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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | stable-baselines3 |
# **TQC** Agent playing **donkey-mountain-track-v0**
This is a trained model of a **TQC** agent playing **donkey-mountain-track-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 ... | {"library_name": "stable-baselines3", "tags": ["donkey-mountain-track-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "donkey-mountain-track-... | araffin/tqc-donkey-mountain-track-v0 | null | [
"stable-baselines3",
"donkey-mountain-track-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-04T19:07:42+00:00 | [] | [] | TAGS
#stable-baselines3 #donkey-mountain-track-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TQC Agent playing donkey-mountain-track-v0
This is a trained model of a TQC agent playing donkey-mountain-track-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 inclu... | [
"# TQC Agent playing donkey-mountain-track-v0\nThis is a trained model of a TQC agent playing donkey-mountain-track-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 a... | [
"TAGS\n#stable-baselines3 #donkey-mountain-track-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TQC Agent playing donkey-mountain-track-v0\nThis is a trained model of a TQC agent playing donkey-mountain-track-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe ... |
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-urdu-asr-commom-voice-9.0_model_final
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-urdu-asr-commom-voice-9.0_model_final", "results": []}]} | Abdullah010/wav2vec2-urdu-asr-commom-voice-9.0_model_final | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T19:16:35+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-urdu-asr-commom-voice-9.0\_model\_final
================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9620
* Wer: 1.0059
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 0.0001\n* t... |
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. -->
# camembert-keyword-discriminator
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "model-index": [{"name": "camembert-keyword-discriminator", "results": []}]} | yanekyuk/camembert-keyword-discriminator | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T19:23:44+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-keyword-discriminator
===============================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2180
* Precision: 0.6646
* Recall: 0.7047
* Accuracy: 0.9344
* F1: 0.6841
* Ent/precision: 0.7185
* Ent/accur... | [
"### 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 #camembert #token-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: 16\n* e... |
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/378800000155926309/6204f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tomcooper26-tomncooper/1654379583668/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tomcooper26-tomncooper | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T20:52:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Tom Cooper & Tom Cooper
@tomcooper26-tomncooper
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"
] |
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. -->
# t5-small_6_3-en-hi_en_LinCE
This model was trained from scratch on the None dataset.
It achieves the following results on the ev... | {"tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small_6_3-en-hi_en_LinCE", "results": []}]} | sayanmandal/t5-small_6_3-en-hi_en_LinCE | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T20:54:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small\_6\_3-en-hi\_en\_LinCE
===============================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2034
* Bleu: 7.8135
* Gen Len: 39.5564
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000... |
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... | jianyang/LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-04T20:57:52+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 | 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... | nutjung/TEST2ppo-LunarLander-v2-4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-04T21:08:07+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... |
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-128k-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-cased](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-128k-keyword-discriminator | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T22:57:09+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| berturk-128k-keyword-discriminator
==================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-128k-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3828
* Precision: 0.6791
* Recall: 0.7234
* Accuracy: 0.9294
* F1: 0.7006
* Ent/pre... | [
"### 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/636093493207666689/dLDyc... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/thundering165/1654388210270/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/thundering165 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T23:15:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Paul Harvey
@thundering165
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"
] |
image-classification | keras |
# Tensorflow Keras implementation of : [CutMix data augmentation for image classification](https://keras.io/examples/vision/cutmix/)
The full credit goes to: [Sayan Nath](https://twitter.com/sayannath2350)
## The Data augmentation strategy
CutMix is a data Augmentation strategy where some portion of the training ex... | {"library_name": "keras", "tags": ["data-augmentation", "image-classification"]} | keras-io/CutMix_data_augmentation_for_image_classification | null | [
"keras",
"tensorboard",
"data-augmentation",
"image-classification",
"arxiv:1905.04899",
"has_space",
"region:us"
] | null | 2022-06-04T23:27:20+00:00 | [
"1905.04899"
] | [] | TAGS
#keras #tensorboard #data-augmentation #image-classification #arxiv-1905.04899 #has_space #region-us
|
# Tensorflow Keras implementation of : CutMix data augmentation for image classification
The full credit goes to: Sayan Nath
## The Data augmentation strategy
CutMix is a data Augmentation strategy where some portion of the training example is removed and pasted with the content from other images in the training se... | [
"# Tensorflow Keras implementation of : CutMix data augmentation for image classification\n\nThe full credit goes to: Sayan Nath",
"## The Data augmentation strategy\n\nCutMix is a data Augmentation strategy where some portion of the training example is removed and pasted with the content from other images in the... | [
"TAGS\n#keras #tensorboard #data-augmentation #image-classification #arxiv-1905.04899 #has_space #region-us \n",
"# Tensorflow Keras implementation of : CutMix data augmentation for image classification\n\nThe full credit goes to: Sayan Nath",
"## The Data augmentation strategy\n\nCutMix is a data Augmentation ... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | murdockthedude/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T23:44:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1078
* Precision: 0.8665
* Recall: 0.8817
* F1: 0.8740
* Accuracy: 0.9717
Model des... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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* le... |
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. -->
# t5-small-gec
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model de... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-gec", "results": []}]} | juancavallotti/t5-small-gec | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T00:06:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-gec
This model is a fine-tuned version of t5-small on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
"# t5-small-gec\n\nThis model is a fine-tuned version of t5-small 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",
"### Training hype... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-gec\n\nThis model is a fine-tuned version of t5-small on the None dataset.",
"## Model description\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425
## Validation Metrics
- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted ... | {"language": "en", "tags": "autotrain", "datasets": ["nitishkumargundapu793/autotrain-data-chat-bot-responses"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.01123534537751425} | nitishkumargundapu793/autotrain-chat-bot-responses-949231426 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:nitishkumargundapu793/autotrain-data-chat-bot-responses",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T02:13:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-nitishkumargundapu793/autotrain-data-chat-bot-responses #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425
## Validation Metrics
- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 949231426\n- CO2 Emissions (in grams): 0.01123534537751425",
"## Validation Metrics\n\n- Loss: 0.26922607421875\n- Accuracy: 1.0\n- Macro F1: 1.0\n- Micro F1: 1.0\n- Weighted F1: 1.0\n- Macro Precision: 1.0\n- Micro Precis... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-nitishkumargundapu793/autotrain-data-chat-bot-responses #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9492314... |
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. -->
# reqbert-tapt-epoch29
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqbert-tapt-epoch29", "results": []}]} | limsc/reqbert-tapt-epoch29 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T02:38:37+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# reqbert-tapt-epoch29
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqbert-tapt-epoch29\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqbert-tapt-epoch29\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:... |
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. -->
# reqbert-tapt-epoch30
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqbert-tapt-epoch30", "results": []}]} | limsc/reqbert-tapt-epoch30 | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T02:50:32+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# reqbert-tapt-epoch30
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqbert-tapt-epoch30\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqbert-tapt-epoch30\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:... |
sentence-similarity | sentence-transformers |
# kimcando/sbert-kornli-knoSTS-trained
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | kimcando/sbert-kornli-knoSTS-trained | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T03:19:09+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# kimcando/sbert-kornli-knoSTS-trained
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers inst... | [
"# kimcando/sbert-kornli-knoSTS-trained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transfo... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# kimcando/sbert-kornli-knoSTS-trained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tas... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ITESM/st_demo_2 | null | [
"sentence-transformers",
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"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T03:37:57+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
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| all-MiniLM-L6-v2
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncase... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ITESM/st_demo_5 | null | [
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"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T03:55:40+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us
| all-MiniLM-L6-v2
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncase... |
sentence-similarity | sentence-transformers |
# ITESM/sentece-embeddings-BETO
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model b... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["stackexchange_xml", "code_search_net"], "pipeline_tag": "sentence-similarity"} | ITESM/sentece-embeddings-BETO | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:stackexchange_xml",
"dataset:code_search_net",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T04:04:52+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dataset-stackexchange_xml #dataset-code_search_net #endpoints_compatible #region-us
|
# ITESM/sentece-embeddings-BETO
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# ITESM/sentece-embeddings-BETO\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers i... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dataset-stackexchange_xml #dataset-code_search_net #endpoints_compatible #region-us \n",
"# ITESM/sentece-embeddings-BETO\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimension... |
sentence-similarity | sentence-transformers |
# bertin-roberta-base-finetuning-esnli
This is a [sentence-transformers](https://www.SBERT.net) model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siamese network... | {"language": ["es"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["hackathon-pln-es/nli-es"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."}, {"text": "La huelga es el m\u00e9todo ... | ITESM/st_demo_6 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"es",
"dataset:hackathon-pln-es/nli-es",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T04:05:57+00:00 | [
"1908.10084"
] | [
"es"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #es #dataset-hackathon-pln-es/nli-es #arxiv-1908.10084 #endpoints_compatible #region-us
| bertin-roberta-base-finetuning-esnli
====================================
This is a sentence-transformers model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siam... | [] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #es #dataset-hackathon-pln-es/nli-es #arxiv-1908.10084 #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# espejelomar/sentece-embeddings-BETO
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this m... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "code_search_net"], "pipeline_tag": "sentence-similarity"} | espejelomar/sentece-embeddings-BETO | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:code_search_net",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T04:32:47+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-code_search_net #endpoints_compatible #region-us
|
# espejelomar/sentece-embeddings-BETO
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers insta... | [
"# espejelomar/sentece-embeddings-BETO\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transfor... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-code_search_net #endpoints_compatible #region-us \n",
"# espejelomar/sentece-embeddings-BETO\n\nThis is a sentence-transformers model: It maps sentences ... |
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-hbtest-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hbtest-2", "results": []}]} | sriiikar/wav2vec2-hbtest-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T05:34:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hbtest-2
=================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9927
* Wer: 1.1562
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
fill-mask | transformers | # BERTpt
```
from transformers import BertModel, BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('joaomsimoes/bertpt-portuguese-portugal')
model = BertModel.from_pretrained("joaomsimoes/bertpt-portuguese-portugal")
text = "Tudo vale a pena quando a alma não é pequena."
encoded_input = tokenizer(text, ... | {} | joaomsimoes/bertpt-portuguese-portugal | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T06:00:08+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BERTpt
Pretrained model on Portuguese (Portugal) language using a masked language modeling (MLM) objective. Notebook
## Training data
Scrapped data from diferent portugues websites, blogs and news channels. Around 2Gb of data.
## Limitations and Bias
| [
"# BERTpt\n\n\n\nPretrained model on Portuguese (Portugal) language using a masked language modeling (MLM) objective. Notebook",
"## Training data\n\nScrapped data from diferent portugues websites, blogs and news channels. Around 2Gb of data.",
"## Limitations and Bias"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERTpt\n\n\n\nPretrained model on Portuguese (Portugal) language using a masked language modeling (MLM) objective. Notebook",
"## Training data\n\nScrapped data from diferent portugues webs... |
text-to-image | null | # ruDALL-E Cities
### Generate illustrations of cities from image prompts and/or text
Finetuned from [Malevich XL](https://huggingface.co/sberbank-ai/rudalle-Malevich) on thousands of anime screenshots of cities. For more information on training, see https://github.com/Xibanya/ru-dalle
<img src="https://huggingface.c... | {"language": ["ru", "en"], "license": "cc-by-nc-4.0", "tags": ["PyTorch", "Transformers"], "pipeline_tag": "text-to-image"} | Xibanya/City | null | [
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-06-05T06:02:45+00:00 | [] | [
"ru",
"en"
] | TAGS
#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-nc-4.0 #has_space #region-us
| # ruDALL-E Cities
### Generate illustrations of cities from image prompts and/or text
Finetuned from Malevich XL on thousands of anime screenshots of cities. For more information on training, see URL
<img src="URL width="1024" height="1024"> | [
"# ruDALL-E Cities",
"### Generate illustrations of cities from image prompts and/or text\n\nFinetuned from Malevich XL on thousands of anime screenshots of cities. For more information on training, see URL\n\n<img src=\"URL width=\"1024\" height=\"1024\">"
] | [
"TAGS\n#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-nc-4.0 #has_space #region-us \n",
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"### Generate illustrations of cities from image prompts and/or text\n\nFinetuned from Malevich XL on thousands of anime screenshots of cities. For more information on training, see URL\n\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_multilingual_XLSum-finetuned-xsum-xsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://hug... | {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-xsum-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"},... | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-xsum | null | [
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"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T07:09:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetuned-xsum-xsum
============================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
* G... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | olpa/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T07:11:22+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1643
* F1: 0.8626
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #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: 5e-05\n* train\\_batch\\_size: 24\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. -->
# xml-roberta-base-finetuned-panx-fr
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": "xml-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | olpa/xml-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T07:27:35+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xml-roberta-base-finetuned-panx-fr
==================================
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: 0.2691
* F1: 0.8394
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: 24\n* eval\\_batch\\_size: 24\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... |
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/1272169107077677057/Cpv0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/cboldisor/1654418897981/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/cboldisor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T07:47:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Costin Boldisor
@cboldisor
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"
] |
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. -->
# layoutlmv2-cord-ner
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/l... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv2-cord-ner", "results": []}]} | renjithks/layoutlmv2-cord-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T07:59:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv2-cord-ner
===================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0952
* Precision: 0.9639
* Recall: 0.9741
* F1: 0.9690
* Accuracy: 0.9911
Model description
-----------------
... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-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: 5e-05\n* tra... |
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_multilingual_XLSum-finetuned-xsum-mlsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://hu... | {"tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-xsum-mlsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "mlsum", "type": "mlsum", "args": "es"}, ... | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T08:56:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetuned-xsum-mlsum
=============================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the mlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
... | [
"### 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 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
text-generation | transformers |
#Raiden from Metal Gear Rising DialoGPT Model | {"tags": ["conversational"]} | Cirilaron/DialoGPT-medium-raiden | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T08:58:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Raiden from Metal Gear Rising DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was driving home from work. He had to stop at a gas station. He bought a lottery ticket and bought some tickets. He bought some lottery tickets and played them all. He got really lucky and was won ... | {} | jppaolim/v52_Large | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T09:25:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was driving home from work. He had to stop at a gas station. He bought a lottery ticket and bought some tickets. He bought some lottery tickets and played them all. He got really lucky and was won ... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur was driving home from work. He had to stop at a gas station. He bought a lottery ticket and bought some tickets. He bought some lottery tickets and played them all. He got really lucky and w... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was driving home from work. He had to stop at a ... |
null | keras | # Tensorflow Keras Implementation of Structured data classification from scratch
This repo contains models and notebook for [Structured data classification from scratch](https://keras.io/examples/structured_data/structured_data_classification_from_scratch/).
This example demonstrates how to do structured data classif... | {"library_name": "keras", "tags": ["structured-data", "tabular-data", "classification"]} | keras-io/structured-data-classification | null | [
"keras",
"tensorboard",
"structured-data",
"tabular-data",
"classification",
"has_space",
"region:us"
] | null | 2022-06-05T09:35:23+00:00 | [] | [] | TAGS
#keras #tensorboard #structured-data #tabular-data #classification #has_space #region-us
| # Tensorflow Keras Implementation of Structured data classification from scratch
This repo contains models and notebook for Structured data classification from scratch.
This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical fe... | [
"# Tensorflow Keras Implementation of Structured data classification from scratch\nThis repo contains models and notebook for Structured data classification from scratch. \nThis example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categor... | [
"TAGS\n#keras #tensorboard #structured-data #tabular-data #classification #has_space #region-us \n",
"# Tensorflow Keras Implementation of Structured data classification from scratch\nThis repo contains models and notebook for Structured data classification from scratch. \nThis example demonstrates how to do str... |
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... | Rgl73/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T09:46:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 0.1446
* F1: 0.8609
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: 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 #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\\_... |
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... | kzvdar42/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T10:24:26+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... |
null | transformers |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This is a pre-trained Ukrainian wav2vec2 XLS-R model with 300m parameters (dataset is 323h, source of speech is **broadcast** programs).
Steps: 400... | {"license": "apache-2.0"} | Yehor/wav2vec2-xls-r-300m-uk | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T10:51:29+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #license-apache-2.0 #endpoints_compatible #region-us
|
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - URL
This is a pre-trained Ukrainian wav2vec2 XLS-R model with 300m parameters (dataset is 323h, source of speech is broadcast programs).
Steps: 400,000
The model is not intended to do inference, i... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #license-apache-2.0 #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. -->
# t5-base-gec
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model descr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-gec", "results": []}]} | juancavallotti/t5-base-gec | null | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T11:00:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #onnx #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-gec
This model is a fine-tuned version of t5-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 following hyper... | [
"# t5-base-gec\n\nThis model is a fine-tuned version of t5-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",
"### Training hyperp... | [
"TAGS\n#transformers #pytorch #tensorboard #onnx #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-gec\n\nThis model is a fine-tuned version of t5-base on the None dataset.",
"## Model descripti... |
text-generation | transformers |
DialoGPT on Russian language
Based on [Grossmend/rudialogpt3_medium_based_on_gpt2](https://huggingface.co/Grossmend/rudialogpt3_medium_based_on_gpt2)
Fine tuned on [2ch /b/ dialogues](https://huggingface.co/datasets/BlackSamorez/2ch_b_dialogues) data. To improve performance replies were filtered by obscenity.
Used... | {"language": ["ru"], "tags": ["conversational"], "datasets": "BlackSamorez/2ch_b_dialogues"} | BlackSamorez/rudialogpt3_medium_based_on_gpt2_2ch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"ru",
"dataset:BlackSamorez/2ch_b_dialogues",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T11:28:06+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ru #dataset-BlackSamorez/2ch_b_dialogues #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
DialoGPT on Russian language
Based on Grossmend/rudialogpt3_medium_based_on_gpt2
Fine tuned on 2ch /b/ dialogues data. To improve performance replies were filtered by obscenity.
Used in Ebanko Telegram bot.
You can find code for deployment on my github.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #ru #dataset-BlackSamorez/2ch_b_dialogues #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | More Information of [KoT5](https://bit.ly/3SrHq36) | {"license": "apache-2.0", "languages": ["ko"]} | psyche/KoT5-paraphrase-generation | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T11:31:06+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| More Information of KoT5 | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# TinyBERT_General_4L_312D-squad
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "TinyBERT_General_4L_312D-squad", "results": []}]} | haritzpuerto/TinyBERT_General_4L_312D-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T11:50:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
| TinyBERT\_General\_4L\_312D-squad
=================================
This model is a fine-tuned version of huawei-noah/TinyBERT\_General\_4L\_312D on the squad dataset.
It achieves the following results on the evaluation set:
* exact\_match: 33.301797540208135
* f1: 45.03886349847048
* Loss: 2.5477
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #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: 20\n* eval\\_batch\\_... |
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="shikhar1997/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"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": ... | shikhar1997/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T11:55:50+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 | 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="rotvderme/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | rotvderme/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T11:58:30+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"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-dequad-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_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "de", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_dequad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Empfangs- und Sendeantenne sollen in ihrer Polarisation \u00fcbereinstimmen, ande... | lmqg/mt5-small-dequad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"de",
"dataset:lmqg/qg_dequad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T12:00:59+00:00 | [
"2210.03992"
] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #de #dataset-lmqg/qg_dequad #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-dequad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_dequad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: de
* Training data: lmqg/qg\_d... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: de\n* Training data: lmqg/qg\\_dequad (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 #de #dataset-lmqg/qg_dequad #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: de\n... |
text-classification | transformers |
# 繁體中文情緒分類: 負面(0)、正面(1)
依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。
# 使用範例:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sent... | {"language": ["tw"], "license": "afl-3.0", "tags": ["albert", "classification"], "metrics": ["Accuracy"]} | clhuang/albert-sentiment | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"albert",
"classification",
"tw",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T12:17:21+00:00 | [] | [
"tw"
] | TAGS
#transformers #pytorch #bert #text-classification #albert #classification #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 繁體中文情緒分類: 負面(0)、正面(1)
依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。
# 使用範例:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sent... | [
"# 繁體中文情緒分類: 負面(0)、正面(1) \n\n依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。",
"# 使用範例:\n\n from transformers import AutoTokenizer, AutoModelForSequenceClassification\n tokenizer = AutoTokenizer.from_pretrained(\"clhuang/albert-sentiment\")\n model = AutoModelForSequenceClassification.from_pretrained(\"cl... | [
"TAGS\n#transformers #pytorch #bert #text-classification #albert #classification #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 繁體中文情緒分類: 負面(0)、正面(1) \n\n依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。",
"# 使用範例:\n\n from transformers import AutoTokenizer, AutoModelForSequ... |
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="shikhar1997/q-Taxi-v3-ver1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fals... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-ver1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.... | shikhar1997/q-Taxi-v3-ver1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T12:17:33+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 | 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="rotvderme/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 +/... | rotvderme/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T12:28:49+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"
] |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was driving his dog on the highway. He felt a breeze approaching him. He took his dog out and let her out. The dog jumped and got lost. Arthur found her under a tree for her.
Arthur goes to the b... | {} | jppaolim/v53_Large_AdaMW | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T12:40:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was driving his dog on the highway. He felt a breeze approaching him. He took his dog out and let her out. The dog jumped and got lost. Arthur found her under a tree for her.
Arthur goes to the b... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur was driving his dog on the highway. He felt a breeze approaching him. He took his dog out and let her out. The dog jumped and got lost. Arthur found her under a tree for her. \nArthur goes ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was driving his dog on the highway. He felt a br... |
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... | Matt00n/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T12:46:01+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... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-dequad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github... | {"language": "de", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_dequad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: Empfangs- und Sendeantenne sollen in ihre... | lmqg/mt5-small-dequad-qg-ae | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"answer extraction",
"de",
"dataset:lmqg/qg_dequad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T13:09:05+00:00 | [
"2210.03992"
] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #de #dataset-lmqg/qg_dequad #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-dequad-qg-ae'
===========================================
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg\_dequad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language:... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: de\n* Training data: lmqg/qg\\_dequad (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 #answer extraction #de #dataset-lmqg/qg_dequad #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-smal... |
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/1496963787655716869/MJrz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/philwornath/1654438397344/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/philwornath | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T13:12:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Phil Wornath 🇪🇺
@philwornath
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="sswt/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"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": ... | sswt/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T13:20:54+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"
] |
text-classification | transformers | DistilBERT pokemon model (uncased)
This model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English.
Model description
DistilBERT Wikipedia Pokemon model ... | {} | mrcoombes/distilbert-wikipedia-pokemon | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-05T14:07:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBERT pokemon model (uncased)
This model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English.
Model description
DistilBERT Wikipedia Pokemon model ... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \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. -->
# layoutlmv2-er-ner
This model is a fine-tuned version of [renjithks/layoutlmv2-cord-ner](https://huggingface.co/renjithks/layoutl... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv2-er-ner", "results": []}]} | renjithks/layoutlmv2-er-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T14:40:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv2-er-ner
=================
This model is a fine-tuned version of renjithks/layoutlmv2-cord-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1217
* Precision: 0.7810
* Recall: 0.8085
* F1: 0.7945
* Accuracy: 0.9747
Model description
-----------------
More inf... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-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: 5e-05\n* tra... |
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. -->
# t5-small_6_3-en-hi_en_bt
This model was trained from scratch on the None dataset.
It achieves the following results on the evalu... | {"tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small_6_3-en-hi_en_bt", "results": []}]} | sayanmandal/t5-small_6_3-en-hi_en_bt | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T14:44:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small\_6\_3-en-hi\_en\_bt
============================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9293
* Bleu: 8.9676
* Gen Len: 33.391
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000... |
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... | thenewcompany/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T14:54:51+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... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC trained on LibriSpeech
This repository provides all the necessary tools to perform au... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Transformer", "pytorch", "speechbrain", "hf-asr-leaderboard"], "datasets": ["librispeech"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "wav2vec2+CTC by Spe... | speechbrain/asr-wav2vec2-librispeech | null | [
"speechbrain",
"wav2vec2",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformer",
"pytorch",
"hf-asr-leaderboard",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"license:apache-2.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-05T15:02:16+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #wav2vec2 #automatic-speech-recognition #CTC #Attention #Transformer #pytorch #hf-asr-leaderboard #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #model-index #has_space #region-us
|
wav2vec 2.0 with CTC trained on LibriSpeech
===========================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (English Language) within
SpeechBrain. For a better experience, we encourage you ... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #wav2vec2 #automatic-speech-recognition #CTC #Attention #Transformer #pytorch #hf-asr-leaderboard #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #model-index #has_space #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform... |
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-cardiffnlp-sentiment-model
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](h... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning-cardiffnlp-sentiment-model", "results": []}]} | anvay/finetuning-cardiffnlp-sentiment-model | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T15:44:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-cardiffnlp-sentiment-model
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2685
- Accuracy: 0.9165
## Model description
More information needed
## Intended uses & limitati... | [
"# finetuning-cardiffnlp-sentiment-model\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2685\n- Accuracy: 0.9165",
"## Model description\n\nMore information needed",
"## Intende... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-cardiffnlp-sentiment-model\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.\nIt... |
null | keras |
## Model description
This model is a 3D convolutional neural network model trained to predict the presence of viral pneumonia in computer tomography scans.
[Spaces link](https://huggingface.co/spaces/keras-io/3D_CNN_Pneumonia)
[Keras Example Link](https://keras.io/examples/vision/3D_image_classification/)
## Trai... | {"library_name": "keras", "tags": ["3D-image-classification"]} | keras-io/3D_CNN_Pneumonia | null | [
"keras",
"tensorboard",
"3D-image-classification",
"has_space",
"region:us"
] | null | 2022-06-05T15:55:48+00:00 | [] | [] | TAGS
#keras #tensorboard #3D-image-classification #has_space #region-us
|
## Model description
This model is a 3D convolutional neural network model trained to predict the presence of viral pneumonia in computer tomography scans.
Spaces link
Keras Example Link
## Training and evaluation data
Subset of the MosMedData: Chest CT Scans with COVID-19 Related Findings
## Training procedure... | [
"## Model description\n\nThis model is a 3D convolutional neural network model trained to predict the presence of viral pneumonia in computer tomography scans.\n\nSpaces link\n\nKeras Example Link",
"## Training and evaluation data\n\nSubset of the MosMedData: Chest CT Scans with COVID-19 Related Findings",
"##... | [
"TAGS\n#keras #tensorboard #3D-image-classification #has_space #region-us \n",
"## Model description\n\nThis model is a 3D convolutional neural network model trained to predict the presence of viral pneumonia in computer tomography scans.\n\nSpaces link\n\nKeras Example Link",
"## Training and evaluation data\n... |
text-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. -->
# gpt2-p4k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-p4k", "results": []}]} | EmileEsmaili/gpt2-p4k | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T16:16:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-p4k
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperpara... | [
"# gpt2-p4k\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparam... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-p4k\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informati... |
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_multilingual_XLSum-finetuned-xsum-mlsum___summary_text
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XL... | {"tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "mlsum", "type": "mlsum", ... | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T16:24:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetuned-xsum-mlsum\_\_\_summary\_text
================================================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the mlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.0
* Rouge... | [
"### 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 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
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. -->
# robingeibel/longformer-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/longformer-base-finetuned-big_pate... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "robingeibel/longformer-base-finetuned-big_patent", "results": []}]} | robingeibel/longformer-base-finetuned-big_patent | null | [
"transformers",
"tf",
"longformer",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T16:24:27+00:00 | [] | [] | TAGS
#transformers #tf #longformer #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| robingeibel/longformer-base-finetuned-big\_patent
=================================================
This model is a fine-tuned version of robingeibel/longformer-base-finetuned-big\_patent on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1860
* Validation Loss: 1.0692
*... | [
"### 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 #longformer #fill-mask #generated_from_keras_callback #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': {'class\\_name': '... |
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. -->
# reqscibert-tapt-epoch49
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqscibert-tapt-epoch49", "results": []}]} | limsc/reqscibert-tapt-epoch49 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T17:01:41+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# reqscibert-tapt-epoch49
This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mo... | [
"# reqscibert-tapt-epoch49\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqscibert-tapt-epoch49\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
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. -->
# reqscibert-tapt-epoch20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqscibert-tapt-epoch20", "results": []}]} | limsc/reqscibert-tapt-epoch20 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T17:10:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# reqscibert-tapt-epoch20
This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mo... | [
"# reqscibert-tapt-epoch20\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqscibert-tapt-epoch20\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
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. -->
# reqscibert-tapt-epoch31
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqscibert-tapt-epoch31", "results": []}]} | limsc/reqscibert-tapt-epoch31 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T17:20:21+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# reqscibert-tapt-epoch31
This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mo... | [
"# reqscibert-tapt-epoch31\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqscibert-tapt-epoch31\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
reinforcement-learning | stable-baselines3 |
# **TQC** Agent playing **donkey-minimonaco-track-v0**
This is a trained model of a **TQC** agent playing **donkey-minimonaco-track-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 ... | {"library_name": "stable-baselines3", "tags": ["donkey-minimonaco-track-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "donkey-minimonaco-tr... | araffin/tqc-donkey-minimonaco-track-v0 | null | [
"stable-baselines3",
"donkey-minimonaco-track-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T17:26:20+00:00 | [] | [] | TAGS
#stable-baselines3 #donkey-minimonaco-track-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TQC Agent playing donkey-minimonaco-track-v0
This is a trained model of a TQC agent playing donkey-minimonaco-track-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 i... | [
"# TQC Agent playing donkey-minimonaco-track-v0\nThis is a trained model of a TQC agent playing donkey-minimonaco-track-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-train... | [
"TAGS\n#stable-baselines3 #donkey-minimonaco-track-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TQC Agent playing donkey-minimonaco-track-v0\nThis is a trained model of a TQC agent playing donkey-minimonaco-track-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n... |
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. -->
# reqscibert-tapt-epoch10
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqscibert-tapt-epoch10", "results": []}]} | limsc/reqscibert-tapt-epoch10 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T17:40:20+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# reqscibert-tapt-epoch10
This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mo... | [
"# reqscibert-tapt-epoch10\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqscibert-tapt-epoch10\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was at the beach. His parents got him a towel for the trip. He lay down and got out of the sand. Arthur put on his towel and went to the ocean. He felt very refreshed as he surfed and swam for a bi... | {} | jppaolim/v54_Large_AdaMW | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T17:46:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was at the beach. His parents got him a towel for the trip. He lay down and got out of the sand. Arthur put on his towel and went to the ocean. He felt very refreshed as he surfed and swam for a bi... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur was at the beach. His parents got him a towel for the trip. He lay down and got out of the sand. Arthur put on his towel and went to the ocean. He felt very refreshed as he surfed and swam f... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 was at the beach. His parents got him a towel fo... |
text-generation | transformers |
Start from sberbank-ai/rugpt3medium_based_on_gpt2 and finetuning on AGIRussia chats (russian).
On this moment - only 3 epoch (perplexity falls reasons)
on progress...
| {"language": ["ru"], "metrics": [{"loss": 3.3}, {"perplexity": 25.7528}], "widget": [{"text": "<IN>\u041a\u0430\u043a \u043d\u0430\u043c \u0432\u0441\u0435-\u0442\u0430\u043a\u0438 \u0441\u0434\u0435\u043b\u0430\u0442\u044c AGI?\n<OUT>"}]} | Nehc/AGIRussia | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T18:49:42+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Start from sberbank-ai/rugpt3medium_based_on_gpt2 and finetuning on AGIRussia chats (russian).
On this moment - only 3 epoch (perplexity falls reasons)
on progress...
| [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ru #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. -->
# finetuning-sentiment-model-finefood
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-finefood", "results": []}]} | AlphaZetta/finetuning-sentiment-model-finefood | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T18:53:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-finefood
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: 0.2269
- Accuracy: 0.95
- F1: 0.9696
## Model description
More information needed
## Intended uses & limitations
More inform... | [
"# finetuning-sentiment-model-finefood\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: 0.2269\n- Accuracy: 0.95\n- F1: 0.9696",
"## Model description\n\nMore information needed",
"## Intended uses & limita... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-finefood\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt ach... |
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-en-af](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/En-Af | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T19:04:44+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-en-af on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- 'eval_bleu': 35.055184951449
- 'eval_loss': 2.225693941116333
After training:
- Loss: 2.0057
- Bleu: 44.2309
## Model description
More informat... | [
"# En-Af\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-af on the None dataset.\nIt achieves the following results on the evaluation set:\nBefore training:\n- 'eval_bleu': 35.055184951449\n- 'eval_loss': 2.225693941116333\n\nAfter training:\n- Loss: 2.0057\n- Bleu: 44.2309",
"## Model descriptio... | [
"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-en-af on the None dataset.\nIt achieves the following results on t... |
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-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Jherb/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T20:00:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
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: 0.3063
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3063\n- Accuracy: 0.8667\n- F1: 0.8667",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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/1572269909513478146/dfyw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/cz_binance/1664010956441/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/cz_binance | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T20:10:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
CZ Binance
@cz\_binance
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 | 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-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T20:16:23+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... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 939531516
- CO2 Emissions (in grams): 11.013963276910237
## Validation Metrics
- Loss: 1.1184396743774414
- Rouge1: 54.9539
- Rouge2: 40.7878
- RougeL: 54.8616
- RougeLsum: 54.8682
- Gen Len: 5.1429
## Usage
You can use cURL to access this ... | {"language": "unk", "tags": "autotrain", "datasets": ["victorlifan/autotrain-data-song_title_generate"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 11.013963276910237} | victorlifan/autotrain-song_title_generate-939531516 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:victorlifan/autotrain-data-song_title_generate",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T20:52:45+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-victorlifan/autotrain-data-song_title_generate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 939531516
- CO2 Emissions (in grams): 11.013963276910237
## Validation Metrics
- Loss: 1.1184396743774414
- Rouge1: 54.9539
- Rouge2: 40.7878
- RougeL: 54.8616
- RougeLsum: 54.8682
- Gen Len: 5.1429
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 939531516\n- CO2 Emissions (in grams): 11.013963276910237",
"## Validation Metrics\n\n- Loss: 1.1184396743774414\n- Rouge1: 54.9539\n- Rouge2: 40.7878\n- RougeL: 54.8616\n- RougeLsum: 54.8682\n- Gen Len: 5.1429",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-victorlifan/autotrain-data-song_title_generate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 93... |
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. -->
# tiny-bert-sst2-distilled-model
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-distilled-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metri... | gokuls/tiny-bert-sst2-distilled-model | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T21:07:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-distilled-model
==============================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2592
* Accuracy: 0.8383
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
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. -->
# bert-uncased-keyword-extractor
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncase... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (\u20ac57bn) cash-and stock deal, massively diversifying the chipmaker\u2019s business a... | yanekyuk/bert-uncased-keyword-extractor | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-05T21:37:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-uncased-keyword-extractor
==============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1247
* Precision: 0.8547
* Recall: 0.8825
* Accuracy: 0.9741
* F1: 0.8684
Model description
--------------... | [
"### 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 #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
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. -->
# reqroberta-tapt-epoch20
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown datase... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqroberta-tapt-epoch20", "results": []}]} | limsc/reqroberta-tapt-epoch20 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:07:19+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# reqroberta-tapt-epoch20
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqroberta-tapt-epoch20\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqroberta-tapt-epoch20\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
... |
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. -->
# reqroberta-tapt-epoch33
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown datase... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqroberta-tapt-epoch33", "results": []}]} | limsc/reqroberta-tapt-epoch33 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:18:14+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# reqroberta-tapt-epoch33
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqroberta-tapt-epoch33\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqroberta-tapt-epoch33\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-itquad-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_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "it", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere t... | lmqg/mt5-small-itquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"it",
"dataset:lmqg/qg_itquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T22:19:44+00:00 | [
"2210.03992"
] | [
"it"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #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-itquad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_itquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: it
* Training data: lmqg/qg\_i... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: it\n* Training data: lmqg/qg\\_itquad (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 #it #dataset-lmqg/qg_itquad #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: it\n... |
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. -->
# reqroberta-tapt-epoch43
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown datase... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqroberta-tapt-epoch43", "results": []}]} | limsc/reqroberta-tapt-epoch43 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:29:13+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# reqroberta-tapt-epoch43
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqroberta-tapt-epoch43\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqroberta-tapt-epoch43\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
... |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | RayY/pegasus-samsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:39:31+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore informat... |
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. -->
# reqroberta-tapt-epoch50
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown datase... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reqroberta-tapt-epoch50", "results": []}]} | limsc/reqroberta-tapt-epoch50 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:40:45+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# reqroberta-tapt-epoch50
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# reqroberta-tapt-epoch50\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# reqroberta-tapt-epoch50\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
... |
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... | phyous/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-05T22:43:39+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... |
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. -->
# t5-grammar-corruption-edits
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-grammar-corruption-edits", "results": []}]} | juancavallotti/t5-grammar-corruption-edits | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T22:47:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-grammar-corruption-edits
This model is a fine-tuned version of t5-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... | [
"# t5-grammar-corruption-edits\n\nThis model is a fine-tuned version of t5-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",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-grammar-corruption-edits\n\nThis model is a fine-tuned version of t5-base on the None dataset.",
"## Model... |
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. -->
# bert-uncased-keyword-discriminator
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (\u20ac57bn) cash-and stock deal, massively diversifying the chipmaker\u2019s business a... | yanekyuk/bert-uncased-keyword-discriminator | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T22:54:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-uncased-keyword-discriminator
==================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1296
* Precision: 0.8439
* Recall: 0.8722
* Accuracy: 0.9727
* F1: 0.8578
* Ent/precision: 0.8723
* ... | [
"### 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 #en #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: 1... |
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="TinySuitStarfish/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additi... | {"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": ... | TinySuitStarfish/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T23:13:27+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 | 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="TinySuitStarfish/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fals... | {"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": "7.48 +/... | TinySuitStarfish/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-05T23:23:34+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"
] |
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 bored and wanted to go the beach. His friends suggest he drive to the beach. Arthur gets a ride and they take off. Arthur takes a nap and has a good time. He has so much fun at the beach he does... | {} | jppaolim/v55_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-05T23:33:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 bored and wanted to go the beach. His friends suggest he drive to the beach. Arthur gets a ride and they take off. Arthur takes a nap and has a good time. He has so much fun at the beach he does... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is bored and wanted to go the beach. His friends suggest he drive to the beach. Arthur gets a ride and they take off. Arthur takes a nap and has a good time. He has so much fun at the beach ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #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 bored and wanted to go the beach. His friends... |
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. -->
# pretrain1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "pretrain1", "results": []}]} | fcx-kilig/pretrain1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-05T23:54:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pretrain1
=========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0888
* Accuracy: 0.7783
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: 5e-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: 6.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 5e-05\n* train\\_batch\\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_branchformer_e18_linear3072`
This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | pyf98/librispeech_branchformer_e18_linear3072 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-06T00:52:25+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_branchformer\_e18\_linear3072'
This model was trained by Yifan Peng using librispeech recipe in espnet.
Branchformer (Peng et al., ICML 2022): URL
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Jun 3 02:25:... | [
"### 'pyf98/librispeech\\_branchformer\\_e18\\_linear3072'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Jun 3 02:25:27 EDT 202... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/librispeech\\_branchformer\\_e18\\_linear3072'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL... |
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="rushic24/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": ... | rushic24/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T01:13:07+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"
] |
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. -->
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-funsd-test", "results": []}]} | Chetan1997/layoutlmv2-finetuned-funsd-test | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:23:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown data... |
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. -->
# bert-semaphore-prediction-w0
This model was trained from scratch on the None dataset.
## Model description
More information ne... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-semaphore-prediction-w0", "results": []}]} | bondi/bert-semaphore-prediction-w0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:32:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-semaphore-prediction-w0
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 followin... | [
"# bert-semaphore-prediction-w0\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",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-semaphore-prediction-w0\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limi... |
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. -->
# bert-semaphore-prediction-w2
This model was trained from scratch on the None dataset.
## Model description
More information ne... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-semaphore-prediction-w2", "results": []}]} | bondi/bert-semaphore-prediction-w2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:33:16+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-semaphore-prediction-w2
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 followin... | [
"# bert-semaphore-prediction-w2\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",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-semaphore-prediction-w2\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limi... |
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. -->
# bert-semaphore-prediction-w4
This model was trained from scratch on the None dataset.
## Model description
More information ne... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-semaphore-prediction-w4", "results": []}]} | bondi/bert-semaphore-prediction-w4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:34:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-semaphore-prediction-w4
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 followin... | [
"# bert-semaphore-prediction-w4\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",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-semaphore-prediction-w4\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limi... |
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. -->
# bert-semaphore-prediction-w8
This model was trained from scratch on the None dataset.
## Model description
More information ne... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-semaphore-prediction-w8", "results": []}]} | bondi/bert-semaphore-prediction-w8 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:35:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-semaphore-prediction-w8
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 followin... | [
"# bert-semaphore-prediction-w8\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",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-semaphore-prediction-w8\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limi... |
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. -->
# chanifrusydi/distillbert-finetuned-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "chanifrusydi/distillbert-finetuned-ner", "results": []}]} | chanifrusydi/distillbert-finetuned-ner | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T01:43:03+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chanifrusydi/distillbert-finetuned-ner
======================================
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: 0.0168
* Validation Loss: 0.0691
* Epoch: 4
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': 2e-05, 'decay\\_steps': 4385, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #distilbert #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', 'learn... |
summarization | 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-small-sandbox1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unkno... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-sandbox1", "results": []}]} | erfangc/mt5-small-sandbox1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T01:57:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-small-sandbox1
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 14.5875
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# mt5-small-sandbox1\n\nThis model is a fine-tuned version of google/mt5-small on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 14.5875\n- Rouge1: 0.0\n- Rouge2: 0.0\n- Rougel: 0.0\n- Rougelsum: 0.0",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-small-sandbox1\n\nThis model is a fine-tuned version of google/mt5-small on an unknown data... |
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. -->
# legalectra-base-spanish-finetuned-squad
This model is a fine-tuned version of [mrm8488/legalectra-base-spanish](https://huggingf... | {"tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "legalectra-base-spanish-finetuned-squad", "results": []}]} | Evelyn18/legalectra-base-spanish-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T03:29:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad_es #endpoints_compatible #region-us
| legalectra-base-spanish-finetuned-squad
=======================================
This model is a fine-tuned version of mrm8488/legalectra-base-spanish on the squad\_es dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9506
Model description
-----------------
More information needed
In... | [
"### 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 #electra #question-answering #generated_from_trainer #dataset-squad_es #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\\_ba... |
text-generation | transformers |
# Rick Dialog GPT Model Medium 12
# Trained on:
# kaggle rick n morty Tv transcript | {"tags": ["conversational"]} | lucataco/DialogGPT-med-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T03:54:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Dialog GPT Model Medium 12
# Trained on:
# kaggle rick n morty Tv transcript | [
"# Rick Dialog GPT Model Medium 12",
"# Trained on:",
"# kaggle rick n morty Tv transcript"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Dialog GPT Model Medium 12",
"# Trained on:",
"# kaggle rick n morty Tv transcript"
] |
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