.0 |
| 3.2332 | 2.95 | 40800 | 3.4415 | 1.0 |
| 3.3714 | 2.96 | 40900 | 3.4521 | 1.0 |
| 3.5205 | 2.97 | 41000 | 3.4395 | 1.0 |
| 4.6267 | 2.98 | 41100 | 3.4622 | 1.0 |
| 6.7747 | 2.98 | 41200 | 3.4407 | 1.0 |
| 3.3091 | 2.99 | 41300 | 3.4422 | 1.0 |
| 3.7135 | 3.0 | 41400 | 3.4383 | 1.0 |
| 3.6261 | 3.0 | 41500 | 3.4482 | 1.0 |
| 3.3323 | 3.01 | 41600 | 3.4366 | 1.0 |
| 3.4544 | 3.02 | 41700 | 3.4376 | 1.0 |
| 3.6486 | 3.03 | 41800 | 3.4511 | 1.0 |
| 3.3333 | 3.03 | 41900 | 3.4397 | 1.0 |
| 3.35 | 3.04 | 42000 | 3.4486 | 1.0 |
| 3.3522 | 3.05 | 42100 | 3.4626 | 1.0 |
| 3.4359 | 3.06 | 42200 | 3.4462 | 1.0 |
| 3.4548 | 3.06 | 42300 | 3.4435 | 1.0 |
| 3.2711 | 3.07 | 42400 | 3.4450 | 1.0 |
| 3.2679 | 3.08 | 42500 | 3.4394 | 1.0 |
| 3.3703 | 3.08 | 42600 | 3.4539 | 1.0 |
| 3.3846 | 3.09 | 42700 | 3.4443 | 1.0 |
| 3.334 | 3.1 | 42800 | 3.4384 | 1.0 |
| 3.3429 | 3.11 | 42900 | 3.4625 | 1.0 |
| 3.282 | 3.11 | 43000 | 3.4419 | 1.0 |
| 3.3503 | 3.12 | 43100 | 3.4653 | 1.0 |
| 3.4923 | 3.13 | 43200 | 3.4380 | 1.0 |
| 3.4309 | 3.13 | 43300 | 3.4534 | 1.0 |
| 3.3292 | 3.14 | 43400 | 3.4448 | 1.0 |
| 3.4219 | 3.15 | 43500 | 3.4665 | 1.0 |
| 3.3848 | 3.16 | 43600 | 3.4473 | 1.0 |
| 3.3004 | 3.16 | 43700 | 3.4509 | 1.0 |
| 3.2002 | 3.17 | 43800 | 3.4493 | 1.0 |
| 3.2654 | 3.18 | 43900 | 3.4384 | 1.0 |
| 3.3394 | 3.19 | 44000 | 3.4388 | 1.0 |
| 3.2365 | 3.19 | 44100 | 3.4491 | 1.0 |
| 3.2846 | 3.2 | 44200 | 3.4404 | 1.0 |
| 3.3973 | 3.21 | 44300 | 3.4426 | 1.0 |
| 3.3367 | 3.21 | 44400 | 3.4690 | 1.0 |
| 3.2747 | 3.22 | 44500 | 3.4378 | 1.0 |
| 3.4307 | 3.23 | 44600 | 3.4395 | 1.0 |
| 3.3685 | 3.24 | 44700 | 3.4431 | 1.0 |
| 3.321 | 3.24 | 44800 | 3.4557 | 1.0 |
| 3.3541 | 3.25 | 44900 | 3.4489 | 1.0 |
| 3.2282 | 3.26 | 45000 | 3.4393 | 1.0 |
| 3.3811 | 3.27 | 45100 | 3.4463 | 1.0 |
| 3.3014 | 3.27 | 45200 | 3.4505 | 1.0 |
| 3.3617 | 3.28 | 45300 | 3.4475 | 1.0 |
| 3.3953 | 3.29 | 45400 | 3.4430 | 1.0 |
| 3.3999 | 3.29 | 45500 | 3.4417 | 1.0 |
| 3.4098 | 3.3 | 45600 | 3.4503 | 1.0 |
| 3.1994 | 3.31 | 45700 | 3.4414 | 1.0 |
| 3.2185 | 3.32 | 45800 | 3.4485 | 1.0 |
| 3.2554 | 3.32 | 45900 | 3.4477 | 1.0 |
| 3.4302 | 3.33 | 46000 | 3.4508 | 1.0 |
| 3.366 | 3.34 | 46100 | 3.4440 | 1.0 |
| 3.4143 | 3.34 | 46200 | 3.4382 | 1.0 |
| 4.318 | 3.35 | 46300 | 3.4524 | 1.0 |
| 3.4233 | 3.36 | 46400 | 3.4451 | 1.0 |
| 3.3492 | 3.37 | 46500 | 3.4526 | 1.0 |
| 3.2399 | 3.37 | 46600 | 3.4462 | 1.0 |
| 3.421 | 3.38 | 46700 | 3.4432 | 1.0 |
| 3.2847 | 3.39 | 46800 | 3.4419 | 1.0 |
| 3.4062 | 3.4 | 46900 | 3.4405 | 1.0 |
| 3.3822 | 3.4 | 47000 | 3.4434 | 1.0 |
| 3.2789 | 3.41 | 47100 | 3.4444 | 1.0 |
| 3.2508 | 3.42 | 47200 | 3.4501 | 1.0 |
| 3.3867 | 3.42 | 47300 | 3.4498 | 1.0 |
| 3.3275 | 3.43 | 47400 | 3.4505 | 1.0 |
| 3.424 | 3.44 | 47500 | 3.4448 | 1.0 |
| 3.2418 | 3.45 | 47600 | 3.4450 | 1.0 |
| 3.3037 | 3.45 | 47700 | 3.4493 | 1.0 |
| 3.2562 | 3.46 | 47800 | 3.4466 | 1.0 |
| 3.3241 | 3.47 | 47900 | 3.4385 | 1.0 |
| 3.5569 | 3.47 | 48000 | 3.4427 | 1.0 |
| 3.298 | 3.48 | 48100 | 3.4667 | 1.0 |
| 3.3401 | 3.49 | 48200 | 3.4440 | 1.0 |
| 3.2824 | 3.5 | 48300 | 3.4427 | 1.0 |
| 3.3829 | 3.5 | 48400 | 3.4398 | 1.0 |
| 3.3595 | 3.51 | 48500 | 3.4421 | 1.0 |
| 3.286 | 3.52 | 48600 | 3.4517 | 1.0 |
| 3.3494 | 3.53 | 48700 | 3.4429 | 1.0 |
| 3.3507 | 3.53 | 48800 | 3.4422 | 1.0 |
| 3.3598 | 3.54 | 48900 | 3.4439 | 1.0 |
| 3.3141 | 3.55 | 49000 | 3.4544 | 1.0 |
| 3.4548 | 3.55 | 49100 | 3.4415 | 1.0 |
| 3.3278 | 3.56 | 49200 | 3.4474 | 1.0 |
| 3.4088 | 3.57 | 49300 | 3.4498 | 1.0 |
| 3.4046 | 3.58 | 49400 | 3.4554 | 1.0 |
| 3.2847 | 3.58 | 49500 | 3.4393 | 1.0 |
| 3.3162 | 3.59 | 49600 | 3.4594 | 1.0 |
| 3.2493 | 3.6 | 49700 | 3.4514 | 1.0 |
| 3.3466 | 3.61 | 49800 | 3.4514 | 1.0 |
| 3.3279 | 3.61 | 49900 | 3.4462 | 1.0 |
| 3.29 | 3.62 | 50000 | 3.4466 | 1.0 |
| 3.2374 | 3.63 | 50100 | 3.4575 | 1.0 |
| 3.3499 | 3.63 | 50200 | 3.4392 | 1.0 |
| 3.251 | 3.64 | 50300 | 3.4556 | 1.0 |
| 3.3692 | 3.65 | 50400 | 3.4498 | 1.0 |
| 3.3743 | 3.66 | 50500 | 3.4569 | 1.0 |
| 3.3662 | 3.66 | 50600 | 3.4463 | 1.0 |
| 3.302 | 3.67 | 50700 | 3.4445 | 1.0 |
| 3.2863 | 3.68 | 50800 | 3.4475 | 1.0 |
| 3.4266 | 3.68 | 50900 | 3.4370 | 1.0 |
| 3.2988 | 3.69 | 51000 | 3.4476 | 1.0 |
| 3.9581 | 3.7 | 51100 | 3.4382 | 1.0 |
| 3.4516 | 3.71 | 51200 | 3.4526 | 1.0 |
| 3.4259 | 3.71 | 51300 | 3.4414 | 1.0 |
| 3.3913 | 3.72 | 51400 | 3.4386 | 1.0 |
| 3.3606 | 3.73 | 51500 | 3.4458 | 1.0 |
| 3.4698 | 3.74 | 51600 | 3.4450 | 1.0 |
| 3.4285 | 3.74 | 51700 | 3.4493 | 1.0 |
| 3.265 | 3.75 | 51800 | 3.4369 | 1.0 |
| 3.4819 | 3.76 | 51900 | 3.4472 | 1.0 |
| 3.2869 | 3.76 | 52000 | 3.4580 | 1.0 |
| 3.2663 | 3.77 | 52100 | 3.4469 | 1.0 |
| 3.4325 | 3.78 | 52200 | 3.4423 | 1.0 |
| 3.3355 | 3.79 | 52300 | 3.4411 | 1.0 |
| 3.4324 | 3.79 | 52400 | 3.4456 | 1.0 |
| 3.3105 | 3.8 | 52500 | 3.4389 | 1.0 |
| 3.3588 | 3.81 | 52600 | 3.4403 | 1.0 |
| 3.3524 | 3.82 | 52700 | 3.4458 | 1.0 |
| 3.2466 | 3.82 | 52800 | 3.4447 | 1.0 |
| 3.2375 | 3.83 | 52900 | 3.4448 | 1.0 |
| 3.4006 | 3.84 | 53000 | 3.4456 | 1.0 |
| 3.3572 | 3.84 | 53100 | 3.4427 | 1.0 |
| 3.6162 | 3.85 | 53200 | 3.4379 | 1.0 |
| 3.3351 | 3.86 | 53300 | 3.4482 | 1.0 |
| 3.7101 | 3.87 | 53400 | 3.4393 | 1.0 |
| 3.3836 | 3.87 | 53500 | 3.4474 | 1.0 |
| 3.3357 | 3.88 | 53600 | 3.4573 | 1.0 |
| 3.3434 | 3.89 | 53700 | 3.4475 | 1.0 |
| 3.3349 | 3.89 | 53800 | 3.4659 | 1.0 |
| 3.3474 | 3.9 | 53900 | 3.4411 | 1.0 |
| 3.4007 | 3.91 | 54000 | 3.4446 | 1.0 |
| 3.4218 | 3.92 | 54100 | 3.4406 | 1.0 |
| 3.2115 | 3.92 | 54200 | 3.4422 | 1.0 |
| 3.2726 | 3.93 | 54300 | 3.4383 | 1.0 |
| 3.2999 | 3.94 | 54400 | 3.4423 | 1.0 |
| 3.3657 | 3.95 | 54500 | 3.4377 | 1.0 |
| 3.4015 | 3.95 | 54600 | 3.4433 | 1.0 |
| 3.3373 | 3.96 | 54700 | 3.4457 | 1.0 |
| 4.9872 | 3.97 | 54800 | 3.4420 | 1.0 |
| 3.3221 | 3.97 | 54900 | 3.4501 | 1.0 |
| 3.8059 | 3.98 | 55000 | 3.4501 | 1.0 |
| 3.2628 | 3.99 | 55100 | 3.4511 | 1.0 |
| 3.3822 | 4.0 | 55200 | 3.4409 | 1.0 |
| 3.5464 | 4.0 | 55300 | 3.4527 | 1.0 |
| 3.3661 | 4.01 | 55400 | 3.4436 | 1.0 |
| 3.4146 | 4.02 | 55500 | 3.4458 | 1.0 |
| 3.5756 | 4.03 | 55600 | 3.4409 | 1.0 |
| 3.3945 | 4.03 | 55700 | 3.4378 | 1.0 |
| 4.5275 | 4.04 | 55800 | 3.4558 | 1.0 |
| 3.7913 | 4.05 | 55900 | 3.4523 | 1.0 |
| 3.4445 | 4.05 | 56000 | 3.4446 | 1.0 |
| 3.51 | 4.06 | 56100 | 3.4488 | 1.0 |
| 6.5935 | 4.07 | 56200 | 3.4497 | 1.0 |
| 3.3548 | 4.08 | 56300 | 3.4443 | 1.0 |
| 3.4544 | 4.08 | 56400 | 3.4547 | 1.0 |
| 3.4206 | 4.09 | 56500 | 3.4476 | 1.0 |
| 3.3979 | 4.1 | 56600 | 3.4459 | 1.0 |
| 3.296 | 4.1 | 56700 | 3.4461 | 1.0 |
| 3.7186 | 4.11 | 56800 | 3.4407 | 1.0 |
| 3.8726 | 4.12 | 56900 | 3.4498 | 1.0 |
| 3.6704 | 4.13 | 57000 | 3.4535 | 1.0 |
| 3.4735 | 4.13 | 57100 | 3.4470 | 1.0 |
| 3.399 | 4.14 | 57200 | 3.4461 | 1.0 |
| 3.3507 | 4.15 | 57300 | 3.4405 | 1.0 |
| 3.3948 | 4.16 | 57400 | 3.4582 | 1.0 |
| 3.613 | 4.16 | 57500 | 3.4462 | 1.0 |
| 3.3553 | 4.17 | 57600 | 3.4507 | 1.0 |
| 3.5798 | 4.18 | 57700 | 3.4476 | 1.0 |
| 7.6315 | 4.18 | 57800 | 3.4412 | 1.0 |
| 3.4873 | 4.19 | 57900 | 3.4605 | 1.0 |
| 3.3193 | 4.2 | 58000 | 3.4458 | 1.0 |
| 3.4065 | 4.21 | 58100 | 3.4368 | 1.0 |
| 3.4813 | 4.21 | 58200 | 3.4464 | 1.0 |
| 3.2523 | 4.22 | 58300 | 3.4601 | 1.0 |
| 3.3384 | 4.23 | 58400 | 3.4449 | 1.0 |
| 3.2839 | 4.24 | 58500 | 3.4544 | 1.0 |
| 3.4564 | 4.24 | 58600 | 3.4412 | 1.0 |
| 3.3995 | 4.25 | 58700 | 3.4408 | 1.0 |
| 3.2107 | 4.26 | 58800 | 3.4463 | 1.0 |
| 4.0565 | 4.26 | 58900 | 3.4402 | 1.0 |
| 3.6744 | 4.27 | 59000 | 3.4537 | 1.0 |
| 3.3658 | 4.28 | 59100 | 3.4435 | 1.0 |
| 3.8134 | 4.29 | 59200 | 3.4491 | 1.0 |
| 3.3783 | 4.29 | 59300 | 3.4480 | 1.0 |
| 3.6206 | 4.3 | 59400 | 3.4403 | 1.0 |
| 3.4018 | 4.31 | 59500 | 3.4433 | 1.0 |
| 3.2325 | 4.31 | 59600 | 3.4419 | 1.0 |
| 3.3935 | 4.32 | 59700 | 3.4420 | 1.0 |
| 3.9773 | 4.33 | 59800 | 3.4477 | 1.0 |
| 3.3477 | 4.34 | 59900 | 3.4557 | 1.0 |
| 3.4817 | 4.34 | 60000 | 3.4421 | 1.0 |
| 3.8685 | 4.35 | 60100 | 3.4470 | 1.0 |
| 3.679 | 4.36 | 60200 | 3.4457 | 1.0 |
| 5.3659 | 4.37 | 60300 | 3.4416 | 1.0 |
| 3.2615 | 4.37 | 60400 | 3.4415 | 1.0 |
| 3.6087 | 4.38 | 60500 | 3.4398 | 1.0 |
| 4.1801 | 4.39 | 60600 | 3.4532 | 1.0 |
| 5.013 | 4.39 | 60700 | 3.4465 | 1.0 |
| 3.333 | 4.4 | 60800 | 3.4498 | 1.0 |
| 3.4247 | 4.41 | 60900 | 3.4542 | 1.0 |
| 3.424 | 4.42 | 61000 | 3.4436 | 1.0 |
| 3.317 | 4.42 | 61100 | 3.4405 | 1.0 |
| 3.4018 | 4.43 | 61200 | 3.4467 | 1.0 |
| 7.2156 | 4.44 | 61300 | 3.4436 | 1.0 |
| 3.3726 | 4.45 | 61400 | 3.4473 | 1.0 |
| 3.2895 | 4.45 | 61500 | 3.4400 | 1.0 |
| 3.2293 | 4.46 | 61600 | 3.4536 | 1.0 |
| 3.8397 | 4.47 | 61700 | 3.4489 | 1.0 |
| 3.3358 | 4.47 | 61800 | 3.4443 | 1.0 |
| 3.4085 | 4.48 | 61900 | 3.4472 | 1.0 |
| 3.4413 | 4.49 | 62000 | 3.4421 | 1.0 |
| 3.4222 | 4.5 | 62100 | 3.4480 | 1.0 |
| 3.4665 | 4.5 | 62200 | 3.4435 | 1.0 |
| 3.4058 | 4.51 | 62300 | 3.4399 | 1.0 |
| 3.4228 | 4.52 | 62400 | 3.4457 | 1.0 |
| 3.3362 | 4.52 | 62500 | 3.4453 | 1.0 |
| 4.3383 | 4.53 | 62600 | 3.4564 | 1.0 |
| 3.2802 | 4.54 | 62700 | 3.4392 | 1.0 |
| 5.0224 | 4.55 | 62800 | 3.4491 | 1.0 |
| 4.1092 | 4.55 | 62900 | 3.4400 | 1.0 |
| 3.6467 | 4.56 | 63000 | 3.4454 | 1.0 |
| 3.4197 | 4.57 | 63100 | 3.4411 | 1.0 |
| 3.4549 | 4.58 | 63200 | 3.4464 | 1.0 |
| 3.2333 | 4.58 | 63300 | 3.4454 | 1.0 |
| 3.3108 | 4.59 | 63400 | 3.4437 | 1.0 |
| 3.3897 | 4.6 | 63500 | 3.4382 | 1.0 |
| 3.2956 | 4.6 | 63600 | 3.4478 | 1.0 |
| 3.4244 | 4.61 | 63700 | 3.4439 | 1.0 |
| 4.3236 | 4.62 | 63800 | 3.4400 | 1.0 |
| 3.263 | 4.63 | 63900 | 3.4542 | 1.0 |
| 3.5322 | 4.63 | 64000 | 3.4548 | 1.0 |
| 3.613 | 4.64 | 64100 | 3.4442 | 1.0 |
| 3.7147 | 4.65 | 64200 | 3.4396 | 1.0 |
| 3.6781 | 4.66 | 64300 | 3.4444 | 1.0 |
| 3.1597 | 4.66 | 64400 | 3.4642 | 1.0 |
| 4.8173 | 4.67 | 64500 | 3.4397 | 1.0 |
| 3.7878 | 4.68 | 64600 | 3.4529 | 1.0 |
| 3.3288 | 4.68 | 64700 | 3.4423 | 1.0 |
| 3.3931 | 4.69 | 64800 | 3.4376 | 1.0 |
| 5.6842 | 4.7 | 64900 | 3.4396 | 1.0 |
| 3.62 | 4.71 | 65000 | 3.4419 | 1.0 |
| 3.3742 | 4.71 | 65100 | 3.4419 | 1.0 |
| 3.3207 | 4.72 | 65200 | 3.4392 | 1.0 |
| 3.6216 | 4.73 | 65300 | 3.4369 | 1.0 |
| 3.2954 | 4.73 | 65400 | 3.4461 | 1.0 |
| 3.3943 | 4.74 | 65500 | 3.4442 | 1.0 |
| 3.5041 | 4.75 | 65600 | 3.4433 | 1.0 |
| 3.5168 | 4.76 | 65700 | 3.4529 | 1.0 |
| 3.3715 | 4.76 | 65800 | 3.4446 | 1.0 |
| 3.3734 | 4.77 | 65900 | 3.4507 | 1.0 |
| 10.6923 | 4.78 | 66000 | 3.4468 | 1.0 |
| 3.4432 | 4.79 | 66100 | 3.4400 | 1.0 |
| 3.5521 | 4.79 | 66200 | 3.4573 | 1.0 |
| 4.9372 | 4.8 | 66300 | 3.4400 | 1.0 |
| 3.48 | 4.81 | 66400 | 3.4374 | 1.0 |
| 3.1794 | 4.81 | 66500 | 3.4379 | 1.0 |
| 3.4121 | 4.82 | 66600 | 3.4364 | 1.0 |
| 3.581 | 4.83 | 66700 | 3.4444 | 1.0 |
| 3.1135 | 4.84 | 66800 | 3.4380 | 1.0 |
| 3.4506 | 4.84 | 66900 | 3.4595 | 1.0 |
| 3.3243 | 4.85 | 67000 | 3.4433 | 1.0 |
| 3.3814 | 4.86 | 67100 | 3.4550 | 1.0 |
| 3.3557 | 4.86 | 67200 | 3.4374 | 1.0 |
| 3.2991 | 4.87 | 67300 | 3.4423 | 1.0 |
| 3.8854 | 4.88 | 67400 | 3.4398 | 1.0 |
| 3.7073 | 4.89 | 67500 | 3.4425 | 1.0 |
| 3.3739 | 4.89 | 67600 | 3.4492 | 1.0 |
| 3.435 | 4.9 | 67700 | 3.4512 | 1.0 |
| 10.5515 | 4.91 | 67800 | 3.4512 | 1.0 |
| 3.5227 | 4.92 | 67900 | 3.4493 | 1.0 |
| 3.2475 | 4.92 | 68000 | 3.4413 | 1.0 |
| 3.3387 | 4.93 | 68100 | 3.4474 | 1.0 |
| 3.365 | 4.94 | 68200 | 3.4426 | 1.0 |
| 4.1377 | 4.94 | 68300 | 3.4457 | 1.0 |
| 3.9188 | 4.95 | 68400 | 3.4437 | 1.0 |
| 3.5646 | 4.96 | 68500 | 3.4438 | 1.0 |
| 3.3686 | 4.97 | 68600 | 3.4477 | 1.0 |
| 3.1943 | 4.97 | 68700 | 3.4508 | 1.0 |
| 3.3747 | 4.98 | 68800 | 3.4453 | 1.0 |
| 3.8971 | 4.99 | 68900 | 3.4560 | 1.0 |
| 3.9434 | 5.0 | 69000 | 3.4457 | 1.0 |
| 3.3862 | 5.0 | 69100 | 3.4575 | 1.0 |
| 3.2693 | 5.01 | 69200 | 3.4436 | 1.0 |
| 3.2971 | 5.02 | 69300 | 3.4494 | 1.0 |
| 3.3175 | 5.02 | 69400 | 3.4432 | 1.0 |
| 3.3889 | 5.03 | 69500 | 3.4371 | 1.0 |
| 3.382 | 5.04 | 69600 | 3.4426 | 1.0 |
| 3.3396 | 5.05 | 69700 | 3.4383 | 1.0 |
| 3.5613 | 5.05 | 69800 | 3.4472 | 1.0 |
| 3.4392 | 5.06 | 69900 | 3.4437 | 1.0 |
| 3.2599 | 5.07 | 70000 | 3.4544 | 1.0 |
| 3.2819 | 5.07 | 70100 | 3.4459 | 1.0 |
| 3.3131 | 5.08 | 70200 | 3.4552 | 1.0 |
| 3.3471 | 5.09 | 70300 | 3.4513 | 1.0 |
| 3.4194 | 5.1 | 70400 | 3.4446 | 1.0 |
| 3.3565 | 5.1 | 70500 | 3.4424 | 1.0 |
| 3.3411 | 5.11 | 70600 | 3.4482 | 1.0 |
| 3.3473 | 5.12 | 70700 | 3.4514 | 1.0 |
| 3.3197 | 5.13 | 70800 | 3.4491 | 1.0 |
| 3.3466 | 5.13 | 70900 | 3.4573 | 1.0 |
| 3.3856 | 5.14 | 71000 | 3.4420 | 1.0 |
| 3.1905 | 5.15 | 71100 | 3.4469 | 1.0 |
| 3.3756 | 5.15 | 71200 | 3.4467 | 1.0 |
| 3.3498 | 5.16 | 71300 | 3.4479 | 1.0 |
| 3.3914 | 5.17 | 71400 | 3.4426 | 1.0 |
| 3.3885 | 5.18 | 71500 | 3.4419 | 1.0 |
| 3.4713 | 5.18 | 71600 | 3.4434 | 1.0 |
| 3.4077 | 5.19 | 71700 | 3.4472 | 1.0 |
| 3.3633 | 5.2 | 71800 | 3.4443 | 1.0 |
| 3.3677 | 5.21 | 71900 | 3.4413 | 1.0 |
| 3.3545 | 5.21 | 72000 | 3.4491 | 1.0 |
| 3.3415 | 5.22 | 72100 | 3.4423 | 1.0 |
| 3.3796 | 5.23 | 72200 | 3.4420 | 1.0 |
| 3.4989 | 5.23 | 72300 | 3.4415 | 1.0 |
| 3.3875 | 5.24 | 72400 | 3.4453 | 1.0 |
| 3.3728 | 5.25 | 72500 | 3.4534 | 1.0 |
| 3.3134 | 5.26 | 72600 | 3.4396 | 1.0 |
| 3.3634 | 5.26 | 72700 | 3.4472 | 1.0 |
| 3.2482 | 5.27 | 72800 | 3.4448 | 1.0 |
| 3.299 | 5.28 | 72900 | 3.4571 | 1.0 |
| 3.3579 | 5.28 | 73000 | 3.4440 | 1.0 |
| 3.6011 | 5.29 | 73100 | 3.4507 | 1.0 |
| 3.2451 | 5.3 | 73200 | 3.4430 | 1.0 |
| 3.399 | 5.31 | 73300 | 3.4443 | 1.0 |
| 3.3605 | 5.31 | 73400 | 3.4525 | 1.0 |
| 3.3511 | 5.32 | 73500 | 3.4520 | 1.0 |
| 3.3946 | 5.33 | 73600 | 3.4402 | 1.0 |
| 3.3602 | 5.34 | 73700 | 3.4383 | 1.0 |
| 3.3105 | 5.34 | 73800 | 3.4492 | 1.0 |
| 3.3346 | 5.35 | 73900 | 3.4428 | 1.0 |
| 3.4219 | 5.36 | 74000 | 3.4534 | 1.0 |
| 3.3491 | 5.36 | 74100 | 3.4603 | 1.0 |
| 3.4207 | 5.37 | 74200 | 3.4512 | 1.0 |
| 3.2418 | 5.38 | 74300 | 3.4474 | 1.0 |
| 3.2637 | 5.39 | 74400 | 3.4402 | 1.0 |
| 3.4331 | 5.39 | 74500 | 3.4576 | 1.0 |
| 3.3483 | 5.4 | 74600 | 3.4518 | 1.0 |
| 3.2825 | 5.41 | 74700 | 3.4526 | 1.0 |
| 3.5443 | 5.42 | 74800 | 3.4380 | 1.0 |
| 3.3637 | 5.42 | 74900 | 3.4525 | 1.0 |
| 3.2016 | 5.43 | 75000 | 3.4483 | 1.0 |
| 3.3641 | 5.44 | 75100 | 3.4389 | 1.0 |
| 3.3869 | 5.44 | 75200 | 3.4511 | 1.0 |
| 3.2595 | 5.45 | 75300 | 3.4498 | 1.0 |
| 3.401 | 5.46 | 75400 | 3.4496 | 1.0 |
| 3.4416 | 5.47 | 75500 | 3.4502 | 1.0 |
| 3.3949 | 5.47 | 75600 | 3.4400 | 1.0 |
| 3.279 | 5.48 | 75700 | 3.4461 | 1.0 |
| 3.343 | 5.49 | 75800 | 3.4419 | 1.0 |
| 3.3848 | 5.49 | 75900 | 3.4470 | 1.0 |
| 3.3605 | 5.5 | 76000 | 3.4430 | 1.0 |
| 3.2786 | 5.51 | 76100 | 3.4479 | 1.0 |
| 3.4013 | 5.52 | 76200 | 3.4469 | 1.0 |
| 3.2064 | 5.52 | 76300 | 3.4420 | 1.0 |
| 3.5022 | 5.53 | 76400 | 3.4475 | 1.0 |
| 3.3093 | 5.54 | 76500 | 3.4431 | 1.0 |
| 3.3647 | 5.55 | 76600 | 3.4392 | 1.0 |
| 3.3971 | 5.55 | 76700 | 3.4434 | 1.0 |
| 3.3352 | 5.56 | 76800 | 3.4485 | 1.0 |
| 3.3756 | 5.57 | 76900 | 3.4453 | 1.0 |
| 3.2675 | 5.57 | 77000 | 3.4456 | 1.0 |
| 3.3187 | 5.58 | 77100 | 3.4471 | 1.0 |
| 3.3915 | 5.59 | 77200 | 3.4434 | 1.0 |
| 3.522 | 5.6 | 77300 | 3.4579 | 1.0 |
| 3.3715 | 5.6 | 77400 | 3.4459 | 1.0 |
| 3.2879 | 5.61 | 77500 | 3.4450 | 1.0 |
| 3.4566 | 5.62 | 77600 | 3.4446 | 1.0 |
| 3.3802 | 5.63 | 77700 | 3.4458 | 1.0 |
| 3.3286 | 5.63 | 77800 | 3.4417 | 1.0 |
| 3.3506 | 5.64 | 77900 | 3.4582 | 1.0 |
| 3.3646 | 5.65 | 78000 | 3.4382 | 1.0 |
| 3.3679 | 5.65 | 78100 | 3.4399 | 1.0 |
| 3.2344 | 5.66 | 78200 | 3.4389 | 1.0 |
| 3.362 | 5.67 | 78300 | 3.4528 | 1.0 |
| 3.3598 | 5.68 | 78400 | 3.4411 | 1.0 |
| 3.4368 | 5.68 | 78500 | 3.4416 | 1.0 |
| 3.3668 | 5.69 | 78600 | 3.4501 | 1.0 |
| 3.4889 | 5.7 | 78700 | 3.4469 | 1.0 |
| 3.5421 | 5.7 | 78800 | 3.4499 | 1.0 |
| 3.4562 | 5.71 | 78900 | 3.4489 | 1.0 |
| 3.4175 | 5.72 | 79000 | 3.4456 | 1.0 |
| 3.3624 | 5.73 | 79100 | 3.4457 | 1.0 |
| 3.338 | 5.73 | 79200 | 3.4480 | 1.0 |
| 3.2783 | 5.74 | 79300 | 3.4398 | 1.0 |
| 3.3664 | 5.75 | 79400 | 3.4454 | 1.0 |
| 3.3883 | 5.76 | 79500 | 3.4511 | 1.0 |
| 3.3578 | 5.76 | 79600 | 3.4480 | 1.0 |
| 3.2831 | 5.77 | 79700 | 3.4425 | 1.0 |
| 3.5258 | 5.78 | 79800 | 3.4522 | 1.0 |
| 3.2697 | 5.78 | 79900 | 3.4398 | 1.0 |
| 3.291 | 5.79 | 80000 | 3.4395 | 1.0 |
| 3.3994 | 5.8 | 80100 | 3.4401 | 1.0 |
| 3.3379 | 5.81 | 80200 | 3.4414 | 1.0 |
| 3.334 | 5.81 | 80300 | 3.4576 | 1.0 |
| 3.4343 | 5.82 | 80400 | 3.4524 | 1.0 |
| 3.3857 | 5.83 | 80500 | 3.4445 | 1.0 |
| 3.3657 | 5.84 | 80600 | 3.4437 | 1.0 |
| 3.3229 | 5.84 | 80700 | 3.4539 | 1.0 |
| 3.2913 | 5.85 | 80800 | 3.4466 | 1.0 |
| 3.2929 | 5.86 | 80900 | 3.4471 | 1.0 |
| 3.4581 | 5.86 | 81000 | 3.4367 | 1.0 |
| 3.3521 | 5.87 | 81100 | 3.4395 | 1.0 |
| 3.6423 | 5.88 | 81200 | 3.4395 | 1.0 |
| 3.3993 | 5.89 | 81300 | 3.4488 | 1.0 |
| 3.3382 | 5.89 | 81400 | 3.4626 | 1.0 |
| 3.2858 | 5.9 | 81500 | 3.4393 | 1.0 |
| 3.3802 | 5.91 | 81600 | 3.4430 | 1.0 |
| 3.4808 | 5.91 | 81700 | 3.4421 | 1.0 |
| 3.2911 | 5.92 | 81800 | 3.4458 | 1.0 |
| 3.199 | 5.93 | 81900 | 3.4411 | 1.0 |
| 3.7089 | 5.94 | 82000 | 3.4402 | 1.0 |
| 3.32 | 5.94 | 82100 | 3.4524 | 1.0 |
| 3.2283 | 5.95 | 82200 | 3.4465 | 1.0 |
| 3.3001 | 5.96 | 82300 | 3.4429 | 1.0 |
| 3.33 | 5.97 | 82400 | 3.4535 | 1.0 |
| 3.3269 | 5.97 | 82500 | 3.4445 | 1.0 |
| 3.3572 | 5.98 | 82600 | 3.4459 | 1.0 |
| 3.2905 | 5.99 | 82700 | 3.4475 | 1.0 |
| 3.4236 | 5.99 | 82800 | 3.4455 | 1.0 |
| 4.1378 | 6.0 | 82900 | 3.4454 | 1.0 |
| 3.4648 | 6.01 | 83000 | 3.4569 | 1.0 |
| 3.2289 | 6.02 | 83100 | 3.4562 | 1.0 |
| 3.511 | 6.02 | 83200 | 3.4452 | 1.0 |
| 5.6152 | 6.03 | 83300 | 3.4684 | 1.0 |
| 3.2102 | 6.04 | 83400 | 3.4555 | 1.0 |
| 3.389 | 6.05 | 83500 | 3.4429 | 1.0 |
| 3.773 | 6.05 | 83600 | 3.4436 | 1.0 |
| 3.3612 | 6.06 | 83700 | 3.4383 | 1.0 |
| 3.316 | 6.07 | 83800 | 3.4421 | 1.0 |
| 3.4754 | 6.07 | 83900 | 3.4444 | 1.0 |
| 3.4536 | 6.08 | 84000 | 3.4461 | 1.0 |
| 3.4987 | 6.09 | 84100 | 3.4441 | 1.0 |
| 3.5025 | 6.1 | 84200 | 3.4423 | 1.0 |
| 3.167 | 6.1 | 84300 | 3.4381 | 1.0 |
| 3.3875 | 6.11 | 84400 | 3.4458 | 1.0 |
| 3.3446 | 6.12 | 84500 | 3.4491 | 1.0 |
| 3.4824 | 6.12 | 84600 | 3.4476 | 1.0 |
| 3.4264 | 6.13 | 84700 | 3.4443 | 1.0 |
| 3.3786 | 6.14 | 84800 | 3.4391 | 1.0 |
| 3.3554 | 6.15 | 84900 | 3.4447 | 1.0 |
| 3.2566 | 6.15 | 85000 | 3.4410 | 1.0 |
| 3.7839 | 6.16 | 85100 | 3.4471 | 1.0 |
| 10.7563 | 6.17 | 85200 | 3.4516 | 1.0 |
| 3.501 | 6.18 | 85300 | 3.4458 | 1.0 |
| 3.3805 | 6.18 | 85400 | 3.4441 | 1.0 |
| 3.3758 | 6.19 | 85500 | 3.4384 | 1.0 |
| 3.4565 | 6.2 | 85600 | 3.4457 | 1.0 |
| 3.3889 | 6.2 | 85700 | 3.4542 | 1.0 |
| 3.6664 | 6.21 | 85800 | 3.4572 | 1.0 |
| 3.4372 | 6.22 | 85900 | 3.4442 | 1.0 |
| 3.3461 | 6.23 | 86000 | 3.4430 | 1.0 |
| 3.3446 | 6.23 | 86100 | 3.4410 | 1.0 |
| 4.1477 | 6.24 | 86200 | 3.4521 | 1.0 |
| 3.2528 | 6.25 | 86300 | 3.4441 | 1.0 |
| 5.4615 | 6.25 | 86400 | 3.4386 | 1.0 |
| 3.3977 | 6.26 | 86500 | 3.4507 | 1.0 |
| 3.3648 | 6.27 | 86600 | 3.4488 | 1.0 |
| 3.875 | 6.28 | 86700 | 3.4477 | 1.0 |
| 3.8437 | 6.28 | 86800 | 3.4421 | 1.0 |
| 3.2904 | 6.29 | 86900 | 3.4458 | 1.0 |
| 3.6029 | 6.3 | 87000 | 3.4536 | 1.0 |
| 3.2774 | 6.31 | 87100 | 3.4452 | 1.0 |
| 3.3557 | 6.31 | 87200 | 3.4491 | 1.0 |
| 3.344 | 6.32 | 87300 | 3.4550 | 1.0 |
| 3.1771 | 6.33 | 87400 | 3.4414 | 1.0 |
| 3.2468 | 6.33 | 87500 | 3.4407 | 1.0 |
| 3.3878 | 6.34 | 87600 | 3.4409 | 1.0 |
| 3.3175 | 6.35 | 87700 | 3.4402 | 1.0 |
| 3.3398 | 6.36 | 87800 | 3.4422 | 1.0 |
| 3.3925 | 6.36 | 87900 | 3.4480 | 1.0 |
| 3.2327 | 6.37 | 88000 | 3.4380 | 1.0 |
| 3.5039 | 6.38 | 88100 | 3.4449 | 1.0 |
| 4.6598 | 6.39 | 88200 | 3.4443 | 1.0 |
| 3.2816 | 6.39 | 88300 | 3.4471 | 1.0 |
| 3.2072 | 6.4 | 88400 | 3.4370 | 1.0 |
| 3.2164 | 6.41 | 88500 | 3.4455 | 1.0 |
| 3.1742 | 6.41 | 88600 | 3.4416 | 1.0 |
| 3.298 | 6.42 | 88700 | 3.4424 | 1.0 |
| 4.2488 | 6.43 | 88800 | 3.4485 | 1.0 |
| 3.3554 | 6.44 | 88900 | 3.4421 | 1.0 |
| 3.469 | 6.44 | 89000 | 3.4442 | 1.0 |
| 3.7796 | 6.45 | 89100 | 3.4478 | 1.0 |
| 3.357 | 6.46 | 89200 | 3.4493 | 1.0 |
| 3.3099 | 6.46 | 89300 | 3.4422 | 1.0 |
| 3.343 | 6.47 | 89400 | 3.4484 | 1.0 |
| 3.1808 | 6.48 | 89500 | 3.4493 | 1.0 |
| 3.3544 | 6.49 | 89600 | 3.4404 | 1.0 |
| 3.2563 | 6.49 | 89700 | 3.4427 | 1.0 |
| 4.8257 | 6.5 | 89800 | 3.4409 | 1.0 |
| 3.3544 | 6.51 | 89900 | 3.4435 | 1.0 |
| 3.3013 | 6.52 | 90000 | 3.4442 | 1.0 |
| 3.4374 | 6.52 | 90100 | 3.4389 | 1.0 |
| 3.3702 | 6.53 | 90200 | 3.4461 | 1.0 |
| 3.8491 | 6.54 | 90300 | 3.4469 | 1.0 |
| 3.3713 | 6.54 | 90400 | 3.4456 | 1.0 |
| 3.36 | 6.55 | 90500 | 3.4600 | 1.0 |
| 3.4559 | 6.56 | 90600 | 3.4541 | 1.0 |
| 3.9838 | 6.57 | 90700 | 3.4411 | 1.0 |
| 3.3675 | 6.57 | 90800 | 3.4448 | 1.0 |
| 3.3384 | 6.58 | 90900 | 3.4437 | 1.0 |
| 3.3098 | 6.59 | 91000 | 3.4401 | 1.0 |
| 3.344 | 6.6 | 91100 | 3.4412 | 1.0 |
| 3.3974 | 6.6 | 91200 | 3.4383 | 1.0 |
| 3.3255 | 6.61 | 91300 | 3.4468 | 1.0 |
| 3.3193 | 6.62 | 91400 | 3.4410 | 1.0 |
| 3.3432 | 6.62 | 91500 | 3.4429 | 1.0 |
| 3.5861 | 6.63 | 91600 | 3.4501 | 1.0 |
| 3.4078 | 6.64 | 91700 | 3.4466 | 1.0 |
| 3.4045 | 6.65 | 91800 | 3.4507 | 1.0 |
| 3.2148 | 6.65 | 91900 | 3.4440 | 1.0 |
| 3.446 | 6.66 | 92000 | 3.4431 | 1.0 |
| 3.2581 | 6.67 | 92100 | 3.4421 | 1.0 |
| 3.4569 | 6.67 | 92200 | 3.4477 | 1.0 |
| 3.3271 | 6.68 | 92300 | 3.4384 | 1.0 |
| 3.3428 | 6.69 | 92400 | 3.4379 | 1.0 |
| 5.7004 | 6.7 | 92500 | 3.4444 | 1.0 |
| 3.3441 | 6.7 | 92600 | 3.4525 | 1.0 |
| 3.4577 | 6.71 | 92700 | 3.4529 | 1.0 |
| 3.2188 | 6.72 | 92800 | 3.4386 | 1.0 |
| 3.3738 | 6.73 | 92900 | 3.4421 | 1.0 |
| 3.309 | 6.73 | 93000 | 3.4421 | 1.0 |
| 3.6994 | 6.74 | 93100 | 3.4476 | 1.0 |
| 3.4694 | 6.75 | 93200 | 3.4479 | 1.0 |
| 3.6629 | 6.75 | 93300 | 3.4433 | 1.0 |
| 3.2603 | 6.76 | 93400 | 3.4455 | 1.0 |
| 3.5258 | 6.77 | 93500 | 3.4466 | 1.0 |
| 3.3443 | 6.78 | 93600 | 3.4444 | 1.0 |
| 3.3363 | 6.78 | 93700 | 3.4389 | 1.0 |
| 3.8168 | 6.79 | 93800 | 3.4411 | 1.0 |
| 3.4222 | 6.8 | 93900 | 3.4447 | 1.0 |
| 3.6458 | 6.81 | 94000 | 3.4432 | 1.0 |
| 3.246 | 6.81 | 94100 | 3.4473 | 1.0 |
| 3.5288 | 6.82 | 94200 | 3.4468 | 1.0 |
| 3.4141 | 6.83 | 94300 | 3.4379 | 1.0 |
| 3.3348 | 6.83 | 94400 | 3.4394 | 1.0 |
| 3.3027 | 6.84 | 94500 | 3.4433 | 1.0 |
| 3.7383 | 6.85 | 94600 | 3.4431 | 1.0 |
| 3.2835 | 6.86 | 94700 | 3.4385 | 1.0 |
| 3.3132 | 6.86 | 94800 | 3.4435 | 1.0 |
| 3.5486 | 6.87 | 94900 | 3.4457 | 1.0 |
| 3.2407 | 6.88 | 95000 | 3.4401 | 1.0 |
| 5.9865 | 6.88 | 95100 | 3.4526 | 1.0 |
| 3.7244 | 6.89 | 95200 | 3.4456 | 1.0 |
| 3.4583 | 6.9 | 95300 | 3.4419 | 1.0 |
| 3.3585 | 6.91 | 95400 | 3.4406 | 1.0 |
| 3.3433 | 6.91 | 95500 | 3.4582 | 1.0 |
| 3.3487 | 6.92 | 95600 | 3.4446 | 1.0 |
| 3.2941 | 6.93 | 95700 | 3.4538 | 1.0 |
| 3.4637 | 6.94 | 95800 | 3.4380 | 1.0 |
| 3.6811 | 6.94 | 95900 | 3.4385 | 1.0 |
| 3.3364 | 6.95 | 96000 | 3.4476 | 1.0 |
| 3.3127 | 6.96 | 96100 | 3.4376 | 1.0 |
| 3.301 | 6.96 | 96200 | 3.4442 | 1.0 |
| 3.407 | 6.97 | 96300 | 3.4419 | 1.0 |
| 3.3103 | 6.98 | 96400 | 3.4444 | 1.0 |
| 3.514 | 6.99 | 96500 | 3.4496 | 1.0 |
| 3.257 | 6.99 | 96600 | 3.4499 | 1.0 |
| 3.4131 | 7.0 | 96700 | 3.4408 | 1.0 |
| 3.3395 | 7.01 | 96800 | 3.4395 | 1.0 |
| 3.3651 | 7.02 | 96900 | 3.4373 | 1.0 |
| 3.4559 | 7.02 | 97000 | 3.4431 | 1.0 |
| 3.8799 | 7.03 | 97100 | 3.4419 | 1.0 |
| 3.4603 | 7.04 | 97200 | 3.4411 | 1.0 |
| 3.3208 | 7.04 | 97300 | 3.4413 | 1.0 |
| 3.3491 | 7.05 | 97400 | 3.4389 | 1.0 |
| 3.3667 | 7.06 | 97500 | 3.4447 | 1.0 |
| 3.3628 | 7.07 | 97600 | 3.4418 | 1.0 |
| 3.322 | 7.07 | 97700 | 3.4448 | 1.0 |
| 3.4562 | 7.08 | 97800 | 3.4479 | 1.0 |
| 3.2331 | 7.09 | 97900 | 3.4522 | 1.0 |
| 3.4535 | 7.09 | 98000 | 3.4465 | 1.0 |
| 3.3035 | 7.1 | 98100 | 3.4444 | 1.0 |
| 3.3541 | 7.11 | 98200 | 3.4380 | 1.0 |
| 3.2874 | 7.12 | 98300 | 3.4413 | 1.0 |
| 3.4224 | 7.12 | 98400 | 3.4519 | 1.0 |
| 3.4403 | 7.13 | 98500 | 3.4447 | 1.0 |
| 3.2964 | 7.14 | 98600 | 3.4424 | 1.0 |
| 3.297 | 7.15 | 98700 | 3.4403 | 1.0 |
| 3.3279 | 7.15 | 98800 | 3.4469 | 1.0 |
| 3.3393 | 7.16 | 98900 | 3.4477 | 1.0 |
| 3.3377 | 7.17 | 99000 | 3.4437 | 1.0 |
| 3.3256 | 7.17 | 99100 | 3.4376 | 1.0 |
| 3.383 | 7.18 | 99200 | 3.4397 | 1.0 |
| 3.3298 | 7.19 | 99300 | 3.4414 | 1.0 |
| 5.1176 | 7.2 | 99400 | 3.4438 | 1.0 |
| 3.2854 | 7.2 | 99500 | 3.4463 | 1.0 |
| 3.3177 | 7.21 | 99600 | 3.4558 | 1.0 |
| 3.3946 | 7.22 | 99700 | 3.4420 | 1.0 |
| 3.3175 | 7.23 | 99800 | 3.4485 | 1.0 |
| 3.3535 | 7.23 | 99900 | 3.4416 | 1.0 |
| 3.332 | 7.24 | 100000 | 3.4375 | 1.0 |
| 3.2779 | 7.25 | 100100 | 3.4437 | 1.0 |
| 3.2977 | 7.25 | 100200 | 3.4438 | 1.0 |
| 3.3777 | 7.26 | 100300 | 3.4448 | 1.0 |
| 3.3096 | 7.27 | 100400 | 3.4414 | 1.0 |
| 3.3538 | 7.28 | 100500 | 3.4464 | 1.0 |
| 3.3164 | 7.28 | 100600 | 3.4456 | 1.0 |
| 3.4028 | 7.29 | 100700 | 3.4494 | 1.0 |
| 3.4322 | 7.3 | 100800 | 3.4554 | 1.0 |
| 3.2851 | 7.3 | 100900 | 3.4499 | 1.0 |
| 3.3666 | 7.31 | 101000 | 3.4394 | 1.0 |
| 3.2821 | 7.32 | 101100 | 3.4396 | 1.0 |
| 3.3335 | 7.33 | 101200 | 3.4454 | 1.0 |
| 3.3327 | 7.33 | 101300 | 3.4484 | 1.0 |
| 3.2771 | 7.34 | 101400 | 3.4416 | 1.0 |
| 3.2928 | 7.35 | 101500 | 3.4433 | 1.0 |
| 3.3341 | 7.36 | 101600 | 3.4482 | 1.0 |
| 3.2928 | 7.36 | 101700 | 3.4420 | 1.0 |
| 3.2428 | 7.37 | 101800 | 3.4428 | 1.0 |
| 3.3266 | 7.38 | 101900 | 3.4455 | 1.0 |
| 3.3004 | 7.38 | 102000 | 3.4481 | 1.0 |
| 3.3588 | 7.39 | 102100 | 3.4414 | 1.0 |
| 3.3312 | 7.4 | 102200 | 3.4510 | 1.0 |
| 3.4165 | 7.41 | 102300 | 3.4375 | 1.0 |
| 3.3087 | 7.41 | 102400 | 3.4522 | 1.0 |
| 3.353 | 7.42 | 102500 | 3.4400 | 1.0 |
| 3.1741 | 7.43 | 102600 | 3.4413 | 1.0 |
| 3.2123 | 7.44 | 102700 | 3.4472 | 1.0 |
| 3.1993 | 7.44 | 102800 | 3.4452 | 1.0 |
| 3.239 | 7.45 | 102900 | 3.4418 | 1.0 |
| 3.3241 | 7.46 | 103000 | 3.4496 | 1.0 |
| 3.2586 | 7.46 | 103100 | 3.4498 | 1.0 |
| 3.5903 | 7.47 | 103200 | 3.4465 | 1.0 |
| 3.3286 | 7.48 | 103300 | 3.4488 | 1.0 |
| 3.4615 | 7.49 | 103400 | 3.4486 | 1.0 |
| 3.3855 | 7.49 | 103500 | 3.4440 | 1.0 |
| 3.3819 | 7.5 | 103600 | 3.4534 | 1.0 |
| 3.3003 | 7.51 | 103700 | 3.4502 | 1.0 |
| 3.4232 | 7.51 | 103800 | 3.4429 | 1.0 |
| 3.2926 | 7.52 | 103900 | 3.4442 | 1.0 |
| 3.7337 | 7.53 | 104000 | 3.4516 | 1.0 |
| 3.3338 | 7.54 | 104100 | 3.4469 | 1.0 |
| 3.32 | 7.54 | 104200 | 3.4545 | 1.0 |
| 3.6807 | 7.55 | 104300 | 3.4449 | 1.0 |
| 3.3397 | 7.56 | 104400 | 3.4479 | 1.0 |
| 3.2993 | 7.57 | 104500 | 3.4424 | 1.0 |
| 3.3652 | 7.57 | 104600 | 3.4507 | 1.0 |
| 3.2885 | 7.58 | 104700 | 3.4437 | 1.0 |
| 3.4006 | 7.59 | 104800 | 3.4403 | 1.0 |
| 3.3361 | 7.59 | 104900 | 3.4432 | 1.0 |
| 3.4084 | 7.6 | 105000 | 3.4423 | 1.0 |
| 3.3251 | 7.61 | 105100 | 3.4418 | 1.0 |
| 3.3079 | 7.62 | 105200 | 3.4398 | 1.0 |
| 3.4738 | 7.62 | 105300 | 3.4497 | 1.0 |
| 3.5048 | 7.63 | 105400 | 3.4429 | 1.0 |
| 3.4189 | 7.64 | 105500 | 3.4410 | 1.0 |
| 3.3132 | 7.64 | 105600 | 3.4437 | 1.0 |
| 3.2738 | 7.65 | 105700 | 3.4457 | 1.0 |
| 3.2876 | 7.66 | 105800 | 3.4404 | 1.0 |
| 3.3413 | 7.67 | 105900 | 3.4458 | 1.0 |
| 3.3014 | 7.67 | 106000 | 3.4535 | 1.0 |
| 3.2244 | 7.68 | 106100 | 3.4436 | 1.0 |
| 3.2715 | 7.69 | 106200 | 3.4470 | 1.0 |
| 3.3593 | 7.7 | 106300 | 3.4410 | 1.0 |
| 3.334 | 7.7 | 106400 | 3.4525 | 1.0 |
| 3.3547 | 7.71 | 106500 | 3.4513 | 1.0 |
| 3.9896 | 7.72 | 106600 | 3.4381 | 1.0 |
| 3.4202 | 7.72 | 106700 | 3.4395 | 1.0 |
| 3.34 | 7.73 | 106800 | 3.4426 | 1.0 |
| 3.3778 | 7.74 | 106900 | 3.4508 | 1.0 |
| 3.3374 | 7.75 | 107000 | 3.4464 | 1.0 |
| 3.4008 | 7.75 | 107100 | 3.4365 | 1.0 |
| 3.2595 | 7.76 | 107200 | 3.4496 | 1.0 |
| 3.3261 | 7.77 | 107300 | 3.4543 | 1.0 |
| 3.2551 | 7.78 | 107400 | 3.4490 | 1.0 |
| 3.2967 | 7.78 | 107500 | 3.4404 | 1.0 |
| 3.4232 | 7.79 | 107600 | 3.4492 | 1.0 |
| 3.3992 | 7.8 | 107700 | 3.4448 | 1.0 |
| 3.3268 | 7.8 | 107800 | 3.4465 | 1.0 |
| 3.283 | 7.81 | 107900 | 3.4424 | 1.0 |
| 3.3488 | 7.82 | 108000 | 3.4446 | 1.0 |
| 3.3232 | 7.83 | 108100 | 3.4432 | 1.0 |
| 3.5081 | 7.83 | 108200 | 3.4460 | 1.0 |
| 3.2686 | 7.84 | 108300 | 3.4499 | 1.0 |
| 3.2465 | 7.85 | 108400 | 3.4429 | 1.0 |
| 3.5602 | 7.85 | 108500 | 3.4398 | 1.0 |
| 3.299 | 7.86 | 108600 | 3.4376 | 1.0 |
| 3.3437 | 7.87 | 108700 | 3.4428 | 1.0 |
| 3.3221 | 7.88 | 108800 | 3.4492 | 1.0 |
| 3.5462 | 7.88 | 108900 | 3.4414 | 1.0 |
| 3.3901 | 7.89 | 109000 | 3.4506 | 1.0 |
| 3.3598 | 7.9 | 109100 | 3.4421 | 1.0 |
| 3.3946 | 7.91 | 109200 | 3.4389 | 1.0 |
| 3.3013 | 7.91 | 109300 | 3.4444 | 1.0 |
| 3.3094 | 7.92 | 109400 | 3.4464 | 1.0 |
| 3.4829 | 7.93 | 109500 | 3.4379 | 1.0 |
| 3.2769 | 7.93 | 109600 | 3.4401 | 1.0 |
| 3.3359 | 7.94 | 109700 | 3.4437 | 1.0 |
| 3.3079 | 7.95 | 109800 | 3.4455 | 1.0 |
| 3.3623 | 7.96 | 109900 | 3.4447 | 1.0 |
| 3.3439 | 7.96 | 110000 | 3.4404 | 1.0 |
| 3.3045 | 7.97 | 110100 | 3.4520 | 1.0 |
| 3.2657 | 7.98 | 110200 | 3.4409 | 1.0 |
| 3.3187 | 7.99 | 110300 | 3.4430 | 1.0 |
| 3.349 | 7.99 | 110400 | 3.4430 | 1.0 |
| 3.3262 | 8.0 | 110500 | 3.4412 | 1.0 |
| 3.2603 | 8.01 | 110600 | 3.4440 | 1.0 |
| 3.4284 | 8.01 | 110700 | 3.4456 | 1.0 |
| 3.5993 | 8.02 | 110800 | 3.4518 | 1.0 |
| 5.6854 | 8.03 | 110900 | 3.4411 | 1.0 |
| 3.3856 | 8.04 | 111000 | 3.4430 | 1.0 |
| 3.5339 | 8.04 | 111100 | 3.4394 | 1.0 |
| 3.2691 | 8.05 | 111200 | 3.4425 | 1.0 |
| 3.3462 | 8.06 | 111300 | 3.4422 | 1.0 |
| 3.3469 | 8.06 | 111400 | 3.4458 | 1.0 |
| 3.3598 | 8.07 | 111500 | 3.4429 | 1.0 |
| 3.554 | 8.08 | 111600 | 3.4438 | 1.0 |
| 3.3207 | 8.09 | 111700 | 3.4480 | 1.0 |
| 3.2963 | 8.09 | 111800 | 3.4434 | 1.0 |
| 3.4644 | 8.1 | 111900 | 3.4417 | 1.0 |
| 3.4265 | 8.11 | 112000 | 3.4404 | 1.0 |
| 3.3026 | 8.12 | 112100 | 3.4442 | 1.0 |
| 3.2747 | 8.12 | 112200 | 3.4433 | 1.0 |
| 7.3735 | 8.13 | 112300 | 3.4403 | 1.0 |
| 3.4803 | 8.14 | 112400 | 3.4464 | 1.0 |
| 4.9879 | 8.14 | 112500 | 3.4454 | 1.0 |
| 3.4249 | 8.15 | 112600 | 3.4421 | 1.0 |
| 3.3493 | 8.16 | 112700 | 3.4403 | 1.0 |
| 3.3514 | 8.17 | 112800 | 3.4445 | 1.0 |
| 3.262 | 8.17 | 112900 | 3.4457 | 1.0 |
| 3.3517 | 8.18 | 113000 | 3.4479 | 1.0 |
| 3.2408 | 8.19 | 113100 | 3.4413 | 1.0 |
| 3.2346 | 8.2 | 113200 | 3.4415 | 1.0 |
| 3.2397 | 8.2 | 113300 | 3.4414 | 1.0 |
| 3.3794 | 8.21 | 113400 | 3.4502 | 1.0 |
| 3.516 | 8.22 | 113500 | 3.4507 | 1.0 |
| 3.4129 | 8.22 | 113600 | 3.4455 | 1.0 |
| 3.3381 | 8.23 | 113700 | 3.4540 | 1.0 |
| 3.3172 | 8.24 | 113800 | 3.4473 | 1.0 |
| 3.5307 | 8.25 | 113900 | 3.4431 | 1.0 |
| 3.3424 | 8.25 | 114000 | 3.4511 | 1.0 |
| 3.4004 | 8.26 | 114100 | 3.4434 | 1.0 |
| 3.4061 | 8.27 | 114200 | 3.4435 | 1.0 |
| 3.5333 | 8.27 | 114300 | 3.4415 | 1.0 |
| 3.2974 | 8.28 | 114400 | 3.4472 | 1.0 |
| 3.3827 | 8.29 | 114500 | 3.4469 | 1.0 |
| 3.5697 | 8.3 | 114600 | 3.4427 | 1.0 |
| 3.4561 | 8.3 | 114700 | 3.4433 | 1.0 |
| 3.5205 | 8.31 | 114800 | 3.4474 | 1.0 |
| 3.2541 | 8.32 | 114900 | 3.4475 | 1.0 |
| 3.4251 | 8.33 | 115000 | 3.4394 | 1.0 |
| 3.2477 | 8.33 | 115100 | 3.4524 | 1.0 |
| 3.4003 | 8.34 | 115200 | 3.4438 | 1.0 |
| 3.3378 | 8.35 | 115300 | 3.4447 | 1.0 |
| 3.2828 | 8.35 | 115400 | 3.4493 | 1.0 |
| 3.6974 | 8.36 | 115500 | 3.4507 | 1.0 |
| 3.3466 | 8.37 | 115600 | 3.4384 | 1.0 |
| 3.2601 | 8.38 | 115700 | 3.4538 | 1.0 |
| 3.8384 | 8.38 | 115800 | 3.4408 | 1.0 |
| 3.5255 | 8.39 | 115900 | 3.4446 | 1.0 |
| 3.3517 | 8.4 | 116000 | 3.4445 | 1.0 |
| 3.37 | 8.41 | 116100 | 3.4530 | 1.0 |
| 3.4486 | 8.41 | 116200 | 3.4446 | 1.0 |
| 3.4104 | 8.42 | 116300 | 3.4447 | 1.0 |
| 3.5267 | 8.43 | 116400 | 3.4410 | 1.0 |
| 3.4422 | 8.43 | 116500 | 3.4546 | 1.0 |
| 3.1616 | 8.44 | 116600 | 3.4400 | 1.0 |
| 3.3557 | 8.45 | 116700 | 3.4458 | 1.0 |
| 3.4674 | 8.46 | 116800 | 3.4443 | 1.0 |
| 3.3114 | 8.46 | 116900 | 3.4390 | 1.0 |
| 3.4986 | 8.47 | 117000 | 3.4405 | 1.0 |
| 3.4579 | 8.48 | 117100 | 3.4459 | 1.0 |
| 3.3369 | 8.48 | 117200 | 3.4403 | 1.0 |
| 3.4802 | 8.49 | 117300 | 3.4480 | 1.0 |
| 3.3244 | 8.5 | 117400 | 3.4447 | 1.0 |
| 3.3096 | 8.51 | 117500 | 3.4525 | 1.0 |
| 3.3415 | 8.51 | 117600 | 3.4516 | 1.0 |
| 3.416 | 8.52 | 117700 | 3.4396 | 1.0 |
| 3.3363 | 8.53 | 117800 | 3.4510 | 1.0 |
| 3.2588 | 8.54 | 117900 | 3.4439 | 1.0 |
| 3.4127 | 8.54 | 118000 | 3.4370 | 1.0 |
| 3.4268 | 8.55 | 118100 | 3.4472 | 1.0 |
| 3.3877 | 8.56 | 118200 | 3.4437 | 1.0 |
| 3.386 | 8.56 | 118300 | 3.4448 | 1.0 |
| 3.9643 | 8.57 | 118400 | 3.4500 | 1.0 |
| 3.2205 | 8.58 | 118500 | 3.4410 | 1.0 |
| 3.3372 | 8.59 | 118600 | 3.4486 | 1.0 |
| 3.3919 | 8.59 | 118700 | 3.4485 | 1.0 |
| 3.3279 | 8.6 | 118800 | 3.4408 | 1.0 |
| 3.3251 | 8.61 | 118900 | 3.4379 | 1.0 |
| 3.2832 | 8.62 | 119000 | 3.4388 | 1.0 |
| 3.2708 | 8.62 | 119100 | 3.4522 | 1.0 |
| 4.0701 | 8.63 | 119200 | 3.4436 | 1.0 |
| 3.5261 | 8.64 | 119300 | 3.4475 | 1.0 |
| 3.2695 | 8.64 | 119400 | 3.4411 | 1.0 |
| 3.4095 | 8.65 | 119500 | 3.4451 | 1.0 |
| 3.2641 | 8.66 | 119600 | 3.4527 | 1.0 |
| 3.6962 | 8.67 | 119700 | 3.4495 | 1.0 |
| 3.407 | 8.67 | 119800 | 3.4523 | 1.0 |
| 3.5073 | 8.68 | 119900 | 3.4612 | 1.0 |
| 3.4697 | 8.69 | 120000 | 3.4491 | 1.0 |
| 3.4643 | 8.69 | 120100 | 3.4427 | 1.0 |
| 3.5253 | 8.7 | 120200 | 3.4457 | 1.0 |
| 3.2562 | 8.71 | 120300 | 3.4545 | 1.0 |
| 3.2946 | 8.72 | 120400 | 3.4570 | 1.0 |
| 3.393 | 8.72 | 120500 | 3.4432 | 1.0 |
| 3.2528 | 8.73 | 120600 | 3.4391 | 1.0 |
| 3.4529 | 8.74 | 120700 | 3.4530 | 1.0 |
| 3.506 | 8.75 | 120800 | 3.4425 | 1.0 |
| 3.3464 | 8.75 | 120900 | 3.4420 | 1.0 |
| 3.3287 | 8.76 | 121000 | 3.4463 | 1.0 |
| 3.3165 | 8.77 | 121100 | 3.4509 | 1.0 |
| 3.3102 | 8.77 | 121200 | 3.4418 | 1.0 |
| 3.4206 | 8.78 | 121300 | 3.4495 | 1.0 |
| 3.5963 | 8.79 | 121400 | 3.4432 | 1.0 |
| 3.2621 | 8.8 | 121500 | 3.4455 | 1.0 |
| 3.3275 | 8.8 | 121600 | 3.4483 | 1.0 |
| 3.3654 | 8.81 | 121700 | 3.4476 | 1.0 |
| 3.4913 | 8.82 | 121800 | 3.4525 | 1.0 |
| 3.4162 | 8.83 | 121900 | 3.4409 | 1.0 |
| 3.221 | 8.83 | 122000 | 3.4415 | 1.0 |
| 3.3024 | 8.84 | 122100 | 3.4385 | 1.0 |
| 3.3451 | 8.85 | 122200 | 3.4428 | 1.0 |
| 3.3909 | 8.85 | 122300 | 3.4417 | 1.0 |
| 3.3237 | 8.86 | 122400 | 3.4472 | 1.0 |
| 3.2992 | 8.87 | 122500 | 3.4406 | 1.0 |
| 3.2422 | 8.88 | 122600 | 3.4492 | 1.0 |
| 3.3713 | 8.88 | 122700 | 3.4411 | 1.0 |
| 3.4062 | 8.89 | 122800 | 3.4412 | 1.0 |
| 3.3616 | 8.9 | 122900 | 3.4464 | 1.0 |
| 3.3811 | 8.9 | 123000 | 3.4382 | 1.0 |
| 3.3592 | 8.91 | 123100 | 3.4442 | 1.0 |
| 3.8331 | 8.92 | 123200 | 3.4423 | 1.0 |
| 3.3764 | 8.93 | 123300 | 3.4492 | 1.0 |
| 3.3964 | 8.93 | 123400 | 3.4390 | 1.0 |
| 3.5063 | 8.94 | 123500 | 3.4411 | 1.0 |
| 3.3627 | 8.95 | 123600 | 3.4467 | 1.0 |
| 4.1315 | 8.96 | 123700 | 3.4409 | 1.0 |
| 3.7114 | 8.96 | 123800 | 3.4456 | 1.0 |
| 3.3446 | 8.97 | 123900 | 3.4413 | 1.0 |
| 3.3777 | 8.98 | 124000 | 3.4464 | 1.0 |
| 3.6232 | 8.98 | 124100 | 3.4478 | 1.0 |
| 3.3275 | 8.99 | 124200 | 3.4474 | 1.0 |
| 3.5736 | 9.0 | 124300 | 3.4427 | 1.0 |
| 3.2052 | 9.01 | 124400 | 3.4455 | 1.0 |
| 3.3101 | 9.01 | 124500 | 3.4485 | 1.0 |
| 3.3523 | 9.02 | 124600 | 3.4389 | 1.0 |
| 3.3095 | 9.03 | 124700 | 3.4433 | 1.0 |
| 3.3152 | 9.03 | 124800 | 3.4402 | 1.0 |
| 3.2351 | 9.04 | 124900 | 3.4452 | 1.0 |
| 3.5137 | 9.05 | 125000 | 3.4458 | 1.0 |
| 3.3489 | 9.06 | 125100 | 3.4431 | 1.0 |
| 3.3822 | 9.06 | 125200 | 3.4370 | 1.0 |
| 3.3842 | 9.07 | 125300 | 3.4359 | 1.0 |
| 3.306 | 9.08 | 125400 | 3.4439 | 1.0 |
| 3.3784 | 9.09 | 125500 | 3.4538 | 1.0 |
| 3.3313 | 9.09 | 125600 | 3.4410 | 1.0 |
| 3.2891 | 9.1 | 125700 | 3.4397 | 1.0 |
| 3.321 | 9.11 | 125800 | 3.4457 | 1.0 |
| 3.2479 | 9.11 | 125900 | 3.4448 | 1.0 |
| 3.3723 | 9.12 | 126000 | 3.4409 | 1.0 |
| 3.3203 | 9.13 | 126100 | 3.4439 | 1.0 |
| 3.2906 | 9.14 | 126200 | 3.4388 | 1.0 |
| 3.2164 | 9.14 | 126300 | 3.4427 | 1.0 |
| 3.2608 | 9.15 | 126400 | 3.4396 | 1.0 |
| 3.3739 | 9.16 | 126500 | 3.4536 | 1.0 |
| 3.3479 | 9.17 | 126600 | 3.4533 | 1.0 |
| 3.4664 | 9.17 | 126700 | 3.4491 | 1.0 |
| 3.326 | 9.18 | 126800 | 3.4402 | 1.0 |
| 3.3056 | 9.19 | 126900 | 3.4398 | 1.0 |
| 3.3528 | 9.19 | 127000 | 3.4424 | 1.0 |
| 3.2717 | 9.2 | 127100 | 3.4409 | 1.0 |
| 3.3564 | 9.21 | 127200 | 3.4497 | 1.0 |
| 3.4015 | 9.22 | 127300 | 3.4435 | 1.0 |
| 3.3325 | 9.22 | 127400 | 3.4478 | 1.0 |
| 3.4459 | 9.23 | 127500 | 3.4479 | 1.0 |
| 3.2151 | 9.24 | 127600 | 3.4519 | 1.0 |
| 3.2456 | 9.24 | 127700 | 3.4408 | 1.0 |
| 3.3108 | 9.25 | 127800 | 3.4430 | 1.0 |
| 3.3965 | 9.26 | 127900 | 3.4427 | 1.0 |
| 3.4911 | 9.27 | 128000 | 3.4430 | 1.0 |
| 3.3996 | 9.27 | 128100 | 3.4458 | 1.0 |
| 3.3408 | 9.28 | 128200 | 3.4435 | 1.0 |
| 3.353 | 9.29 | 128300 | 3.4468 | 1.0 |
| 3.5449 | 9.3 | 128400 | 3.4401 | 1.0 |
| 3.3564 | 9.3 | 128500 | 3.4481 | 1.0 |
| 3.4768 | 9.31 | 128600 | 3.4450 | 1.0 |
| 3.3972 | 9.32 | 128700 | 3.4467 | 1.0 |
| 3.3295 | 9.32 | 128800 | 3.4385 | 1.0 |
| 3.3181 | 9.33 | 128900 | 3.4435 | 1.0 |
| 3.3224 | 9.34 | 129000 | 3.4467 | 1.0 |
| 3.3471 | 9.35 | 129100 | 3.4415 | 1.0 |
| 3.3379 | 9.35 | 129200 | 3.4458 | 1.0 |
| 3.3991 | 9.36 | 129300 | 3.4420 | 1.0 |
| 3.4037 | 9.37 | 129400 | 3.4433 | 1.0 |
| 3.3157 | 9.38 | 129500 | 3.4450 | 1.0 |
| 3.3739 | 9.38 | 129600 | 3.4426 | 1.0 |
| 3.2556 | 9.39 | 129700 | 3.4473 | 1.0 |
| 3.3451 | 9.4 | 129800 | 3.4413 | 1.0 |
| 3.3694 | 9.4 | 129900 | 3.4462 | 1.0 |
| 3.343 | 9.41 | 130000 | 3.4408 | 1.0 |
| 3.4286 | 9.42 | 130100 | 3.4495 | 1.0 |
| 3.4468 | 9.43 | 130200 | 3.4450 | 1.0 |
| 3.3417 | 9.43 | 130300 | 3.4457 | 1.0 |
| 3.4661 | 9.44 | 130400 | 3.4409 | 1.0 |
| 3.2859 | 9.45 | 130500 | 3.4412 | 1.0 |
| 3.3164 | 9.45 | 130600 | 3.4495 | 1.0 |
| 3.3542 | 9.46 | 130700 | 3.4428 | 1.0 |
| 3.2783 | 9.47 | 130800 | 3.4398 | 1.0 |
| 3.421 | 9.48 | 130900 | 3.4408 | 1.0 |
| 3.3765 | 9.48 | 131000 | 3.4443 | 1.0 |
| 3.3822 | 9.49 | 131100 | 3.4458 | 1.0 |
| 3.2261 | 9.5 | 131200 | 3.4437 | 1.0 |
| 3.362 | 9.51 | 131300 | 3.4388 | 1.0 |
| 3.3203 | 9.51 | 131400 | 3.4498 | 1.0 |
| 3.2326 | 9.52 | 131500 | 3.4415 | 1.0 |
| 3.3897 | 9.53 | 131600 | 3.4556 | 1.0 |
| 3.3434 | 9.53 | 131700 | 3.4421 | 1.0 |
| 3.3297 | 9.54 | 131800 | 3.4394 | 1.0 |
| 3.4889 | 9.55 | 131900 | 3.4420 | 1.0 |
| 3.3502 | 9.56 | 132000 | 3.4425 | 1.0 |
| 3.4079 | 9.56 | 132100 | 3.4370 | 1.0 |
| 3.213 | 9.57 | 132200 | 3.4479 | 1.0 |
| 3.3935 | 9.58 | 132300 | 3.4433 | 1.0 |
| 3.2598 | 9.59 | 132400 | 3.4431 | 1.0 |
| 3.3968 | 9.59 | 132500 | 3.4442 | 1.0 |
| 3.338 | 9.6 | 132600 | 3.4433 | 1.0 |
| 3.3268 | 9.61 | 132700 | 3.4447 | 1.0 |
| 3.3656 | 9.61 | 132800 | 3.4394 | 1.0 |
| 3.3782 | 9.62 | 132900 | 3.4397 | 1.0 |
| 3.3787 | 9.63 | 133000 | 3.4440 | 1.0 |
| 5.5557 | 9.64 | 133100 | 3.4396 | 1.0 |
| 3.4011 | 9.64 | 133200 | 3.4448 | 1.0 |
| 3.7319 | 9.65 | 133300 | 3.4447 | 1.0 |
| 3.5717 | 9.66 | 133400 | 3.4387 | 1.0 |
| 3.3051 | 9.66 | 133500 | 3.4460 | 1.0 |
| 3.3485 | 9.67 | 133600 | 3.4513 | 1.0 |
| 3.4845 | 9.68 | 133700 | 3.4506 | 1.0 |
| 3.335 | 9.69 | 133800 | 3.4415 | 1.0 |
| 3.2942 | 9.69 | 133900 | 3.4439 | 1.0 |
| 3.2748 | 9.7 | 134000 | 3.4390 | 1.0 |
| 3.392 | 9.71 | 134100 | 3.4490 | 1.0 |
| 3.3396 | 9.72 | 134200 | 3.4463 | 1.0 |
| 3.3097 | 9.72 | 134300 | 3.4440 | 1.0 |
| 3.3421 | 9.73 | 134400 | 3.4498 | 1.0 |
| 3.5204 | 9.74 | 134500 | 3.4514 | 1.0 |
| 3.8217 | 9.74 | 134600 | 3.4463 | 1.0 |
| 3.3094 | 9.75 | 134700 | 3.4402 | 1.0 |
| 3.3267 | 9.76 | 134800 | 3.4425 | 1.0 |
| 3.3396 | 9.77 | 134900 | 3.4429 | 1.0 |
| 3.3117 | 9.77 | 135000 | 3.4415 | 1.0 |
| 3.4302 | 9.78 | 135100 | 3.4406 | 1.0 |
| 3.2691 | 9.79 | 135200 | 3.4405 | 1.0 |
| 3.337 | 9.8 | 135300 | 3.4416 | 1.0 |
| 3.3437 | 9.8 | 135400 | 3.4427 | 1.0 |
| 3.3744 | 9.81 | 135500 | 3.4477 | 1.0 |
| 3.3151 | 9.82 | 135600 | 3.4388 | 1.0 |
| 3.3742 | 9.82 | 135700 | 3.4448 | 1.0 |
| 3.3093 | 9.83 | 135800 | 3.4462 | 1.0 |
| 3.4145 | 9.84 | 135900 | 3.4413 | 1.0 |
| 3.3858 | 9.85 | 136000 | 3.4459 | 1.0 |
| 3.3464 | 9.85 | 136100 | 3.4432 | 1.0 |
| 3.3831 | 9.86 | 136200 | 3.4467 | 1.0 |
| 3.2715 | 9.87 | 136300 | 3.4442 | 1.0 |
| 3.3594 | 9.87 | 136400 | 3.4444 | 1.0 |
| 3.3679 | 9.88 | 136500 | 3.4498 | 1.0 |
| 3.346 | 9.89 | 136600 | 3.4380 | 1.0 |
| 3.3156 | 9.9 | 136700 | 3.4501 | 1.0 |
| 3.3689 | 9.9 | 136800 | 3.4403 | 1.0 |
| 3.3157 | 9.91 | 136900 | 3.4461 | 1.0 |
| 3.2955 | 9.92 | 137000 | 3.4460 | 1.0 |
| 3.2288 | 9.93 | 137100 | 3.4429 | 1.0 |
| 3.3068 | 9.93 | 137200 | 3.4442 | 1.0 |
| 3.3965 | 9.94 | 137300 | 3.4400 | 1.0 |
| 3.3238 | 9.95 | 137400 | 3.4464 | 1.0 |
| 3.3469 | 9.95 | 137500 | 3.4496 | 1.0 |
| 3.3818 | 9.96 | 137600 | 3.4446 | 1.0 |
| 3.3677 | 9.97 | 137700 | 3.4487 | 1.0 |
| 3.4811 | 9.98 | 137800 | 3.4441 | 1.0 |
| 3.3636 | 9.98 | 137900 | 3.4456 | 1.0 |
| 3.3305 | 9.99 | 138000 | 3.4417 | 1.0 |
| 3.4025 | 10.0 | 138100 | 3.4401 | 1.0 |
| 3.4951 | 10.01 | 138200 | 3.4392 | 1.0 |
| 3.2803 | 10.01 | 138300 | 3.4411 | 1.0 |
| 4.6095 | 10.02 | 138400 | 3.4446 | 1.0 |
| 3.3677 | 10.03 | 138500 | 3.4465 | 1.0 |
| 3.4183 | 10.03 | 138600 | 3.4434 | 1.0 |
| 3.3482 | 10.04 | 138700 | 3.4430 | 1.0 |
| 3.2795 | 10.05 | 138800 | 3.4449 | 1.0 |
| 3.282 | 10.06 | 138900 | 3.4455 | 1.0 |
| 3.2617 | 10.06 | 139000 | 3.4442 | 1.0 |
| 3.5404 | 10.07 | 139100 | 3.4375 | 1.0 |
| 3.3432 | 10.08 | 139200 | 3.4447 | 1.0 |
| 3.3643 | 10.08 | 139300 | 3.4429 | 1.0 |
| 3.3022 | 10.09 | 139400 | 3.4415 | 1.0 |
| 3.4062 | 10.1 | 139500 | 3.4415 | 1.0 |
| 3.374 | 10.11 | 139600 | 3.4405 | 1.0 |
| 3.2843 | 10.11 | 139700 | 3.4435 | 1.0 |
| 3.6033 | 10.12 | 139800 | 3.4473 | 1.0 |
| 3.3374 | 10.13 | 139900 | 3.4428 | 1.0 |
| 3.3877 | 10.14 | 140000 | 3.4513 | 1.0 |
| 3.3533 | 10.14 | 140100 | 3.4484 | 1.0 |
| 3.3678 | 10.15 | 140200 | 3.4481 | 1.0 |
| 3.276 | 10.16 | 140300 | 3.4416 | 1.0 |
| 3.3052 | 10.16 | 140400 | 3.4483 | 1.0 |
| 3.4821 | 10.17 | 140500 | 3.4390 | 1.0 |
| 3.2748 | 10.18 | 140600 | 3.4389 | 1.0 |
| 3.2742 | 10.19 | 140700 | 3.4482 | 1.0 |
| 3.2824 | 10.19 | 140800 | 3.4416 | 1.0 |
| 3.37 | 10.2 | 140900 | 3.4435 | 1.0 |
| 3.3768 | 10.21 | 141000 | 3.4458 | 1.0 |
| 3.2652 | 10.22 | 141100 | 3.4454 | 1.0 |
| 3.4041 | 10.22 | 141200 | 3.4425 | 1.0 |
| 3.4062 | 10.23 | 141300 | 3.4465 | 1.0 |
| 3.2338 | 10.24 | 141400 | 3.4438 | 1.0 |
| 3.4214 | 10.24 | 141500 | 3.4425 | 1.0 |
| 3.3741 | 10.25 | 141600 | 3.4389 | 1.0 |
| 3.3156 | 10.26 | 141700 | 3.4468 | 1.0 |
| 3.43 | 10.27 | 141800 | 3.4430 | 1.0 |
| 3.3447 | 10.27 | 141900 | 3.4456 | 1.0 |
| 3.2682 | 10.28 | 142000 | 3.4517 | 1.0 |
| 3.3296 | 10.29 | 142100 | 3.4484 | 1.0 |
| 3.2508 | 10.29 | 142200 | 3.4420 | 1.0 |
| 3.3328 | 10.3 | 142300 | 3.4472 | 1.0 |
| 3.2838 | 10.31 | 142400 | 3.4439 | 1.0 |
| 3.3274 | 10.32 | 142500 | 3.4408 | 1.0 |
| 3.4848 | 10.32 | 142600 | 3.4448 | 1.0 |
| 3.5383 | 10.33 | 142700 | 3.4423 | 1.0 |
| 3.231 | 10.34 | 142800 | 3.4463 | 1.0 |
| 3.1536 | 10.35 | 142900 | 3.4437 | 1.0 |
| 3.281 | 10.35 | 143000 | 3.4436 | 1.0 |
| 3.2452 | 10.36 | 143100 | 3.4393 | 1.0 |
| 3.5728 | 10.37 | 143200 | 3.4406 | 1.0 |
| 3.3216 | 10.37 | 143300 | 3.4403 | 1.0 |
| 3.3496 | 10.38 | 143400 | 3.4397 | 1.0 |
| 3.3177 | 10.39 | 143500 | 3.4559 | 1.0 |
| 3.3153 | 10.4 | 143600 | 3.4460 | 1.0 |
| 3.4076 | 10.4 | 143700 | 3.4441 | 1.0 |
| 3.4137 | 10.41 | 143800 | 3.4397 | 1.0 |
| 3.3806 | 10.42 | 143900 | 3.4488 | 1.0 |
| 3.366 | 10.42 | 144000 | 3.4462 | 1.0 |
| 3.4151 | 10.43 | 144100 | 3.4446 | 1.0 |
| 3.3399 | 10.44 | 144200 | 3.4447 | 1.0 |
| 3.3705 | 10.45 | 144300 | 3.4392 | 1.0 |
| 3.5029 | 10.45 | 144400 | 3.4513 | 1.0 |
| 3.3149 | 10.46 | 144500 | 3.4458 | 1.0 |
| 3.3677 | 10.47 | 144600 | 3.4442 | 1.0 |
| 3.408 | 10.48 | 144700 | 3.4403 | 1.0 |
| 3.3738 | 10.48 | 144800 | 3.4405 | 1.0 |
| 3.2886 | 10.49 | 144900 | 3.4447 | 1.0 |
| 3.3321 | 10.5 | 145000 | 3.4455 | 1.0 |
| 3.4341 | 10.5 | 145100 | 3.4476 | 1.0 |
| 3.4789 | 10.51 | 145200 | 3.4436 | 1.0 |
| 3.4361 | 10.52 | 145300 | 3.4488 | 1.0 |
| 3.3073 | 10.53 | 145400 | 3.4495 | 1.0 |
| 3.3372 | 10.53 | 145500 | 3.4461 | 1.0 |
| 3.31 | 10.54 | 145600 | 3.4512 | 1.0 |
| 3.4571 | 10.55 | 145700 | 3.4473 | 1.0 |
| 3.3517 | 10.56 | 145800 | 3.4435 | 1.0 |
| 3.4304 | 10.56 | 145900 | 3.4428 | 1.0 |
| 3.4364 | 10.57 | 146000 | 3.4369 | 1.0 |
| 3.5522 | 10.58 | 146100 | 3.4431 | 1.0 |
| 3.421 | 10.58 | 146200 | 3.4426 | 1.0 |
| 3.3087 | 10.59 | 146300 | 3.4436 | 1.0 |
| 3.2905 | 10.6 | 146400 | 3.4417 | 1.0 |
| 3.4746 | 10.61 | 146500 | 3.4419 | 1.0 |
| 3.3347 | 10.61 | 146600 | 3.4396 | 1.0 |
| 3.2969 | 10.62 | 146700 | 3.4471 | 1.0 |
| 3.3403 | 10.63 | 146800 | 3.4453 | 1.0 |
| 3.8747 | 10.63 | 146900 | 3.4447 | 1.0 |
| 3.3049 | 10.64 | 147000 | 3.4458 | 1.0 |
| 3.3451 | 10.65 | 147100 | 3.4441 | 1.0 |
| 3.4467 | 10.66 | 147200 | 3.4439 | 1.0 |
| 3.3037 | 10.66 | 147300 | 3.4425 | 1.0 |
| 3.3891 | 10.67 | 147400 | 3.4427 | 1.0 |
| 3.2158 | 10.68 | 147500 | 3.4436 | 1.0 |
| 3.3726 | 10.69 | 147600 | 3.4438 | 1.0 |
| 3.3391 | 10.69 | 147700 | 3.4548 | 1.0 |
| 3.2352 | 10.7 | 147800 | 3.4414 | 1.0 |
| 3.3604 | 10.71 | 147900 | 3.4408 | 1.0 |
| 3.3056 | 10.71 | 148000 | 3.4407 | 1.0 |
| 3.3201 | 10.72 | 148100 | 3.4404 | 1.0 |
| 3.4137 | 10.73 | 148200 | 3.4423 | 1.0 |
| 3.3336 | 10.74 | 148300 | 3.4455 | 1.0 |
| 3.317 | 10.74 | 148400 | 3.4426 | 1.0 |
| 3.2644 | 10.75 | 148500 | 3.4427 | 1.0 |
| 3.4462 | 10.76 | 148600 | 3.4429 | 1.0 |
| 3.448 | 10.77 | 148700 | 3.4479 | 1.0 |
| 3.8269 | 10.77 | 148800 | 3.4428 | 1.0 |
| 3.2383 | 10.78 | 148900 | 3.4400 | 1.0 |
| 3.4066 | 10.79 | 149000 | 3.4412 | 1.0 |
| 3.2348 | 10.79 | 149100 | 3.4491 | 1.0 |
| 3.2971 | 10.8 | 149200 | 3.4464 | 1.0 |
| 3.2493 | 10.81 | 149300 | 3.4509 | 1.0 |
| 3.4274 | 10.82 | 149400 | 3.4420 | 1.0 |
| 3.4327 | 10.82 | 149500 | 3.4441 | 1.0 |
| 3.7189 | 10.83 | 149600 | 3.4377 | 1.0 |
| 3.3102 | 10.84 | 149700 | 3.4484 | 1.0 |
| 3.4991 | 10.84 | 149800 | 3.4460 | 1.0 |
| 3.2776 | 10.85 | 149900 | 3.4428 | 1.0 |
| 3.4605 | 10.86 | 150000 | 3.4469 | 1.0 |
| 3.8307 | 10.87 | 150100 | 3.4500 | 1.0 |
| 3.3874 | 10.87 | 150200 | 3.4454 | 1.0 |
| 3.3007 | 10.88 | 150300 | 3.4433 | 1.0 |
| 3.4145 | 10.89 | 150400 | 3.4434 | 1.0 |
| 3.1793 | 10.9 | 150500 | 3.4401 | 1.0 |
| 3.27 | 10.9 | 150600 | 3.4459 | 1.0 |
| 3.3434 | 10.91 | 150700 | 3.4400 | 1.0 |
| 3.3301 | 10.92 | 150800 | 3.4389 | 1.0 |
| 3.622 | 10.92 | 150900 | 3.4451 | 1.0 |
| 3.2369 | 10.93 | 151000 | 3.4417 | 1.0 |
| 3.4093 | 10.94 | 151100 | 3.4520 | 1.0 |
| 3.3885 | 10.95 | 151200 | 3.4448 | 1.0 |
| 3.4032 | 10.95 | 151300 | 3.4453 | 1.0 |
| 3.4659 | 10.96 | 151400 | 3.4445 | 1.0 |
| 5.0434 | 10.97 | 151500 | 3.4457 | 1.0 |
| 3.5397 | 10.98 | 151600 | 3.4409 | 1.0 |
| 3.4057 | 10.98 | 151700 | 3.4426 | 1.0 |
| 3.2813 | 10.99 | 151800 | 3.4471 | 1.0 |
| 3.2432 | 11.0 | 151900 | 3.4465 | 1.0 |
| 3.3493 | 11.0 | 152000 | 3.4466 | 1.0 |
| 3.4295 | 11.01 | 152100 | 3.4379 | 1.0 |
| 3.2836 | 11.02 | 152200 | 3.4421 | 1.0 |
| 3.3436 | 11.03 | 152300 | 3.4429 | 1.0 |
| 3.2982 | 11.03 | 152400 | 3.4473 | 1.0 |
| 3.3687 | 11.04 | 152500 | 3.4428 | 1.0 |
| 3.362 | 11.05 | 152600 | 3.4387 | 1.0 |
| 3.3621 | 11.05 | 152700 | 3.4410 | 1.0 |
| 3.4442 | 11.06 | 152800 | 3.4392 | 1.0 |
| 3.247 | 11.07 | 152900 | 3.4536 | 1.0 |
| 3.3843 | 11.08 | 153000 | 3.4479 | 1.0 |
| 3.3548 | 11.08 | 153100 | 3.4425 | 1.0 |
| 3.376 | 11.09 | 153200 | 3.4394 | 1.0 |
| 3.3866 | 11.1 | 153300 | 3.4389 | 1.0 |
| 3.3348 | 11.11 | 153400 | 3.4484 | 1.0 |
| 3.3206 | 11.11 | 153500 | 3.4468 | 1.0 |
| 3.4335 | 11.12 | 153600 | 3.4445 | 1.0 |
| 3.3921 | 11.13 | 153700 | 3.4456 | 1.0 |
| 3.434 | 11.13 | 153800 | 3.4422 | 1.0 |
| 3.3742 | 11.14 | 153900 | 3.4434 | 1.0 |
| 3.3157 | 11.15 | 154000 | 3.4444 | 1.0 |
| 3.4209 | 11.16 | 154100 | 3.4411 | 1.0 |
| 3.3413 | 11.16 | 154200 | 3.4457 | 1.0 |
| 3.3626 | 11.17 | 154300 | 3.4451 | 1.0 |
| 3.3541 | 11.18 | 154400 | 3.4391 | 1.0 |
| 3.2927 | 11.19 | 154500 | 3.4515 | 1.0 |
| 3.3222 | 11.19 | 154600 | 3.4498 | 1.0 |
| 3.2971 | 11.2 | 154700 | 3.4521 | 1.0 |
| 3.3817 | 11.21 | 154800 | 3.4482 | 1.0 |
| 3.3806 | 11.21 | 154900 | 3.4467 | 1.0 |
| 3.2959 | 11.22 | 155000 | 3.4417 | 1.0 |
| 3.4212 | 11.23 | 155100 | 3.4438 | 1.0 |
| 3.3606 | 11.24 | 155200 | 3.4382 | 1.0 |
| 3.3119 | 11.24 | 155300 | 3.4381 | 1.0 |
| 3.4004 | 11.25 | 155400 | 3.4403 | 1.0 |
| 3.2865 | 11.26 | 155500 | 3.4469 | 1.0 |
| 3.3606 | 11.26 | 155600 | 3.4492 | 1.0 |
| 3.2771 | 11.27 | 155700 | 3.4407 | 1.0 |
| 3.3281 | 11.28 | 155800 | 3.4461 | 1.0 |
| 3.3006 | 11.29 | 155900 | 3.4505 | 1.0 |
| 3.3116 | 11.29 | 156000 | 3.4440 | 1.0 |
| 3.4326 | 11.3 | 156100 | 3.4475 | 1.0 |
| 3.2976 | 11.31 | 156200 | 3.4517 | 1.0 |
| 3.3424 | 11.32 | 156300 | 3.4429 | 1.0 |
| 3.5005 | 11.32 | 156400 | 3.4398 | 1.0 |
| 3.2623 | 11.33 | 156500 | 3.4382 | 1.0 |
| 3.331 | 11.34 | 156600 | 3.4472 | 1.0 |
| 3.3657 | 11.34 | 156700 | 3.4413 | 1.0 |
| 3.3101 | 11.35 | 156800 | 3.4496 | 1.0 |
| 3.3516 | 11.36 | 156900 | 3.4465 | 1.0 |
| 3.752 | 11.37 | 157000 | 3.4419 | 1.0 |
| 3.2446 | 11.37 | 157100 | 3.4416 | 1.0 |
| 3.2753 | 11.38 | 157200 | 3.4406 | 1.0 |
| 3.2386 | 11.39 | 157300 | 3.4420 | 1.0 |
| 3.3541 | 11.4 | 157400 | 3.4409 | 1.0 |
| 3.4276 | 11.4 | 157500 | 3.4430 | 1.0 |
| 3.2635 | 11.41 | 157600 | 3.4442 | 1.0 |
| 3.4478 | 11.42 | 157700 | 3.4413 | 1.0 |
| 3.3043 | 11.42 | 157800 | 3.4491 | 1.0 |
| 3.3014 | 11.43 | 157900 | 3.4413 | 1.0 |
| 3.3542 | 11.44 | 158000 | 3.4436 | 1.0 |
| 3.3745 | 11.45 | 158100 | 3.4465 | 1.0 |
| 3.3318 | 11.45 | 158200 | 3.4463 | 1.0 |
| 3.3373 | 11.46 | 158300 | 3.4444 | 1.0 |
| 3.4279 | 11.47 | 158400 | 3.4386 | 1.0 |
| 3.3588 | 11.47 | 158500 | 3.4449 | 1.0 |
| 3.338 | 11.48 | 158600 | 3.4399 | 1.0 |
| 3.4119 | 11.49 | 158700 | 3.4376 | 1.0 |
| 3.2989 | 11.5 | 158800 | 3.4462 | 1.0 |
| 3.1883 | 11.5 | 158900 | 3.4398 | 1.0 |
| 3.277 | 11.51 | 159000 | 3.4457 | 1.0 |
| 3.2838 | 11.52 | 159100 | 3.4481 | 1.0 |
| 3.3205 | 11.53 | 159200 | 3.4496 | 1.0 |
| 3.2713 | 11.53 | 159300 | 3.4435 | 1.0 |
| 3.3927 | 11.54 | 159400 | 3.4441 | 1.0 |
| 3.5806 | 11.55 | 159500 | 3.4466 | 1.0 |
| 3.3704 | 11.55 | 159600 | 3.4462 | 1.0 |
| 3.3217 | 11.56 | 159700 | 3.4444 | 1.0 |
| 3.2637 | 11.57 | 159800 | 3.4481 | 1.0 |
| 3.2525 | 11.58 | 159900 | 3.4456 | 1.0 |
| 3.3364 | 11.58 | 160000 | 3.4445 | 1.0 |
| 3.3219 | 11.59 | 160100 | 3.4431 | 1.0 |
| 3.3982 | 11.6 | 160200 | 3.4489 | 1.0 |
| 3.2253 | 11.61 | 160300 | 3.4409 | 1.0 |
| 3.2497 | 11.61 | 160400 | 3.4427 | 1.0 |
| 3.3137 | 11.62 | 160500 | 3.4454 | 1.0 |
| 3.566 | 11.63 | 160600 | 3.4419 | 1.0 |
| 3.3203 | 11.63 | 160700 | 3.4460 | 1.0 |
| 3.3048 | 11.64 | 160800 | 3.4439 | 1.0 |
| 3.371 | 11.65 | 160900 | 3.4432 | 1.0 |
| 3.249 | 11.66 | 161000 | 3.4412 | 1.0 |
| 3.2731 | 11.66 | 161100 | 3.4430 | 1.0 |
| 3.3787 | 11.67 | 161200 | 3.4426 | 1.0 |
| 3.2696 | 11.68 | 161300 | 3.4479 | 1.0 |
| 3.7056 | 11.68 | 161400 | 3.4417 | 1.0 |
| 3.3999 | 11.69 | 161500 | 3.4455 | 1.0 |
| 3.292 | 11.7 | 161600 | 3.4458 | 1.0 |
| 3.2673 | 11.71 | 161700 | 3.4398 | 1.0 |
| 3.4488 | 11.71 | 161800 | 3.4445 | 1.0 |
| 3.2858 | 11.72 | 161900 | 3.4422 | 1.0 |
| 3.4464 | 11.73 | 162000 | 3.4466 | 1.0 |
| 3.2651 | 11.74 | 162100 | 3.4460 | 1.0 |
| 3.2518 | 11.74 | 162200 | 3.4520 | 1.0 |
| 3.4483 | 11.75 | 162300 | 3.4447 | 1.0 |
| 3.2609 | 11.76 | 162400 | 3.4373 | 1.0 |
| 3.398 | 11.76 | 162500 | 3.4432 | 1.0 |
| 3.5529 | 11.77 | 162600 | 3.4435 | 1.0 |
| 3.3348 | 11.78 | 162700 | 3.4452 | 1.0 |
| 3.398 | 11.79 | 162800 | 3.4393 | 1.0 |
| 3.5933 | 11.79 | 162900 | 3.4418 | 1.0 |
| 3.3373 | 11.8 | 163000 | 3.4434 | 1.0 |
| 3.3553 | 11.81 | 163100 | 3.4463 | 1.0 |
| 3.3234 | 11.81 | 163200 | 3.4421 | 1.0 |
| 3.3678 | 11.82 | 163300 | 3.4417 | 1.0 |
| 3.2942 | 11.83 | 163400 | 3.4454 | 1.0 |
| 3.5065 | 11.84 | 163500 | 3.4490 | 1.0 |
| 3.2952 | 11.84 | 163600 | 3.4468 | 1.0 |
| 3.7354 | 11.85 | 163700 | 3.4450 | 1.0 |
| 3.3021 | 11.86 | 163800 | 3.4439 | 1.0 |
| 3.3754 | 11.87 | 163900 | 3.4455 | 1.0 |
| 3.2568 | 11.87 | 164000 | 3.4400 | 1.0 |
| 3.3191 | 11.88 | 164100 | 3.4391 | 1.0 |
| 3.379 | 11.89 | 164200 | 3.4435 | 1.0 |
| 3.3221 | 11.89 | 164300 | 3.4440 | 1.0 |
| 3.3765 | 11.9 | 164400 | 3.4452 | 1.0 |
| 3.2364 | 11.91 | 164500 | 3.4445 | 1.0 |
| 3.6366 | 11.92 | 164600 | 3.4424 | 1.0 |
| 3.3871 | 11.92 | 164700 | 3.4398 | 1.0 |
| 3.3257 | 11.93 | 164800 | 3.4414 | 1.0 |
| 3.298 | 11.94 | 164900 | 3.4388 | 1.0 |
| 3.2322 | 11.95 | 165000 | 3.4410 | 1.0 |
| 3.4019 | 11.95 | 165100 | 3.4453 | 1.0 |
| 3.5989 | 11.96 | 165200 | 3.4435 | 1.0 |
| 3.3113 | 11.97 | 165300 | 3.4439 | 1.0 |
| 3.3364 | 11.97 | 165400 | 3.4416 | 1.0 |
| 3.3256 | 11.98 | 165500 | 3.4465 | 1.0 |
| 3.3355 | 11.99 | 165600 | 3.4434 | 1.0 |
| 3.3243 | 12.0 | 165700 | 3.4420 | 1.0 |
| 3.277 | 12.0 | 165800 | 3.4429 | 1.0 |
| 3.3413 | 12.01 | 165900 | 3.4418 | 1.0 |
| 3.3576 | 12.02 | 166000 | 3.4432 | 1.0 |
| 3.2624 | 12.02 | 166100 | 3.4493 | 1.0 |
| 3.4131 | 12.03 | 166200 | 3.4429 | 1.0 |
| 3.3717 | 12.04 | 166300 | 3.4460 | 1.0 |
| 3.4403 | 12.05 | 166400 | 3.4413 | 1.0 |
| 3.3418 | 12.05 | 166500 | 3.4425 | 1.0 |
| 3.2016 | 12.06 | 166600 | 3.4429 | 1.0 |
| 3.2851 | 12.07 | 166700 | 3.4427 | 1.0 |
| 3.3627 | 12.08 | 166800 | 3.4436 | 1.0 |
| 3.176 | 12.08 | 166900 | 3.4473 | 1.0 |
| 3.3159 | 12.09 | 167000 | 3.4431 | 1.0 |
| 3.335 | 12.1 | 167100 | 3.4425 | 1.0 |
| 3.2585 | 12.1 | 167200 | 3.4438 | 1.0 |
| 3.311 | 12.11 | 167300 | 3.4420 | 1.0 |
| 3.2594 | 12.12 | 167400 | 3.4402 | 1.0 |
| 3.3877 | 12.13 | 167500 | 3.4427 | 1.0 |
| 3.3837 | 12.13 | 167600 | 3.4468 | 1.0 |
| 3.4012 | 12.14 | 167700 | 3.4431 | 1.0 |
| 3.3258 | 12.15 | 167800 | 3.4405 | 1.0 |
| 3.5918 | 12.16 | 167900 | 3.4420 | 1.0 |
| 3.1809 | 12.16 | 168000 | 3.4487 | 1.0 |
| 3.2878 | 12.17 | 168100 | 3.4453 | 1.0 |
| 3.3626 | 12.18 | 168200 | 3.4469 | 1.0 |
| 3.3128 | 12.18 | 168300 | 3.4452 | 1.0 |
| 3.3257 | 12.19 | 168400 | 3.4466 | 1.0 |
| 3.3226 | 12.2 | 168500 | 3.4416 | 1.0 |
| 3.5412 | 12.21 | 168600 | 3.4479 | 1.0 |
| 3.2933 | 12.21 | 168700 | 3.4476 | 1.0 |
| 3.5552 | 12.22 | 168800 | 3.4431 | 1.0 |
| 3.3288 | 12.23 | 168900 | 3.4424 | 1.0 |
| 3.4587 | 12.23 | 169000 | 3.4423 | 1.0 |
| 3.3286 | 12.24 | 169100 | 3.4449 | 1.0 |
| 3.2894 | 12.25 | 169200 | 3.4432 | 1.0 |
| 4.5148 | 12.26 | 169300 | 3.4424 | 1.0 |
| 3.3809 | 12.26 | 169400 | 3.4472 | 1.0 |
| 3.2641 | 12.27 | 169500 | 3.4456 | 1.0 |
| 3.3429 | 12.28 | 169600 | 3.4443 | 1.0 |
| 3.2988 | 12.29 | 169700 | 3.4423 | 1.0 |
| 3.3795 | 12.29 | 169800 | 3.4408 | 1.0 |
| 3.2812 | 12.3 | 169900 | 3.4468 | 1.0 |
| 3.2393 | 12.31 | 170000 | 3.4415 | 1.0 |
| 3.3997 | 12.31 | 170100 | 3.4426 | 1.0 |
| 3.3112 | 12.32 | 170200 | 3.4424 | 1.0 |
| 3.4299 | 12.33 | 170300 | 3.4434 | 1.0 |
| 3.486 | 12.34 | 170400 | 3.4454 | 1.0 |
| 3.2899 | 12.34 | 170500 | 3.4451 | 1.0 |
| 3.4311 | 12.35 | 170600 | 3.4456 | 1.0 |
| 3.2727 | 12.36 | 170700 | 3.4472 | 1.0 |
| 3.3182 | 12.37 | 170800 | 3.4409 | 1.0 |
| 3.5047 | 12.37 | 170900 | 3.4412 | 1.0 |
| 3.3801 | 12.38 | 171000 | 3.4403 | 1.0 |
| 3.3643 | 12.39 | 171100 | 3.4400 | 1.0 |
| 3.3132 | 12.39 | 171200 | 3.4417 | 1.0 |
| 3.3558 | 12.4 | 171300 | 3.4440 | 1.0 |
| 3.4187 | 12.41 | 171400 | 3.4470 | 1.0 |
| 3.3376 | 12.42 | 171500 | 3.4450 | 1.0 |
| 3.3095 | 12.42 | 171600 | 3.4456 | 1.0 |
| 3.3304 | 12.43 | 171700 | 3.4465 | 1.0 |
| 3.4092 | 12.44 | 171800 | 3.4500 | 1.0 |
| 3.4149 | 12.44 | 171900 | 3.4459 | 1.0 |
| 5.8155 | 12.45 | 172000 | 3.4422 | 1.0 |
| 3.3086 | 12.46 | 172100 | 3.4405 | 1.0 |
| 3.2699 | 12.47 | 172200 | 3.4439 | 1.0 |
| 3.2727 | 12.47 | 172300 | 3.4465 | 1.0 |
| 3.4084 | 12.48 | 172400 | 3.4495 | 1.0 |
| 3.3246 | 12.49 | 172500 | 3.4451 | 1.0 |
| 3.4584 | 12.5 | 172600 | 3.4404 | 1.0 |
| 3.3491 | 12.5 | 172700 | 3.4407 | 1.0 |
| 3.3103 | 12.51 | 172800 | 3.4417 | 1.0 |
| 3.3413 | 12.52 | 172900 | 3.4452 | 1.0 |
| 3.3625 | 12.52 | 173000 | 3.4437 | 1.0 |
| 3.3988 | 12.53 | 173100 | 3.4452 | 1.0 |
| 3.3915 | 12.54 | 173200 | 3.4428 | 1.0 |
| 3.2812 | 12.55 | 173300 | 3.4445 | 1.0 |
| 3.2952 | 12.55 | 173400 | 3.4450 | 1.0 |
| 3.4923 | 12.56 | 173500 | 3.4419 | 1.0 |
| 3.4275 | 12.57 | 173600 | 3.4420 | 1.0 |
| 3.8005 | 12.58 | 173700 | 3.4465 | 1.0 |
| 3.5748 | 12.58 | 173800 | 3.4437 | 1.0 |
| 3.283 | 12.59 | 173900 | 3.4441 | 1.0 |
| 3.3727 | 12.6 | 174000 | 3.4444 | 1.0 |
| 3.285 | 12.6 | 174100 | 3.4443 | 1.0 |
| 3.4836 | 12.61 | 174200 | 3.4422 | 1.0 |
| 3.5803 | 12.62 | 174300 | 3.4426 | 1.0 |
| 3.2655 | 12.63 | 174400 | 3.4420 | 1.0 |
| 3.3653 | 12.63 | 174500 | 3.4463 | 1.0 |
| 3.3581 | 12.64 | 174600 | 3.4464 | 1.0 |
| 3.2738 | 12.65 | 174700 | 3.4435 | 1.0 |
| 3.3552 | 12.65 | 174800 | 3.4409 | 1.0 |
| 3.3571 | 12.66 | 174900 | 3.4467 | 1.0 |
| 3.3093 | 12.67 | 175000 | 3.4423 | 1.0 |
| 3.6147 | 12.68 | 175100 | 3.4444 | 1.0 |
| 3.2892 | 12.68 | 175200 | 3.4420 | 1.0 |
| 3.4071 | 12.69 | 175300 | 3.4455 | 1.0 |
| 3.3201 | 12.7 | 175400 | 3.4502 | 1.0 |
| 3.308 | 12.71 | 175500 | 3.4428 | 1.0 |
| 3.3885 | 12.71 | 175600 | 3.4452 | 1.0 |
| 3.3285 | 12.72 | 175700 | 3.4418 | 1.0 |
| 3.3647 | 12.73 | 175800 | 3.4446 | 1.0 |
| 3.2559 | 12.73 | 175900 | 3.4433 | 1.0 |
| 3.4547 | 12.74 | 176000 | 3.4484 | 1.0 |
| 3.395 | 12.75 | 176100 | 3.4464 | 1.0 |
| 3.4244 | 12.76 | 176200 | 3.4468 | 1.0 |
| 3.4961 | 12.76 | 176300 | 3.4441 | 1.0 |
| 3.4281 | 12.77 | 176400 | 3.4419 | 1.0 |
| 3.4241 | 12.78 | 176500 | 3.4407 | 1.0 |
| 3.2563 | 12.79 | 176600 | 3.4430 | 1.0 |
| 3.3779 | 12.79 | 176700 | 3.4437 | 1.0 |
| 3.3268 | 12.8 | 176800 | 3.4457 | 1.0 |
| 3.4255 | 12.81 | 176900 | 3.4437 | 1.0 |
| 3.3086 | 12.81 | 177000 | 3.4422 | 1.0 |
| 3.3619 | 12.82 | 177100 | 3.4447 | 1.0 |
| 3.2334 | 12.83 | 177200 | 3.4457 | 1.0 |
| 3.4318 | 12.84 | 177300 | 3.4413 | 1.0 |
| 3.2553 | 12.84 | 177400 | 3.4425 | 1.0 |
| 3.225 | 12.85 | 177500 | 3.4435 | 1.0 |
| 3.3984 | 12.86 | 177600 | 3.4518 | 1.0 |
| 3.5566 | 12.86 | 177700 | 3.4481 | 1.0 |
| 4.3006 | 12.87 | 177800 | 3.4463 | 1.0 |
| 3.2232 | 12.88 | 177900 | 3.4454 | 1.0 |
| 3.2224 | 12.89 | 178000 | 3.4452 | 1.0 |
| 3.3974 | 12.89 | 178100 | 3.4430 | 1.0 |
| 3.4688 | 12.9 | 178200 | 3.4441 | 1.0 |
| 3.293 | 12.91 | 178300 | 3.4422 | 1.0 |
| 3.7722 | 12.92 | 178400 | 3.4459 | 1.0 |
| 3.3155 | 12.92 | 178500 | 3.4451 | 1.0 |
| 3.3955 | 12.93 | 178600 | 3.4438 | 1.0 |
| 3.2985 | 12.94 | 178700 | 3.4411 | 1.0 |
| 3.3729 | 12.94 | 178800 | 3.4415 | 1.0 |
| 3.3966 | 12.95 | 178900 | 3.4433 | 1.0 |
| 3.2917 | 12.96 | 179000 | 3.4422 | 1.0 |
| 3.3772 | 12.97 | 179100 | 3.4426 | 1.0 |
| 3.2921 | 12.97 | 179200 | 3.4458 | 1.0 |
| 3.2751 | 12.98 | 179300 | 3.4429 | 1.0 |
| 3.4227 | 12.99 | 179400 | 3.4429 | 1.0 |
| 3.3031 | 13.0 | 179500 | 3.4463 | 1.0 |
| 3.3257 | 13.0 | 179600 | 3.4496 | 1.0 |
| 3.3472 | 13.01 | 179700 | 3.4436 | 1.0 |
| 3.4014 | 13.02 | 179800 | 3.4484 | 1.0 |
| 3.4494 | 13.02 | 179900 | 3.4418 | 1.0 |
| 3.559 | 13.03 | 180000 | 3.4425 | 1.0 |
| 3.3253 | 13.04 | 180100 | 3.4412 | 1.0 |
| 3.2797 | 13.05 | 180200 | 3.4464 | 1.0 |
| 3.3854 | 13.05 | 180300 | 3.4484 | 1.0 |
| 3.24 | 13.06 | 180400 | 3.4446 | 1.0 |
| 3.2406 | 13.07 | 180500 | 3.4453 | 1.0 |
| 3.3609 | 13.07 | 180600 | 3.4425 | 1.0 |
| 3.3496 | 13.08 | 180700 | 3.4465 | 1.0 |
| 3.2963 | 13.09 | 180800 | 3.4437 | 1.0 |
| 3.2781 | 13.1 | 180900 | 3.4481 | 1.0 |
| 3.1707 | 13.1 | 181000 | 3.4465 | 1.0 |
| 3.5305 | 13.11 | 181100 | 3.4460 | 1.0 |
| 3.3498 | 13.12 | 181200 | 3.4423 | 1.0 |
| 3.276 | 13.13 | 181300 | 3.4402 | 1.0 |
| 3.2264 | 13.13 | 181400 | 3.4432 | 1.0 |
| 3.2517 | 13.14 | 181500 | 3.4408 | 1.0 |
| 3.3312 | 13.15 | 181600 | 3.4455 | 1.0 |
| 3.4057 | 13.15 | 181700 | 3.4476 | 1.0 |
| 3.34 | 13.16 | 181800 | 3.4415 | 1.0 |
| 3.2458 | 13.17 | 181900 | 3.4409 | 1.0 |
| 3.3949 | 13.18 | 182000 | 3.4405 | 1.0 |
| 3.289 | 13.18 | 182100 | 3.4431 | 1.0 |
| 3.4016 | 13.19 | 182200 | 3.4393 | 1.0 |
| 3.256 | 13.2 | 182300 | 3.4410 | 1.0 |
| 3.2597 | 13.2 | 182400 | 3.4391 | 1.0 |
| 3.2483 | 13.21 | 182500 | 3.4387 | 1.0 |
| 3.3637 | 13.22 | 182600 | 3.4409 | 1.0 |
| 3.2936 | 13.23 | 182700 | 3.4399 | 1.0 |
| 3.2666 | 13.23 | 182800 | 3.4458 | 1.0 |
| 3.3675 | 13.24 | 182900 | 3.4494 | 1.0 |
| 3.3538 | 13.25 | 183000 | 3.4430 | 1.0 |
| 3.3276 | 13.26 | 183100 | 3.4442 | 1.0 |
| 3.3851 | 13.26 | 183200 | 3.4425 | 1.0 |
| 3.3579 | 13.27 | 183300 | 3.4410 | 1.0 |
| 3.2882 | 13.28 | 183400 | 3.4400 | 1.0 |
| 3.3541 | 13.28 | 183500 | 3.4436 | 1.0 |
| 3.392 | 13.29 | 183600 | 3.4445 | 1.0 |
| 3.3857 | 13.3 | 183700 | 3.4477 | 1.0 |
| 3.3084 | 13.31 | 183800 | 3.4463 | 1.0 |
| 3.327 | 13.31 | 183900 | 3.4451 | 1.0 |
| 3.3967 | 13.32 | 184000 | 3.4483 | 1.0 |
| 3.3657 | 13.33 | 184100 | 3.4471 | 1.0 |
| 3.3732 | 13.34 | 184200 | 3.4465 | 1.0 |
| 3.366 | 13.34 | 184300 | 3.4459 | 1.0 |
| 3.2545 | 13.35 | 184400 | 3.4451 | 1.0 |
| 4.2873 | 13.36 | 184500 | 3.4425 | 1.0 |
| 3.6525 | 13.36 | 184600 | 3.4432 | 1.0 |
| 3.2921 | 13.37 | 184700 | 3.4437 | 1.0 |
| 3.273 | 13.38 | 184800 | 3.4420 | 1.0 |
| 3.267 | 13.39 | 184900 | 3.4445 | 1.0 |
| 3.3585 | 13.39 | 185000 | 3.4459 | 1.0 |
| 3.3271 | 13.4 | 185100 | 3.4424 | 1.0 |
| 3.3752 | 13.41 | 185200 | 3.4406 | 1.0 |
| 3.2715 | 13.41 | 185300 | 3.4424 | 1.0 |
| 3.2668 | 13.42 | 185400 | 3.4440 | 1.0 |
| 3.4546 | 13.43 | 185500 | 3.4464 | 1.0 |
| 3.2931 | 13.44 | 185600 | 3.4444 | 1.0 |
| 3.4428 | 13.44 | 185700 | 3.4443 | 1.0 |
| 3.4004 | 13.45 | 185800 | 3.4475 | 1.0 |
| 3.3416 | 13.46 | 185900 | 3.4447 | 1.0 |
| 3.3598 | 13.47 | 186000 | 3.4458 | 1.0 |
| 3.3348 | 13.47 | 186100 | 3.4420 | 1.0 |
| 3.2879 | 13.48 | 186200 | 3.4410 | 1.0 |
| 3.3791 | 13.49 | 186300 | 3.4481 | 1.0 |
| 3.3066 | 13.49 | 186400 | 3.4440 | 1.0 |
| 3.2824 | 13.5 | 186500 | 3.4447 | 1.0 |
| 3.4092 | 13.51 | 186600 | 3.4447 | 1.0 |
| 3.2679 | 13.52 | 186700 | 3.4443 | 1.0 |
| 3.3921 | 13.52 | 186800 | 3.4447 | 1.0 |
| 3.3348 | 13.53 | 186900 | 3.4424 | 1.0 |
| 3.2365 | 13.54 | 187000 | 3.4392 | 1.0 |
| 3.3355 | 13.55 | 187100 | 3.4387 | 1.0 |
| 3.2654 | 13.55 | 187200 | 3.4393 | 1.0 |
| 3.3085 | 13.56 | 187300 | 3.4404 | 1.0 |
| 3.3127 | 13.57 | 187400 | 3.4400 | 1.0 |
| 3.219 | 13.57 | 187500 | 3.4422 | 1.0 |
| 3.3733 | 13.58 | 187600 | 3.4391 | 1.0 |
| 3.2622 | 13.59 | 187700 | 3.4420 | 1.0 |
| 3.2188 | 13.6 | 187800 | 3.4445 | 1.0 |
| 3.2977 | 13.6 | 187900 | 3.4437 | 1.0 |
| 3.2994 | 13.61 | 188000 | 3.4463 | 1.0 |
| 3.2897 | 13.62 | 188100 | 3.4438 | 1.0 |
| 3.3194 | 13.62 | 188200 | 3.4452 | 1.0 |
| 3.3566 | 13.63 | 188300 | 3.4446 | 1.0 |
| 3.3442 | 13.64 | 188400 | 3.4509 | 1.0 |
| 3.58 | 13.65 | 188500 | 3.4509 | 1.0 |
| 3.4537 | 13.65 | 188600 | 3.4479 | 1.0 |
| 3.342 | 13.66 | 188700 | 3.4428 | 1.0 |
| 3.2765 | 13.67 | 188800 | 3.4410 | 1.0 |
| 3.2765 | 13.68 | 188900 | 3.4422 | 1.0 |
| 3.3381 | 13.68 | 189000 | 3.4400 | 1.0 |
| 3.2883 | 13.69 | 189100 | 3.4411 | 1.0 |
| 3.2861 | 13.7 | 189200 | 3.4417 | 1.0 |
| 3.3049 | 13.7 | 189300 | 3.4431 | 1.0 |
| 3.7184 | 13.71 | 189400 | 3.4446 | 1.0 |
| 3.3307 | 13.72 | 189500 | 3.4449 | 1.0 |
| 3.3274 | 13.73 | 189600 | 3.4456 | 1.0 |
| 3.3481 | 13.73 | 189700 | 3.4417 | 1.0 |
| 3.3763 | 13.74 | 189800 | 3.4439 | 1.0 |
| 3.3005 | 13.75 | 189900 | 3.4442 | 1.0 |
| 3.3775 | 13.76 | 190000 | 3.4458 | 1.0 |
| 3.284 | 13.76 | 190100 | 3.4427 | 1.0 |
| 3.2496 | 13.77 | 190200 | 3.4465 | 1.0 |
| 3.4141 | 13.78 | 190300 | 3.4422 | 1.0 |
| 3.3689 | 13.78 | 190400 | 3.4441 | 1.0 |
| 3.2925 | 13.79 | 190500 | 3.4446 | 1.0 |
| 3.334 | 13.8 | 190600 | 3.4447 | 1.0 |
| 3.4054 | 13.81 | 190700 | 3.4442 | 1.0 |
| 3.5985 | 13.81 | 190800 | 3.4418 | 1.0 |
| 3.307 | 13.82 | 190900 | 3.4437 | 1.0 |
| 3.2475 | 13.83 | 191000 | 3.4418 | 1.0 |
| 3.4217 | 13.83 | 191100 | 3.4429 | 1.0 |
| 3.2629 | 13.84 | 191200 | 3.4417 | 1.0 |
| 3.4471 | 13.85 | 191300 | 3.4420 | 1.0 |
| 3.3174 | 13.86 | 191400 | 3.4400 | 1.0 |
| 3.3505 | 13.86 | 191500 | 3.4430 | 1.0 |
| 3.4601 | 13.87 | 191600 | 3.4409 | 1.0 |
| 3.2617 | 13.88 | 191700 | 3.4439 | 1.0 |
| 3.4259 | 13.89 | 191800 | 3.4451 | 1.0 |
| 3.4135 | 13.89 | 191900 | 3.4424 | 1.0 |
| 3.2713 | 13.9 | 192000 | 3.4425 | 1.0 |
| 3.3399 | 13.91 | 192100 | 3.4450 | 1.0 |
| 3.375 | 13.91 | 192200 | 3.4440 | 1.0 |
| 3.2318 | 13.92 | 192300 | 3.4449 | 1.0 |
| 3.2925 | 13.93 | 192400 | 3.4430 | 1.0 |
| 3.416 | 13.94 | 192500 | 3.4440 | 1.0 |
| 3.283 | 13.94 | 192600 | 3.4441 | 1.0 |
| 3.249 | 13.95 | 192700 | 3.4436 | 1.0 |
| 3.3415 | 13.96 | 192800 | 3.4435 | 1.0 |
| 3.3123 | 13.97 | 192900 | 3.4427 | 1.0 |
| 3.3019 | 13.97 | 193000 | 3.4414 | 1.0 |
| 3.3949 | 13.98 | 193100 | 3.4409 | 1.0 |
| 3.3118 | 13.99 | 193200 | 3.4413 | 1.0 |
| 3.4302 | 13.99 | 193300 | 3.4431 | 1.0 |
| 3.382 | 14.0 | 193400 | 3.4439 | 1.0 |
| 3.4496 | 14.01 | 193500 | 3.4429 | 1.0 |
| 3.2643 | 14.02 | 193600 | 3.4454 | 1.0 |
| 3.2298 | 14.02 | 193700 | 3.4439 | 1.0 |
| 3.3804 | 14.03 | 193800 | 3.4429 | 1.0 |
| 3.2049 | 14.04 | 193900 | 3.4429 | 1.0 |
| 3.3818 | 14.04 | 194000 | 3.4420 | 1.0 |
| 3.2901 | 14.05 | 194100 | 3.4433 | 1.0 |
| 3.2989 | 14.06 | 194200 | 3.4419 | 1.0 |
| 3.2548 | 14.07 | 194300 | 3.4434 | 1.0 |
| 3.454 | 14.07 | 194400 | 3.4432 | 1.0 |
| 3.3365 | 14.08 | 194500 | 3.4433 | 1.0 |
| 3.3799 | 14.09 | 194600 | 3.4443 | 1.0 |
| 3.3536 | 14.1 | 194700 | 3.4438 | 1.0 |
| 3.5929 | 14.1 | 194800 | 3.4441 | 1.0 |
| 4.2116 | 14.11 | 194900 | 3.4433 | 1.0 |
| 3.4121 | 14.12 | 195000 | 3.4437 | 1.0 |
| 3.3715 | 14.12 | 195100 | 3.4442 | 1.0 |
| 3.4325 | 14.13 | 195200 | 3.4467 | 1.0 |
| 3.3585 | 14.14 | 195300 | 3.4450 | 1.0 |
| 3.3374 | 14.15 | 195400 | 3.4421 | 1.0 |
| 3.3519 | 14.15 | 195500 | 3.4421 | 1.0 |
| 3.4128 | 14.16 | 195600 | 3.4416 | 1.0 |
| 3.3448 | 14.17 | 195700 | 3.4412 | 1.0 |
| 3.4239 | 14.18 | 195800 | 3.4418 | 1.0 |
| 3.6124 | 14.18 | 195900 | 3.4440 | 1.0 |
| 3.3607 | 14.19 | 196000 | 3.4444 | 1.0 |
| 3.3141 | 14.2 | 196100 | 3.4433 | 1.0 |
| 3.4431 | 14.2 | 196200 | 3.4432 | 1.0 |
| 3.4539 | 14.21 | 196300 | 3.4426 | 1.0 |
| 3.3409 | 14.22 | 196400 | 3.4418 | 1.0 |
| 3.2736 | 14.23 | 196500 | 3.4422 | 1.0 |
| 3.8002 | 14.23 | 196600 | 3.4431 | 1.0 |
| 3.501 | 14.24 | 196700 | 3.4421 | 1.0 |
| 3.3537 | 14.25 | 196800 | 3.4420 | 1.0 |
| 3.4373 | 14.25 | 196900 | 3.4412 | 1.0 |
| 3.359 | 14.26 | 197000 | 3.4412 | 1.0 |
| 3.302 | 14.27 | 197100 | 3.4425 | 1.0 |
| 3.3282 | 14.28 | 197200 | 3.4424 | 1.0 |
| 3.3941 | 14.28 | 197300 | 3.4424 | 1.0 |
| 4.4183 | 14.29 | 197400 | 3.4435 | 1.0 |
| 3.4406 | 14.3 | 197500 | 3.4432 | 1.0 |
| 3.285 | 14.31 | 197600 | 3.4432 | 1.0 |
| 3.3289 | 14.31 | 197700 | 3.4442 | 1.0 |
| 3.3085 | 14.32 | 197800 | 3.4426 | 1.0 |
| 3.2033 | 14.33 | 197900 | 3.4446 | 1.0 |
| 3.3691 | 14.33 | 198000 | 3.4448 | 1.0 |
| 3.3715 | 14.34 | 198100 | 3.4448 | 1.0 |
| 4.5572 | 14.35 | 198200 | 3.4432 | 1.0 |
| 3.3509 | 14.36 | 198300 | 3.4431 | 1.0 |
| 3.3179 | 14.36 | 198400 | 3.4426 | 1.0 |
| 3.2891 | 14.37 | 198500 | 3.4436 | 1.0 |
| 3.3872 | 14.38 | 198600 | 3.4436 | 1.0 |
| 3.3177 | 14.38 | 198700 | 3.4442 | 1.0 |
| 3.4302 | 14.39 | 198800 | 3.4446 | 1.0 |
| 3.3834 | 14.4 | 198900 | 3.4441 | 1.0 |
| 3.4318 | 14.41 | 199000 | 3.4430 | 1.0 |
| 3.4176 | 14.41 | 199100 | 3.4431 | 1.0 |
| 4.6882 | 14.42 | 199200 | 3.4431 | 1.0 |
| 3.2657 | 14.43 | 199300 | 3.4436 | 1.0 |
| 3.3929 | 14.44 | 199400 | 3.4436 | 1.0 |
| 5.337 | 14.44 | 199500 | 3.4432 | 1.0 |
| 3.4289 | 14.45 | 199600 | 3.4432 | 1.0 |
| 3.2498 | 14.46 | 199700 | 3.4435 | 1.0 |
| 3.3635 | 14.46 | 199800 | 3.4432 | 1.0 |
| 5.4355 | 14.47 | 199900 | 3.4418 | 1.0 |
| 3.2158 | 14.48 | 200000 | 3.4427 | 1.0 |
| 3.4885 | 14.49 | 200100 | 3.4435 | 1.0 |
| 3.3739 | 14.49 | 200200 | 3.4430 | 1.0 |
| 3.4712 | 14.5 | 200300 | 3.4434 | 1.0 |
| 3.3742 | 14.51 | 200400 | 3.4444 | 1.0 |
| 3.3465 | 14.52 | 200500 | 3.4429 | 1.0 |
| 3.3277 | 14.52 | 200600 | 3.4430 | 1.0 |
| 3.3073 | 14.53 | 200700 | 3.4431 | 1.0 |
| 3.33 | 14.54 | 200800 | 3.4432 | 1.0 |
| 3.3857 | 14.54 | 200900 | 3.4436 | 1.0 |
| 3.4481 | 14.55 | 201000 | 3.4430 | 1.0 |
| 3.546 | 14.56 | 201100 | 3.4416 | 1.0 |
| 3.4435 | 14.57 | 201200 | 3.4404 | 1.0 |
| 3.3237 | 14.57 | 201300 | 3.4408 | 1.0 |
| 3.3347 | 14.58 | 201400 | 3.4420 | 1.0 |
| 4.5461 | 14.59 | 201500 | 3.4420 | 1.0 |
| 3.3307 | 14.59 | 201600 | 3.4430 | 1.0 |
| 3.3899 | 14.6 | 201700 | 3.4439 | 1.0 |
| 3.2613 | 14.61 | 201800 | 3.4435 | 1.0 |
| 3.2693 | 14.62 | 201900 | 3.4426 | 1.0 |
| 3.3621 | 14.62 | 202000 | 3.4430 | 1.0 |
| 3.4383 | 14.63 | 202100 | 3.4434 | 1.0 |
| 3.5096 | 14.64 | 202200 | 3.4444 | 1.0 |
| 3.3962 | 14.65 | 202300 | 3.4445 | 1.0 |
| 3.3854 | 14.65 | 202400 | 3.4441 | 1.0 |
| 3.3116 | 14.66 | 202500 | 3.4445 | 1.0 |
| 3.3691 | 14.67 | 202600 | 3.4445 | 1.0 |
| 3.3821 | 14.67 | 202700 | 3.4440 | 1.0 |
| 3.2872 | 14.68 | 202800 | 3.4431 | 1.0 |
| 3.3575 | 14.69 | 202900 | 3.4431 | 1.0 |
| 3.2881 | 14.7 | 203000 | 3.4435 | 1.0 |
| 3.4115 | 14.7 | 203100 | 3.4440 | 1.0 |
| 3.3814 | 14.71 | 203200 | 3.4439 | 1.0 |
| 3.3609 | 14.72 | 203300 | 3.4435 | 1.0 |
| 3.3261 | 14.73 | 203400 | 3.4430 | 1.0 |
| 3.2983 | 14.73 | 203500 | 3.4435 | 1.0 |
| 3.3094 | 14.74 | 203600 | 3.4431 | 1.0 |
| 3.2582 | 14.75 | 203700 | 3.4431 | 1.0 |
| 3.2963 | 14.75 | 203800 | 3.4435 | 1.0 |
| 3.361 | 14.76 | 203900 | 3.4435 | 1.0 |
| 3.2636 | 14.77 | 204000 | 3.4440 | 1.0 |
| 3.2908 | 14.78 | 204100 | 3.4439 | 1.0 |
| 3.4743 | 14.78 | 204200 | 3.4445 | 1.0 |
| 3.2633 | 14.79 | 204300 | 3.4444 | 1.0 |
| 3.6696 | 14.8 | 204400 | 3.4440 | 1.0 |
| 3.4295 | 14.8 | 204500 | 3.4439 | 1.0 |
| 3.2838 | 14.81 | 204600 | 3.4439 | 1.0 |
| 3.285 | 14.82 | 204700 | 3.4439 | 1.0 |
| 3.2501 | 14.83 | 204800 | 3.4443 | 1.0 |
| 3.2872 | 14.83 | 204900 | 3.4443 | 1.0 |
| 3.3486 | 14.84 | 205000 | 3.4443 | 1.0 |
| 3.2943 | 14.85 | 205100 | 3.4443 | 1.0 |
| 3.2908 | 14.86 | 205200 | 3.4438 | 1.0 |
| 4.0962 | 14.86 | 205300 | 3.4443 | 1.0 |
| 3.2306 | 14.87 | 205400 | 3.4433 | 1.0 |
| 3.4682 | 14.88 | 205500 | 3.4433 | 1.0 |
| 3.2785 | 14.88 | 205600 | 3.4438 | 1.0 |
| 3.4161 | 14.89 | 205700 | 3.4438 | 1.0 |
| 3.299 | 14.9 | 205800 | 3.4438 | 1.0 |
| 3.3116 | 14.91 | 205900 | 3.4438 | 1.0 |
| 3.3456 | 14.91 | 206000 | 3.4439 | 1.0 |
| 3.263 | 14.92 | 206100 | 3.4439 | 1.0 |
| 3.4408 | 14.93 | 206200 | 3.4444 | 1.0 |
| 3.3478 | 14.94 | 206300 | 3.4443 | 1.0 |
| 3.1718 | 14.94 | 206400 | 3.4438 | 1.0 |
| 3.2811 | 14.95 | 206500 | 3.4439 | 1.0 |
| 3.4132 | 14.96 | 206600 | 3.4439 | 1.0 |
| 3.2337 | 14.96 | 206700 | 3.4439 | 1.0 |
| 3.3859 | 14.97 | 206800 | 3.4439 | 1.0 |
| 3.3501 | 14.98 | 206900 | 3.4439 | 1.0 |
| 3.5111 | 14.99 | 207000 | 3.4439 | 1.0 |
| 3.5375 | 14.99 | 207100 | 3.4439 | 1.0 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.