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text2text-generation | transformers | Korean Dialect Translator: Standard > Gyeongsang
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(๊ฒฝ์๋)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation
| {} | eunjin/kobart_gyeongsang_translator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T04:13:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Korean Dialect Translator: Standard > Gyeongsang
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(๊ฒฝ์๋)
- Used Model : SKT-KoBART
- URL
- Reference Code
- URL
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Korean Dialect Translator: Standard > Jeju
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(์ ์ฃผ๋)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation
| {} | eunjin/kobart_jeju_translator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T04:23:56+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Korean Dialect Translator: Standard > Jeju
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(์ ์ฃผ๋)
- Used Model : SKT-KoBART
- URL
- Reference Code
- URL
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Korean Dialect Translator: Jeju > Standard
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(์ ์ฃผ๋)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation
| {} | eunjin/kobart_jeju_to_standard_translator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T04:27:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Korean Dialect Translator: Jeju > Standard
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(์ ์ฃผ๋)
- Used Model : SKT-KoBART
- URL
- Reference Code
- URL
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Korean Dialect Translator: Gyeongsang > Standard
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(๊ฒฝ์๋)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation | {} | eunjin/kobart_gyeongsang_to_standard_translator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T04:30:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Korean Dialect Translator: Gyeongsang > Standard
- Used Data : AI hub ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(๊ฒฝ์๋)
- Used Model : SKT-KoBART
- URL
- Reference Code
- URL | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | anjankumar/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T04:37:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3557
- Bleu: 37.1286
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.3557\n- Bleu: 37.1286",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | mindwrapped/dqn-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T05:07:19+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/roberta-base-attribute-correction-qa-attribute-correction-qa
This model is a fine-tuned version of [ksabeh/roberta-base-attribu... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/roberta-base-attribute-correction-qa-attribute-correction-qa", "results": []}]} | ksabeh/roberta-base-attribute-correction | null | [
"transformers",
"tf",
"tensorboard",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T05:49:17+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| ksabeh/roberta-base-attribute-correction-qa-attribute-correction-qa
===================================================================
This model is a fine-tuned version of ksabeh/roberta-base-attribute-correction-qa on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.12... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 36783, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'P... |
fill-mask | transformers |
<!-- 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. -->
# wangchanberta-base-att-spm-uncased-finetuned-cosme
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wangchanberta-base-att-spm-uncased-finetuned-cosme", "results": []}]} | Nawaphong-zax/wangchanberta-base-att-spm-uncased-finetuned-cosme | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:12:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| wangchanberta-base-att-spm-uncased-finetuned-cosme
==================================================
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9973
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch... |
text-classification | transformers | Model trained on IBMArgRank30k for 2 epochs with a learning rate of 3e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
```
@inproceedings{lauscher-etal-2020-rhetoric... | {"license": "mit"} | anlausch/aq_bert_ibm | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:28:24+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Model trained on IBMArgRank30k for 2 epochs with a learning rate of 3e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- 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. -->
# bart-paraphrase-finetuned-xsum-v3
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/euge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum-v3", "results": []}]} | yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:29:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-finetuned-xsum-v3
=================================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3377
* Rouge1: 99.9461
* Rouge2: 72.6619
* Rougel: 99.9461
* Rougelsum: 99.9461
* Gen Len: 9.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
automatic-speech-recognition | transformers | Indonesia XLRS model | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian by Ridho", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech ... | ridhoalattqas/xlrs-best-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:37:38+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Indonesia XLRS model | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
null | transformers | Multi-task learning model (flat architecture) trained on GAQCorpus for 4 epochs with a learning rate of 2e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
```
@inpro... | {"license": "mit"} | anlausch/aq_bert_gaq_mt | null | [
"transformers",
"pytorch",
"bert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:41:55+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us
| Multi-task learning model (flat architecture) trained on GAQCorpus for 4 epochs with a learning rate of 2e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
| [] | [
"TAGS\n#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us \n"
] |
translation | transformers |
<!-- 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-Af
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-af-en](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on t... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Af", "results": []}]} | kabelomalapane/Af-En | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:54:17+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# En-Af
This model is a fine-tuned version of Helsinki-NLP/opus-mt-af-en on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- 'eval_bleu': 46.1522519
- 'eval_loss': 2.5693612
After training:
- Loss: 1.7516168
- Bleu: 55.3924697
## Model description
More information ... | [
"# En-Af\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-af-en on the None dataset.\nIt achieves the following results on the evaluation set:\nBefore training:\n- 'eval_bleu': 46.1522519\n- 'eval_loss': 2.5693612\n\nAfter training:\n- Loss: 1.7516168\n- Bleu: 55.3924697",
"## Model description\n\nMo... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# En-Af\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-af-en on the None dataset.\nIt achieves the following results on t... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HWJin/SMU-NLP-assignment2-finetuned-best
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "HWJin/SMU-NLP-assignment2-finetuned-best", "results": []}]} | HWJin/SMU-NLP-assignment2-finetuned-best | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T06:55:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| HWJin/SMU-NLP-assignment2-finetuned-best
========================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9936
* Validation Loss: 0.9867
* Epoch: 13
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-church | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T07:58:49+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
automatic-speech-recognition | transformers |
# Thai Wav2Vec2 with CommonVoice V8 (newmm tokenizer) + language model
This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). It was finetune [wav2vec2-large-xls... | {"language": ["th"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | wannaphong/wav2vec2-large-xlsr-53-th-cv8-newmm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:common_voice",
"arxiv:2208.04799",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:01:59+00:00 | [
"2208.04799"
] | [
"th"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #th #dataset-common_voice #arxiv-2208.04799 #license-apache-2.0 #endpoints_compatible #region-us
| Thai Wav2Vec2 with CommonVoice V8 (newmm tokenizer) + language model
====================================================================
This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in airesearch/wav2vec2-large-xlsr-53-th. It was finetune wav2vec2-large-... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #th #dataset-common_voice #arxiv-2208.04799 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# depression_tweet
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "depression_tweet", "results": []}]} | ziq/depression_tweet | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:02:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| depression\_tweet
=================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1606
* Accuracy: 0.9565
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_b... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | botika/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:27:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1500
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Copninich/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:28:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-koquad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "ko", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "1990\ub144 \uc601\ud654 \u300a <hl> \ub0a8\ubd80\uad70 <hl> \u300b\uc5d0\uc11c \u... | lmqg/mt5-small-koquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ko",
"dataset:lmqg/qg_koquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T08:31:39+00:00 | [
"2210.03992"
] | [
"ko"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-koquad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: ko
* Training data: lmqg/qg\_k... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ko\n* Training data: lmqg/qg\\_koquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ko\n... |
token-classification | transformers |
<!-- 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. -->
# berturk-uncased-keyword-extractor
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co... | {"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz... | yanekyuk/berturk-uncased-keyword-extractor | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:33:44+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| berturk-uncased-keyword-extractor
=================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3931
* Precision: 0.6631
* Recall: 0.6728
* Accuracy: 0.9188
* F1: 0.6679
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text2text-generation | transformers |
<!-- 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. -->
# rob2rand_merged_w_prefix_c_fc_field
This model was trained from scratch on the None dataset.
## Model description
More informa... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rob2rand_merged_w_prefix_c_fc_field", "results": []}]} | imamnurby/rob2rand_merged_w_prefix_c_fc_field | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-06T08:38:04+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# rob2rand_merged_w_prefix_c_fc_field
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# rob2rand_merged_w_prefix_c_fc_field\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# rob2rand_merged_w_prefix_c_fc_field\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information neede... |
text-classification | transformers |
# Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncas... | {"language": "en", "license": "apache-2.0", "tags": ["text-classification", "neural-compressor", "int8"], "datasets": ["sst2", "glue"], "metrics": ["accuracy"]} | echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"neural-compressor",
"int8",
"en",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:51:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2
## Table of Contents
- Model Details
- How to Get Started With the Model
## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to obtain a sparsi... | [
"# Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2",
"## Table of Contents\n- Model Details\n- How to Get Started With the Model",
"## Model Details\nModel Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2",
"## Table of Contents\n- Model Det... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | sayakpramanik/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T08:52:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2166
* Accuracy: 0.923
* F1: 0.9229
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
<!-- 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. -->
# ainize-kobart-news-eb-finetuned-xsum
This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/ko... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "ainize-kobart-news-eb-finetuned-xsum", "results": []}]} | eunbeee/ainize-kobart-news-eb-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T09:01:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ainize-kobart-news-eb-finetuned-xsum
====================================
This model is a fine-tuned version of ainize/kobart-news on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2147
* Rouge1: 60.732
* Rouge2: 39.1933
* Rougel: 60.6507
* Rougelsum: 60.6712
* Gen Len: 19.3417... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | stig/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T09:07:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8545
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 928531583
- CO2 Emissions (in grams): 3.4552892403407167
## Validation Metrics
- Loss: 2.1122372150421143
- Rouge1: 68.7226
- Rouge2: 50.1638
- RougeL: 59.7235
- RougeLsum: 62.3458
- Gen Len: 63.2505
## Usage
You can use cURL to access this... | {"language": "en", "tags": "autotrain", "datasets": ["spy24/autotrain-data-expand"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.4552892403407167} | spy24/autotrain-expand-928531583 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:spy24/autotrain-data-expand",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T09:07:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-spy24/autotrain-data-expand #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 928531583
- CO2 Emissions (in grams): 3.4552892403407167
## Validation Metrics
- Loss: 2.1122372150421143
- Rouge1: 68.7226
- Rouge2: 50.1638
- RougeL: 59.7235
- RougeLsum: 62.3458
- Gen Len: 63.2505
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 928531583\n- CO2 Emissions (in grams): 3.4552892403407167",
"## Validation Metrics\n\n- Loss: 2.1122372150421143\n- Rouge1: 68.7226\n- Rouge2: 50.1638\n- RougeL: 59.7235\n- RougeLsum: 62.3458\n- Gen Len: 63.2505",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-spy24/autotrain-data-expand #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 928531583\n- CO2 Emissions (in grams): 3.455... |
text-classification | transformers |
<!-- 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. -->
# ECHR_test_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the lex_g... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lex_glue"], "model-index": [{"name": "ECHR_test_2", "results": []}]} | mpsb00/ECHR_test_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:lex_glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T09:11:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ECHR\_test\_2
=============
This model is a fine-tuned version of prajjwal1/bert-tiny on the lex\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2487
* Macro-f1: 0.4052
* Micro-f1: 0.5660
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* trai... |
null | null | asdf | {} | sj5lee/testmodel | null | [
"region:us"
] | null | 2022-06-06T09:44:12+00:00 | [] | [] | TAGS
#region-us
| asdf | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- 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-finnish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-finnish", "results": []}]} | bekirbakar/wav2vec2-large-xls-r-300m-finnish | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T09:46:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-finnish
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4747
* Wer: 0.5143
Training procedure
------------------
### Training hyperparam... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
null | null | # MidcurveNN
Midcurve by Neural Networks
---
license: apache-2.0
---
## Description
- Goal: Given a 2D closed shape (closed polygon) find its midcurve (polyline, closed or open)
- Input: set of points or ... | {} | yogeshkulkarni/MidcurveNN | null | [
"arxiv:1904.0429",
"region:us"
] | null | 2022-06-06T09:55:33+00:00 | [
"1904.0429"
] | [] | TAGS
#arxiv-1904.0429 #region-us
| # MidcurveNN
Midcurve by Neural Networks
!Midcurve
---
license: apache-2.0
---
## Description
- Goal: Given a 2D closed shape (closed polygon) find its midcurve (polyline, closed or open)
- Input: set of points or set of connected lines, non-intersecting, simple, convex, closed polygon
- Output: another set of poin... | [
"# MidcurveNN\nMidcurve by Neural Networks\n\n!Midcurve\n\n---\nlicense: apache-2.0\n---",
"## Description\n- Goal: Given a 2D closed shape (closed polygon) find its midcurve (polyline, closed or open)\n- Input: set of points or set of connected lines, non-intersecting, simple, convex, closed polygon \n- Output: ... | [
"TAGS\n#arxiv-1904.0429 #region-us \n",
"# MidcurveNN\nMidcurve by Neural Networks\n\n!Midcurve\n\n---\nlicense: apache-2.0\n---",
"## Description\n- Goal: Given a 2D closed shape (closed polygon) find its midcurve (polyline, closed or open)\n- Input: set of points or set of connected lines, non-intersecting, s... |
fill-mask | transformers |
# BanglaBERT
This repository contains the pretrained generator checkpoint of the model [**BanglaBERT**](). This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali corpora.
**Note**: This model was pretrai... | {"language": ["bn", "en"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglabert_generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"bn",
"en",
"arxiv:2101.00204",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T10:01:12+00:00 | [
"2101.00204"
] | [
"bn",
"en"
] | TAGS
#transformers #pytorch #electra #fill-mask #bn #en #arxiv-2101.00204 #autotrain_compatible #endpoints_compatible #region-us
|
# BanglaBERT
This repository contains the pretrained generator checkpoint of the model [BanglaBERT](). This is an ELECTRA generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali corpora.
Note: This model was pretrained using a specific normalization pipeline availabl... | [
"# BanglaBERT\n\nThis repository contains the pretrained generator checkpoint of the model [BanglaBERT](). This is an ELECTRA generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali corpora.\n\n\nNote: This model was pretrained using a specific normalization pipeline... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #bn #en #arxiv-2101.00204 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BanglaBERT\n\nThis repository contains the pretrained generator checkpoint of the model [BanglaBERT](). This is an ELECTRA generator model pretrained with the Masked Language ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Pa... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "sr... | binaya-s/xls-r-300m-en | null | [
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T10:04:29+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Base-960h
==================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Au... | [] | [
"TAGS\n#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- 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. -->
# repo_name
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the N... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "repo_name", "results": []}]} | zakria/repo_name | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T10:09:44+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# repo_name
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# repo_name\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# repo_name\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Int... |
text-classification | transformers |
<!-- 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. -->
# rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [cointegrated/rubert-tiny2]... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear", "results": []}]} | mmillet/rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T10:20:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| rubert-tiny2\_finetuned\_emotion\_experiment\_augmented\_anger\_fear
====================================================================
This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4049
* Accuracy: 0.8779
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets help at the beach.
Arthur goes to the beach. Arthur is feeli... | {} | jppaolim/v56_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T10:30:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets help at the beach.
Arthur goes to the beach. Arthur is feeli... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets help at the beach. \nArthur goes to the beach. Arthur ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They ... |
automatic-speech-recognition | transformers |
<!-- 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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | Kabir5296/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T10:35:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4102
* Wer: 0.3165
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text2text-generation | transformers |
<!-- 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. -->
# VN_ja_to_en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "VN_ja_to_en", "results": []}]} | twieland/VN_ja_to_en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:09:18+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| VN\_ja\_to\_en
==============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0411
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
text-classification | transformers | # tweet-topic-19-multi
This is a RoBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)) and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi).... | {} | cardiffnlp/tweet-topic-19-multi | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2202.03829",
"arxiv:2209.09824",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:14:49+00:00 | [
"2202.03829",
"2209.09824"
] | [] | TAGS
#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us
| tweet-topic-19-multi
====================
This is a RoBERTa-base model trained on ~90m tweets until the end of 2019 (see here) and finetuned for multi-label topic classification on a corpus of 11,267 tweets.
The original RoBERTa-base model can be found here and the original reference paper is TweetEval. This model is... | [] | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-cat | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:21:08+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-bedroom | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:21:20+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-cifar10 | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:21:38+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | galbraun/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:30:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5277
* Matthews Correlation: 0.5518
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | finBert_10k is a model that summarises the 10k documents, which are an essential part of the Investment management, so what's required is the text input and it is expected to give the summarized version of the text. It's fined tuned to the financial news summaries. | {} | Shivam29rathore/finBert_10k | null | [
"region:us"
] | null | 2022-06-06T11:34:59+00:00 | [] | [] | TAGS
#region-us
| finBert_10k is a model that summarises the 10k documents, which are an essential part of the Investment management, so what's required is the text input and it is expected to give the summarized version of the text. It's fined tuned to the financial news summaries. | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# finetuning_results
This model is a fine-tuned version of [DanielSM/finetuning_results](https://huggingface.co/DanielSM/finetunin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning_results", "results": []}]} | DanielSM/finetuning_results2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:36:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuning\_results
===================
This model is a fine-tuned version of DanielSM/finetuning\_results on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 12",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-banking77
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["banking77"], "metrics": ["accuracy", "f1"], "widget": [{"text": "Could you assist me in finding my lost card?", "example_title": "Example 1"}, {"text": "I found my lost card. Am I still able to use it?", "example_title": "Example 2"}, {"text": ... | optimum/distilbert-base-uncased-finetuned-banking77 | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T11:50:49+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-banking77 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-banking77
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the banking77 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2935
* Accuracy: 0.925
* F1: 0.9250
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.686210354742596e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-banking77 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Nitika/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Nitika/distilbert-base-uncased-finetuned-cola", "results": []}]} | Nitika/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T12:23:16+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Nitika/distilbert-base-uncased-finetuned-cola
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1924
* Validation Loss: 0.4890
* Train Matthews Correlation: 0.540... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1481727546186211329/U8Ae... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/byelihoff/1654564001530/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/byelihoff | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T12:43:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Eli Hoff
@byelihoff
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1459686915498819587/cYF4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bigmanbakar/1654523350313/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bigmanbakar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T12:48:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
AbuBakar Siddiq
@bigmanbakar
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rushic24/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | rushic24/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T12:48:55+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | spacy |
# Essay Grammar Checker
Essay Grammar Checker trained on [Russian Error-Annotated Learner English Corpus](https://realec.org).
## Training information
The checker consists of 6 pipelines each trained on specific error types.
Error Categories used for pipeline mapping:
```
"spelling":{"Spelling", "Capitalisati... | {"language": ["en"], "license": "cc-by-sa-3.0", "tags": ["Token Classification", "spacy", "SpanCategorizer", "grammar_checker", "essay_checker"]} | iproskurina/en_grammar_checker | null | [
"spacy",
"Token Classification",
"SpanCategorizer",
"grammar_checker",
"essay_checker",
"en",
"license:cc-by-sa-3.0",
"region:us"
] | null | 2022-06-06T12:50:13+00:00 | [] | [
"en"
] | TAGS
#spacy #Token Classification #SpanCategorizer #grammar_checker #essay_checker #en #license-cc-by-sa-3.0 #region-us
|
# Essay Grammar Checker
Essay Grammar Checker trained on Russian Error-Annotated Learner English Corpus.
## Training information
The checker consists of 6 pipelines each trained on specific error types.
Error Categories used for pipeline mapping:
Detailed information
Example usage in Colab | [
"# Essay Grammar Checker\n\nEssay Grammar Checker trained on Russian Error-Annotated Learner English Corpus.",
"## Training information\nThe checker consists of 6 pipelines each trained on specific error types.\nError Categories used for pipeline mapping: \n\n \n\nDetailed information\n\nExample usage in Colab"
... | [
"TAGS\n#spacy #Token Classification #SpanCategorizer #grammar_checker #essay_checker #en #license-cc-by-sa-3.0 #region-us \n",
"# Essay Grammar Checker\n\nEssay Grammar Checker trained on Russian Error-Annotated Learner English Corpus.",
"## Training information\nThe checker consists of 6 pipelines each trained... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1335009788212748291/X5Ey... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/briangrimmett/1654524569583/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/briangrimmett | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T12:51:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Brian Grimmett
@briangrimmett
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-esquad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "es", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_esquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.", "example_... | lmqg/mt5-small-esquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"es",
"dataset:lmqg/qg_esquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T12:52:09+00:00 | [
"2210.03992"
] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-esquad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_esquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: es
* Training data: lmqg/qg\_e... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: es\n* Training data: lmqg/qg\\_esquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: es\n... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ianspektor/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | ianspektor/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T12:58:11+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers | # tweet-topic-19-single
This is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)), and finetuned for single-label topic classification on a corpus of 6,997 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_sing... | {} | cardiffnlp/tweet-topic-19-single | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2202.03829",
"arxiv:2209.09824",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T13:06:50+00:00 | [
"2202.03829",
"2209.09824"
] | [] | TAGS
#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us
| # tweet-topic-19-single
This is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see here), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
The original roBERTa-base model can be found here and the original reference paper is TweetEval. This model is suitable for Eng... | [
"# tweet-topic-19-single\n\nThis is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see here), and finetuned for single-label topic classification on a corpus of 6,997 tweets.\nThe original roBERTa-base model can be found here and the original reference paper is TweetEval. This model is suitable... | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tweet-topic-19-single\n\nThis is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see here), and finetuned for single-label topic cl... |
automatic-speech-recognition | transformers |
<!-- 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-slovenian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-slovenian", "results": []}]} | bekirbakar/wav2vec2-large-xls-r-300m-slovenian | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T13:23:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-slovenian
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4462
* Wer: 0.3271
Training procedure
------------------
### Training Hyper-... | [
"### Training Hyper-parameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training Hyper-parameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* ... |
text-classification | transformers |
<!-- 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. -->
# ECHR_test_2 Task A
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["lex_glue"], "model-index": [{"name": "ECHR_test_2", "results": []}]} | QuentinKemperino/ECHR_test_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:lex_glue",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T13:24:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ECHR\_test\_2 Task A
====================
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the lex\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1998
* Macro-f1: 0.5295
* Micro-f1: 0.6157
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-0... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ubiqtuitin/deeprltutorial1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T13:30:07+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
# BanglishBERT
This repository contains the pretrained generator checkpoint of the model [**BanglishBERT**](). This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali and English corpora.
**Note**: This m... | {"language": ["bn", "en"], "tags": ["fill-mask"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglishbert_generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"bn",
"en",
"arxiv:2101.00204",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T13:37:28+00:00 | [
"2101.00204"
] | [
"bn",
"en"
] | TAGS
#transformers #pytorch #electra #fill-mask #bn #en #arxiv-2101.00204 #autotrain_compatible #endpoints_compatible #region-us
|
# BanglishBERT
This repository contains the pretrained generator checkpoint of the model [BanglishBERT](). This is an ELECTRA generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali and English corpora.
Note: This model was pretrained using a specific normalization p... | [
"# BanglishBERT\n\nThis repository contains the pretrained generator checkpoint of the model [BanglishBERT](). This is an ELECTRA generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali and English corpora.\n\n\nNote: This model was pretrained using a specific normal... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #bn #en #arxiv-2101.00204 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BanglishBERT\n\nThis repository contains the pretrained generator checkpoint of the model [BanglishBERT](). This is an ELECTRA generator model pretrained with the Masked Langu... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532336212412977152/TWPq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dkostanjsak-nonewthing/1654527393385/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dkostanjsak-nonewthing | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T13:47:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
AI & Domagoj Kostanjลกak
@dkostanjsak-nonewthing
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | # tweet-topic-21-single
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for single-label topic classification on a corpus of 6,997 [tweets](https://huggingface.co/datasets/cardiffnlp/t... | {} | cardiffnlp/tweet-topic-21-single | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2202.03829",
"arxiv:2209.09824",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T13:50:25+00:00 | [
"2202.03829",
"2209.09824"
] | [] | TAGS
#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us
| # tweet-topic-21-single
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see here), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
The original roBERTa-base model can be found here and the original reference paper is TweetEval. This model is su... | [
"# tweet-topic-21-single\n\nThis is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see here), and finetuned for single-label topic classification on a corpus of 6,997 tweets.\nThe original roBERTa-base model can be found here and the original reference paper is TweetEval. This mod... | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #arxiv-2202.03829 #arxiv-2209.09824 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tweet-topic-21-single\n\nThis is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see here), and finetuned for single-l... |
text-classification | transformers |
# tweet-topic-21-multi
This model is based on a [TimeLMs](https://github.com/cardiffnlp/timelms) language model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 1... | {"language": "en", "license": "mit", "datasets": ["cardiffnlp/tweet_topic_multi"], "metrics": ["f1", "accuracy"], "widget": [{"text": "It is great to see athletes promoting awareness for climate change."}], "pipeline_tag": "text-classification"} | cardiffnlp/tweet-topic-21-multi | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"dataset:cardiffnlp/tweet_topic_multi",
"arxiv:2209.09824",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-06T13:52:42+00:00 | [
"2209.09824"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #roberta #text-classification #en #dataset-cardiffnlp/tweet_topic_multi #arxiv-2209.09824 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| tweet-topic-21-multi
====================
This model is based on a TimeLMs language model trained on ~124M tweets from January 2018 to December 2021 (see here), and finetuned for multi-label topic classification on a corpus of 11,267 tweets. This model is suitable for English.
* Reference Paper: TweetTopic (COLING ... | [
"### BibTeX entry and citation info\n\n\nPlease cite the reference paper if you use this model."
] | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #en #dataset-cardiffnlp/tweet_topic_multi #arxiv-2209.09824 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info\n\n\nPlease cite the reference paper if you use this model."
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | ubiqtuitin/PPO_CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T13:58:31+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
token-classification | transformers |
<!-- 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. -->
# berturk-uncased-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingfac... | {"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz... | yanekyuk/berturk-uncased-keyword-discriminator | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T14:01:04+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| berturk-uncased-keyword-discriminator
=====================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3989
* Precision: 0.6234
* Recall: 0.6508
* Accuracy: 0.9145
* F1: 0.6368
* Ent/... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1318130998757019649/R8dW... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/aksumfootball-geirjordet-slawekmorawski/1654528907750/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/aksumfootball-geirjordet-slawekmorawski | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T14:10:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Geir Jordet & Karl Marius Aksum & Sลawek Morawski
@aksumfootball-geirjordet-slawekmorawski
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- 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-sla-en-finetuned-uk-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Hel... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus100"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-sla-en-finetuned-uk-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus100", "type": "opus1... | stopdoingmath/opus-mt-sla-en-finetuned-uk-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T14:18:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-sla-en-finetuned-uk-to-en
=================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-sla-en on the opus100 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7232
* Bleu: 27.7684
* Gen Len: 12.2485
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1505206395595104264/y3dW... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jeffwhou/1654530271923/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jeffwhou | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T14:33:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
URL
@jeffwhou
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | keras |
## Model description
This repo contains model weights for the the probabilistic model from [Probabilistic Bayesian Neural Networks](https://keras.io/examples/keras_recipes/bayesian_neural_networks/). This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of ... | {"library_name": "keras", "tags": ["probabilistic-models", "regression"]} | keras-io/ProbabalisticBayesianModel-Wine | null | [
"keras",
"tensorboard",
"probabilistic-models",
"regression",
"region:us"
] | null | 2022-06-06T14:36:50+00:00 | [] | [] | TAGS
#keras #tensorboard #probabilistic-models #regression #region-us
| Model description
-----------------
This repo contains model weights for the the probabilistic model from Probabilistic Bayesian Neural Networks. This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of uncertainty. We use TensorFlow Probability library, wh... | [
"### Training hyperparameters"
] | [
"TAGS\n#keras #tensorboard #probabilistic-models #regression #region-us \n",
"### Training hyperparameters"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ianspektor/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | ianspektor/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T14:56:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | pytorch |
# MemeBERT
Bert model fine-tined with [Memes dataset](https://github.com/mrsndmn/memes-dataset) | {"language": ["en"], "license": "mit", "library_name": "pytorch", "tags": ["meme classification"], "metrics": ["accuracy"]} | garutyunov/meme-bert | null | [
"pytorch",
"distilbert",
"meme classification",
"en",
"license:mit",
"region:us"
] | null | 2022-06-06T14:58:40+00:00 | [] | [
"en"
] | TAGS
#pytorch #distilbert #meme classification #en #license-mit #region-us
|
# MemeBERT
Bert model fine-tined with Memes dataset | [
"# MemeBERT\n\nBert model fine-tined with Memes dataset"
] | [
"TAGS\n#pytorch #distilbert #meme classification #en #license-mit #region-us \n",
"# MemeBERT\n\nBert model fine-tined with Memes dataset"
] |
null | null | Unconditional 256x256 Diffusion model trained on ~4100 hand-picked pixel art pieces.\
*Outputs* made with this model may be used however you wish without attribution--although attribution is always nice!
However, if you use this model in your own tool/app/notebook/commercial product/whatever, you MUST credit KaliYuga-... | {"license": "cc-by-3.0"} | KaliYuga/pixelartdiffusion4k | null | [
"license:cc-by-3.0",
"region:us"
] | null | 2022-06-06T14:59:40+00:00 | [] | [] | TAGS
#license-cc-by-3.0 #region-us
| Unconditional 256x256 Diffusion model trained on ~4100 hand-picked pixel art pieces.\
*Outputs* made with this model may be used however you wish without attribution--although attribution is always nice!
However, if you use this model in your own tool/app/notebook/commercial product/whatever, you MUST credit KaliYuga-... | [] | [
"TAGS\n#license-cc-by-3.0 #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/494875249347788801/0uf8T... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mattcocco/1654531718885/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mattcocco | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T15:06:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Matt Cocco
@mattcocco
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vjeansel/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | vjeansel/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T16:00:31+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | poltoran/RL-course-1-unit-ppo-LunarLander-v2-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T16:01:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vjeansel/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"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 +/... | vjeansel/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T16:02:48+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | ubiqtuitin/PPO_CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T16:09:22+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532336212412977152/TWPq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/nonewthing | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T16:49:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
AI
@nonewthing
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
Th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | miyagawaorj/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:06:58+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2466
* Accuracy: 0.9506
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
null | transformers |
<!-- 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-xlsr-greek-speech-emotion-recognition
This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek]... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-xlsr-greek-speech-emotion-recognition", "results": []}]} | cammy/wav2vec2-xlsr-greek-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:14:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-greek-speech-emotion-recognition
==============================================
This model is a fine-tuned version of lighteternal/wav2vec2-large-xlsr-53-greek on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7699
* Accuracy: 0.8168
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* ... |
text-classification | transformers |
<!-- 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. -->
# amazon_sentiment_sample_of_1900
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "amazon_sentiment_sample_of_1900", "results": []}]} | ett1112/amazon_sentiment_sample_of_1900 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:19:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# amazon_sentiment_sample_of_1900
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2185
- Accuracy: 0.9162
- F1: 0.9192
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# amazon_sentiment_sample_of_1900\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2185\n- Accuracy: 0.9162\n- F1: 0.9192",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# amazon_sentiment_sample_of_1900\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves ... |
text-classification | transformers |
<!-- 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. -->
# rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis
This model is a fine-tuned version of [cointegrated/rub... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis", "results": []}]} | mmillet/rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:22:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| rubert-tiny2\_finetuned\_emotion\_experiment\_augmented\_anger\_fear\_no\_emojis
================================================================================
This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
zero-shot-classification | transformers |
# DeBERTa-v3-large-mnli-fever-anli-ling-wanli
## Model description
This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli))... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "zero-shot-classification"], "datasets": ["multi_nli", "anli", "fever", "lingnli", "alisawuffles/WANLI"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "model-index": [{"name": "DeBERTa-v3-large-mnli-fever-anli-ling-wanli", "r... | MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:anli",
"dataset:fever",
"dataset:lingnli",
"dataset:alisawuffles/WANLI",
"arxiv:2104.07179",
"arxiv:2111.09543",
"license:mit",
"mode... | null | 2022-06-06T17:28:10+00:00 | [
"2104.07179",
"2111.09543"
] | [
"en"
] | TAGS
#transformers #pytorch #onnx #safetensors #deberta-v2 #text-classification #zero-shot-classification #en #dataset-multi_nli #dataset-anli #dataset-fever #dataset-lingnli #dataset-alisawuffles/WANLI #arxiv-2104.07179 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space ... | DeBERTa-v3-large-mnli-fever-anli-ling-wanli
===========================================
Model description
-----------------
This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing... | [
"### How to use the model",
"#### Simple zero-shot classification pipeline",
"#### NLI use-case",
"### Training data\n\n\nDeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs.... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #deberta-v2 #text-classification #zero-shot-classification #en #dataset-multi_nli #dataset-anli #dataset-fever #dataset-lingnli #dataset-alisawuffles/WANLI #arxiv-2104.07179 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-vios-v4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "wav2vec2-base-vios-v4", "results": []}]} | tclong/wav2vec2-base-vios-v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:29:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-vios-v4
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3198
* Wer: 0.2169
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* t... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/bert_attrs_qa_large
This model is a fine-tuned version of [ksabeh/distilbert-attribute-correction-mlm](https://huggingface.co/k... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/bert_attrs_qa_large", "results": []}]} | ksabeh/distilbert-attribute-correction-mlm-titles | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:32:03+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ksabeh/bert\_attrs\_qa\_large
=============================
This model is a fine-tuned version of ksabeh/distilbert-attribute-correction-mlm on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0560
* Validation Loss: 0.0722
* Epoch: 1
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 23878, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Poly... |
null | keras |
## Model Description
### Keras Implementation of Convolutional autoencoder for image denoising
This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.
Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder
Keras Example Link:- h... | {"license": "gpl-3.0"} | keras-io/conv_autoencoder | null | [
"keras",
"tensorboard",
"license:gpl-3.0",
"has_space",
"region:us"
] | null | 2022-06-06T17:37:58+00:00 | [] | [] | TAGS
#keras #tensorboard #license-gpl-3.0 #has_space #region-us
|
## Model Description
### Keras Implementation of Convolutional autoencoder for image denoising
This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.
Spaces Link:- URL
Keras Example Link:- URL
## Intended uses & limitations
- The trained mod... | [
"## Model Description",
"### Keras Implementation of Convolutional autoencoder for image denoising\n\nThis repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.\n\nSpaces Link:- URL\n\nKeras Example Link:- URL",
"## Intended uses & limitations... | [
"TAGS\n#keras #tensorboard #license-gpl-3.0 #has_space #region-us \n",
"## Model Description",
"### Keras Implementation of Convolutional autoencoder for image denoising\n\nThis repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.\n\nSpaces L... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468670117357789192/sStr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/russellriesjr/1654541578565/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/russellriesjr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T17:47:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Russell Ries Jr.
@russellriesjr
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# amazon_sentiment_sample_of_1900_with_summary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "amazon_sentiment_sample_of_1900_with_summary", "results": []}]} | ett1112/amazon_sentiment_sample_of_1900_with_summary | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T17:56:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# amazon_sentiment_sample_of_1900_with_summary
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1062
- Accuracy: 0.9581
- F1: 0.9579
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# amazon_sentiment_sample_of_1900_with_summary\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1062\n- Accuracy: 0.9581\n- F1: 0.9579",
"## Model description\n\nMore information needed",
"## Intended uses... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# amazon_sentiment_sample_of_1900_with_summary\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/americasnlp22-asr-bzd`
This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't... | {"language": "bzd", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["americasnlp22"]} | espnet/americasnlp22-asr-bzd | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"bzd",
"dataset:americasnlp22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-06T18:06:19+00:00 | [
"1804.00015"
] | [
"bzd"
] | TAGS
#espnet #audio #automatic-speech-recognition #bzd #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/americasnlp22-asr-bzd'
This model was trained by Pavel Denisov using americasnlp22 recipe in espnet.
### Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
RESULTS
=======
Environments
------------
* dat... | [
"### 'espnet/americasnlp22-asr-bzd'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Jun ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #bzd #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/americasnlp22-asr-bzd'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet in... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/americasnlp22-asr-gvc`
This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 66ca5df9f08b6084dbde4d9f312fa8ba0a47ecfc
pip install -e .
cd egs2/americasnlp22/... | {"language": "gvc", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["americasnlp22"]} | espnet/americasnlp22-asr-gvc | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"gvc",
"dataset:americasnlp22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-06T18:07:35+00:00 | [
"1804.00015"
] | [
"gvc"
] | TAGS
#espnet #audio #automatic-speech-recognition #gvc #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/americasnlp22-asr-gvc'
This model was trained by Pavel Denisov using americasnlp22 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Jun 5 03:29:33 CEST 2022'
* python version: '3.9.13 (main, May 18 2022, ... | [
"### 'espnet/americasnlp22-asr-gvc'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Jun 5 03:29:33 CEST 2022'\n* python version: '3.9.13 (main, May 18 2022, 00:00:00) [GCC ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #gvc #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/americasnlp22-asr-gvc'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/americasnlp22-asr-tav`
This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't... | {"language": "tav", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["americasnlp22"]} | espnet/americasnlp22-asr-tav | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"tav",
"dataset:americasnlp22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-06T18:08:34+00:00 | [
"1804.00015"
] | [
"tav"
] | TAGS
#espnet #audio #automatic-speech-recognition #tav #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/americasnlp22-asr-tav'
This model was trained by Pavel Denisov using americasnlp22 recipe in espnet.
### Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
RESULTS
=======
Environments
------------
* dat... | [
"### 'espnet/americasnlp22-asr-tav'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Jun ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #tav #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/americasnlp22-asr-tav'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet in... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juancopi81/marian-finetuned-kde4-en-to-es
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/He... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "juancopi81/marian-finetuned-kde4-en-to-es", "results": []}]} | juancopi81/marian-finetuned-kde4-en-to-es | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T18:40:46+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| juancopi81/marian-finetuned-kde4-en-to-es
=========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-es on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6269
* Validation Loss: 0.7437
* Epoch: 2
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 18447, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text2text-generation | transformers |
<!-- 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. -->
# rob2rand_merged_w_prefix_c_fc_interactive
This model was trained from scratch on the None dataset.
## Model description
More i... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rob2rand_merged_w_prefix_c_fc_interactive", "results": []}]} | imamnurby/rob2rand_merged_w_prefix_c_fc_interactive | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-06T18:45:22+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# rob2rand_merged_w_prefix_c_fc_interactive
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# rob2rand_merged_w_prefix_c_fc_interactive\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# rob2rand_merged_w_prefix_c_fc_interactive\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jplago/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jplago/bert-finetuned-ner", "results": []}]} | jplago/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T18:58:03+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jplago/bert-finetuned-ner
=========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0270
* Validation Loss: 0.0550
* Epoch: 2
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
null | null | Prueba | {} | danifelpo/GPT2-Poems-Generation | null | [
"region:us"
] | null | 2022-06-06T19:14:32+00:00 | [] | [] | TAGS
#region-us
| Prueba | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# amazon_sentiment_sample_of_1900_with_summary_larger_test
This model is a fine-tuned version of [distilbert-base-uncased](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "amazon_sentiment_sample_of_1900_with_summary_larger_test", "results": []}]} | daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T19:47:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# amazon_sentiment_sample_of_1900_with_summary_larger_test
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1489
- Accuracy: 0.9503
- F1: 0.9504
## Model description
More information needed
## Intended uses & lim... | [
"# amazon_sentiment_sample_of_1900_with_summary_larger_test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1489\n- Accuracy: 0.9503\n- F1: 0.9504",
"## Model description\n\nMore information needed",
"## I... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# amazon_sentiment_sample_of_1900_with_summary_larger_test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the No... |
reinforcement-learning | stable-baselines3 |
# **TQC** Agent playing **RocketLander-v0**
This is a trained model of a **TQC** agent playing **RocketLander-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["RocketLander-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "RocketLander-v0", "type": "Rock... | araffin/tqc-RocketLander-v0 | null | [
"stable-baselines3",
"RocketLander-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-06T19:48:41+00:00 | [] | [] | TAGS
#stable-baselines3 #RocketLander-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TQC Agent playing RocketLander-v0
This is a trained model of a TQC agent playing RocketLander-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (wi... | [
"# TQC Agent playing RocketLander-v0\nThis is a trained model of a TQC agent playing RocketLander-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #RocketLander-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TQC Agent playing RocketLander-v0\nThis is a trained model of a TQC agent playing RocketLander-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framew... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Cole/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T19:49:59+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1428
* F1: 0.8662
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text2text-generation | transformers |
<!-- 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. -->
# mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name":... | nestoralvaro/mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T21:08:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-mlsum\_\_\_summary\_text\_google\_mt5\_base
===================================================================
This model is a fine-tuned version of google/mt5-base on the mlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 8.9973
* Rouge2: 0.9036
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
text-classification | transformers |
<!-- 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. -->
# IA_Trabalho01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "IA_Trabalho01", "results": []}]} | Lorenzo1708/IA_Trabalho01 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T21:13:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# IA_Trabalho01
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2717
- Accuracy: 0.8990
- F1: 0.8987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# IA_Trabalho01\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2717\n- Accuracy: 0.8990\n- F1: 0.8987",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# IA_Trabalho01\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following re... |
null | null |
# Model Card for model-card-testing
<!-- Provide a quick summary of what the model is/does. [Optional] -->
This is a placeholder summary.
<details>
<summary> Click to expand policymaker version of model card </summary>
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and L... | {"language": ["en", "fr", "multilingual"], "license": "mit"} | Marissa/model-card-testing | null | [
"en",
"fr",
"multilingual",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-06-06T21:16:21+00:00 | [
"1910.09700"
] | [
"en",
"fr",
"multilingual"
] | TAGS
#en #fr #multilingual #arxiv-1910.09700 #license-mit #region-us
|
# Model Card for model-card-testing
This is a placeholder summary.
<details>
<summary> Click to expand policymaker version of model card </summary>
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Model Examination
5. Environmental Impact
6. Citation
7. Glossary
8. More Informatio... | [
"# Model Card for model-card-testing\n\n\nThis is a placeholder summary.\n\n\n<details>\n<summary> Click to expand policymaker version of model card </summary>",
"# Table of Contents \n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Model Examination\n5. Environmental Impact\n6. Citation\n7. Glos... | [
"TAGS\n#en #fr #multilingual #arxiv-1910.09700 #license-mit #region-us \n",
"# Model Card for model-card-testing\n\n\nThis is a placeholder summary.\n\n\n<details>\n<summary> Click to expand policymaker version of model card </summary>",
"# Table of Contents \n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Li... |
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