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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 12:31:03
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11.7k
| library_name
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Huamin/santacoder-finetuned-the-stack-bash
|
Huamin
| 2023-07-14T18:49:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:bigcode-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-08T19:30:54Z |
---
license: bigcode-openrail-m
tags:
- generated_from_trainer
model-index:
- name: santacoder-finetuned-the-stack-bash
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. -->
# santacoder-finetuned-the-stack-bash
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3174
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6951 | 0.1 | 500 | 1.8041 |
| 1.69 | 0.2 | 1000 | 1.5214 |
| 1.3821 | 0.3 | 1500 | 1.5855 |
| 1.5861 | 0.4 | 2000 | 1.4657 |
| 1.6196 | 0.5 | 2500 | 1.4089 |
| 1.6839 | 0.6 | 3000 | 1.3801 |
| 1.3929 | 0.7 | 3500 | 1.3493 |
| 1.471 | 0.8 | 4000 | 1.3278 |
| 1.3222 | 0.9 | 4500 | 1.3203 |
| 1.4529 | 1.0 | 5000 | 1.3174 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Danish-summarisation/DanSumT5-large
|
Danish-summarisation
| 2023-07-14T18:43:21Z | 29 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"da",
"arxiv:1804.11283",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-04-10T12:21:06Z |
---
pipeline_tag: summarization
license: apache-2.0
language:
- da
---
# mT5-large fine-tuned for News article Summarisation ✏️🧾
[Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task.
# Model summary
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-large.
The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
# References
Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283
Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
|
BigSalmon/InformalToFormalLincoln104Paraphrase
|
BigSalmon
| 2023-07-14T18:36:51Z | 209 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T01:28:43Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln104Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln104Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
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:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
```
dull: clean
emotional heft: spotless, immaculate, pristine
***
dull: hot
emotional heft: scorching, searing, blistering
***
dull: happy
emotional heft: euphoric
```
```
text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ).
```
```
system: i'm not sure i agree with your assessment of the situation.
user: replace "assessment" with a more casual term.
chatbot: ( take / read / view ).
system: i've been feeling really busy lately, trying to balance work and family responsibilities.
user: replace "busy" with a more intense word that conveys it is too much.
chatbot: overwhelmed.
system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected.
user: replace "nebulous" with a word that conveys a lack of detail.
chatbot: ( vague / ambiguous ).
```
```
user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression]
chatbot: intellectual horizons.
user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”?
chatbot: academic enlightenment.
```
```
key: calculate.
syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute.
ant: guess, conjecture, hit, chance, risk, stake, miscalculate.
```
```
description: more forceful version of curious that is less forceful than nosy
answer: inquisitive
description: more forceful version of hopeful that is less forceful than overconfident
answer: optimistic
```
```
key: inquisitive
positive: curious, interested
negative: nosy, prying
***
key: witty
positive: clever, humorous
negative: sarcastic, caustic
***
key: influential
positive: impactful, powerful
negative: overbearing, domineering
```
```
defective: the blogger's { use of language imprecise } confused an already complicated issue.
precise: the blogger's ( vague wording ) confused an already complicated issue.
defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals.
precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals.
```
```
example: the new car uses gas.
boring: uses
stronger: guzzles
example: he hates people that are rude.
boring: hates
stronger: loathes, abhors, despises, scorns, detests
```
```
initial: The music at the party was [ loud; replace with a word that suggests a more uncomfortable noise level ] and overwhelming.
modified: The music at the party was { ear-splitting } and overwhelming.
initial: their house is [ small; replace with a positive spin ].
modified: their house is { cozy }.
```
```
defective: they spent the weekend enjoying { time do what you want }.
precise: they spent the weekend enjoying ( leisure activities).
defective: the author rightly notes the inequities perpetuated by { employment based on who you know }.
precise: the author rightly notes the inequities perpetuated by ( nepotism ).
defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals.
precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals.
```
```
persona: human resources manager
buzzwords: pipeline, talent, retention, compensation, flexible, recruitment, personnel, resume, competitive, quality, onboard
```
|
giocs2017/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
|
giocs2017
| 2023-07-14T18:35:09Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-14T17:01:58Z |
---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Accuracy: 0.9
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9627 | 1.0 | 112 | 0.7284 | 0.75 |
| 0.3776 | 1.99 | 224 | 0.4641 | 0.83 |
| 0.4536 | 3.0 | 337 | 0.5534 | 0.85 |
| 0.0602 | 4.0 | 449 | 0.4999 | 0.86 |
| 0.1927 | 4.99 | 561 | 0.5989 | 0.85 |
| 0.0122 | 6.0 | 674 | 0.7778 | 0.85 |
| 0.0006 | 6.99 | 786 | 0.4095 | 0.9 |
| 0.0005 | 8.0 | 899 | 0.5149 | 0.9 |
| 0.1723 | 9.0 | 1011 | 0.4558 | 0.9 |
| 0.0001 | 9.99 | 1123 | 0.4700 | 0.9 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Ruborobot/roberta-large-finetuned-TeacherMomentsConfusion
|
Ruborobot
| 2023-07-14T18:34:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T16:43:00Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-TeacherMomentsConfusion
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-large-finetuned-TeacherMomentsConfusion
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7027
- Accuracy: 0.8655
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Balanced Accuracy: 0.5
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Balanced Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------:|
| No log | 1.0 | 295 | 0.7027 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7205 | 2.0 | 590 | 0.6718 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7205 | 3.0 | 885 | 0.6768 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6976 | 4.0 | 1180 | 0.7014 | 0.1345 | 0.1345 | 1.0 | 0.2371 | 0.5 |
| 0.6976 | 5.0 | 1475 | 0.6722 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6994 | 6.0 | 1770 | 0.6900 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6961 | 7.0 | 2065 | 0.6845 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6961 | 8.0 | 2360 | 0.6897 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6958 | 9.0 | 2655 | 0.6854 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6958 | 10.0 | 2950 | 0.6878 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hafeezmhk6/vikas_trail2
|
hafeezmhk6
| 2023-07-14T18:14:36Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-14T18:10:26Z |
---
tags:
- generated_from_trainer
model-index:
- name: vikas_trail2
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. -->
# vikas_trail2
This model is a fine-tuned version of [](https://huggingface.co/) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 10
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/gpt2-concat-open-rarity-all-no-cut
|
NasimB
| 2023-07-14T17:50:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T15:59:40Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-open-rarity-all-no-cut
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. -->
# gpt2-concat-open-rarity-all-no-cut
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3821
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.7448 | 0.3 | 500 | 5.6602 |
| 5.394 | 0.61 | 1000 | 5.2370 |
| 5.0391 | 0.91 | 1500 | 4.9974 |
| 4.77 | 1.22 | 2000 | 4.8671 |
| 4.6175 | 1.52 | 2500 | 4.7345 |
| 4.5073 | 1.83 | 3000 | 4.6299 |
| 4.3477 | 2.13 | 3500 | 4.5740 |
| 4.2108 | 2.44 | 4000 | 4.5190 |
| 4.1708 | 2.74 | 4500 | 4.4512 |
| 4.0895 | 3.05 | 5000 | 4.4226 |
| 3.883 | 3.35 | 5500 | 4.4025 |
| 3.873 | 3.66 | 6000 | 4.3664 |
| 3.8532 | 3.96 | 6500 | 4.3286 |
| 3.6257 | 4.27 | 7000 | 4.3401 |
| 3.5945 | 4.57 | 7500 | 4.3264 |
| 3.5796 | 4.88 | 8000 | 4.3114 |
| 3.4673 | 5.18 | 8500 | 4.3227 |
| 3.4 | 5.48 | 9000 | 4.3223 |
| 3.3995 | 5.79 | 9500 | 4.3209 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
CMunch/fine_tuned_temp_real
|
CMunch
| 2023-07-14T17:34:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T17:35:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: fine_tuned_temp_real
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93116
---
<!-- 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. -->
# fine_tuned_temp_real
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2334
- Accuracy: 0.9312
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2349 | 1.0 | 1563 | 0.1965 | 0.9247 |
| 0.1521 | 2.0 | 3126 | 0.2334 | 0.9312 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
chunwoolee0/my_paircls_klue_nli_beomi_kcbert_base_model
|
chunwoolee0
| 2023-07-14T17:24:46Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T16:23:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- klue
model-index:
- name: my_paircls_klue_nli_beomi_kcbert_base_model
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. -->
# my_paircls_klue_nli_beomi_kcbert_base_model
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9825
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 391 | 1.4004 |
| 0.1479 | 2.0 | 782 | 1.2491 |
| 0.167 | 3.0 | 1173 | 1.3786 |
| 0.0803 | 4.0 | 1564 | 1.7437 |
| 0.0803 | 5.0 | 1955 | 1.9825 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
DanGalt/distilhubert-finetuned-gtzan
|
DanGalt
| 2023-07-14T17:07:56Z | 167 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-02T16:13:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7162
- Accuracy: 0.88
## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
- label_smoothing_factor: 0.05
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5923 | 1.0 | 113 | 1.7310 | 0.44 |
| 1.2071 | 2.0 | 226 | 1.2546 | 0.62 |
| 1.0673 | 3.0 | 339 | 0.9320 | 0.76 |
| 0.8149 | 4.0 | 452 | 0.8768 | 0.81 |
| 0.4999 | 5.0 | 565 | 0.7154 | 0.86 |
| 0.3562 | 6.0 | 678 | 0.6631 | 0.89 |
| 0.3852 | 7.0 | 791 | 0.7136 | 0.87 |
| 0.4476 | 8.0 | 904 | 0.7162 | 0.88 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
achang/fin_forecast_0
|
achang
| 2023-07-14T17:06:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-03T00:24:42Z |
commit: 1559705809d9ef7ac5e7dfe8e739c13c1b26f77a
model train on https://huggingface.co/datasets/achang/stock_forecast_0
|
Imadeddineibrahim/SustainEcho
|
Imadeddineibrahim
| 2023-07-14T17:04:20Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T17:04:20Z |
---
license: creativeml-openrail-m
---
|
grace-pro/afro-xlmr-base-hausa-2e-5
|
grace-pro
| 2023-07-14T17:02:55Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-14T16:32:12Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: afro-xlmr-base-hausa-2e-5
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. -->
# afro-xlmr-base-hausa-2e-5
This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1327
- Precision: 0.7423
- Recall: 0.5632
- F1: 0.6405
- Accuracy: 0.9613
## 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: 8
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.165 | 1.0 | 1312 | 0.1422 | 0.6965 | 0.4508 | 0.5473 | 0.9530 |
| 0.1317 | 2.0 | 2624 | 0.1309 | 0.7152 | 0.5191 | 0.6016 | 0.9571 |
| 0.1113 | 3.0 | 3936 | 0.1311 | 0.7385 | 0.5341 | 0.6199 | 0.9593 |
| 0.0977 | 4.0 | 5248 | 0.1297 | 0.7474 | 0.5412 | 0.6278 | 0.9604 |
| 0.0862 | 5.0 | 6560 | 0.1327 | 0.7423 | 0.5632 | 0.6405 | 0.9613 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
joystick/Viyan
|
joystick
| 2023-07-14T16:58:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T16:55:04Z |
---
license: creativeml-openrail-m
---
|
FinancialSupport/gpt2-ft-medical-qa
|
FinancialSupport
| 2023-07-14T16:56:47Z | 221 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T15:27:42Z |
---
pipeline_tag: text-generation
widget:
- text: "Question: My belly hurts.\nAnswer:"
example_title: "Q&A"
---
Hello!
We finetuned gpt2 to a q&a medical dataset
This is just a test
---
license: mit
datasets:
- medical_dialog
language:
- en
---
|
manosp/audio_inversion_cat
|
manosp
| 2023-07-14T16:46:07Z | 48 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T13:04:34Z |
---
license: creativeml-openrail-m
base_model: /home/plitsis/text-inv/audioldm-m-full
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - manosp/audio_inversion_cat
These are textual inversion adaption weights for /home/plitsis/text-inv/audioldm-m-full. You can find some example images in the following.
|
chh6/dqn-SpaceInvadersNoFrameskip-v4
|
chh6
| 2023-07-14T16:41:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T16:40:42Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 481.00 +/- 179.37
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga chh6 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga chh6 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga chh6
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
FrancescoBonzi/speecht5_finetuned_voxpopuli_it
|
FrancescoBonzi
| 2023-07-14T16:39:52Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-07-14T14:17:17Z |
---
license: mit
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_it
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. -->
# speecht5_finetuned_voxpopuli_it
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4871
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5505 | 6.13 | 1000 | 0.5092 |
| 0.5245 | 12.26 | 2000 | 0.4941 |
| 0.5192 | 18.39 | 3000 | 0.4885 |
| 0.5126 | 24.52 | 4000 | 0.4871 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Akriel/ResNetYoloV1
|
Akriel
| 2023-07-14T16:36:14Z | 0 | 0 | null |
[
"tensorboard",
"computer_vision",
"vision_models_playground",
"custom-implementation",
"region:us"
] | null | 2023-07-10T16:40:25Z |
---
tags:
- computer_vision
- vision_models_playground
- custom-implementation
---
# **Vision Models Playground**
This is a trained model from the Vision Models Playground repository.
Link to the repository: https://github.com/Akrielz/vision_models_playground
## **Model**
This model is a custom implementation of **ResNetYoloV1** from the ```vision_models_playground.models.segmentation.yolo_v1``` module.
Please look in the config file for more information about the model architecture.
## **Usage**
To load the torch model, you can use the following code snippet:
```python
import torch
from vision_models_playground.utility.hub import load_vmp_model_from_hub
model = load_vmp_model_from_hub("Akriel/ResNetYoloV1")
x = torch.randn(...)
y = model(x) # y will be of type torch.Tensor
```
To load the pipeline that includes the model, you can use the following code snippet:
```python
from vision_models_playground.utility.hub import load_vmp_pipeline_from_hub
pipeline = load_vmp_pipeline_from_hub("Akriel/ResNetYoloV1")
x = raw_data # raw_data will be of type pipeline.input_type
y = pipeline(x) # y will be of type pipeline.output_type
```
## **Metrics**
The model was evaluated on the following dataset: **YoloPascalVocDataset** from ```vision_models_playground.datasets.yolo_pascal_voc_dataset```
These are the results of the evaluation:
- MulticlassAccuracy: 0.7241
- MulticlassAveragePrecision: 0.7643
- MulticlassAUROC: 0.9684
- Dice: 0.7241
- MulticlassF1Score: 0.7241
- LossTracker: 4.1958
## **Additional Information**
The train and evaluation runs are also saved using tensorboard. You can use the following command to visualize the runs:
```bash
tensorboard --logdir ./model
```
```bash
tensorboard --logdir ./eval
```
|
giocs2017/distilhubert-finetuned-gtzan
|
giocs2017
| 2023-07-14T16:32:55Z | 160 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-13T01:20:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8934
- Accuracy: 0.82
## 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: 2
- eval_batch_size: 2
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.681 | 1.0 | 450 | 1.7351 | 0.5 |
| 1.5534 | 2.0 | 900 | 1.2192 | 0.66 |
| 0.6835 | 3.0 | 1350 | 1.0462 | 0.71 |
| 1.069 | 4.0 | 1800 | 0.5503 | 0.83 |
| 0.1563 | 5.0 | 2250 | 0.9394 | 0.78 |
| 0.0077 | 6.0 | 2700 | 0.9394 | 0.81 |
| 0.7444 | 7.0 | 3150 | 0.8934 | 0.82 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.13.1
- Tokenizers 0.13.2
|
NasimB/gpt-concat-open-rarity-no-cut
|
NasimB
| 2023-07-14T16:16:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T14:26:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt-concat-open-rarity-no-cut
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. -->
# gpt-concat-open-rarity-no-cut
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3829
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.7388 | 0.3 | 500 | 5.6607 |
| 5.3931 | 0.61 | 1000 | 5.2422 |
| 5.0383 | 0.91 | 1500 | 4.9910 |
| 4.7635 | 1.22 | 2000 | 4.8625 |
| 4.6209 | 1.52 | 2500 | 4.7279 |
| 4.5025 | 1.83 | 3000 | 4.6289 |
| 4.3387 | 2.13 | 3500 | 4.5691 |
| 4.2095 | 2.44 | 4000 | 4.5097 |
| 4.1709 | 2.74 | 4500 | 4.4588 |
| 4.0873 | 3.05 | 5000 | 4.4166 |
| 3.879 | 3.35 | 5500 | 4.3989 |
| 3.8803 | 3.66 | 6000 | 4.3657 |
| 3.8458 | 3.96 | 6500 | 4.3271 |
| 3.6225 | 4.27 | 7000 | 4.3446 |
| 3.5936 | 4.57 | 7500 | 4.3251 |
| 3.579 | 4.88 | 8000 | 4.3130 |
| 3.4673 | 5.18 | 8500 | 4.3251 |
| 3.4024 | 5.48 | 9000 | 4.3237 |
| 3.4004 | 5.79 | 9500 | 4.3225 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
giocs2017/Taxi-v3_v1
|
giocs2017
| 2023-07-14T16:00:46Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T15:47:30Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="giocs2017/Taxi-v3_v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
giocs2017/q-FrozenLake-v1-4x4-noSlippery
|
giocs2017
| 2023-07-14T15:59:09Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T15:46:03Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="giocs2017/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Dlychan/Nadhieraa
|
Dlychan
| 2023-07-14T15:47:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T15:42:59Z |
---
license: creativeml-openrail-m
---
|
PlankyxD/Reinforce-CartPole-v1
|
PlankyxD
| 2023-07-14T15:46:00Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T02:12:53Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 466.10 +/- 71.75
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Daniil-plotnikov/russian-vision-v5-beta-2
|
Daniil-plotnikov
| 2023-07-14T15:39:16Z | 25 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"ru",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T18:54:00Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
language:
- ru
- en
---
### Russian-Vision-V5-beta-2
Бета 5 версии модели.
|
Junr-syl/sentiments_analysis_DISTILBERT
|
Junr-syl
| 2023-07-14T15:24:03Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T15:05:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiments_analysis_DISTILBERT
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. -->
# sentiments_analysis_DISTILBERT
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: 0.5860
- Accuracy: 0.765
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7053 | 1.0 | 500 | 0.6420 | 0.7325 |
| 0.5671 | 2.0 | 1000 | 0.5860 | 0.765 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
nightdude/stonefish-7b
|
nightdude
| 2023-07-14T15:17:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-14T15:17:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Vladislav-HuggingFace/Taxi-v3
|
Vladislav-HuggingFace
| 2023-07-14T15:17:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T14:47:39Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.75
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Vladislav-HuggingFace/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
digiplay/YabaLMixAnimeRealistic_V2.0
|
digiplay
| 2023-07-14T15:04:45Z | 384 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-14T11:25:39Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/79579?modelVersionId=111467
Original Author's DEMO images :
.jpeg)
.jpeg)
|
ahmadnajm/KurdGPT
|
ahmadnajm
| 2023-07-14T14:58:36Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"chemistry",
"biology",
"legal",
"art",
"medical",
"text-classification",
"en",
"ku",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] |
text-classification
| 2023-06-16T23:54:34Z |
---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
- ku
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- code
- chemistry
- biology
- legal
- art
- medical
---
|
ingo-m/bloom-560m-onnx
|
ingo-m
| 2023-07-14T14:57:03Z | 5 | 0 |
transformers
|
[
"transformers",
"onnx",
"bloom",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T14:35:32Z |
---
{}
---
`bigscience/bloom-560m` model converted to ONNX format.
|
grace-pro/xlmr-base-igbo-5e-5
|
grace-pro
| 2023-07-14T14:49:57Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-14T14:18:19Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlmr-base-igbo-5e-5
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. -->
# xlmr-base-igbo-5e-5
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- Precision: 0.7064
- Recall: 0.5075
- F1: 0.5907
- Accuracy: 0.9212
## 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: 16
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2768 | 1.0 | 1257 | 0.2611 | 0.7212 | 0.3117 | 0.4353 | 0.9055 |
| 0.2299 | 2.0 | 2514 | 0.2606 | 0.7395 | 0.3797 | 0.5018 | 0.9134 |
| 0.1966 | 3.0 | 3771 | 0.2224 | 0.7252 | 0.4496 | 0.5550 | 0.9186 |
| 0.1697 | 4.0 | 5028 | 0.2290 | 0.7273 | 0.4775 | 0.5765 | 0.9208 |
| 0.1449 | 5.0 | 6285 | 0.2368 | 0.7064 | 0.5075 | 0.5907 | 0.9212 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Vladislav-HuggingFace/q-Taxi-v3
|
Vladislav-HuggingFace
| 2023-07-14T14:44:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T14:44:30Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Vladislav-HuggingFace/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
shahafw/ppo-CartPole-v1
|
shahafw
| 2023-07-14T14:39:10Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T14:35:39Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -160.58 +/- 79.59
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'shahafw/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
WALIDALI/imentunisly
|
WALIDALI
| 2023-07-14T14:36:26Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-14T14:30:28Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### imentunisly Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Vladislav-HuggingFace/q-FrozenLake-v1-4x4-Slippery
|
Vladislav-HuggingFace
| 2023-07-14T14:35:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T14:13:08Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.67 +/- 0.47
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Vladislav-HuggingFace/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
lokpalai/lokpalgpt
|
lokpalai
| 2023-07-14T14:25:58Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T14:17:01Z |
# LokPalAI: Bridging the Gap to Legal Empowerment
LokPalAI is an advanced language model finetuned for Indian scenarios, specifically designed to bridge the gap between individuals and legal empowerment. With LokPalAI, users can interact with a powerful query box to seek information and guidance related to Indian law.
## Features:
1. Interact with LokPalAI’s Query Box: LokPalAI provides a user-friendly query box interface where users can input their legal queries and receive accurate and relevant responses. Whether you need information about a specific law, legal procedure, or any other legal matter, LokPalAI is here to assist you.
2. Enhanced with Rail Guards: To ensure the accuracy and reliability of the information provided, LokPalAI incorporates rail guards. These safeguards help prevent the generation of misleading or incorrect legal advice. We understand the importance of reliable legal information, and our rail guards are designed to maintain the highest standards of accuracy.
3. Real-Time Responses using RAG: LokPalAI leverages the Retrieve and Generate (RAG) framework to provide real-time responses to your legal queries. RAG combines the power of retrieval-based models with generation-based models, ensuring that the information provided is both contextually relevant and up to date.
4. Thorough Testing and Maintenance: We understand the criticality of maintaining a reliable and accurate legal information system. LokPalAI undergoes extensive testing to ensure its performance and reliability. We continuously monitor and update the model to account for changes in Indian law, ensuring that the information provided is always accurate and up to date.
|
PaulineJamin/cart-pole-v1
|
PaulineJamin
| 2023-07-14T14:13:25Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T09:45:08Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cart-pole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.40 +/- 39.85
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ivankott/allo-model-vicuna7b-ftuned
|
ivankott
| 2023-07-14T14:04:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-14T14:04:07Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
NasimB/gpt2-concat-guten-mod-rarity-all-bnc-rarity
|
NasimB
| 2023-07-14T14:02:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T12:07:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-guten-mod-rarity-all-bnc-rarity
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. -->
# gpt2-concat-guten-mod-rarity-all-bnc-rarity
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3295
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6878 | 0.29 | 500 | 5.6376 |
| 5.3505 | 0.59 | 1000 | 5.2050 |
| 5.0121 | 0.88 | 1500 | 4.9566 |
| 4.7224 | 1.17 | 2000 | 4.8154 |
| 4.575 | 1.47 | 2500 | 4.6855 |
| 4.4597 | 1.76 | 3000 | 4.5818 |
| 4.3368 | 2.05 | 3500 | 4.4994 |
| 4.1447 | 2.35 | 4000 | 4.4538 |
| 4.1132 | 2.64 | 4500 | 4.3938 |
| 4.0745 | 2.93 | 5000 | 4.3415 |
| 3.864 | 3.23 | 5500 | 4.3406 |
| 3.8154 | 3.52 | 6000 | 4.3078 |
| 3.7945 | 3.81 | 6500 | 4.2773 |
| 3.684 | 4.11 | 7000 | 4.2795 |
| 3.5318 | 4.4 | 7500 | 4.2759 |
| 3.5281 | 4.69 | 8000 | 4.2591 |
| 3.5091 | 4.99 | 8500 | 4.2453 |
| 3.343 | 5.28 | 9000 | 4.2609 |
| 3.332 | 5.58 | 9500 | 4.2587 |
| 3.3376 | 5.87 | 10000 | 4.2585 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Junr-syl/sentiments_analysis_upgrade
|
Junr-syl
| 2023-07-14T14:02:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T11:58:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiments_analysis_upgrade
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. -->
# sentiments_analysis_upgrade
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1984
- Accuracy: 0.7455
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7286 | 1.0 | 1000 | 0.7253 | 0.696 |
| 0.6246 | 2.0 | 2000 | 0.6452 | 0.735 |
| 0.5072 | 3.0 | 3000 | 0.6872 | 0.7565 |
| 0.3384 | 4.0 | 4000 | 1.0357 | 0.7485 |
| 0.243 | 5.0 | 5000 | 1.1984 | 0.7455 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
HuengchI/my_awesome_eli5_clm-model
|
HuengchI
| 2023-07-14T13:59:42Z | 198 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T13:46:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
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. -->
# my_awesome_eli5_clm-model
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: 3.7590
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 3.7798 |
| 3.8857 | 2.0 | 564 | 3.7630 |
| 3.8857 | 3.0 | 846 | 3.7590 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Vladislav-HuggingFace/q-FrozenLake-v1-4x4-noSlippery
|
Vladislav-HuggingFace
| 2023-07-14T13:56:48Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T13:56:46Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Vladislav-HuggingFace/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
YanJiangJerry/sentiment-roberta-e6-b16-v2-w0.01
|
YanJiangJerry
| 2023-07-14T13:53:31Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T13:34:34Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-e6-b16-v2-w0.01
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. -->
# sentiment-roberta-e6-b16-v2-w0.01
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2099
- F1: 0.7898
- Recall: 0.7898
- Precision: 0.7898
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.8152 | 0.7251 | 0.7251 | 0.7251 |
| 0.7707 | 2.0 | 750 | 0.7114 | 0.7817 | 0.7817 | 0.7817 |
| 0.4129 | 3.0 | 1125 | 0.9667 | 0.7763 | 0.7763 | 0.7763 |
| 0.2466 | 4.0 | 1500 | 0.9866 | 0.7763 | 0.7763 | 0.7763 |
| 0.2466 | 5.0 | 1875 | 1.2385 | 0.7871 | 0.7871 | 0.7871 |
| 0.139 | 6.0 | 2250 | 1.2099 | 0.7898 | 0.7898 | 0.7898 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
barnobarno666/ppo-LunarLander-v2
|
barnobarno666
| 2023-07-14T13:45:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T13:44:06Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -207.20 +/- 110.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DiazOnFire/FoodVision
|
DiazOnFire
| 2023-07-14T13:45:20Z | 219 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-14T13:43:14Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: FoodVision
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8660714030265808
---
# FoodVision
Put any image from the 5 foods shown in the examples and the model
will classify it accurately
## Example Images
#### burger
#### doughnuts
#### pasta
#### pizza
#### sandwich
|
Balajb/t5-small-finetuned-xsum-bala
|
Balajb
| 2023-07-14T13:44:29Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:test-bala",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-11T11:48:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- test-bala
model-index:
- name: t5-small-finetuned-xsum-bala
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. -->
# t5-small-finetuned-xsum-bala
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the test-bala 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 3 | 1.0014 | 23.9552 | 6.6667 | 17.8146 | 18.494 | 19.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.2
|
ZoeVN/sam_full_finetune_breastcancer_lora
|
ZoeVN
| 2023-07-14T13:35:09Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-14T13:34:48Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
laura63/wav2vec2-base-finetuned-ks
|
laura63
| 2023-07-14T13:34:41Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-06-27T14:34:02Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: wav2vec2-base-finetuned-ks
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-finetuned-ks
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: 0.9236
- Accuracy: 0.8129
- F1: 0.7991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
BirdClef2023 (Top 20 species represented)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.6415 | 1.0 | 1467 | 1.8215 | 0.4724 | 0.3482 |
| 1.4247 | 2.0 | 2934 | 1.3207 | 0.6457 | 0.5854 |
| 1.0871 | 3.0 | 4401 | 1.0206 | 0.7469 | 0.7164 |
| 0.4278 | 4.0 | 5868 | 0.9235 | 0.8006 | 0.7821 |
| 0.3165 | 5.0 | 7335 | 0.9236 | 0.8129 | 0.7991 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
csalaam/bias-classification-setfit-model-islamic_bias
|
csalaam
| 2023-07-14T13:23:07Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-14T13:22:50Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# csalaam/bias-classification-setfit-model-islamic_bias
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("csalaam/bias-classification-setfit-model-islamic_bias")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
MredK/Ferdiv1
|
MredK
| 2023-07-14T13:20:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-14T13:16:14Z |
---
license: openrail
---
6 Dklık Dataset İle Yapıldı \
Train Bana Aittir\
200 Epoch\
Türkçe Model
|
MredK/Mertv1
|
MredK
| 2023-07-14T13:20:05Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-14T13:13:16Z |
---
license: openrail
---
6 Dklık Dataset İle Yapıldı \
Train Bana Aittir\
150 Epoch\
Türkçe Model
|
3b3r/vit_model
|
3b3r
| 2023-07-14T12:58:50Z | 228 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-14T00:39:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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. -->
# vit_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.9925
## 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: 0.0002
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1498 | 3.85 | 500 | 0.0414 | 0.9925 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
prognosis/cardio_qanda_openassistant_v1
|
prognosis
| 2023-07-14T12:57:43Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2023-07-14T11:59:52Z |
---
tags:
- generated_from_trainer
model-index:
- name: cardio_qanda_openassistant_v1
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. -->
# cardio_qanda_openassistant_v1
This model is a fine-tuned version of [prognosis/falcon7b_merged](https://huggingface.co/prognosis/falcon7b_merged) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
csalaam/bias-classification-setfit-model-blackbias
|
csalaam
| 2023-07-14T12:40:15Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-06-01T15:42:35Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# csalaam/bias-classification-setfit-model-blackbias
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("csalaam/bias-classification-setfit-model-blackbias")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
CamposCaio/distilbert-base-uncased-finetuned-cola
|
CamposCaio
| 2023-07-14T12:28:04Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T12:23:29Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CamposCaio/distilbert-base-uncased-finetuned-cola
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. -->
# CamposCaio/distilbert-base-uncased-finetuned-cola
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: 0.1904
- Validation Loss: 0.5521
- Train Matthews Correlation: 0.5153
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5200 | 0.4695 | 0.4727 | 0 |
| 0.3210 | 0.4581 | 0.5159 | 1 |
| 0.1904 | 0.5521 | 0.5153 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
rightspeed/spacehope6
|
rightspeed
| 2023-07-14T12:16:58Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T12:16:21Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 6.50 +/- 10.74
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rightspeed -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rightspeed -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rightspeed
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
YanJiangJerry/sentiment-roberta-clean-e8-b16-data2
|
YanJiangJerry
| 2023-07-14T12:12:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T11:47:40Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-clean-e8-b16-data2
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. -->
# sentiment-roberta-clean-e8-b16-data2
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9974
- F1: 0.7709
- Recall: 0.7709
- Precision: 0.7709
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.7813 | 0.7412 | 0.7412 | 0.7412 |
| 0.5973 | 2.0 | 750 | 0.7826 | 0.7332 | 0.7332 | 0.7332 |
| 0.2098 | 3.0 | 1125 | 1.1970 | 0.7547 | 0.7547 | 0.7547 |
| 0.1231 | 4.0 | 1500 | 1.5843 | 0.7520 | 0.7520 | 0.7520 |
| 0.1231 | 5.0 | 1875 | 1.7089 | 0.7520 | 0.7520 | 0.7520 |
| 0.0457 | 6.0 | 2250 | 1.7845 | 0.7601 | 0.7601 | 0.7601 |
| 0.0228 | 7.0 | 2625 | 2.0039 | 0.7601 | 0.7601 | 0.7601 |
| 0.0134 | 8.0 | 3000 | 1.9974 | 0.7709 | 0.7709 | 0.7709 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
peft-internal-testing/tiny_StableDiffusionPipelineUnet-lora
|
peft-internal-testing
| 2023-07-14T12:11:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-14T12:11:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
cgisky/RWKV-safetensors-fp16
|
cgisky
| 2023-07-14T12:09:12Z | 0 | 10 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-12T16:50:42Z |
---
license: apache-2.0
---
This is an implementation of the language model of RWKV in pure WebGPU.
https://github.com/cryscan/web-rwkv
|
PranjaliS/my_setiment_analysis_model2
|
PranjaliS
| 2023-07-14T12:02:52Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T11:06:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: PranjaliS/my_setiment_analysis_model2
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. -->
# PranjaliS/my_setiment_analysis_model2
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: 0.4203
- Validation Loss: 0.3669
- Train Accuracy: 0.846
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4203 | 0.3669 | 0.846 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jondurbin/mpt-30b-qlora-compatible
|
jondurbin
| 2023-07-14T11:50:40Z | 5 | 11 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-23T15:04:00Z |
Slightly modified mpt-30b, which has some updates to allow gradient checkpointing/etc., to be compatible with qlora training code.
Original model: https://huggingface.co/mosaicml/mpt-30b
My fork of qlora with mpt-30b support: https://github.com/jondurbin/qlora
Differences in the qlora scripts:
- requires adding `--mpt True` for mpt-based models
- uses `--num_train_epochs` instead of `--max_steps`
- uses airoboros prompt format (mostly 1:1 with vicuna) rather than alpaca, and expects an input file in JSONL format with "instruction" and "response"
__I think there's a bug in gradient accumulation, so if you try this, maybe set gradient accumulation steps to 1__
*my first attempts used batch size 6, with gradient accumulation steps 16, but results of three epochs with gradient accumulation vs without were quite a bit worse*
__5 epochs seemed to achieve the best results, but YMMV__
Full example of tuning (used for airoboros-mpt-30b-gpt4-1.4):
```
source /workspace/venv/bin/activate
export PYTHONPATH=./mpt-30b
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=airoboros-mpt-30b-gpt4-1.4
python qlora.py \
--model_name_or_path ./mpt-30b \
--output_dir ./$WANDB_PROJECT-checkpoints \
--num_train_epochs 5 \
--logging_steps 1 \
--save_strategy steps \
--data_seed 11422 \
--save_steps 100 \
--save_total_limit 3 \
--evaluation_strategy "no" \
--eval_dataset_size 2 \
--max_new_tokens 8192 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--double_quant \
--quant_type nf4 \
--bf16 \
--bits 4 \
--warmup_ratio 0.03 \
--lr_scheduler_type constant \
--dataset ./instructions.jsonl \
--dataset_format airoboros \
--model_max_len 8192 \
--gradient_checkpointing \
--per_device_train_batch_size 6 \
--gradient_accumulation_steps 1 \
--learning_rate 0.0001 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.05 \
--weight_decay 0.0 \
--seed 11422 \
--trust_remote_code \
--mpt True \
--report_to wandb
```
### Merged model
Run the `merge_weights.py` script in the qlora repo: https://github.com/jondurbin/qlora/blob/main/merge_weights.py
Then, copy all of the original python files from the mpt-30b repo into your output directory: https://huggingface.co/mosaicml/mpt-30b/tree/main
|
Ojimi/anime-kawai-diffusion
|
Ojimi
| 2023-07-14T11:39:06Z | 1,082 | 154 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"anime",
"pytorch",
"art",
"stable diffusion",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-09T15:30:12Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- anime
- pytorch
- diffusers
- art
- stable diffusion
---
## Introduction:
- I don't know how to introduce it, but it's been renamed several times. It is an open, free to use and fine-tune AI-art model. It was created by my curiosity. Hope you will like it. Have fun! (●'◡'●).
## Use:
- For 🧨Diffusers:
```python
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Ojimi/anime-kawai-diffusion")
pipe = pipe.to("cuda")
prompt = "1girl, animal ears, long hair, solo, cat ears, choker, bare shoulders, red eyes, fang, looking at viewer, animal ear fluff, upper body, black hair, blush, closed mouth, off shoulder, bangs, bow, collarbone"
image = pipe(prompt, negative_prompt="lowres, bad anatomy").images[0]
```
## Tips:
- The `masterpiece` and `best quality` tags are not necessary, as it sometimes leads to contradictory results, but if it is distorted or discolored, add them now.
- The CGF scale should be 7.5 and the step count 28 for the best quality and best performance.
- Use a sample photo for your idea. `Interrogate DeepBooru` and change the prompts to suit what you want.
- You should use it as a supportive tool for creating works of art, and not rely on it completely.
- The Clip skip should be 2.
## **Limitations:**
- The drawing is hard, not soft.
- Loss of detail, errors, bad human-like (six-fingered hand) details, deformation, blurring, and unclear images are inevitable.
- ⚠️Content may not be appropriate for all ages: As it is trained on data that includes adult content, the generated images may contain content not suitable for children (depending on your country there will be a specific regulation about it). If you do not want to appear adult content, make sure you have additional safety measures in place, such as adding "nsfw" to the negative prompt.
- The results generated by the model are considered impressive. But unfortunately, currently, it only supports the English language, to use multilingual, consider using third-party translation programs.
- The model is trained on the `Danbooru` and `Nai` tagging system, so the long text may result in poor results.
- My amount of money: 0 USD =((.
## **Desires:**
As it is a version made only by myself and my small associates, the model will not be perfect and may differ from what people expect. Any contributions from everyone will be respected.
Want to support me? Thank you, please help me make it better. ❤️
## Special Thank:
This wouldn't have happened if they hadn't made a breakthrough.
- [Runwayml](https://huggingface.co/runwayml/): Base model.
- [CompVis](https://github.com/CompVis/): VAE Trainer.
- stabilityai: [stabilityai/sd-vae-ft-mse-original · Hugging Face](https://huggingface.co/stabilityai/sd-vae-ft-mse-original)
- [d8ahazard](https://github.com/d8ahazard/.sd_dreambooth_extension) : Dreambooth.
- [Automatic1111](https://github.com/AUTOMATIC1111/) : Web UI.
- [Mikubill](https://github.com/Mikubill/): Where my ideas started.
- Chat-GPT: Help me do crazy things that I thought I would never do.
- Novel AI, Anything Model, Abyss Orange Model: Dataset images. An AI made me thousands of pictures without worrying about copyright or dispute.
- Danbooru: Help me write the correct tag.
- My friend and others: Get quality images.
- And You 🫵❤️
## Copyright:
This license allows anyone to copy, and modify the model, but please follow the terms of the CreativeML Open RAIL-M. You can learn more about the CreativeML Open RAIL-M [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license).
If any part of the model does not comply with the terms of the GNU General Public License, the copyright and other rights of the model will still be valid.
All AI-generated images are yours, you can do whatever you want, but please obey the laws of your country. We will not be responsible for any problems you cause.
We allow you to merge with another model, but if you share that merge model, don't forget to add me to the credits.
Don't forget me.
# Have fun with your waifu! (●'◡'●)
Do you want to sponsor computing resources for us? Thank you . Please sponsor to me on Ko-fi at https://ko-fi.com/projectk.
|
lakecrimsonn/distilbert-base-uncased-finetuned-squad-d5716d28
|
lakecrimsonn
| 2023-07-14T11:38:32Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-14T10:57:46Z |
---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
jordyvl/swin-base_tobacco
|
jordyvl
| 2023-07-14T11:26:32Z | 132 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T13:08:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-base_tobacco
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. -->
# swin-base_tobacco
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6059
- Accuracy: 0.835
- Brier Loss: 0.2576
- Nll: 1.2824
- F1 Micro: 0.835
- F1 Macro: 0.8348
- Ece: 0.1310
- Aurc: 0.0387
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 3 | 2.3165 | 0.11 | 0.9031 | 7.6310 | 0.11 | 0.0604 | 0.2004 | 0.8718 |
| No log | 1.96 | 6 | 2.2894 | 0.155 | 0.8975 | 6.8146 | 0.155 | 0.0944 | 0.2230 | 0.8555 |
| No log | 2.96 | 9 | 2.2481 | 0.215 | 0.8888 | 5.1480 | 0.2150 | 0.1472 | 0.2492 | 0.8119 |
| No log | 3.96 | 12 | 2.1955 | 0.275 | 0.8770 | 4.2879 | 0.275 | 0.1939 | 0.2844 | 0.6562 |
| No log | 4.96 | 15 | 2.1326 | 0.36 | 0.8619 | 3.8809 | 0.36 | 0.2199 | 0.3357 | 0.4962 |
| No log | 5.96 | 18 | 2.0568 | 0.375 | 0.8415 | 3.9254 | 0.375 | 0.2309 | 0.3377 | 0.4471 |
| No log | 6.96 | 21 | 1.9639 | 0.375 | 0.8126 | 3.8158 | 0.375 | 0.2319 | 0.3195 | 0.4534 |
| No log | 7.96 | 24 | 1.8621 | 0.375 | 0.7781 | 3.3244 | 0.375 | 0.2456 | 0.2924 | 0.4833 |
| No log | 8.96 | 27 | 1.7100 | 0.44 | 0.7273 | 2.8211 | 0.44 | 0.3136 | 0.3188 | 0.3515 |
| No log | 9.96 | 30 | 1.5377 | 0.535 | 0.6611 | 2.4560 | 0.535 | 0.4259 | 0.3557 | 0.2259 |
| No log | 10.96 | 33 | 1.3588 | 0.595 | 0.5825 | 2.3216 | 0.595 | 0.4933 | 0.2986 | 0.1795 |
| No log | 11.96 | 36 | 1.2072 | 0.62 | 0.5215 | 2.3831 | 0.62 | 0.5352 | 0.2927 | 0.1541 |
| No log | 12.96 | 39 | 1.0766 | 0.67 | 0.4715 | 2.2078 | 0.67 | 0.5966 | 0.2727 | 0.1219 |
| No log | 13.96 | 42 | 0.9699 | 0.675 | 0.4408 | 1.8028 | 0.675 | 0.5961 | 0.2568 | 0.1215 |
| No log | 14.96 | 45 | 0.8660 | 0.68 | 0.4011 | 1.4772 | 0.68 | 0.5978 | 0.2176 | 0.1014 |
| No log | 15.96 | 48 | 0.7907 | 0.725 | 0.3709 | 1.4755 | 0.7250 | 0.6768 | 0.2055 | 0.0904 |
| No log | 16.96 | 51 | 0.7362 | 0.75 | 0.3501 | 1.3822 | 0.75 | 0.7077 | 0.2042 | 0.0806 |
| No log | 17.96 | 54 | 0.6867 | 0.76 | 0.3322 | 1.3191 | 0.76 | 0.7177 | 0.1926 | 0.0724 |
| No log | 18.96 | 57 | 0.6572 | 0.78 | 0.3203 | 1.2996 | 0.78 | 0.7424 | 0.1920 | 0.0699 |
| No log | 19.96 | 60 | 0.6074 | 0.785 | 0.2967 | 1.3136 | 0.785 | 0.7686 | 0.1705 | 0.0589 |
| No log | 20.96 | 63 | 0.6050 | 0.795 | 0.2956 | 1.3729 | 0.795 | 0.7793 | 0.1762 | 0.0600 |
| No log | 21.96 | 66 | 0.5748 | 0.83 | 0.2785 | 1.3558 | 0.83 | 0.8113 | 0.1744 | 0.0529 |
| No log | 22.96 | 69 | 0.5722 | 0.815 | 0.2756 | 1.3937 | 0.815 | 0.8097 | 0.1767 | 0.0489 |
| No log | 23.96 | 72 | 0.5689 | 0.795 | 0.2750 | 1.3641 | 0.795 | 0.7947 | 0.1452 | 0.0539 |
| No log | 24.96 | 75 | 0.5536 | 0.825 | 0.2718 | 1.2773 | 0.825 | 0.8068 | 0.1698 | 0.0509 |
| No log | 25.96 | 78 | 0.5464 | 0.805 | 0.2726 | 1.2772 | 0.805 | 0.7888 | 0.1499 | 0.0487 |
| No log | 26.96 | 81 | 0.5455 | 0.81 | 0.2626 | 1.3607 | 0.81 | 0.8080 | 0.1750 | 0.0471 |
| No log | 27.96 | 84 | 0.5542 | 0.815 | 0.2609 | 1.3643 | 0.815 | 0.8089 | 0.1521 | 0.0466 |
| No log | 28.96 | 87 | 0.5480 | 0.82 | 0.2710 | 1.2996 | 0.82 | 0.8227 | 0.1422 | 0.0468 |
| No log | 29.96 | 90 | 0.5507 | 0.83 | 0.2654 | 1.3425 | 0.83 | 0.8320 | 0.1491 | 0.0475 |
| No log | 30.96 | 93 | 0.5608 | 0.815 | 0.2591 | 1.4365 | 0.815 | 0.8145 | 0.1405 | 0.0442 |
| No log | 31.96 | 96 | 0.5473 | 0.825 | 0.2622 | 1.3600 | 0.825 | 0.8198 | 0.1339 | 0.0424 |
| No log | 32.96 | 99 | 0.5296 | 0.83 | 0.2588 | 1.2906 | 0.83 | 0.8311 | 0.1373 | 0.0416 |
| No log | 33.96 | 102 | 0.5370 | 0.82 | 0.2522 | 1.2895 | 0.82 | 0.8214 | 0.1428 | 0.0436 |
| No log | 34.96 | 105 | 0.5578 | 0.8 | 0.2707 | 1.3364 | 0.8000 | 0.8056 | 0.1708 | 0.0481 |
| No log | 35.96 | 108 | 0.5193 | 0.825 | 0.2484 | 1.2883 | 0.825 | 0.8250 | 0.1316 | 0.0405 |
| No log | 36.96 | 111 | 0.5306 | 0.815 | 0.2569 | 1.2856 | 0.815 | 0.8093 | 0.1344 | 0.0420 |
| No log | 37.96 | 114 | 0.5824 | 0.815 | 0.2729 | 1.3994 | 0.815 | 0.8182 | 0.1418 | 0.0479 |
| No log | 38.96 | 117 | 0.5486 | 0.82 | 0.2549 | 1.2974 | 0.82 | 0.8259 | 0.1312 | 0.0443 |
| No log | 39.96 | 120 | 0.5421 | 0.83 | 0.2545 | 1.3575 | 0.83 | 0.8316 | 0.1491 | 0.0415 |
| No log | 40.96 | 123 | 0.5477 | 0.81 | 0.2700 | 1.3251 | 0.81 | 0.8166 | 0.1499 | 0.0418 |
| No log | 41.96 | 126 | 0.5404 | 0.825 | 0.2553 | 1.3186 | 0.825 | 0.8309 | 0.1519 | 0.0414 |
| No log | 42.96 | 129 | 0.5698 | 0.83 | 0.2598 | 1.3249 | 0.83 | 0.8386 | 0.1396 | 0.0452 |
| No log | 43.96 | 132 | 0.5538 | 0.815 | 0.2605 | 1.3122 | 0.815 | 0.8212 | 0.1410 | 0.0430 |
| No log | 44.96 | 135 | 0.5369 | 0.81 | 0.2586 | 1.3030 | 0.81 | 0.8141 | 0.1404 | 0.0409 |
| No log | 45.96 | 138 | 0.5614 | 0.825 | 0.2615 | 1.3881 | 0.825 | 0.8278 | 0.1404 | 0.0427 |
| No log | 46.96 | 141 | 0.5636 | 0.825 | 0.2601 | 1.4077 | 0.825 | 0.8286 | 0.1345 | 0.0421 |
| No log | 47.96 | 144 | 0.5783 | 0.83 | 0.2684 | 1.3350 | 0.83 | 0.8304 | 0.1373 | 0.0422 |
| No log | 48.96 | 147 | 0.5749 | 0.825 | 0.2663 | 1.3167 | 0.825 | 0.8241 | 0.1308 | 0.0424 |
| No log | 49.96 | 150 | 0.5802 | 0.82 | 0.2692 | 1.3191 | 0.82 | 0.8194 | 0.1217 | 0.0461 |
| No log | 50.96 | 153 | 0.5696 | 0.82 | 0.2639 | 1.3330 | 0.82 | 0.8175 | 0.1372 | 0.0429 |
| No log | 51.96 | 156 | 0.5827 | 0.84 | 0.2656 | 1.3975 | 0.8400 | 0.8444 | 0.1378 | 0.0426 |
| No log | 52.96 | 159 | 0.5725 | 0.805 | 0.2669 | 1.3172 | 0.805 | 0.7997 | 0.1459 | 0.0422 |
| No log | 53.96 | 162 | 0.5769 | 0.805 | 0.2691 | 1.3111 | 0.805 | 0.7991 | 0.1457 | 0.0434 |
| No log | 54.96 | 165 | 0.5883 | 0.805 | 0.2647 | 1.4581 | 0.805 | 0.8104 | 0.1405 | 0.0430 |
| No log | 55.96 | 168 | 0.5834 | 0.835 | 0.2543 | 1.4586 | 0.835 | 0.8349 | 0.1346 | 0.0407 |
| No log | 56.96 | 171 | 0.5875 | 0.835 | 0.2543 | 1.3211 | 0.835 | 0.8358 | 0.1320 | 0.0402 |
| No log | 57.96 | 174 | 0.5741 | 0.84 | 0.2533 | 1.3027 | 0.8400 | 0.8405 | 0.1290 | 0.0395 |
| No log | 58.96 | 177 | 0.5737 | 0.82 | 0.2624 | 1.3104 | 0.82 | 0.8167 | 0.1437 | 0.0396 |
| No log | 59.96 | 180 | 0.5796 | 0.815 | 0.2603 | 1.4021 | 0.815 | 0.8154 | 0.1286 | 0.0406 |
| No log | 60.96 | 183 | 0.5711 | 0.83 | 0.2553 | 1.4016 | 0.83 | 0.8306 | 0.1272 | 0.0390 |
| No log | 61.96 | 186 | 0.5670 | 0.825 | 0.2591 | 1.3136 | 0.825 | 0.8263 | 0.1429 | 0.0406 |
| No log | 62.96 | 189 | 0.5736 | 0.825 | 0.2592 | 1.3077 | 0.825 | 0.8231 | 0.1244 | 0.0417 |
| No log | 63.96 | 192 | 0.5730 | 0.83 | 0.2531 | 1.3007 | 0.83 | 0.8274 | 0.1275 | 0.0401 |
| No log | 64.96 | 195 | 0.6130 | 0.82 | 0.2687 | 1.3014 | 0.82 | 0.8246 | 0.1484 | 0.0414 |
| No log | 65.96 | 198 | 0.6023 | 0.825 | 0.2596 | 1.3107 | 0.825 | 0.8254 | 0.1373 | 0.0404 |
| No log | 66.96 | 201 | 0.5923 | 0.825 | 0.2599 | 1.3078 | 0.825 | 0.8263 | 0.1312 | 0.0411 |
| No log | 67.96 | 204 | 0.6197 | 0.81 | 0.2766 | 1.3046 | 0.81 | 0.8035 | 0.1373 | 0.0451 |
| No log | 68.96 | 207 | 0.5918 | 0.805 | 0.2651 | 1.3019 | 0.805 | 0.8044 | 0.1407 | 0.0404 |
| No log | 69.96 | 210 | 0.5908 | 0.835 | 0.2544 | 1.3286 | 0.835 | 0.8344 | 0.1354 | 0.0394 |
| No log | 70.96 | 213 | 0.5941 | 0.83 | 0.2558 | 1.3019 | 0.83 | 0.8324 | 0.1402 | 0.0401 |
| No log | 71.96 | 216 | 0.5994 | 0.82 | 0.2588 | 1.2998 | 0.82 | 0.8215 | 0.1297 | 0.0411 |
| No log | 72.96 | 219 | 0.6083 | 0.825 | 0.2638 | 1.3525 | 0.825 | 0.8257 | 0.1379 | 0.0410 |
| No log | 73.96 | 222 | 0.5980 | 0.825 | 0.2609 | 1.3515 | 0.825 | 0.8295 | 0.1457 | 0.0394 |
| No log | 74.96 | 225 | 0.5945 | 0.83 | 0.2568 | 1.3670 | 0.83 | 0.8302 | 0.1324 | 0.0390 |
| No log | 75.96 | 228 | 0.5982 | 0.845 | 0.2535 | 1.4552 | 0.845 | 0.8476 | 0.1246 | 0.0390 |
| No log | 76.96 | 231 | 0.5850 | 0.83 | 0.2507 | 1.3700 | 0.83 | 0.8287 | 0.1348 | 0.0391 |
| No log | 77.96 | 234 | 0.5859 | 0.825 | 0.2566 | 1.2917 | 0.825 | 0.8232 | 0.1309 | 0.0394 |
| No log | 78.96 | 237 | 0.6085 | 0.835 | 0.2630 | 1.3516 | 0.835 | 0.8370 | 0.1329 | 0.0420 |
| No log | 79.96 | 240 | 0.6108 | 0.835 | 0.2621 | 1.2943 | 0.835 | 0.8370 | 0.1395 | 0.0414 |
| No log | 80.96 | 243 | 0.6061 | 0.81 | 0.2596 | 1.2898 | 0.81 | 0.8119 | 0.1313 | 0.0413 |
| No log | 81.96 | 246 | 0.6006 | 0.815 | 0.2564 | 1.2952 | 0.815 | 0.8122 | 0.1453 | 0.0406 |
| No log | 82.96 | 249 | 0.6050 | 0.825 | 0.2577 | 1.2998 | 0.825 | 0.8283 | 0.1271 | 0.0400 |
| No log | 83.96 | 252 | 0.6197 | 0.835 | 0.2658 | 1.3021 | 0.835 | 0.8386 | 0.1222 | 0.0414 |
| No log | 84.96 | 255 | 0.6086 | 0.825 | 0.2651 | 1.2889 | 0.825 | 0.8251 | 0.1207 | 0.0404 |
| No log | 85.96 | 258 | 0.5965 | 0.83 | 0.2587 | 1.2929 | 0.83 | 0.8304 | 0.1323 | 0.0397 |
| No log | 86.96 | 261 | 0.5897 | 0.82 | 0.2550 | 1.2980 | 0.82 | 0.8171 | 0.1372 | 0.0394 |
| No log | 87.96 | 264 | 0.5887 | 0.83 | 0.2551 | 1.2950 | 0.83 | 0.8290 | 0.1251 | 0.0391 |
| No log | 88.96 | 267 | 0.5958 | 0.82 | 0.2598 | 1.2871 | 0.82 | 0.8180 | 0.1319 | 0.0392 |
| No log | 89.96 | 270 | 0.6088 | 0.82 | 0.2658 | 1.2805 | 0.82 | 0.8184 | 0.1513 | 0.0396 |
| No log | 90.96 | 273 | 0.6192 | 0.825 | 0.2692 | 1.2772 | 0.825 | 0.8263 | 0.1258 | 0.0402 |
| No log | 91.96 | 276 | 0.6230 | 0.825 | 0.2689 | 1.2777 | 0.825 | 0.8263 | 0.1416 | 0.0404 |
| No log | 92.96 | 279 | 0.6223 | 0.83 | 0.2667 | 1.2792 | 0.83 | 0.8318 | 0.1296 | 0.0401 |
| No log | 93.96 | 282 | 0.6145 | 0.83 | 0.2627 | 1.2797 | 0.83 | 0.8321 | 0.1265 | 0.0394 |
| No log | 94.96 | 285 | 0.6105 | 0.83 | 0.2610 | 1.2807 | 0.83 | 0.8321 | 0.1352 | 0.0392 |
| No log | 95.96 | 288 | 0.6095 | 0.83 | 0.2602 | 1.2815 | 0.83 | 0.8321 | 0.1360 | 0.0390 |
| No log | 96.96 | 291 | 0.6076 | 0.835 | 0.2590 | 1.2824 | 0.835 | 0.8348 | 0.1255 | 0.0389 |
| No log | 97.96 | 294 | 0.6060 | 0.835 | 0.2578 | 1.2827 | 0.835 | 0.8348 | 0.1281 | 0.0388 |
| No log | 98.96 | 297 | 0.6058 | 0.835 | 0.2575 | 1.2825 | 0.835 | 0.8348 | 0.1410 | 0.0387 |
| No log | 99.96 | 300 | 0.6059 | 0.835 | 0.2576 | 1.2824 | 0.835 | 0.8348 | 0.1310 | 0.0387 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
NasimB/gpt2-concat-cbt-rarity-all-end-p5k
|
NasimB
| 2023-07-14T11:21:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T09:26:19Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-cbt-rarity-all-end-p5k
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. -->
# gpt2-concat-cbt-rarity-all-end-p5k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3074
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7056 | 0.29 | 500 | 5.6370 |
| 5.3385 | 0.58 | 1000 | 5.2064 |
| 4.9896 | 0.87 | 1500 | 4.9537 |
| 4.7182 | 1.17 | 2000 | 4.8020 |
| 4.5599 | 1.46 | 2500 | 4.6783 |
| 4.448 | 1.75 | 3000 | 4.5720 |
| 4.3216 | 2.04 | 3500 | 4.4947 |
| 4.1234 | 2.33 | 4000 | 4.4478 |
| 4.0942 | 2.62 | 4500 | 4.3887 |
| 4.0687 | 2.92 | 5000 | 4.3369 |
| 3.8617 | 3.21 | 5500 | 4.3314 |
| 3.7939 | 3.5 | 6000 | 4.2976 |
| 3.7824 | 3.79 | 6500 | 4.2652 |
| 3.6941 | 4.08 | 7000 | 4.2608 |
| 3.5091 | 4.37 | 7500 | 4.2583 |
| 3.5104 | 4.66 | 8000 | 4.2411 |
| 3.5011 | 4.96 | 8500 | 4.2278 |
| 3.3458 | 5.25 | 9000 | 4.2417 |
| 3.319 | 5.54 | 9500 | 4.2395 |
| 3.3151 | 5.83 | 10000 | 4.2387 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
YanJiangJerry/sentiment-roberta-latest-e8-b16-data2
|
YanJiangJerry
| 2023-07-14T11:02:31Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T10:52:47Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-latest-e8-b16-data2
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. -->
# sentiment-roberta-latest-e8-b16-data2
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0416
- F1: 0.7439
- Recall: 0.7439
- Precision: 0.7439
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.8354 | 0.7278 | 0.7278 | 0.7278 |
| 0.446 | 2.0 | 750 | 1.0641 | 0.7655 | 0.7655 | 0.7655 |
| 0.1559 | 3.0 | 1125 | 1.4651 | 0.7278 | 0.7278 | 0.7278 |
| 0.0812 | 4.0 | 1500 | 1.8105 | 0.7412 | 0.7412 | 0.7412 |
| 0.0812 | 5.0 | 1875 | 1.9380 | 0.7358 | 0.7358 | 0.7358 |
| 0.0254 | 6.0 | 2250 | 2.0327 | 0.7412 | 0.7412 | 0.7412 |
| 0.0125 | 7.0 | 2625 | 2.1059 | 0.7412 | 0.7412 | 0.7412 |
| 0.0071 | 8.0 | 3000 | 2.0416 | 0.7439 | 0.7439 | 0.7439 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
HoaAn2003/q-FrozenLake-v1-v8x8-Slippery
|
HoaAn2003
| 2023-07-14T11:02:24Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T11:02:14Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-v8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="HoaAn2003/q-FrozenLake-v1-v8x8-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
YanJiangJerry/sentiment-roberta-e6-b16-data2
|
YanJiangJerry
| 2023-07-14T10:43:12Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T10:25:22Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-e6-b16-data2
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. -->
# sentiment-roberta-e6-b16-data2
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4505
- F1: 0.7682
- Recall: 0.7682
- Precision: 0.7682
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.7961 | 0.7089 | 0.7089 | 0.7089 |
| 0.6924 | 2.0 | 750 | 0.6880 | 0.7601 | 0.7601 | 0.7601 |
| 0.3191 | 3.0 | 1125 | 1.1324 | 0.7520 | 0.7520 | 0.7520 |
| 0.1802 | 4.0 | 1500 | 1.2056 | 0.7682 | 0.7682 | 0.7682 |
| 0.1802 | 5.0 | 1875 | 1.3942 | 0.7736 | 0.7736 | 0.7736 |
| 0.088 | 6.0 | 2250 | 1.4505 | 0.7682 | 0.7682 | 0.7682 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
tuanhnh/dqn-SpaceInvadersNoFrameskip-v4
|
tuanhnh
| 2023-07-14T10:40:55Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T10:40:19Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 580.00 +/- 96.85
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tuanhnh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tuanhnh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tuanhnh
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
NechuBM/mi-super-modelo
|
NechuBM
| 2023-07-14T10:37:57Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T10:18:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: mi-super-modelo
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_review_full
split: test
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.225
---
<!-- 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. -->
# mi-super-modelo
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6404
- Accuracy: 0.225
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7058 | 0.5 | 5 | 1.7046 | 0.225 |
| 1.6208 | 1.0 | 10 | 1.6404 | 0.225 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
HoaAn2003/q-FrozenLake-v1-4x4-noSlippery
|
HoaAn2003
| 2023-07-14T10:25:48Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T10:25:26Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.31 +/- 0.46
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="HoaAn2003/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
photel/taxi-v3
|
photel
| 2023-07-14T10:18:46Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T07:45:46Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="photel/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Python/ACROSS-m2o-eng-base
|
Python
| 2023-07-14T10:17:34Z | 153 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-14T09:20:46Z |
# ACROSS-m2o-eng-base
## How to use
```python
from transformers import MT5ForConditionalGeneration, AutoTokenizer
model = MT5ForConditionalGeneration.from_pretrained('Python/ACROSS-m2o-eng-base')
tokenizer = AutoTokenizer.from_pretrained('Python/ACROSS-m2o-eng-base', use_fast=False)
input_text = '冈山县的倉敷市整个泡在泥水之中，数千户人家停水停电 这是日本近30多年来因为降雨而造成的死亡人数最多的一次水灾。究竟为何如此严重？仍然是每个人心中的疑问。 日本一向被视为是“防灾强国”，日本人对地震、台风、海啸等自然灾难绝对不陌生。 但这次暴雨引发水灾和土石流，竟然出现如此惊人的天灾死亡人数，也令许多人感到震惊。 短短几日的降雨量达到整个7月正常降雨量的三倍之多 超大降雨 究其原因，首先是短时间之内的超大降雨。 日本气象厅上周对西日本多个地方发布“大雨特别警报”，警告西部地方会受到“数十年一遇”的豪大雨，结果一共有93个观测站录得史上雨量第一的纪录。 从上周四开始的短短几日之内，日本西部地区多个地方的降雨量达到整个7月正常降雨量的三倍之多。 日本此次降雨多个地方超过上千毫米，日本气象厅也将这次豪雨正式命名为“平成30年7月豪雨”。 一共有7万多人参与救灾工作 河川溃堤 此外，超大豪雨超过河川疏洪承受度，短时间涌入巨大水量造成河川溃堤，沿岸市镇整个泡在泥水之中。 日本《每日新闻》报道说，冈山县的小田川溃堤，至少4600户都被洪水淹没，许多长者逃生不及淹死在自己家中。 暴雨过后被毁坏的家园 回水现象 据《产经新闻》报导，冈山县仓敷市真备町内的高梁川各支流共有5处溃堤，是因为大雨让河川主流水位上升，导致原本要和主流汇集的的支流无法流入，因此溃堤淹没附近区域，这样的状况被称之为“回水现象”。 有专家指出，“回水现象”也是这次豪雨水灾如此严重的原因之一。 救难人员抓紧时间在土石堆和残垣断壁下搜寻抢救生还者 山体滑坡 除了超大豪雨之外，日本地形多山，还有板块和花岗岩地质层，不少民宅都建筑在山坡地，一旦遇上大雨容易发生山体滑坡现象。 《日本经济新闻》报道说，这次日本暴雨灾难，多个地方发生大规模山体滑坡灾害，导致遇难人数增加。 受灾区的15个县有大约12000人安置到学校和体育馆等避难中心 该报引述京都大学防灾研究所的应用地质学教授千木良雅弘分析说，灾区是花岗岩的分布地区，其表层由“风化花岗岩”砂土覆盖，一旦降雨，表层滑坡就成为土石流，涌入住宅区。 专家也指出，表层滑坡导致的灾害近年来频频发生，原因多半是局部性暴雨所导致，需要检讨是否要在可能发生表层滑坡的地区建设住宅。'
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors='pt')
generate_ids = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
num_beams=5,
min_length=10,
length_penalty=0.8,
max_length=84
)
print(tokenizer.decode(generate_ids[0], skip_special_tokens=True))
```
|
YanJiangJerry/sentiment-roberta-e4-b16
|
YanJiangJerry
| 2023-07-14T09:55:48Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T09:40:45Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-e4-b16
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. -->
# sentiment-roberta-e4-b16
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1904
- F1: 0.7547
- Recall: 0.7547
- Precision: 0.7547
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.7026 | 0.7520 | 0.7520 | 0.7520 |
| 0.7289 | 2.0 | 750 | 0.7406 | 0.7385 | 0.7385 | 0.7385 |
| 0.3702 | 3.0 | 1125 | 1.1224 | 0.7385 | 0.7385 | 0.7385 |
| 0.2018 | 4.0 | 1500 | 1.1904 | 0.7547 | 0.7547 | 0.7547 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
mjv256/controlnet_qrcode-control_v11p_sd21
|
mjv256
| 2023-07-14T09:42:26Z | 20 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"controlnet",
"image-to-image",
"en",
"license:openrail++",
"endpoints_compatible",
"region:us"
] |
image-to-image
| 2023-07-13T19:22:14Z |
---
tags:
- stable-diffusion
- controlnet
- image-to-image
license: openrail++
language:
- en
pipeline_tag: image-to-image
duplicated_from: DionTimmer/controlnet_qrcode-control_v11p_sd21
---
# QR Code Conditioned ControlNet Models for Stable Diffusion 2.1
## Model Description
This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v2.1.
The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version.
## How to use with diffusers
```bash
pip -q install diffusers transformers accelerate torch xformers
```
```python
import torch
from PIL import Image
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.utils import load_image
controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v11p_sd21",
torch_dtype=torch.float16)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
# play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image
# qr code image
source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png")
# initial image, anything
init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg")
condition_image = resize_for_condition_image(source_image, 768)
init_image = resize_for_condition_image(init_image, 768)
generator = torch.manual_seed(123121231)
image = pipe(prompt="a bilboard in NYC with a qrcode",
negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
image=init_image,
control_image=condition_image,
width=768,
height=768,
guidance_scale=20,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150,
)
image.images[0]
```
## Performance and Limitations
These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).**
To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
## Installation
The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application.
For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail.
Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.
|
snolyai/resultsfinalgerman
|
snolyai
| 2023-07-14T09:41:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-14T07:57:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resultsfinalgerman
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. -->
# resultsfinalgerman
This model is a fine-tuned version of [padmalcom/wav2vec2-large-emotion-detection-german](https://huggingface.co/padmalcom/wav2vec2-large-emotion-detection-german) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6302
- Accuracy: 0.6429
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7053 | 1.0 | 13 | 0.6971 | 0.3571 |
| 0.6994 | 2.0 | 26 | 0.6930 | 0.5714 |
| 0.686 | 3.0 | 39 | 0.6891 | 0.5714 |
| 0.6759 | 4.0 | 52 | 0.6889 | 0.5714 |
| 0.6865 | 5.0 | 65 | 0.6870 | 0.5714 |
| 0.6916 | 6.0 | 78 | 0.6847 | 0.5714 |
| 0.6764 | 7.0 | 91 | 0.6854 | 0.5714 |
| 0.6768 | 8.0 | 104 | 0.6869 | 0.5714 |
| 0.6546 | 9.0 | 117 | 0.6882 | 0.5714 |
| 0.6806 | 10.0 | 130 | 0.6875 | 0.5714 |
| 0.6742 | 11.0 | 143 | 0.6893 | 0.5714 |
| 0.6675 | 12.0 | 156 | 0.6897 | 0.5714 |
| 0.6762 | 13.0 | 169 | 0.6903 | 0.5714 |
| 0.6451 | 14.0 | 182 | 0.6920 | 0.5714 |
| 0.6641 | 15.0 | 195 | 0.6928 | 0.5714 |
| 0.634 | 16.0 | 208 | 0.6974 | 0.5714 |
| 0.6342 | 17.0 | 221 | 0.6983 | 0.5714 |
| 0.6526 | 18.0 | 234 | 0.6992 | 0.5714 |
| 0.6498 | 19.0 | 247 | 0.6926 | 0.5714 |
| 0.6293 | 20.0 | 260 | 0.6842 | 0.5714 |
| 0.5946 | 21.0 | 273 | 0.6833 | 0.5714 |
| 0.6281 | 22.0 | 286 | 0.6761 | 0.5 |
| 0.6084 | 23.0 | 299 | 0.6748 | 0.5 |
| 0.6055 | 24.0 | 312 | 0.6655 | 0.5 |
| 0.5806 | 25.0 | 325 | 0.6670 | 0.7143 |
| 0.62 | 26.0 | 338 | 0.6550 | 0.5714 |
| 0.5741 | 27.0 | 351 | 0.6578 | 0.7143 |
| 0.6261 | 28.0 | 364 | 0.6675 | 0.6429 |
| 0.5069 | 29.0 | 377 | 0.6661 | 0.6429 |
| 0.5526 | 30.0 | 390 | 0.6602 | 0.6429 |
| 0.5145 | 31.0 | 403 | 0.6545 | 0.6429 |
| 0.5634 | 32.0 | 416 | 0.6553 | 0.6429 |
| 0.4619 | 33.0 | 429 | 0.6493 | 0.6429 |
| 0.5694 | 34.0 | 442 | 0.6487 | 0.6429 |
| 0.5045 | 35.0 | 455 | 0.6436 | 0.6429 |
| 0.4623 | 36.0 | 468 | 0.6448 | 0.6429 |
| 0.5001 | 37.0 | 481 | 0.6465 | 0.6429 |
| 0.4779 | 38.0 | 494 | 0.6439 | 0.6429 |
| 0.4751 | 39.0 | 507 | 0.6329 | 0.6429 |
| 0.4426 | 40.0 | 520 | 0.6294 | 0.6429 |
| 0.4341 | 41.0 | 533 | 0.6270 | 0.6429 |
| 0.4282 | 42.0 | 546 | 0.6265 | 0.6429 |
| 0.4908 | 43.0 | 559 | 0.6269 | 0.6429 |
| 0.4073 | 44.0 | 572 | 0.6251 | 0.6429 |
| 0.4207 | 45.0 | 585 | 0.6261 | 0.6429 |
| 0.4757 | 46.0 | 598 | 0.6277 | 0.6429 |
| 0.4357 | 47.0 | 611 | 0.6294 | 0.6429 |
| 0.4473 | 48.0 | 624 | 0.6302 | 0.6429 |
| 0.4047 | 49.0 | 637 | 0.6302 | 0.6429 |
| 0.4881 | 50.0 | 650 | 0.6302 | 0.6429 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
at2507/gpt_output
|
at2507
| 2023-07-14T09:39:17Z | 137 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikisql",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T08:22:21Z |
---
tags:
- generated_from_trainer
datasets:
- wikisql
model-index:
- name: gpt_output
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. -->
# gpt_output
This model was trained from scratch on the wikisql 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: 2
- 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jordyvl/vit-base_rvl_tobacco
|
jordyvl
| 2023-07-14T09:35:21Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-14T08:31:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_rvl_tobacco
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. -->
# vit-base_rvl_tobacco
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4152
- Accuracy: 0.905
- Brier Loss: 0.1584
- Nll: 0.7130
- F1 Micro: 0.905
- F1 Macro: 0.9056
- Ece: 0.1601
- Aurc: 0.0196
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 3 | 2.3234 | 0.045 | 0.9050 | 9.6090 | 0.045 | 0.0479 | 0.1570 | 0.9674 |
| No log | 1.96 | 6 | 2.3007 | 0.05 | 0.9005 | 8.5690 | 0.0500 | 0.0549 | 0.1567 | 0.9599 |
| No log | 2.96 | 9 | 2.2614 | 0.095 | 0.8924 | 6.9011 | 0.095 | 0.0853 | 0.1807 | 0.9128 |
| No log | 3.96 | 12 | 2.2062 | 0.255 | 0.8804 | 5.5442 | 0.255 | 0.1609 | 0.2738 | 0.7469 |
| No log | 4.96 | 15 | 2.1348 | 0.385 | 0.8636 | 4.0613 | 0.3850 | 0.2330 | 0.3605 | 0.4157 |
| No log | 5.96 | 18 | 2.0473 | 0.48 | 0.8410 | 2.5353 | 0.48 | 0.3152 | 0.4376 | 0.2329 |
| No log | 6.96 | 21 | 1.9483 | 0.64 | 0.8128 | 2.0469 | 0.64 | 0.5131 | 0.5355 | 0.1314 |
| No log | 7.96 | 24 | 1.8371 | 0.735 | 0.7783 | 1.7309 | 0.735 | 0.6333 | 0.5897 | 0.0802 |
| No log | 8.96 | 27 | 1.7227 | 0.775 | 0.7393 | 1.3371 | 0.775 | 0.6937 | 0.6049 | 0.0560 |
| No log | 9.96 | 30 | 1.6124 | 0.805 | 0.6978 | 1.1320 | 0.805 | 0.7319 | 0.5981 | 0.0462 |
| No log | 10.96 | 33 | 1.4990 | 0.82 | 0.6518 | 0.9973 | 0.82 | 0.7658 | 0.5882 | 0.0444 |
| No log | 11.96 | 36 | 1.3922 | 0.855 | 0.6064 | 0.8830 | 0.855 | 0.8127 | 0.5823 | 0.0397 |
| No log | 12.96 | 39 | 1.2985 | 0.865 | 0.5653 | 0.8957 | 0.865 | 0.8350 | 0.5604 | 0.0365 |
| No log | 13.96 | 42 | 1.2141 | 0.89 | 0.5271 | 0.6892 | 0.89 | 0.8733 | 0.5564 | 0.0331 |
| No log | 14.96 | 45 | 1.1402 | 0.895 | 0.4926 | 0.6695 | 0.895 | 0.8803 | 0.5341 | 0.0321 |
| No log | 15.96 | 48 | 1.0699 | 0.91 | 0.4596 | 0.6407 | 0.91 | 0.8999 | 0.5185 | 0.0285 |
| No log | 16.96 | 51 | 1.0037 | 0.91 | 0.4282 | 0.6163 | 0.91 | 0.8979 | 0.4831 | 0.0270 |
| No log | 17.96 | 54 | 0.9457 | 0.915 | 0.4004 | 0.6126 | 0.915 | 0.9011 | 0.4618 | 0.0247 |
| No log | 18.96 | 57 | 0.8914 | 0.915 | 0.3742 | 0.6066 | 0.915 | 0.9011 | 0.4426 | 0.0242 |
| No log | 19.96 | 60 | 0.8405 | 0.92 | 0.3495 | 0.5898 | 0.92 | 0.9102 | 0.4314 | 0.0216 |
| No log | 20.96 | 63 | 0.7995 | 0.915 | 0.3291 | 0.5934 | 0.915 | 0.9049 | 0.4033 | 0.0204 |
| No log | 21.96 | 66 | 0.7583 | 0.915 | 0.3089 | 0.5883 | 0.915 | 0.9049 | 0.3818 | 0.0206 |
| No log | 22.96 | 69 | 0.7228 | 0.915 | 0.2915 | 0.5835 | 0.915 | 0.9049 | 0.3707 | 0.0199 |
| No log | 23.96 | 72 | 0.6889 | 0.925 | 0.2747 | 0.5703 | 0.925 | 0.9169 | 0.3649 | 0.0191 |
| No log | 24.96 | 75 | 0.6624 | 0.925 | 0.2614 | 0.5769 | 0.925 | 0.9200 | 0.3375 | 0.0190 |
| No log | 25.96 | 78 | 0.6373 | 0.925 | 0.2491 | 0.5764 | 0.925 | 0.9218 | 0.3206 | 0.0191 |
| No log | 26.96 | 81 | 0.6106 | 0.93 | 0.2363 | 0.5570 | 0.93 | 0.9251 | 0.3276 | 0.0186 |
| No log | 27.96 | 84 | 0.5945 | 0.93 | 0.2281 | 0.5721 | 0.93 | 0.9251 | 0.3201 | 0.0187 |
| No log | 28.96 | 87 | 0.5780 | 0.92 | 0.2206 | 0.5668 | 0.92 | 0.9190 | 0.3008 | 0.0200 |
| No log | 29.96 | 90 | 0.5613 | 0.925 | 0.2125 | 0.5709 | 0.925 | 0.9218 | 0.2961 | 0.0191 |
| No log | 30.96 | 93 | 0.5456 | 0.925 | 0.2051 | 0.6155 | 0.925 | 0.9175 | 0.2764 | 0.0182 |
| No log | 31.96 | 96 | 0.5354 | 0.91 | 0.2008 | 0.6139 | 0.91 | 0.9104 | 0.2600 | 0.0187 |
| No log | 32.96 | 99 | 0.5248 | 0.91 | 0.1961 | 0.6078 | 0.91 | 0.9104 | 0.2610 | 0.0194 |
| No log | 33.96 | 102 | 0.5151 | 0.91 | 0.1915 | 0.6158 | 0.91 | 0.9084 | 0.2529 | 0.0186 |
| No log | 34.96 | 105 | 0.5066 | 0.91 | 0.1880 | 0.6121 | 0.91 | 0.9084 | 0.2409 | 0.0186 |
| No log | 35.96 | 108 | 0.4986 | 0.91 | 0.1846 | 0.6070 | 0.91 | 0.9084 | 0.2429 | 0.0186 |
| No log | 36.96 | 111 | 0.4920 | 0.91 | 0.1817 | 0.6208 | 0.91 | 0.9084 | 0.2380 | 0.0187 |
| No log | 37.96 | 114 | 0.4858 | 0.91 | 0.1793 | 0.6081 | 0.91 | 0.9084 | 0.2319 | 0.0185 |
| No log | 38.96 | 117 | 0.4792 | 0.91 | 0.1766 | 0.6044 | 0.91 | 0.9084 | 0.2276 | 0.0184 |
| No log | 39.96 | 120 | 0.4753 | 0.91 | 0.1749 | 0.6671 | 0.91 | 0.9084 | 0.2245 | 0.0185 |
| No log | 40.96 | 123 | 0.4704 | 0.905 | 0.1731 | 0.6137 | 0.905 | 0.9056 | 0.2321 | 0.0186 |
| No log | 41.96 | 126 | 0.4656 | 0.91 | 0.1714 | 0.6028 | 0.91 | 0.9084 | 0.2259 | 0.0187 |
| No log | 42.96 | 129 | 0.4624 | 0.91 | 0.1703 | 0.6048 | 0.91 | 0.9084 | 0.2080 | 0.0189 |
| No log | 43.96 | 132 | 0.4604 | 0.905 | 0.1695 | 0.6674 | 0.905 | 0.9056 | 0.2167 | 0.0187 |
| No log | 44.96 | 135 | 0.4553 | 0.905 | 0.1678 | 0.6190 | 0.905 | 0.9056 | 0.2130 | 0.0185 |
| No log | 45.96 | 138 | 0.4512 | 0.905 | 0.1663 | 0.6002 | 0.905 | 0.9056 | 0.2182 | 0.0186 |
| No log | 46.96 | 141 | 0.4513 | 0.905 | 0.1665 | 0.6681 | 0.905 | 0.9056 | 0.1902 | 0.0185 |
| No log | 47.96 | 144 | 0.4480 | 0.905 | 0.1656 | 0.6661 | 0.905 | 0.9056 | 0.1900 | 0.0186 |
| No log | 48.96 | 147 | 0.4451 | 0.905 | 0.1647 | 0.6085 | 0.905 | 0.9056 | 0.1969 | 0.0185 |
| No log | 49.96 | 150 | 0.4429 | 0.905 | 0.1638 | 0.6729 | 0.905 | 0.9056 | 0.1954 | 0.0186 |
| No log | 50.96 | 153 | 0.4416 | 0.905 | 0.1637 | 0.7300 | 0.905 | 0.9056 | 0.1730 | 0.0188 |
| No log | 51.96 | 156 | 0.4390 | 0.905 | 0.1627 | 0.6832 | 0.905 | 0.9056 | 0.1881 | 0.0187 |
| No log | 52.96 | 159 | 0.4377 | 0.905 | 0.1625 | 0.6708 | 0.905 | 0.9056 | 0.1724 | 0.0187 |
| No log | 53.96 | 162 | 0.4360 | 0.905 | 0.1620 | 0.7300 | 0.905 | 0.9056 | 0.1714 | 0.0189 |
| No log | 54.96 | 165 | 0.4338 | 0.905 | 0.1613 | 0.6734 | 0.905 | 0.9056 | 0.1923 | 0.0190 |
| No log | 55.96 | 168 | 0.4321 | 0.905 | 0.1609 | 0.6635 | 0.905 | 0.9056 | 0.1846 | 0.0189 |
| No log | 56.96 | 171 | 0.4326 | 0.905 | 0.1614 | 0.6722 | 0.905 | 0.9056 | 0.1851 | 0.0190 |
| No log | 57.96 | 174 | 0.4322 | 0.905 | 0.1613 | 0.7871 | 0.905 | 0.9056 | 0.1850 | 0.0191 |
| No log | 58.96 | 177 | 0.4286 | 0.905 | 0.1600 | 0.6660 | 0.905 | 0.9056 | 0.1733 | 0.0190 |
| No log | 59.96 | 180 | 0.4267 | 0.905 | 0.1596 | 0.6581 | 0.905 | 0.9056 | 0.1720 | 0.0190 |
| No log | 60.96 | 183 | 0.4277 | 0.905 | 0.1601 | 0.7252 | 0.905 | 0.9056 | 0.1772 | 0.0189 |
| No log | 61.96 | 186 | 0.4274 | 0.905 | 0.1601 | 0.7841 | 0.905 | 0.9056 | 0.1866 | 0.0192 |
| No log | 62.96 | 189 | 0.4264 | 0.905 | 0.1598 | 0.7830 | 0.905 | 0.9056 | 0.1669 | 0.0191 |
| No log | 63.96 | 192 | 0.4246 | 0.905 | 0.1595 | 0.7188 | 0.905 | 0.9056 | 0.1671 | 0.0191 |
| No log | 64.96 | 195 | 0.4236 | 0.905 | 0.1592 | 0.7170 | 0.905 | 0.9056 | 0.1762 | 0.0193 |
| No log | 65.96 | 198 | 0.4238 | 0.905 | 0.1594 | 0.7235 | 0.905 | 0.9056 | 0.1757 | 0.0192 |
| No log | 66.96 | 201 | 0.4227 | 0.905 | 0.1591 | 0.7218 | 0.905 | 0.9056 | 0.1724 | 0.0192 |
| No log | 67.96 | 204 | 0.4220 | 0.905 | 0.1590 | 0.7195 | 0.905 | 0.9056 | 0.1715 | 0.0191 |
| No log | 68.96 | 207 | 0.4214 | 0.905 | 0.1589 | 0.7201 | 0.905 | 0.9056 | 0.1708 | 0.0191 |
| No log | 69.96 | 210 | 0.4210 | 0.905 | 0.1588 | 0.7210 | 0.905 | 0.9056 | 0.1703 | 0.0193 |
| No log | 70.96 | 213 | 0.4211 | 0.905 | 0.1590 | 0.7226 | 0.905 | 0.9056 | 0.1697 | 0.0193 |
| No log | 71.96 | 216 | 0.4201 | 0.905 | 0.1587 | 0.7165 | 0.905 | 0.9056 | 0.1785 | 0.0193 |
| No log | 72.96 | 219 | 0.4194 | 0.905 | 0.1587 | 0.7145 | 0.905 | 0.9056 | 0.1780 | 0.0194 |
| No log | 73.96 | 222 | 0.4194 | 0.905 | 0.1587 | 0.7189 | 0.905 | 0.9056 | 0.1777 | 0.0194 |
| No log | 74.96 | 225 | 0.4192 | 0.905 | 0.1587 | 0.7193 | 0.905 | 0.9056 | 0.1770 | 0.0194 |
| No log | 75.96 | 228 | 0.4188 | 0.905 | 0.1586 | 0.7186 | 0.905 | 0.9056 | 0.1764 | 0.0192 |
| No log | 76.96 | 231 | 0.4180 | 0.905 | 0.1585 | 0.7148 | 0.905 | 0.9056 | 0.1786 | 0.0192 |
| No log | 77.96 | 234 | 0.4174 | 0.905 | 0.1584 | 0.7121 | 0.905 | 0.9056 | 0.1746 | 0.0193 |
| No log | 78.96 | 237 | 0.4178 | 0.905 | 0.1585 | 0.7159 | 0.905 | 0.9056 | 0.1720 | 0.0195 |
| No log | 79.96 | 240 | 0.4177 | 0.905 | 0.1586 | 0.7161 | 0.905 | 0.9056 | 0.1627 | 0.0195 |
| No log | 80.96 | 243 | 0.4173 | 0.905 | 0.1585 | 0.7147 | 0.905 | 0.9056 | 0.1627 | 0.0195 |
| No log | 81.96 | 246 | 0.4171 | 0.905 | 0.1585 | 0.7159 | 0.905 | 0.9056 | 0.1650 | 0.0195 |
| No log | 82.96 | 249 | 0.4162 | 0.905 | 0.1582 | 0.7135 | 0.905 | 0.9056 | 0.1742 | 0.0194 |
| No log | 83.96 | 252 | 0.4163 | 0.905 | 0.1584 | 0.7138 | 0.905 | 0.9056 | 0.1522 | 0.0196 |
| No log | 84.96 | 255 | 0.4161 | 0.905 | 0.1583 | 0.7136 | 0.905 | 0.9056 | 0.1616 | 0.0195 |
| No log | 85.96 | 258 | 0.4163 | 0.905 | 0.1585 | 0.7143 | 0.905 | 0.9056 | 0.1615 | 0.0196 |
| No log | 86.96 | 261 | 0.4161 | 0.905 | 0.1585 | 0.7132 | 0.905 | 0.9056 | 0.1614 | 0.0195 |
| No log | 87.96 | 264 | 0.4159 | 0.905 | 0.1584 | 0.7133 | 0.905 | 0.9056 | 0.1514 | 0.0195 |
| No log | 88.96 | 267 | 0.4157 | 0.905 | 0.1584 | 0.7132 | 0.905 | 0.9056 | 0.1513 | 0.0195 |
| No log | 89.96 | 270 | 0.4156 | 0.905 | 0.1584 | 0.7134 | 0.905 | 0.9056 | 0.1511 | 0.0195 |
| No log | 90.96 | 273 | 0.4153 | 0.905 | 0.1583 | 0.7124 | 0.905 | 0.9056 | 0.1605 | 0.0195 |
| No log | 91.96 | 276 | 0.4153 | 0.905 | 0.1584 | 0.7121 | 0.905 | 0.9056 | 0.1604 | 0.0195 |
| No log | 92.96 | 279 | 0.4154 | 0.905 | 0.1584 | 0.7127 | 0.905 | 0.9056 | 0.1603 | 0.0195 |
| No log | 93.96 | 282 | 0.4154 | 0.905 | 0.1585 | 0.7131 | 0.905 | 0.9056 | 0.1603 | 0.0195 |
| No log | 94.96 | 285 | 0.4154 | 0.905 | 0.1585 | 0.7132 | 0.905 | 0.9056 | 0.1603 | 0.0195 |
| No log | 95.96 | 288 | 0.4154 | 0.905 | 0.1585 | 0.7135 | 0.905 | 0.9056 | 0.1603 | 0.0196 |
| No log | 96.96 | 291 | 0.4153 | 0.905 | 0.1585 | 0.7133 | 0.905 | 0.9056 | 0.1602 | 0.0195 |
| No log | 97.96 | 294 | 0.4152 | 0.905 | 0.1584 | 0.7132 | 0.905 | 0.9056 | 0.1601 | 0.0196 |
| No log | 98.96 | 297 | 0.4152 | 0.905 | 0.1584 | 0.7130 | 0.905 | 0.9056 | 0.1601 | 0.0196 |
| No log | 99.96 | 300 | 0.4152 | 0.905 | 0.1584 | 0.7130 | 0.905 | 0.9056 | 0.1601 | 0.0196 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
xian79/poca-SoccerTwos
|
xian79
| 2023-07-14T09:29:04Z | 105 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-12T14:44:00Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: xian79/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Lazycuber/Janemalion-6B
|
Lazycuber
| 2023-07-14T09:26:25Z | 1,479 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-20T12:43:53Z |
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- conversational
---
This is a model mix of Pyg V8 + GPT-J-6B-Janeway 60/40
Mainly use for SFW and NSFW roleplay.
It kinda... works?
|
dead-owwl/falcon7b-ft-gpt
|
dead-owwl
| 2023-07-14T09:25:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-14T09:06:21Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
NasimB/gpt2-concat-guten-rarity-all-end-2p5k
|
NasimB
| 2023-07-14T09:19:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-14T07:35:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-guten-rarity-all-end-2p5k
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. -->
# gpt2-concat-guten-rarity-all-end-2p5k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3145
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7001 | 0.29 | 500 | 5.6295 |
| 5.338 | 0.59 | 1000 | 5.1950 |
| 4.9888 | 0.88 | 1500 | 4.9517 |
| 4.7126 | 1.17 | 2000 | 4.8011 |
| 4.5624 | 1.47 | 2500 | 4.6851 |
| 4.4509 | 1.76 | 3000 | 4.5701 |
| 4.3163 | 2.05 | 3500 | 4.4938 |
| 4.1282 | 2.35 | 4000 | 4.4485 |
| 4.1062 | 2.64 | 4500 | 4.3915 |
| 4.0665 | 2.93 | 5000 | 4.3373 |
| 3.8497 | 3.23 | 5500 | 4.3328 |
| 3.7992 | 3.52 | 6000 | 4.3061 |
| 3.783 | 3.81 | 6500 | 4.2676 |
| 3.6751 | 4.11 | 7000 | 4.2689 |
| 3.5191 | 4.4 | 7500 | 4.2617 |
| 3.5079 | 4.69 | 8000 | 4.2467 |
| 3.4984 | 4.99 | 8500 | 4.2327 |
| 3.3306 | 5.28 | 9000 | 4.2471 |
| 3.3203 | 5.57 | 9500 | 4.2451 |
| 3.3192 | 5.87 | 10000 | 4.2444 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
keehun/textual_inversion_slime_up
|
keehun
| 2023-07-14T09:03:48Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-14T07:32:24Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - keehun/textual_inversion_slime_up
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
keehun/textual_inversion_slime_noup
|
keehun
| 2023-07-14T09:02:48Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-14T07:31:06Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - keehun/textual_inversion_slime_noup
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
Saed2023/layoutlmv3-finetuned-UsingAlgoDataset_427Images
|
Saed2023
| 2023-07-14T09:01:59Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-14T08:51:28Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-UsingAlgoDataset_427Images
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. -->
# layoutlmv3-finetuned-UsingAlgoDataset_427Images
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0022
- Precision: 0.9892
- Recall: 0.9880
- F1: 0.9886
- Accuracy: 0.9997
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.62 | 50 | 0.0349 | 0.7521 | 0.6300 | 0.6857 | 0.9926 |
| No log | 1.25 | 100 | 0.0080 | 0.9538 | 0.9405 | 0.9471 | 0.9985 |
| No log | 1.88 | 150 | 0.0044 | 0.9750 | 0.9723 | 0.9736 | 0.9992 |
| No log | 2.5 | 200 | 0.0032 | 0.9834 | 0.9827 | 0.9831 | 0.9995 |
| No log | 3.12 | 250 | 0.0037 | 0.9710 | 0.9784 | 0.9747 | 0.9992 |
| No log | 3.75 | 300 | 0.0026 | 0.9861 | 0.9852 | 0.9857 | 0.9996 |
| No log | 4.38 | 350 | 0.0023 | 0.9880 | 0.9871 | 0.9875 | 0.9996 |
| No log | 5.0 | 400 | 0.0022 | 0.9883 | 0.9871 | 0.9877 | 0.9997 |
| No log | 5.62 | 450 | 0.0022 | 0.9892 | 0.9880 | 0.9886 | 0.9997 |
| 0.029 | 6.25 | 500 | 0.0022 | 0.9892 | 0.9880 | 0.9886 | 0.9997 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ccattomio/Reinforce-Pixelcopter-PLE-v0
|
ccattomio
| 2023-07-14T08:50:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T08:50:52Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 53.80 +/- 52.76
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ronig/protein_biencoder
|
ronig
| 2023-07-14T08:43:35Z | 180 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"custom_code",
"en",
"dataset:ronig/protein_binding_sequences",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-06-23T20:31:43Z |
---
language: en
license: mit
datasets:
- ronig/protein_binding_sequences
---
## Peptriever: A Bi-Encoder for large-scale protein-peptide binding search
For training details see our [Application Note](https://www.biorxiv.org/content/10.1101/2023.07.13.548811v1).
Training code can be found in our [Github repo](https://github.com/RoniGurvich/Peptriever).
A live demo is available on our [application page](https://peptriever.app)
## Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ronig/protein_biencoder")
model = AutoModel.from_pretrained("ronig/protein_biencoder", trust_remote_code=True)
model.eval()
peptide_sequence = "AAA"
protein_sequence = "MMM"
encoded_peptide = tokenizer.encode_plus(peptide_sequence, return_tensors='pt')
encoded_protein = tokenizer.encode_plus(protein_sequence, return_tensors='pt')
with torch.no_grad():
peptide_output = model.forward1(encoded_peptide)
protein_output = model.forward2(encoded_protein)
print("distance: ", torch.norm(peptide_output - protein_output, p=2))
```
## Version
Model checkpint: `peptriever_2023-06-23T16:07:24.508460`
|
Winmodel/rl_course_vizdoom_health_gathering_supreme
|
Winmodel
| 2023-07-14T08:41:22Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T08:41:04Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.15 +/- 4.35
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Winmodel/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
SachinKaushik/open_llama_7b_tuned_model
|
SachinKaushik
| 2023-07-14T08:37:39Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-07-14T05:34:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: open_llama_7b_tuned_model
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. -->
# open_llama_7b_tuned_model
This model is a fine-tuned version of [openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b) 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: 0.0002
- 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_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
tuanhnh/Taxi-v3
|
tuanhnh
| 2023-07-14T08:29:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T08:29:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tuanhnh/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
YanJiangJerry/sentiment-roberta-latest-large-e6-b16
|
YanJiangJerry
| 2023-07-14T08:27:00Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T08:20:43Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-latest-large-e6-b16
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. -->
# sentiment-roberta-latest-large-e6-b16
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8762
- F1: 0.7412
- Recall: 0.7412
- Precision: 0.7412
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.8620 | 0.7412 | 0.7412 | 0.7412 |
| 0.447 | 2.0 | 750 | 1.0118 | 0.7574 | 0.7574 | 0.7574 |
| 0.1491 | 3.0 | 1125 | 1.4604 | 0.7520 | 0.7520 | 0.7520 |
| 0.072 | 4.0 | 1500 | 1.7418 | 0.7412 | 0.7412 | 0.7412 |
| 0.072 | 5.0 | 1875 | 1.8522 | 0.7412 | 0.7412 | 0.7412 |
| 0.0228 | 6.0 | 2250 | 1.8762 | 0.7412 | 0.7412 | 0.7412 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jwchung/bert-finetuned-squad
|
jwchung
| 2023-07-14T08:24:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-14T06:05:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-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: 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Diandiandian/path-to-save-model_v1-5
|
Diandiandian
| 2023-07-14T08:17:43Z | 31 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-14T07:46:44Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - Diandiandian/path-to-save-model_v1-5
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
YanJiangJerry/sentiment-roberta-large-e12-b16
|
YanJiangJerry
| 2023-07-14T08:17:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-14T07:49:24Z |
---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-large-e12-b16
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. -->
# sentiment-roberta-large-e12-b16
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8728
- F1: 0.7682
- Recall: 0.7682
- Precision: 0.7682
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.7403 | 0.7278 | 0.7278 | 0.7278 |
| 0.7133 | 2.0 | 750 | 0.7236 | 0.7709 | 0.7709 | 0.7709 |
| 0.3747 | 3.0 | 1125 | 0.9717 | 0.7601 | 0.7601 | 0.7601 |
| 0.2548 | 4.0 | 1500 | 1.1395 | 0.7628 | 0.7628 | 0.7628 |
| 0.2548 | 5.0 | 1875 | 1.3756 | 0.7709 | 0.7709 | 0.7709 |
| 0.1466 | 6.0 | 2250 | 1.5808 | 0.7790 | 0.7790 | 0.7790 |
| 0.1039 | 7.0 | 2625 | 1.7365 | 0.7412 | 0.7412 | 0.7412 |
| 0.0639 | 8.0 | 3000 | 1.8293 | 0.7547 | 0.7547 | 0.7547 |
| 0.0639 | 9.0 | 3375 | 1.8055 | 0.7628 | 0.7628 | 0.7628 |
| 0.0452 | 10.0 | 3750 | 1.7496 | 0.7682 | 0.7682 | 0.7682 |
| 0.0262 | 11.0 | 4125 | 1.8558 | 0.7655 | 0.7655 | 0.7655 |
| 0.0183 | 12.0 | 4500 | 1.8728 | 0.7682 | 0.7682 | 0.7682 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
tuanhnh/q-FrozenLake-v1-4x4-noSlippery
|
tuanhnh
| 2023-07-14T08:11:58Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T08:11:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tuanhnh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
edwardjjj/Taxi-v3
|
edwardjjj
| 2023-07-14T07:57:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-14T06:33:35Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="edwardjjj/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
sail-rvc/testingjul_e25
|
sail-rvc
| 2023-07-14T07:50:14Z | 1 | 0 |
transformers
|
[
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] |
audio-to-audio
| 2023-07-14T07:45:09Z |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# testingjul_e25
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:50:14
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
tahawarsi360/videomae-base-finetuned-cricket-classification
|
tahawarsi360
| 2023-07-14T07:50:03Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-07-14T05:13:30Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-cricket-classification
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. -->
# videomae-base-finetuned-cricket-classification
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3123
- Accuracy: 0.9452
## 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: 1
- eval_batch_size: 1
- 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: 288
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7699 | 0.25 | 72 | 0.5352 | 0.8493 |
| 0.127 | 1.25 | 144 | 0.5993 | 0.8356 |
| 0.0019 | 2.25 | 216 | 0.2100 | 0.9589 |
| 0.0007 | 3.25 | 288 | 0.3123 | 0.9452 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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