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Gummybear05/wav2vec2-large-xls-r-300m-korean_speed
|
Gummybear05
| 2023-12-14T15:06:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-14T12:47:43Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean_speed
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-korean_speed
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3672
- Cer: 98.3846
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 34.1959 | 0.13 | 200 | 5.0888 | 100.0 |
| 5.0127 | 0.26 | 400 | 4.8530 | 100.0 |
| 4.8574 | 0.39 | 600 | 4.8649 | 100.0 |
| 4.8596 | 0.52 | 800 | 4.6823 | 100.0 |
| 4.7742 | 0.64 | 1000 | 4.9200 | 100.0 |
| 4.7557 | 0.77 | 1200 | 4.6647 | 100.0 |
| 4.7367 | 0.9 | 1400 | 4.6535 | 100.0 |
| 4.7075 | 1.03 | 1600 | 4.6910 | 100.0 |
| 4.6994 | 1.16 | 1800 | 4.7617 | 100.0 |
| 4.6886 | 1.29 | 2000 | 4.7125 | 100.0 |
| 4.6495 | 1.42 | 2200 | 4.7181 | 100.0 |
| 4.651 | 1.55 | 2400 | 4.7701 | 100.0 |
| 4.5945 | 1.68 | 2600 | 4.7640 | 100.0 |
| 4.5589 | 1.81 | 2800 | 5.2212 | 100.0 |
| 4.5125 | 1.93 | 3000 | 4.6738 | 99.9178 |
| 4.4526 | 2.06 | 3200 | 4.6993 | 99.8649 |
| 4.3971 | 2.19 | 3400 | 4.6336 | 99.7063 |
| 4.3061 | 2.32 | 3600 | 4.6569 | 99.2775 |
| 4.2643 | 2.45 | 3800 | 4.5247 | 99.4243 |
| 4.1763 | 2.58 | 4000 | 4.5722 | 99.1189 |
| 4.106 | 2.71 | 4200 | 4.4703 | 99.0778 |
| 4.0419 | 2.84 | 4400 | 4.4161 | 97.9617 |
| 4.0283 | 2.97 | 4600 | 4.3672 | 98.3846 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
lepifort/apored
|
lepifort
| 2023-12-14T12:50:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T12:49:42Z |
---
license: openrail
---
|
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bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-20
|
bio-datasets
| 2023-12-14T12:49:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T12:49:47Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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] |
Prosoft82/Ahkam
|
Prosoft82
| 2023-12-14T12:50:29Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T12:50:29Z |
---
license: mit
---
|
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sahilmohsin111/bart-base-finetuned-BBC-SM
|
sahilmohsin111
| 2023-12-14T12:51:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T12:51:26Z |
Entry not found
|
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alitolga/electra-base-generator-peft
|
alitolga
| 2023-12-17T21:21:29Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:google/electra-base-generator",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T12:53:38Z |
---
license: apache-2.0
base_model: google/electra-base-generator
tags:
- generated_from_trainer
model-index:
- name: electra-base-generator-peft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-generator-peft
This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0679
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.934 | 1.0 | 258 | 3.3565 |
| 3.2271 | 2.0 | 516 | 3.1055 |
| 3.1653 | 3.0 | 774 | 3.0679 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
dipkumar007/dip_v1
|
dipkumar007
| 2023-12-14T12:59:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T12:54:20Z |
---
license: openrail
---
|
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] |
louistichelman/opus-mt-en-ro-finetuned-en-to-ro
|
louistichelman
| 2023-12-14T16:52:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"base_model:Helsinki-NLP/opus-mt-en-ro",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-14T12:55:54Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ro
tags:
- generated_from_trainer
datasets:
- wmt16
model-index:
- name: opus-mt-en-ro-finetuned-en-to-ro
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 188 | 1.3874 | 28.0995 | 34.094 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.1+cpu
- Datasets 2.15.0
- Tokenizers 0.14.1
|
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] |
TinyPixel/pythia-lm
|
TinyPixel
| 2023-12-14T12:59:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T12:57:48Z |
Entry not found
|
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] |
alitolga/bart-base-peft
|
alitolga
| 2023-12-17T21:25:06Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T13:00:20Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-base-peft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart-base-peft
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7796
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.2266 | 1.0 | 389 | 5.8530 |
| 5.4129 | 2.0 | 778 | 4.9682 |
| 5.152 | 3.0 | 1167 | 4.7796 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
kanishka/smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-3
|
kanishka
| 2023-12-14T17:33:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:02:16Z |
Entry not found
|
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LarryAIDraw/keqing_genshin
|
LarryAIDraw
| 2023-12-14T13:16:08Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:02:18Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/130952/keqing-genshin-impact
|
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LarryAIDraw/mangoKeqing-v2_0
|
LarryAIDraw
| 2023-12-14T13:16:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:02:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/11990/keqing
|
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multimodalart/test-polizao
|
multimodalart
| 2023-12-14T13:03:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:03:05Z |
Entry not found
|
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] |
LarryAIDraw/misaki_V1
|
LarryAIDraw
| 2023-12-14T13:16:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:03:16Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/28655/shokuhou-misaki
|
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Weyaxi/metadata-error-test
|
Weyaxi
| 2023-12-17T11:41:47Z | 0 | 0 | null |
[
"generated_from_trainer",
"text-generation",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:mistralai/Mistral-7B-v0.1",
"license:mit",
"model-index",
"region:us"
] |
text-generation
| 2023-12-14T13:03:25Z |
---
tags:
- generated_from_trainer
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: mistralai/Mistral-7B-v0.1
widget:
- text: "<|system|>\nYou are a pirate chatbot who always responds with Arr!</s>\n<|user|>\nThere's a llama on my lawn, how can I get rid of him?</s>\n<|assistant|>\n"
output:
text: "Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr!"
pipeline_tag: text-generation
model-index:
- name: zephyr-7b-beta
results:
# AI2 Reasoning Challenge (25-Shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
name: normalized accuracy
value: 62.03071672354948
# HellaSwag (10-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
name: normalized accuracy
value: 84.35570603465445
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# DROP (3-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: Drop (3-Shot)
type: drop
split: validation
args:
num_few_shot: 3
metrics:
- type: f1
name: f1 score
value: 9.662437080536909
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# TruthfulQA (0-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.44916942762855
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# GSM8k (5-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 12.736921910538287
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# MMLU (5-Shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 61.07
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# Winogrande (5-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 77.74269928966061
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# AlpacaEval (taken from model card)
- task:
type: text-generation
name: Text Generation
dataset:
name: AlpacaEval
type: tatsu-lab/alpaca_eval
metrics:
- type: unknown
name: win rate
value: 0.9060
source:
url: https://tatsu-lab.github.io/alpaca_eval/
# MT-Bench (taken from model card)
- task:
type: text-generation
name: Text Generation
dataset:
name: MT-Bench
type: unknown
metrics:
- type: unknown
name: score
value: 7.34
source:
url: https://huggingface.co/spaces/lmsys/mt-bench
---
# Readme copied expection test
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 52.15 |
| ARC (25-shot) | 62.03 |
| HellaSwag (10-shot) | 84.36 |
| MMLU (5-shot) | 61.07 |
| TruthfulQA (0-shot) | 57.45 |
| Winogrande (5-shot) | 77.74 |
| GSM8K (5-shot) | 12.74 |
| DROP (3-shot) | 9.66 |
|
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] |
LarryAIDraw/misaki_shokuhou_s3-lora-nochekaiser
|
LarryAIDraw
| 2023-12-14T13:16:44Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:03:48Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/99954/misaki-shokuhou-a-certain-scientific-railgun
|
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] |
LarryAIDraw/ajitaniHifumi
|
LarryAIDraw
| 2023-12-14T13:16:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:04:29Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/120993/ajitani-hifumi-blue-archive
|
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] |
LarryAIDraw/BA_AjitaniHifumi_epoch-000003
|
LarryAIDraw
| 2023-12-14T13:17:06Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:04:51Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/7050/bluearchiveajitanihifumi
|
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LarryAIDraw/hifumi1-000005
|
LarryAIDraw
| 2023-12-14T13:17:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:05:22Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/82066/ajitani-hifumi-blue-archive-or-character-lora-1261
|
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LarryAIDraw/blue_archive_hifumi-v1
|
LarryAIDraw
| 2023-12-14T13:17:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:05:45Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/61307/ajitani-hifumi-blue-archive
|
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LarryAIDraw/hifumiswim
|
LarryAIDraw
| 2023-12-14T13:17:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:06:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/136509/costumeblue-archive-ajitani-hifumis-bikini-swimsuit
|
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LarryAIDraw/bagpipe_t_v1
|
LarryAIDraw
| 2023-12-14T13:18:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:06:28Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/20128/bagpipearknights
|
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] |
LarryAIDraw/Bagpipe_ark_
|
LarryAIDraw
| 2023-12-14T13:18:17Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:07:00Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/177126/bagpipe-arknights
|
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LarryAIDraw/BagpipeAOM2H
|
LarryAIDraw
| 2023-12-14T13:18:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:07:26Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/16047/arknights-bagpipe
|
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LarryAIDraw/blanc-nikke-richy-v1
|
LarryAIDraw
| 2023-12-14T13:19:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:07:58Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/162841/blanc-nikke-lora
|
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] |
LarryAIDraw/blanc_nikke
|
LarryAIDraw
| 2023-12-14T13:19:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:08:26Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/220545/blanc-nikke-goddess-of-victory-or-goofy-ai
|
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iarbel/llama-1.3b-sft_new_v0
|
iarbel
| 2023-12-14T13:08:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:08:26Z |
Entry not found
|
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] |
Dexter-Juan/DDJ
|
Dexter-Juan
| 2023-12-14T13:08:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:08:55Z |
Entry not found
|
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reciprocate/stablelm-3b-dpo-full
|
reciprocate
| 2023-12-14T13:12:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:12:19Z |
Entry not found
|
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Hengky123123/Dea.Aulia
|
Hengky123123
| 2023-12-14T13:18:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:12:59Z |
Entry not found
|
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bnurpek/mgpt-tr-5
|
bnurpek
| 2023-12-14T13:15:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:15:02Z |
Entry not found
|
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alitolga/gpt2-peft
|
alitolga
| 2023-12-17T21:29:55Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"region:us"
] | null | 2023-12-14T13:16:42Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-peft
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-peft
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9468
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4264 | 1.0 | 379 | 2.1343 |
| 2.1396 | 2.0 | 758 | 1.9787 |
| 2.073 | 3.0 | 1137 | 1.9468 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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faustinoaq/nanoGPTshakespeare_char
|
faustinoaq
| 2023-12-14T13:30:45Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-12-14T13:17:22Z |
Entry not found
|
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w11wo/bge-small-en-v1.5-isarcasm
|
w11wo
| 2023-12-14T13:20:21Z | 0 | 0 |
setfit
|
[
"setfit",
"pytorch",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-small-en-v1.5",
"model-index",
"region:us"
] |
text-classification
| 2023-12-14T13:18:05Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: so i am currently stuck in an automatic revolving door .
- text: ah my favorite pastime , watching logan and crying
- text: i have a new instagram account ! go give theollyjackson a follow
- text: guess they are not rich enough to get their precious cars in a garage .
- text: last day in my twenties
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: iSarcasm
type: isarcasm
split: test
metrics:
- type: accuracy
value: 0.6617812852311161
name: Accuracy
- type: f1
value: 0.3951612903225807
name: F1
- type: precision
value: 0.2890855457227139
name: Precision
- type: recall
value: 0.6242038216560509
name: Recall
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for 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.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:--------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| NON_SARCASTIC | <ul><li>'so the newer devices have the ios screenshot i m still on ios but my ipad mini 1 st gen shows the ios screenshot . odd .'</li><li>'why do amazon need a test authorisation when i add a new payment card , as well as the authorisation they get when i actually use it ?'</li><li>'waterboarding sounds like a lot of fun until you find out what it is'</li></ul> |
| SARCASTIC | <ul><li>"have you been reading long ? you are not very good at it . it has nothing to do with who i like , especially since i am not a fan of corbyn anyway . it ' s that in one case someone was literally slapped in the face , and in the other someone wore a milkshake . battery > being annoying"</li><li>'wish one of the many people dressed as killers were actually one n killed me'</li><li>'is it even christmas if there isn t a fight with neighbours and a broken wrist ?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy | F1 | Precision | Recall |
|:--------|:---------|:-------|:----------|:-------|
| **all** | 0.6618 | 0.3952 | 0.2891 | 0.6242 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("w11wo/bge-small-en-v1.5-isarcasm")
# Run inference
preds = model("last day in my twenties")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 19.8489 | 63 |
| Label | Training Sample Count |
|:--------------|:----------------------|
| NON_SARCASTIC | 609 |
| SARCASTIC | 609 |
### Training Hyperparameters
- batch_size: (256, 16)
- num_epochs: (3, 8)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 5e-06)
- head_learning_rate: 0.002
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.2571 | - |
| 0.0172 | 50 | 0.251 | - |
| 0.0344 | 100 | 0.2556 | - |
| 0.0517 | 150 | 0.2513 | - |
| 0.0689 | 200 | 0.2531 | - |
| 0.0861 | 250 | 0.2518 | - |
| 0.1033 | 300 | 0.2553 | - |
| 0.1206 | 350 | 0.2501 | - |
| 0.1378 | 400 | 0.2546 | - |
| 0.1550 | 450 | 0.2506 | - |
| 0.1722 | 500 | 0.2317 | - |
| 0.1895 | 550 | 0.093 | - |
| 0.2067 | 600 | 0.0139 | - |
| 0.2239 | 650 | 0.0166 | - |
| 0.2411 | 700 | 0.0053 | - |
| 0.2584 | 750 | 0.0013 | - |
| 0.2756 | 800 | 0.0121 | - |
| 0.2928 | 850 | 0.0096 | - |
| 0.3100 | 900 | 0.0043 | - |
| 0.3272 | 950 | 0.0014 | - |
| 0.3445 | 1000 | 0.0009 | - |
| 0.3617 | 1050 | 0.0117 | - |
| 0.3789 | 1100 | 0.0144 | - |
| 0.3961 | 1150 | 0.0084 | - |
| 0.4134 | 1200 | 0.0006 | - |
| 0.4306 | 1250 | 0.0005 | - |
| 0.4478 | 1300 | 0.0081 | - |
| 0.4650 | 1350 | 0.0144 | - |
| 0.4823 | 1400 | 0.0045 | - |
| 0.4995 | 1450 | 0.0042 | - |
| 0.5167 | 1500 | 0.0005 | - |
| 0.5339 | 1550 | 0.003 | - |
| 0.5512 | 1600 | 0.0004 | - |
| 0.5684 | 1650 | 0.0005 | - |
| 0.5856 | 1700 | 0.0004 | - |
| 0.6028 | 1750 | 0.0004 | - |
| 0.6200 | 1800 | 0.0026 | - |
| 0.6373 | 1850 | 0.0004 | - |
| 0.6545 | 1900 | 0.0004 | - |
| 0.6717 | 1950 | 0.0003 | - |
| 0.6889 | 2000 | 0.0014 | - |
| 0.7062 | 2050 | 0.0004 | - |
| 0.7234 | 2100 | 0.0003 | - |
| 0.7406 | 2150 | 0.0003 | - |
| 0.7578 | 2200 | 0.0004 | - |
| 0.7751 | 2250 | 0.0003 | - |
| 0.7923 | 2300 | 0.0003 | - |
| 0.8095 | 2350 | 0.0003 | - |
| 0.8267 | 2400 | 0.0003 | - |
| 0.8440 | 2450 | 0.0003 | - |
| 0.8612 | 2500 | 0.0003 | - |
| 0.8784 | 2550 | 0.0003 | - |
| 0.8956 | 2600 | 0.0003 | - |
| 0.9128 | 2650 | 0.0003 | - |
| 0.9301 | 2700 | 0.0003 | - |
| 0.9473 | 2750 | 0.0004 | - |
| 0.9645 | 2800 | 0.0003 | - |
| 0.9817 | 2850 | 0.0003 | - |
| 0.9990 | 2900 | 0.0036 | - |
| 1.0162 | 2950 | 0.0003 | - |
| 1.0334 | 3000 | 0.0003 | - |
| 1.0506 | 3050 | 0.0002 | - |
| 1.0679 | 3100 | 0.0002 | - |
| 1.0851 | 3150 | 0.0002 | - |
| 1.1023 | 3200 | 0.0002 | - |
| 1.1195 | 3250 | 0.0002 | - |
| 1.1368 | 3300 | 0.0003 | - |
| 1.1540 | 3350 | 0.0004 | - |
| 1.1712 | 3400 | 0.0002 | - |
| 1.1884 | 3450 | 0.0002 | - |
| 1.2056 | 3500 | 0.0002 | - |
| 1.2229 | 3550 | 0.0002 | - |
| 1.2401 | 3600 | 0.0002 | - |
| 1.2573 | 3650 | 0.0009 | - |
| 1.2745 | 3700 | 0.0002 | - |
| 1.2918 | 3750 | 0.0002 | - |
| 1.3090 | 3800 | 0.0002 | - |
| 1.3262 | 3850 | 0.0002 | - |
| 1.3434 | 3900 | 0.0002 | - |
| 1.3607 | 3950 | 0.0002 | - |
| 1.3779 | 4000 | 0.0002 | - |
| 1.3951 | 4050 | 0.0002 | - |
| 1.4123 | 4100 | 0.0002 | - |
| 1.4296 | 4150 | 0.0002 | - |
| 1.4468 | 4200 | 0.0003 | - |
| 1.4640 | 4250 | 0.0002 | - |
| 1.4812 | 4300 | 0.0002 | - |
| 1.4984 | 4350 | 0.0002 | - |
| 1.5157 | 4400 | 0.0002 | - |
| 1.5329 | 4450 | 0.0002 | - |
| 1.5501 | 4500 | 0.0002 | - |
| 1.5673 | 4550 | 0.0002 | - |
| 1.5846 | 4600 | 0.0002 | - |
| 1.6018 | 4650 | 0.0002 | - |
| 1.6190 | 4700 | 0.0002 | - |
| 1.6362 | 4750 | 0.0002 | - |
| 1.6535 | 4800 | 0.0002 | - |
| 1.6707 | 4850 | 0.0002 | - |
| 1.6879 | 4900 | 0.0002 | - |
| 1.7051 | 4950 | 0.0002 | - |
| 1.7224 | 5000 | 0.0003 | - |
| 1.7396 | 5050 | 0.0002 | - |
| 1.7568 | 5100 | 0.0002 | - |
| 1.7740 | 5150 | 0.0002 | - |
| 1.7913 | 5200 | 0.0002 | - |
| 1.8085 | 5250 | 0.0002 | - |
| 1.8257 | 5300 | 0.0038 | - |
| 1.8429 | 5350 | 0.0002 | - |
| 1.8601 | 5400 | 0.0002 | - |
| 1.8774 | 5450 | 0.0002 | - |
| 1.8946 | 5500 | 0.0002 | - |
| 1.9118 | 5550 | 0.0002 | - |
| 1.9290 | 5600 | 0.0005 | - |
| 1.9463 | 5650 | 0.0002 | - |
| 1.9635 | 5700 | 0.0002 | - |
| 1.9807 | 5750 | 0.0002 | - |
| 1.9979 | 5800 | 0.0002 | - |
| 2.0152 | 5850 | 0.0001 | - |
| 2.0324 | 5900 | 0.0002 | - |
| 2.0496 | 5950 | 0.0002 | - |
| 2.0668 | 6000 | 0.0002 | - |
| 2.0841 | 6050 | 0.0002 | - |
| 2.1013 | 6100 | 0.0002 | - |
| 2.1185 | 6150 | 0.0002 | - |
| 2.1357 | 6200 | 0.0001 | - |
| 2.1529 | 6250 | 0.0002 | - |
| 2.1702 | 6300 | 0.0002 | - |
| 2.1874 | 6350 | 0.0001 | - |
| 2.2046 | 6400 | 0.0001 | - |
| 2.2218 | 6450 | 0.0001 | - |
| 2.2391 | 6500 | 0.0001 | - |
| 2.2563 | 6550 | 0.0001 | - |
| 2.2735 | 6600 | 0.0001 | - |
| 2.2907 | 6650 | 0.0001 | - |
| 2.3080 | 6700 | 0.0001 | - |
| 2.3252 | 6750 | 0.0001 | - |
| 2.3424 | 6800 | 0.0001 | - |
| 2.3596 | 6850 | 0.0001 | - |
| 2.3769 | 6900 | 0.0001 | - |
| 2.3941 | 6950 | 0.0001 | - |
| 2.4113 | 7000 | 0.0001 | - |
| 2.4285 | 7050 | 0.0001 | - |
| 2.4457 | 7100 | 0.0001 | - |
| 2.4630 | 7150 | 0.0001 | - |
| 2.4802 | 7200 | 0.0001 | - |
| 2.4974 | 7250 | 0.0001 | - |
| 2.5146 | 7300 | 0.0001 | - |
| 2.5319 | 7350 | 0.0001 | - |
| 2.5491 | 7400 | 0.0001 | - |
| 2.5663 | 7450 | 0.0001 | - |
| 2.5835 | 7500 | 0.0001 | - |
| 2.6008 | 7550 | 0.0001 | - |
| 2.6180 | 7600 | 0.0001 | - |
| 2.6352 | 7650 | 0.0001 | - |
| 2.6524 | 7700 | 0.0001 | - |
| 2.6697 | 7750 | 0.0001 | - |
| 2.6869 | 7800 | 0.0001 | - |
| 2.7041 | 7850 | 0.0001 | - |
| 2.7213 | 7900 | 0.0001 | - |
| 2.7385 | 7950 | 0.0001 | - |
| 2.7558 | 8000 | 0.0001 | - |
| 2.7730 | 8050 | 0.0001 | - |
| 2.7902 | 8100 | 0.0001 | - |
| 2.8074 | 8150 | 0.0001 | - |
| 2.8247 | 8200 | 0.0001 | - |
| 2.8419 | 8250 | 0.0001 | - |
| 2.8591 | 8300 | 0.0001 | - |
| 2.8763 | 8350 | 0.0001 | - |
| 2.8936 | 8400 | 0.0001 | - |
| 2.9108 | 8450 | 0.0001 | - |
| 2.9280 | 8500 | 0.0001 | - |
| 2.9452 | 8550 | 0.0001 | - |
| 2.9625 | 8600 | 0.0001 | - |
| 2.9797 | 8650 | 0.0001 | - |
| 2.9969 | 8700 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.32.0
- PyTorch: 2.1.1+cu121
- Datasets: 2.14.5
- Tokenizers: 0.13.3
## Citation
### BibTeX
```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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
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LarryAIDraw/blanc-v2_5-naivae-final-5ep
|
LarryAIDraw
| 2023-12-14T13:20:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T13:20:03Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/66724/characterblanc-nikke
|
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ZKKS/Ziran
|
ZKKS
| 2023-12-14T13:22:19Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2023-12-14T13:22:19Z |
---
license: artistic-2.0
---
|
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AdnanRiaz107/codebertbasePEFTLORA-finetuned-the-stack-bash
|
AdnanRiaz107
| 2023-12-14T17:28:18Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/codebert-base",
"region:us"
] | null | 2023-12-14T13:23:53Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/codebert-base
model-index:
- name: codebertbasePEFTLORA-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. -->
# codebertbasePEFTLORA-finetuned-the-stack-bash
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6253
## 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: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 8.0071 | 0.05 | 500 | 7.3041 |
| 6.7238 | 0.1 | 1000 | 6.7353 |
| 6.0485 | 0.15 | 1500 | 6.4389 |
| 7.5991 | 0.2 | 2000 | 6.1635 |
| 7.0524 | 0.25 | 2500 | 6.0566 |
| 6.4077 | 0.3 | 3000 | 5.9759 |
| 6.2579 | 0.35 | 3500 | 5.9255 |
| 6.2236 | 0.4 | 4000 | 5.9026 |
| 6.6512 | 0.45 | 4500 | 5.7760 |
| 6.0908 | 0.5 | 5000 | 5.7303 |
| 6.3444 | 0.55 | 5500 | 5.6846 |
| 7.097 | 0.6 | 6000 | 5.7699 |
| 5.9796 | 0.65 | 6500 | 5.6752 |
| 5.2663 | 0.7 | 7000 | 5.7492 |
| 6.065 | 0.75 | 7500 | 5.6354 |
| 6.5333 | 0.8 | 8000 | 5.6294 |
| 6.1995 | 0.85 | 8500 | 5.6278 |
| 6.0109 | 0.9 | 9000 | 5.6193 |
| 5.8204 | 0.95 | 9500 | 5.6142 |
| 6.7558 | 1.0 | 10000 | 5.6253 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Shadows231/chatyoutube
|
Shadows231
| 2023-12-14T13:25:19Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T13:25:19Z |
---
license: mit
---
|
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imagepipeline/LCM-LoRA-SDXL
|
imagepipeline
| 2023-12-14T16:02:14Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-14T13:26:31Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## LCM-LoRA-SDXL
<img src="https://f005.backblazeb2.com/b2api/v2/b2_download_file_by_id?fileId=4_zfdf0a8ed59e8666b89b10713_f102e27ef30cbc5ad_d20231202_m035349_c005_v0501009_t0041_u01701489229022" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - For use with LCM Scheduler
[](https://imagepipeline.io/models/LCM-LoRA-SDXL?id=a33fceb5-eb91-4953-81a7-206e7ecd13df)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "sdxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "a33fceb5-eb91-4953-81a7-206e7ecd13df",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
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] |
KhoiC/esm2_t12_35M_UR50D-finetuned-localization
|
KhoiC
| 2023-12-14T13:59:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T13:31:09Z |
Entry not found
|
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] |
FanFierik/Phoenix2.0
|
FanFierik
| 2023-12-14T13:33:46Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T13:31:38Z |
---
license: openrail
---
|
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dhiyagavaeikar/fine_tuned_model_health
|
dhiyagavaeikar
| 2023-12-14T13:47:29Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-14T13:32:18Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
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agusroccohd/thiagogriffo
|
agusroccohd
| 2023-12-14T13:36:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:34:46Z |
Entry not found
|
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] |
LoneStriker/Ana-v1-m7-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-14T13:37:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:35:36Z |
---
license: cc-by-nc-4.0
language:
- en
---
Ana - A model solely focused on the RP / ERP Experience.
<br> - Little to no Censorship *during* Roleplay. May be kinda censored at 0 Conext. Does not affect Roleplays.
<br> - Little to no Positivity Bias *during* Roleplay, from my tests atleast.
<br> - Verbose
<br> - Kinda Smart
<br> - *Slightly* Horny by default.
Purely experimental ----- I do not know what I am doing, this is for fun. A merge + train. Final qLoRA train took 3 hours on a 4090.
Why no 70Bs? Because the 4090 I have currently broke down. falling back to 3060 12gb.. I'm runpod training them for now.
|
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mellaflame/OUTPUT_DIR
|
mellaflame
| 2023-12-14T13:36:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:36:07Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_19
|
ADISH007
| 2023-12-14T13:36:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T13:36:24Z |
Entry not found
|
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hieungo1410/NMT_LaVi
|
hieungo1410
| 2023-12-14T14:57:28Z | 0 | 0 | null |
[
"translation",
"vi",
"arxiv:2112.15272",
"license:afl-3.0",
"region:us"
] |
translation
| 2023-12-14T13:36:58Z |
---
license: afl-3.0
language:
- vi
metrics:
- bleu
pipeline_tag: translation
---
# Mô hình dịch máy Lào - Việt
Ứng dụng dự án MultilingualMT-UET-KC4.0 vào bài toán dịch máy Lào-Việt trong chủ đề bài tập lớn lớp Xử lý ngôn ngữ tự nhiên INT3406E 20.
## Nội dung
- [Cài đặt môi trường, thư viện](#cài-đặt-môi-trường-thư-viện)
- [Chuẩn bị dữ liệu](#bước-1-chuẩn-bị-dữ-liệu)
- [Huấn luyện mô hình](#bước-2-huấn-luyện-mô-hình)
- [Dịch](#bước-3-dịch)
- [Đánh giá chất lượng dựa trên điểm BLEU](#bước-4-đánh-giá-chất-lượng-dựa-trên-điểm-bleu)
- [Thành viên trong nhóm](#thành-viên-trong-nhóm)
## Cài đặt môi trường, thư viện
**Note**:
Lưu ý:
Phiên bản hiện tại chỉ tương thích với python>=3.6
```
git clone https://github.com/UwU-tao/NMT-LaVi.git
cd NMT-LaVi
pip install -r requirements.txt
```
## Bước 1: Chuẩn bị dữ liệu
Ví dụ thực nghiệm dựa trên cặp dữ liệu Lào - Việt với 80k cặp câu.
```bash
cd data/pre_processed
```
Dữ liệu bao gồm câu nguồn (`src`) và câu đích (`tgt`) dữ liệu đã được tách từ:
* `train2023.lo`
* `train2023.vi`
* `dev2023.lo`
* `dev2023.vi`
* `test.lo`
* `test.vi`
| Data set | Sentences | Download |
| :---------: | :--------: | :--------: |
| Training | 75,872 | via GitHub |
| Development | 1,822 | via GitHub |
| Test | 1,000 | via GitHub |
**Note**:
Lưu ý:
- Dữ liệu trước khi đưa vào huấn luyện cần phải được tokenize.
- $CONFIG là đường dẫn tới vị trí chứa file config
Tách dữ liệu dev để tính toán hội tụ trong quá trình huấn luyện, thường không lớn hơn 5k câu.
## Bước 2: Huấn luyện mô hình
Để huấn luyện một mô hình mới **hãy chỉnh sửa file YAML config**:
Cần phải sửa lại file config en_vi.yml chỉnh siêu tham số và đường dẫn tới dữ liệu huấn luyện:
```yaml
# data location and config section
data:
train_data_location: data/pre_processed/train2023
eval_data_location: data/pre_processed/dev2023
src_lang: .lo
trg_lang: .vi
log_file_models: 'model.log'
lowercase: false
build_vocab_kwargs: # additional arguments for build_vocab. See torchtext.vocab.Vocab for mode details
# max_size: 50000
min_freq: 5
# model parameters section
device: cuda
d_model: 512
n_layers: 6
heads: 8
# inference section
eval_batch_size: 8
decode_strategy: BeamSearch
decode_strategy_kwargs:
beam_size: 5 # beam search size
length_normalize: 0.6 # recalculate beam position by length. Currently only work in default BeamSearch
replace_unk: # tuple of layer/head attention to replace unknown words
- 0 # layer
- 0 # head
input_max_length: 400 # input longer than this value will be trimmed in inference. Note that this values are to be used during cached PE, hence, validation set with more than this much tokens will call a warning for the trimming.
max_length: 160 # only perform up to this much timestep during inference
train_max_length: 150 # training samples with this much length in src/trg will be discarded
# optimizer and learning arguments section
lr: 0.2
optimizer: AdaBelief
optimizer_params:
betas:
- 0.9 # beta1
- 0.98 # beta2
eps: !!float 1e-9
n_warmup_steps: 4000
label_smoothing: 0.1
dropout: 0.1
# training config, evaluation, save & load section
batch_size: 64
epochs: 20
printevery: 200
save_checkpoint_epochs: 1
maximum_saved_model_eval: 5
maximum_saved_model_train: 5
```
Sau đó có thể chạy với câu lệnh:
```bash
python -m bin.main train --model Transformer --model_dir $MODEL/en-vi.model --config $CONFIG/en_vi.yml
```
**Note**:
Ở đây:
- $MODEL là dường dẫn tới vị trí lưu mô hình. Sau khi huấn luyện mô hình, thư mục chứa mô hình bao gồm mô hình huyến luyện, file config, file log, vocab.
- $CONFIG là đường dẫn tới vị trí chứa file config
## Bước 3: Dịch
Mô hình dịch dựa trên thuật toán beam search và lưu bản dịch tại `$your_data_path/translate.la2vi.vi`.
```bash
python -m bin.main infer --model Transformer --model_dir $MODEL/la_vi.model --features_file $your_data_path/test.lo --predictions_file $your_data_path/translate.la2vi.vi
```
## Bước 4: Đánh giá chất lượng dựa trên điểm BLEU
Đánh giá điểm BLEU dựa trên multi-bleu
```bash
perl thrid-party/multi-bleu.perl $your_data_path/translate.la2vi.vi < $your_data_path/test.vi
```
| MODEL | BLEU (Beam Search) |
| :-----------------:| :----------------: |
| Transformer (Base) | 19.72 |
## Thành viên trong nhóm
Ngô Thượng Hiếu - 21021491\
Ngô Vịt Huy - 21020046\
Đồng Văn Dương - 21021470
## Chi tiết tham khảo tại
[nmtuet.ddns.net](http://nmtuet.ddns.net:1190/)
## Nếu có ý kiến đóng góp, xin hãy gửi thư tới địa chỉ mail [email protected]
## Xin trích dẫn bài báo sau:
```bash
@inproceedings{ViNMT2022,
title = {ViNMT: Neural Machine Translation Toolkit},
author = {Nguyen Hoang Quan, Nguyen Thanh Dat, Nguyen Hoang Minh Cong, Nguyen Van Vinh, Ngo Thi Vinh, Nguyen Phuong Thai, Tran Hong Viet},
booktitle = {https://arxiv.org/abs/2112.15272},
year = {2022},
}
```
|
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] |
hkivancoral/smids_5x_beit_base_adamax_0001_fold4
|
hkivancoral
| 2023-12-14T15:39:11Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T13:37:55Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8716666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_beit_base_adamax_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5977
- Accuracy: 0.8717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3006 | 1.0 | 375 | 0.3431 | 0.865 |
| 0.2258 | 2.0 | 750 | 0.3974 | 0.8433 |
| 0.1308 | 3.0 | 1125 | 0.4504 | 0.8633 |
| 0.1539 | 4.0 | 1500 | 0.4844 | 0.865 |
| 0.0793 | 5.0 | 1875 | 0.5671 | 0.8683 |
| 0.0565 | 6.0 | 2250 | 0.6745 | 0.865 |
| 0.0735 | 7.0 | 2625 | 0.5104 | 0.8983 |
| 0.0686 | 8.0 | 3000 | 0.7410 | 0.8767 |
| 0.0395 | 9.0 | 3375 | 0.6717 | 0.88 |
| 0.057 | 10.0 | 3750 | 0.5892 | 0.885 |
| 0.0561 | 11.0 | 4125 | 0.7571 | 0.8733 |
| 0.0534 | 12.0 | 4500 | 0.9537 | 0.8583 |
| 0.0346 | 13.0 | 4875 | 0.8297 | 0.85 |
| 0.0136 | 14.0 | 5250 | 0.9195 | 0.8667 |
| 0.0218 | 15.0 | 5625 | 0.9025 | 0.87 |
| 0.0052 | 16.0 | 6000 | 1.0564 | 0.865 |
| 0.0551 | 17.0 | 6375 | 0.9490 | 0.8633 |
| 0.0659 | 18.0 | 6750 | 0.9404 | 0.8717 |
| 0.0263 | 19.0 | 7125 | 0.9533 | 0.875 |
| 0.0009 | 20.0 | 7500 | 0.9914 | 0.87 |
| 0.0008 | 21.0 | 7875 | 1.1404 | 0.855 |
| 0.0028 | 22.0 | 8250 | 1.0363 | 0.8733 |
| 0.0035 | 23.0 | 8625 | 1.0823 | 0.865 |
| 0.0007 | 24.0 | 9000 | 1.0248 | 0.8733 |
| 0.0408 | 25.0 | 9375 | 0.9782 | 0.8683 |
| 0.0262 | 26.0 | 9750 | 1.0392 | 0.8683 |
| 0.0167 | 27.0 | 10125 | 1.0282 | 0.8717 |
| 0.0115 | 28.0 | 10500 | 0.9251 | 0.885 |
| 0.0242 | 29.0 | 10875 | 1.0471 | 0.8767 |
| 0.0002 | 30.0 | 11250 | 1.0729 | 0.8817 |
| 0.0011 | 31.0 | 11625 | 1.1808 | 0.87 |
| 0.0 | 32.0 | 12000 | 1.2243 | 0.8617 |
| 0.0 | 33.0 | 12375 | 1.2017 | 0.8733 |
| 0.0002 | 34.0 | 12750 | 1.2888 | 0.865 |
| 0.0 | 35.0 | 13125 | 1.1910 | 0.8717 |
| 0.0002 | 36.0 | 13500 | 1.3235 | 0.8683 |
| 0.0 | 37.0 | 13875 | 1.2685 | 0.8617 |
| 0.0109 | 38.0 | 14250 | 1.3836 | 0.865 |
| 0.0001 | 39.0 | 14625 | 1.3162 | 0.8733 |
| 0.0002 | 40.0 | 15000 | 1.3327 | 0.86 |
| 0.0 | 41.0 | 15375 | 1.4460 | 0.8567 |
| 0.0 | 42.0 | 15750 | 1.5521 | 0.8583 |
| 0.0 | 43.0 | 16125 | 1.6052 | 0.86 |
| 0.0 | 44.0 | 16500 | 1.5790 | 0.865 |
| 0.0 | 45.0 | 16875 | 1.5481 | 0.8667 |
| 0.0 | 46.0 | 17250 | 1.5829 | 0.8667 |
| 0.0 | 47.0 | 17625 | 1.6010 | 0.8717 |
| 0.0008 | 48.0 | 18000 | 1.5864 | 0.87 |
| 0.0 | 49.0 | 18375 | 1.5922 | 0.8683 |
| 0.0001 | 50.0 | 18750 | 1.5977 | 0.8717 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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multimodalart/poli-test-anotha-no-prior
|
multimodalart
| 2023-12-14T13:38:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:38:45Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError0.0_Seed102
|
behzadnet
| 2023-12-14T13:40:00Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-14T13:39:53Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.7.0.dev0
## 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 0.7.0.dev0
|
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] |
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError0.0_Seed102
|
behzadnet
| 2023-12-14T13:40:07Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-14T13:40:05Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## 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 0.7.0.dev0
|
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] |
LoneStriker/Ana-v1-m7-4.0bpw-h6-exl2
|
LoneStriker
| 2023-12-14T13:42:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:40:31Z |
---
license: cc-by-nc-4.0
language:
- en
---
Ana - A model solely focused on the RP / ERP Experience.
<br> - Little to no Censorship *during* Roleplay. May be kinda censored at 0 Conext. Does not affect Roleplays.
<br> - Little to no Positivity Bias *during* Roleplay, from my tests atleast.
<br> - Verbose
<br> - Kinda Smart
<br> - *Slightly* Horny by default.
Purely experimental ----- I do not know what I am doing, this is for fun. A merge + train. Final qLoRA train took 3 hours on a 4090.
Why no 70Bs? Because the 4090 I have currently broke down. falling back to 3060 12gb.. I'm runpod training them for now.
|
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moiduy04/sliver_spoon_lao_vi
|
moiduy04
| 2023-12-14T13:42:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:41:15Z |
Entry not found
|
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] |
ksanchis/Reinforce-Pixelcopter-PLE-v0
|
ksanchis
| 2023-12-14T13:41:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T13:41:43Z |
---
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: 14.00 +/- 11.51
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
|
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multimodalart/politest-anotha-no-prior-2
|
multimodalart
| 2023-12-14T13:42:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:42:20Z |
Entry not found
|
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samitarias/modelo_analisis_sentimiento
|
samitarias
| 2023-12-14T13:43:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:43:05Z |
Entry not found
|
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andrealexroom/mistral-7B_0.1_LexPC_v0.0.0.7
|
andrealexroom
| 2023-12-14T15:56:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:43:12Z |
Entry not found
|
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] |
suzukiairi/test2
|
suzukiairi
| 2023-12-14T13:43:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T13:43:37Z |
---
license: apache-2.0
---
|
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] |
ksanchis/Reinforce-Pixelcopter-PLE-v1
|
ksanchis
| 2023-12-14T14:07:11Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T13:44:30Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.30 +/- 26.75
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
|
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yvo04622/test_model
|
yvo04622
| 2023-12-14T13:45:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:45:09Z |
Entry not found
|
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] |
w11wo/roberta-base-isarcasm
|
w11wo
| 2023-12-14T13:50:19Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T13:45:20Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-isarcasm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-isarcasm
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6208
- Accuracy: 0.7982
- F1: 0.4317
- Precision: 0.4304
- Recall: 0.4331
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 215 | 0.7162 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 430 | 0.9168 | 0.7143 | 0.1667 | 0.1667 | 0.1667 |
| 0.6391 | 3.0 | 645 | 1.2142 | 0.6857 | 0.1538 | 0.1429 | 0.1667 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
hkivancoral/smids_5x_deit_tiny_adamax_00001_fold1
|
hkivancoral
| 2023-12-17T22:45:00Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T13:45:46Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8831385642737897
---
<!-- 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. -->
# smids_5x_deit_tiny_adamax_00001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9436
- Accuracy: 0.8831
## 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: 32
- eval_batch_size: 32
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3072 | 1.0 | 376 | 0.3156 | 0.8765 |
| 0.1866 | 2.0 | 752 | 0.3111 | 0.8798 |
| 0.1992 | 3.0 | 1128 | 0.2782 | 0.8881 |
| 0.1175 | 4.0 | 1504 | 0.2771 | 0.8932 |
| 0.0885 | 5.0 | 1880 | 0.3109 | 0.8948 |
| 0.0629 | 6.0 | 2256 | 0.3306 | 0.9015 |
| 0.0575 | 7.0 | 2632 | 0.3619 | 0.8948 |
| 0.0255 | 8.0 | 3008 | 0.4740 | 0.8881 |
| 0.0161 | 9.0 | 3384 | 0.5352 | 0.8765 |
| 0.0279 | 10.0 | 3760 | 0.5956 | 0.8815 |
| 0.0384 | 11.0 | 4136 | 0.6406 | 0.8831 |
| 0.0026 | 12.0 | 4512 | 0.7055 | 0.8748 |
| 0.0045 | 13.0 | 4888 | 0.7435 | 0.8715 |
| 0.0194 | 14.0 | 5264 | 0.7535 | 0.8798 |
| 0.0002 | 15.0 | 5640 | 0.8056 | 0.8748 |
| 0.0001 | 16.0 | 6016 | 0.8509 | 0.8781 |
| 0.0001 | 17.0 | 6392 | 0.8210 | 0.8765 |
| 0.0001 | 18.0 | 6768 | 0.8304 | 0.8781 |
| 0.0 | 19.0 | 7144 | 0.8626 | 0.8798 |
| 0.0304 | 20.0 | 7520 | 0.8719 | 0.8765 |
| 0.0 | 21.0 | 7896 | 0.9044 | 0.8781 |
| 0.0001 | 22.0 | 8272 | 0.8924 | 0.8881 |
| 0.0 | 23.0 | 8648 | 0.8973 | 0.8815 |
| 0.0 | 24.0 | 9024 | 0.8953 | 0.8798 |
| 0.0 | 25.0 | 9400 | 0.9328 | 0.8698 |
| 0.0041 | 26.0 | 9776 | 0.9144 | 0.8781 |
| 0.0 | 27.0 | 10152 | 0.9041 | 0.8798 |
| 0.0 | 28.0 | 10528 | 0.9154 | 0.8815 |
| 0.0022 | 29.0 | 10904 | 0.9165 | 0.8831 |
| 0.0086 | 30.0 | 11280 | 0.9177 | 0.8815 |
| 0.0 | 31.0 | 11656 | 0.9151 | 0.8815 |
| 0.0 | 32.0 | 12032 | 0.9187 | 0.8765 |
| 0.0 | 33.0 | 12408 | 0.9203 | 0.8815 |
| 0.0 | 34.0 | 12784 | 0.9308 | 0.8781 |
| 0.0 | 35.0 | 13160 | 0.9377 | 0.8781 |
| 0.0 | 36.0 | 13536 | 0.9294 | 0.8765 |
| 0.0 | 37.0 | 13912 | 0.9284 | 0.8831 |
| 0.0 | 38.0 | 14288 | 0.9287 | 0.8831 |
| 0.0 | 39.0 | 14664 | 0.9291 | 0.8831 |
| 0.0 | 40.0 | 15040 | 0.9319 | 0.8815 |
| 0.0 | 41.0 | 15416 | 0.9457 | 0.8781 |
| 0.0 | 42.0 | 15792 | 0.9438 | 0.8781 |
| 0.0 | 43.0 | 16168 | 0.9428 | 0.8798 |
| 0.0 | 44.0 | 16544 | 0.9398 | 0.8831 |
| 0.0 | 45.0 | 16920 | 0.9423 | 0.8831 |
| 0.0059 | 46.0 | 17296 | 0.9455 | 0.8781 |
| 0.0 | 47.0 | 17672 | 0.9433 | 0.8831 |
| 0.0 | 48.0 | 18048 | 0.9441 | 0.8815 |
| 0.0 | 49.0 | 18424 | 0.9445 | 0.8815 |
| 0.0047 | 50.0 | 18800 | 0.9436 | 0.8831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Ca555/mysupermodel
|
Ca555
| 2023-12-14T13:46:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T13:46:02Z |
---
license: apache-2.0
---
|
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phoenixLisha/mBart_xlsum
|
phoenixLisha
| 2023-12-14T13:47:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:47:32Z |
Entry not found
|
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] |
moiduy04/homemade_lo_vi
|
moiduy04
| 2023-12-14T22:26:49Z | 0 | 0 |
pytorch
|
[
"pytorch",
"text2text-generation",
"vi",
"lo",
"region:us"
] |
text2text-generation
| 2023-12-14T13:47:44Z |
---
library_name: pytorch
pipeline_tag: text2text-generation
language:
- vi
- lo
metrics:
- bleu
---
|
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] |
dhiyagavaeikar/fine_tuned_model_health_trainer
|
dhiyagavaeikar
| 2023-12-15T10:57:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:48:30Z |
Entry not found
|
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dhiyagavaeikar/results
|
dhiyagavaeikar
| 2023-12-14T13:48:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:48:50Z |
Entry not found
|
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] |
LoneStriker/Ana-v1-m7-6.0bpw-h6-exl2
|
LoneStriker
| 2023-12-14T13:53:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:50:21Z |
---
license: cc-by-nc-4.0
language:
- en
---
Ana - A model solely focused on the RP / ERP Experience.
<br> - Little to no Censorship *during* Roleplay. May be kinda censored at 0 Conext. Does not affect Roleplays.
<br> - Little to no Positivity Bias *during* Roleplay, from my tests atleast.
<br> - Verbose
<br> - Kinda Smart
<br> - *Slightly* Horny by default.
Purely experimental ----- I do not know what I am doing, this is for fun. A merge + train. Final qLoRA train took 3 hours on a 4090.
Why no 70Bs? Because the 4090 I have currently broke down. falling back to 3060 12gb.. I'm runpod training them for now.
|
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imdatta0/llama_2_13b_oasst_top1_2023_08_25_upNqNk_r8_lr0.0001_bg28_alpha32_run2
|
imdatta0
| 2023-12-14T13:50:30Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | 2023-12-14T13:50:26Z |
---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: llama_2_13b_oasst_top1_2023_08_25_upNqNk_r8_lr0.0001_bg28_alpha32_run2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# llama_2_13b_oasst_top1_2023_08_25_upNqNk_r8_lr0.0001_bg28_alpha32_run2
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9861
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.04
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1254 | 0.04 | 32 | 1.0310 |
| 1.033 | 0.08 | 64 | 1.0088 |
| 0.9889 | 0.12 | 96 | 1.0025 |
| 1.033 | 0.16 | 128 | 0.9999 |
| 0.9883 | 0.2 | 160 | 0.9984 |
| 0.9968 | 0.24 | 192 | 0.9955 |
| 1.005 | 0.28 | 224 | 0.9944 |
| 1.0207 | 0.32 | 256 | 0.9951 |
| 0.998 | 0.36 | 288 | 0.9924 |
| 0.9874 | 0.4 | 320 | 0.9913 |
| 1.0115 | 0.44 | 352 | 0.9919 |
| 0.9845 | 0.48 | 384 | 0.9896 |
| 0.9927 | 0.52 | 416 | 0.9893 |
| 1.0157 | 0.56 | 448 | 0.9892 |
| 1.0244 | 0.61 | 480 | 0.9881 |
| 0.9835 | 0.65 | 512 | 0.9875 |
| 0.9597 | 0.69 | 544 | 0.9868 |
| 1.0001 | 0.73 | 576 | 0.9870 |
| 0.9891 | 0.77 | 608 | 0.9869 |
| 1.0154 | 0.81 | 640 | 0.9867 |
| 0.9912 | 0.85 | 672 | 0.9867 |
| 0.9688 | 0.89 | 704 | 0.9862 |
| 0.9928 | 0.93 | 736 | 0.9861 |
| 0.991 | 0.97 | 768 | 0.9861 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.5.2
- Tokenizers 0.14.1
|
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] |
jlvdoorn/whisper-tiny-atcosim
|
jlvdoorn
| 2023-12-14T15:29:10Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-14T13:51:03Z |
Entry not found
|
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] |
w11wo/deberta-v3-base-isarcasm
|
w11wo
| 2023-12-14T13:57:55Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T13:51:57Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: microsoft/deberta-v3-base
model-index:
- name: deberta-v3-base-isarcasm
results:
- task:
type: text-classification
dataset:
name: iSarcasm
type: isarcasm
split: test
metrics:
- type: f1
value: 0.47887323943661975
name: f1
- type: accuracy
value: 0.8331454340473506
name: accuracy
- type: recall
value: 0.43312101910828027
name: recall
- type: precision
value: 0.5354330708661418
name: precision
---
<!-- 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. -->
# deberta-v3-base-isarcasm
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3693
- Accuracy: 0.8331
- F1: 0.4789
- Precision: 0.5354
- Recall: 0.4331
## 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 215 | 0.7833 | 0.8 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 430 | 1.1913 | 0.8 | 0.0 | 0.0 | 0.0 |
| 0.577 | 3.0 | 645 | 1.5866 | 0.7714 | 0.2 | 0.25 | 0.1667 |
| 0.577 | 4.0 | 860 | 2.3199 | 0.8 | 0.2222 | 0.3333 | 0.1667 |
| 0.2047 | 5.0 | 1075 | 2.4911 | 0.8 | 0.2222 | 0.3333 | 0.1667 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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vidiaoilpoland/VidiaOilPoland
|
vidiaoilpoland
| 2023-12-14T13:53:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:52:41Z |
Vidia Oil Pomaga złagodzić szumy uszne i problemy ze słuchem. Przeczytaj recenzje, cenę i rabat! Poland
Vidia Oil to skuteczna formuła wspomagająca słuch, która przynosi ulgę w problemach ze słuchem bez żadnych skutków ubocznych. Vidia Oil Poland
Vidia Oil Kup Teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskaj teraz 50% zniżki !! Pośpiesz się!!
Czytaj więcej: https://www.canvasclinic.com/VidOilPola
➢Nazwa produktu — Vidia Oil
➢Kategoria - Wspomaganie zdrowego słuchu
➢Główne korzyści — Formuła wspomagania słuchu
➢ Skład — Naturalny Związek Organiczny
➢ Skutki uboczne – NA
➢Ocena końcowa: — 4.8
➢ Dostępność — online
➢Oferty i rabaty; OSZCZĘDŹ DZIŚ! KUP TERAZ, ABY KUPOWAĆ OFERTĘ SPECJALNĄ!!!
CO TO JEST Vidia Oil?
Vidia Oil to w 100% naturalna formuła łagodząca szumy uszne i inne problemy ze słuchem. Składniki składają się z witamin, aminokwasów, ziół i ekstraktów roślinnych. Vidia Oilpoprawia zdrowie słuchu i działa skutecznie u każdej osoby. Laryngolog (ucho-nos-gardło), że produkt jest czysty i działa naturalnie. Daje szybką ulgę, nie powodując przy tym skutków ubocznych. Dzięki Vidia Oil nie musisz poddawać się operacji ucha ani stosować leków na receptę, które mogą powodować niekorzystne skutki uboczne. Doskonale przywraca zdrowie uszu w ciągu kilku tygodni lub miesięcy stosowania. Zatrzymuje inne problemy związane z uszami i zapobiega dalszym uszkodzeniom.
Vidia Oil Kup Teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskaj teraz 50% zniżki !! Pośpiesz się!!
Czytaj więcej: https://www.canvasclinic.com/VidOilPola
https://sites.google.com/view/vidia-oil-poland/home
Vidia Oil Vidia Oil Cena Vidia Oil Opinie Vidia Oil Składniki Vidia Oil Korzyści Vidia Oil Skutki uboczne Vidia Oil Kompozycja Vidia Oil Reklamacja Vidia Oil Gdzie kupić Vidia Oil Jak używać Vidia Oil koszt Vidia Oil Pracuje Vidia Oil forum Vidia Oil oryginał Vidia Oil Apteka
https://www.canvasclinic.com/VidOilPola
https://sites.google.com/view/vidia-oil-poland/home
https://healthtoned.blogspot.com/2023/12/vidia-oil-poland.html
https://www.weddingwire.com/website/vidia-oil-and-poland
https://medium.com/@healthytalk24x7/vidia-oil-poland-81ddf0283705
https://medium.com/@healthytalk24x7/vidia-oil-pomaga-z%C5%82agodzi%C4%87-szumy-uszne-i-problemy-ze-s%C5%82uchem-3e9273f224ec
https://infogram.com/vidia-oil-poland-1h7v4pwo0xmwj6k?live
https://softisenilspain.hashnode.dev/vidia-oil-poland
https://vidiaoilpoland.company.site/
https://sway.office.com/iFPpgT4xMlujdpY9
https://gamma.app/docs/Vidia-Oil-Poland-p8h6lvuuf6j502u?mode=doc
https://groups.google.com/g/snshine/c/egSwbam-JuQ
https://healthytalk24x7.wixsite.com/sunshine/post/vidia-oil-poland
https://www.sunflower-cissp.com/glossary/cissp/7250/vidia-oil-poland
https://nepal.tribe.so/post/vidia-oil-poland-657b06bb64de3c12cf39a964
https://enkling.com/read-blog/8919
https://community.thebatraanumerology.com/
https://community.thebatraanumerology.com/post/vidia-oil-pomaga-zlagodzic-szumy-uszne-i-problemy-ze-sluchem-przeczytaj-rec--657b07a89e19757294d1cb23
https://soundcloud.com/vidiaoilpoland/vidia-oil-poland?si=13fe7963e69845afa16f34bdf92d71bb&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/vidiaoilpoland/art/Vidia-Oil-Poland-1001896906
|
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] |
Felladrin/gguf-llama-160m
|
Felladrin
| 2023-12-14T13:55:37Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"en",
"dataset:wikipedia",
"base_model:JackFram/llama-160m",
"license:other",
"region:us"
] |
text-generation
| 2023-12-14T13:53:01Z |
---
license: other
language:
- en
datasets:
- wikipedia
pipeline_tag: text-generation
base_model: JackFram/llama-160m
---
Quantized GGUF version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m).
|
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] |
hkivancoral/smids_5x_beit_base_adamax_00001_fold4
|
hkivancoral
| 2023-12-14T21:22:38Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T13:53:16Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8866666666666667
---
<!-- 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. -->
# smids_5x_beit_base_adamax_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0773
- Accuracy: 0.8867
## 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: 32
- eval_batch_size: 32
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2789 | 1.0 | 375 | 0.3658 | 0.8517 |
| 0.2051 | 2.0 | 750 | 0.3746 | 0.855 |
| 0.0849 | 3.0 | 1125 | 0.3986 | 0.87 |
| 0.1357 | 4.0 | 1500 | 0.4367 | 0.8633 |
| 0.0524 | 5.0 | 1875 | 0.4518 | 0.8867 |
| 0.0622 | 6.0 | 2250 | 0.5510 | 0.8867 |
| 0.0413 | 7.0 | 2625 | 0.6374 | 0.8767 |
| 0.0498 | 8.0 | 3000 | 0.6614 | 0.88 |
| 0.0181 | 9.0 | 3375 | 0.6937 | 0.8867 |
| 0.013 | 10.0 | 3750 | 0.7663 | 0.8817 |
| 0.0052 | 11.0 | 4125 | 0.8001 | 0.8783 |
| 0.0189 | 12.0 | 4500 | 0.8578 | 0.8833 |
| 0.0009 | 13.0 | 4875 | 0.8974 | 0.88 |
| 0.0122 | 14.0 | 5250 | 0.9013 | 0.885 |
| 0.0628 | 15.0 | 5625 | 0.9434 | 0.875 |
| 0.0114 | 16.0 | 6000 | 0.9650 | 0.8733 |
| 0.0367 | 17.0 | 6375 | 0.9453 | 0.885 |
| 0.0138 | 18.0 | 6750 | 0.9235 | 0.8833 |
| 0.0185 | 19.0 | 7125 | 0.9504 | 0.8783 |
| 0.0147 | 20.0 | 7500 | 0.9928 | 0.8883 |
| 0.0067 | 21.0 | 7875 | 0.9826 | 0.8867 |
| 0.0174 | 22.0 | 8250 | 1.0361 | 0.8817 |
| 0.0198 | 23.0 | 8625 | 1.0257 | 0.8767 |
| 0.0001 | 24.0 | 9000 | 1.0258 | 0.885 |
| 0.0333 | 25.0 | 9375 | 1.0061 | 0.885 |
| 0.0008 | 26.0 | 9750 | 1.0277 | 0.8867 |
| 0.014 | 27.0 | 10125 | 1.0251 | 0.8833 |
| 0.0001 | 28.0 | 10500 | 1.0047 | 0.89 |
| 0.0006 | 29.0 | 10875 | 0.9963 | 0.89 |
| 0.0315 | 30.0 | 11250 | 1.0164 | 0.8833 |
| 0.0036 | 31.0 | 11625 | 1.0188 | 0.8883 |
| 0.0321 | 32.0 | 12000 | 1.0423 | 0.885 |
| 0.0001 | 33.0 | 12375 | 1.0241 | 0.89 |
| 0.0007 | 34.0 | 12750 | 1.0270 | 0.8883 |
| 0.0001 | 35.0 | 13125 | 1.0742 | 0.89 |
| 0.0009 | 36.0 | 13500 | 1.0587 | 0.8883 |
| 0.0086 | 37.0 | 13875 | 1.0716 | 0.8833 |
| 0.0329 | 38.0 | 14250 | 1.0601 | 0.8917 |
| 0.0002 | 39.0 | 14625 | 1.0796 | 0.8867 |
| 0.0003 | 40.0 | 15000 | 1.0705 | 0.88 |
| 0.0076 | 41.0 | 15375 | 1.0541 | 0.8833 |
| 0.0006 | 42.0 | 15750 | 1.0775 | 0.885 |
| 0.0002 | 43.0 | 16125 | 1.0701 | 0.885 |
| 0.0068 | 44.0 | 16500 | 1.0578 | 0.8933 |
| 0.0 | 45.0 | 16875 | 1.0747 | 0.8883 |
| 0.0158 | 46.0 | 17250 | 1.0767 | 0.89 |
| 0.0066 | 47.0 | 17625 | 1.0837 | 0.89 |
| 0.0106 | 48.0 | 18000 | 1.0810 | 0.8867 |
| 0.0002 | 49.0 | 18375 | 1.0785 | 0.885 |
| 0.0001 | 50.0 | 18750 | 1.0773 | 0.8867 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
heisguyy/distilgpt2-squad
|
heisguyy
| 2023-12-15T08:56:33Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:54:30Z |
Entry not found
|
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adamdad/stable_zero123
|
adamdad
| 2023-12-14T13:54:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:54:44Z |
Entry not found
|
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] |
LoneStriker/Ana-v1-m7-8.0bpw-h8-exl2
|
LoneStriker
| 2023-12-14T13:59:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T13:55:00Z |
---
license: cc-by-nc-4.0
language:
- en
---
Ana - A model solely focused on the RP / ERP Experience.
<br> - Little to no Censorship *during* Roleplay. May be kinda censored at 0 Conext. Does not affect Roleplays.
<br> - Little to no Positivity Bias *during* Roleplay, from my tests atleast.
<br> - Verbose
<br> - Kinda Smart
<br> - *Slightly* Horny by default.
Purely experimental ----- I do not know what I am doing, this is for fun. A merge + train. Final qLoRA train took 3 hours on a 4090.
Why no 70Bs? Because the 4090 I have currently broke down. falling back to 3060 12gb.. I'm runpod training them for now.
|
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jiyoun-kim/vit-base-patch16-224-in21k-finetuned-lora-food101
|
jiyoun-kim
| 2023-12-14T14:44:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T13:57:56Z |
Entry not found
|
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bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-40
|
bio-datasets
| 2023-12-14T13:59:06Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T13:59:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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dmnkbckr/q-FrozenLake-v1-4x4-noSlippery
|
dmnkbckr
| 2023-12-14T14:00:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T14:00:11Z |
---
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="dmnkbckr/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"])
```
|
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] |
FanFierik/FreddyFazbear
|
FanFierik
| 2023-12-14T14:05:42Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T14:02:33Z |
---
license: openrail
---
|
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] |
pariakashani/en-multinerd-masked-ner
|
pariakashani
| 2023-12-14T16:02:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T14:02:35Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: en-multinerd-masked-ner
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. -->
# en-multinerd-masked-ner
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: 0.0251
- Precision: 0.9353
- Recall: 0.9420
- F1: 0.9386
- Accuracy: 0.9917
- Per-precision: 0.9945
- Per-recall: 0.9953
- Per-f1: 0.9949
- Org-precision: 0.9261
- Org-recall: 0.9434
- Org-f1: 0.9347
- Loc-precision: 0.9679
- Loc-recall: 0.9718
- Loc-f1: 0.9699
- Dis-precision: 0.7003
- Dis-recall: 0.7156
- Dis-f1: 0.7079
- Anim-precision: 0.7021
- Anim-recall: 0.7169
- Anim-f1: 0.7094
## 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 | Precision | Recall | F1 | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0235 | 1.0 | 8205 | 0.0268 | 0.9171 | 0.9466 | 0.9316 | 0.9907 | 0.9943 | 0.9960 | 0.9951 | 0.8943 | 0.9521 | 0.9223 | 0.9595 | 0.9713 | 0.9654 | 0.6531 | 0.7298 | 0.6893 | 0.6518 | 0.7593 | 0.7014 |
| 0.0151 | 2.0 | 16410 | 0.0251 | 0.9353 | 0.9420 | 0.9386 | 0.9917 | 0.9945 | 0.9953 | 0.9949 | 0.9261 | 0.9434 | 0.9347 | 0.9679 | 0.9718 | 0.9699 | 0.7003 | 0.7156 | 0.7079 | 0.7021 | 0.7169 | 0.7094 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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dmnkbckr/q-Taxi-v3
|
dmnkbckr
| 2023-12-14T14:03:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T14:03:19Z |
---
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: 7.54 +/- 2.74
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="dmnkbckr/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"])
```
|
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Potatonumbertwo/genshin
|
Potatonumbertwo
| 2023-12-14T14:04:24Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-14T14:04:24Z |
---
license: cc-by-4.0
---
|
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NMEX/pretrained
|
NMEX
| 2023-12-14T14:29:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T14:04:38Z |
---
license: mit
---
|
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] |
allanfear/KOZ
|
allanfear
| 2023-12-14T14:07:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T14:06:47Z |
---
license: apache-2.0
---
|
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Eringers/phi_1.5_alpaca_python_instruct_225k
|
Eringers
| 2023-12-15T13:51:50Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T14:07:41Z |
Entry not found
|
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Ouiaoui/OUI
|
Ouiaoui
| 2023-12-14T14:10:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:10:26Z |
Entry not found
|
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] |
jiyoun-kim/vit-base-patch16-224-in21k-finetuned-food101
|
jiyoun-kim
| 2023-12-14T17:22:18Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T14:10:46Z |
Entry not found
|
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ntanta03/MachineTranslation
|
ntanta03
| 2023-12-14T16:19:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:11:45Z |
Entry not found
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GRIZZLY3/Restaurant
|
GRIZZLY3
| 2023-12-14T14:12:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:12:25Z |
![Uploading Coffee Shop no exterior.mp4…]()
|
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johjonah/CUAD-Mistral
|
johjonah
| 2023-12-14T14:15:22Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T14:14:02Z |
Entry not found
|
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akshitapps/autoplace-models
|
akshitapps
| 2023-12-14T14:14:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:14:33Z |
Entry not found
|
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] |
fhusia/bert-finetuned-ner
|
fhusia
| 2023-12-14T14:18:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T14:16:03Z |
Entry not found
|
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] |
4our5ive/tomato
|
4our5ive
| 2023-12-14T14:17:31Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-12-14T14:17:31Z |
---
license: bigscience-openrail-m
---
|
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] |
mikeee/test1
|
mikeee
| 2023-12-14T14:17:39Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-14T14:17:35Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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] |
lulu5131/distilbert-base-uncased-finetuned-emotion
|
lulu5131
| 2023-12-14T17:44:28Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T14:17:59Z |
Entry not found
|
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veriga/adm_letters
|
veriga
| 2023-12-14T14:21:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:21:01Z |
Entry not found
|
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EmmaGthn/results_prefix_2_2000_moji
|
EmmaGthn
| 2023-12-14T16:12:11Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-14T14:22:30Z |
Entry not found
|
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