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null | null | Simple testing model for Kipoi/pytorch by Roman Kreuzhuber
| {"tags": ["kipoi"]} | dnouri-kipoi/pyt | null | [
"kipoi",
"region:us"
] | null | 2022-06-24T10:20:03+00:00 | [] | [] | TAGS
#kipoi #region-us
| Simple testing model for Kipoi/pytorch by Roman Kreuzhuber
| [] | [
"TAGS\n#kipoi #region-us \n"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielense_user_model_cos_384 | null | [
"keras",
"region:us"
] | null | 2022-06-24T10:32:14+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
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"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielense_movie_model_cos_384 | null | [
"keras",
"region:us"
] | null | 2022-06-24T10:33:18+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_9_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pt-colab", "results": []}]} | robertodtg/wav2vec2-large-xls-r-300m-pt-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_9_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T10:52:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-pt-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_9\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2975
* Wer: 0.1736
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\... |
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. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1
This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_w... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1", "results": []}]} | gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T10:57:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-v1
=======================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING2 dataset.
It achieves the following results on the evaluati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* ... |
automatic-speech-recognition | transformers | Hello, World! | {} | MahmoudAbdullah99/wav2vec-speech-model | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T11:20:14+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Hello, World! | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
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. -->
# BeardedJohn/bert-finetuned-ner-ubb-conll
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-case... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BeardedJohn/bert-finetuned-ner-ubb-conll", "results": []}]} | BeardedJohn/bert-finetuned-ner-ubb-conll | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T11:42:22+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BeardedJohn/bert-finetuned-ner-ubb-conll
========================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0351
* Validation Loss: 0.0581
* Epoch: 2
Model description
-----------------
Mo... | [
"### 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': 1317, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
token-classification | transformers |
This model is fine-tuned for Multiconer22 task on Hindi dataset.
hi_test.csv is preprocessed Hindi test dataset, which conll format is provided by Multiconer22 task.
This model can predict NER tag for Hindi sentnces using colab notebook https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scr... | {"license": "afl-3.0"} | sumitrsch/muril_large_multiconer22_hi | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T12:06:21+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is fine-tuned for Multiconer22 task on Hindi dataset.
hi_test.csv is preprocessed Hindi test dataset, which conll format is provided by Multiconer22 task.
This model can predict NER tag for Hindi sentnces using colab notebook URL
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #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="Lakshya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"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": ... | Lakshya/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-24T12:10:30+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"
] |
audio-classification | transformers |
# wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
### Model description
This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large) on the [speech_commands](https://huggingface.co/datasets/speech_commands) dataset.
It... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["speech_commands"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-conformer-rel-pos-large-finetuned-speech-commands", "results": [{"task": {"type": "audio-classification", "name": "audio classification"}, "dataset... | juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2-conformer",
"audio-classification",
"generated_from_trainer",
"en",
"dataset:speech_commands",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-24T12:11:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2-conformer #audio-classification #generated_from_trainer #en #dataset-speech_commands #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
==========================================================
### Model description
This model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the speech\_commands dataset.
It achieves the following results on the evaluation set:
* ... | [
"### Model description\n\n\nThis model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the speech\\_commands dataset.\n\n\nIt achieves the following results on the evaluation set:\n\n\n* Loss: 0.5245\n* Accuracy: 0.9724",
"#### Intended uses & limitations\n\n\nThe model can spot one of the... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2-conformer #audio-classification #generated_from_trainer #en #dataset-speech_commands #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Model description\n\n\nThis model is a fine-tuned version of facebook/wav2vec2-conformer-... |
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. -->
# BioM-ALBERT-xxlarge-finetuned-DAGPap22
This model is a fine-tuned version of [sultan/BioM-ALBERT-xxlarge](https://huggingface.co... | {"tags": ["text-classification", "generated_from_trainer"], "model-index": [{"name": "BioM-ALBERT-xxlarge-finetuned-DAGPap22", "results": []}]} | domenicrosati/BioM-ALBERT-xxlarge-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T12:25:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# BioM-ALBERT-xxlarge-finetuned-DAGPap22
This model is a fine-tuned version of sultan/BioM-ALBERT-xxlarge on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# BioM-ALBERT-xxlarge-finetuned-DAGPap22\n\nThis model is a fine-tuned version of sultan/BioM-ALBERT-xxlarge 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 #albert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioM-ALBERT-xxlarge-finetuned-DAGPap22\n\nThis model is a fine-tuned version of sultan/BioM-ALBERT-xxlarge on an unknown dataset.",
"## Model description... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | Corianas/ppo-QbertNoFrameskip-v4_3 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T12:33:03+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | Corianas/ppo-QbertNoFrameskip-v4_3.load-best | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T12:55:03+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-header-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-header-classifier", "results": []}]} | alk/distilbert-base-uncased-finetuned-header-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T13:26:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-header-classifier
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# distilbert-base-uncased-finetuned-header-classifier\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-header-classifier\n\nThis model is a fine-tuned version of distilbert-base-uncased o... |
text-generation | transformers | This is an utility repo for testing inference methods. Please use [bigscience/bloom](https://huggingface.co/bigscience/bloom) to access the latest model. | {} | bigscience/test-bloomd | null | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T15:04:45+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bloom #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is an utility repo for testing inference methods. Please use bigscience/bloom to access the latest model. | [] | [
"TAGS\n#transformers #pytorch #safetensors #bloom #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
image-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. -->
# swin-tiny-patch4-window7-224-finetuned-plantdisease
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-22... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-plantdisease", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "t... | gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T15:27:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-plantdisease
===================================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1032
* Accuracy: 0.9690
Model descript... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
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="gballoccu/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | gballoccu/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-24T15:58:42+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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"
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"# 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="gballoccu/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": ... | gballoccu/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-24T16:01:39+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 |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056
## Validation Metrics
- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-bert_wikipedia_sst_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 17.051424016530056} | deepesh0x/autotrain-bert_wikipedia_sst_2-1034235509 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-bert_wikipedia_sst_2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T16:17:01+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056
## Validation Metrics
- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9... | [
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"## Validation Metrics\n\n- Loss: 0.14414940774440765\n- Accuracy: 0.954046028210839\n- Precision: 0.9583831937242387\n- Recall: 0.9592760180995475\n- AUC: 0.98726237... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1034235509\n- CO2 Emi... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034235513
- CO2 Emissions (in grams): 16.686945384446037
## Validation Metrics
- Loss: 0.14450643956661224
- Accuracy: 0.9527839643652561
- Precision: 0.9565852363250132
- Recall: 0.9588767633750332
- AUC: 0.9872179498202862
- F1: 0.... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-bert_wikipedia_sst_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 16.686945384446037} | deepesh0x/autotrain-bert_wikipedia_sst_2-1034235513 | null | [
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"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-bert_wikipedia_sst_2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T16:17:14+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034235513
- CO2 Emissions (in grams): 16.686945384446037
## Validation Metrics
- Loss: 0.14450643956661224
- Accuracy: 0.9527839643652561
- Precision: 0.9565852363250132
- Recall: 0.9588767633750332
- AUC: 0.9872179498202862
- F1: 0.... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1034235513\n- CO2 Emi... |
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="gballoccu/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 +/... | gballoccu/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-24T16:22:28+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"
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"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"license": "apache-2.0", "tags": ["fastai"], "title": "Blurr Sentiment Classification", "emoji": "\ud83d\udc20", "colorFrom": "green", "colorTo": "indigo", "sdk": "gradio", "sdk_version": "2.9.4", "app_file": "app.py", "pinned": false} | msivanes/blurr_IMDB_distilbert_cls | null | [
"fastai",
"license:apache-2.0",
"region:us"
] | null | 2022-06-24T16:23:09+00:00 | [] | [] | TAGS
#fastai #license-apache-2.0 #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #license-apache-2.0 #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using ... |
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"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | pitronalldak/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T16:24:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0709
* Precision: 0.8442
* Recall: 0.8364
* F1: 0.8403
* Accuracy: 0.9794
Model descript... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
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. -->
# wav2vec_cv
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_cv", "results": []}]} | eugenetanjc/wav2vec_cv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T16:27:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_cv
===========
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1760
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 12\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 6\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034335535
- CO2 Emissions (in grams): 7.1805069109958835
## Validation Metrics
- Loss: 0.05866553634405136
- Accuracy: 0.9793615441722346
- Precision: 0.9811170212765957
- Recall: 0.9819004524886877
- AUC: 0.9976735725727466
- F1: 0.... | {"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-finetunedmodelbert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.1805069109958835} | deepesh0x/autotrain-finetunedmodelbert-1034335535 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-finetunedmodelbert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T16:56:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-finetunedmodelbert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034335535
- CO2 Emissions (in grams): 7.1805069109958835
## Validation Metrics
- Loss: 0.05866553634405136
- Accuracy: 0.9793615441722346
- Precision: 0.9811170212765957
- Recall: 0.9819004524886877
- AUC: 0.9976735725727466
- F1: 0.... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1034335535\n- CO2 ... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens_user_model_cos_384 | null | [
"keras",
"region:us"
] | null | 2022-06-24T17:40:19+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens_movie_model_cos_384 | null | [
"keras",
"region:us"
] | null | 2022-06-24T17:42:02+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306
## Validation Metrics
- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.94... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-finetunedmodel1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 29.194903746653306} | deepesh0x/autotrain-finetunedmodel1-1034535555 | null | [
"transformers",
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"distilbert",
"text-classification",
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"unk",
"dataset:deepesh0x/autotrain-data-finetunedmodel1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T17:43:40+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-finetunedmodel1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306
## Validation Metrics
- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.94... | [
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"## Validation Metrics\n\n- Loss: 0.16423887014389038\n- Accuracy: 0.9402375649591685\n- Precision: 0.94876254180602\n- Recall: 0.9438381687516636\n- AUC: 0.984396833... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1034535555\n- CO2 Em... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens_user_model_cos_32 | null | [
"keras",
"region:us"
] | null | 2022-06-24T18:16:33+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens_movie_model_cos_32 | null | [
"keras",
"region:us"
] | null | 2022-06-24T18:16:54+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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] | [
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"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
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... | sharanharsoor/RL-work-Try | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T18:31:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# hsohn3/ehr-bert-base-uncased-cchs-wordlevel
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/ehr-bert-base-uncased-cchs-wordlevel", "results": []}]} | hsohn3/ehr-bert-base-uncased-cchs-wordlevel | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T18:46:55+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/ehr-bert-base-uncased-cchs-wordlevel
===========================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.7374
* Epoch: 9
Model description
-----------------
* model: bert-base-u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32\n*... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 1e-0... |
text-classification | generic |
# Optimized and Quantized DistilBERT with a custom pipeline with handler.py
> NOTE: Blog post coming soon
This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the require... | {"library_name": "generic", "tags": ["text-classification", "endpoints-template", "optimum"]} | philschmid/distilbert-onnx-banking77 | null | [
"generic",
"onnx",
"text-classification",
"endpoints-template",
"optimum",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-24T18:53:29+00:00 | [] | [] | TAGS
#generic #onnx #text-classification #endpoints-template #optimum #endpoints_compatible #has_space #region-us
|
# Optimized and Quantized DistilBERT with a custom pipeline with URL
> NOTE: Blog post coming soon
This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements b... | [
"# Optimized and Quantized DistilBERT with a custom pipeline with URL\n\n> NOTE: Blog post coming soon\n\nThis is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requ... | [
"TAGS\n#generic #onnx #text-classification #endpoints-template #optimum #endpoints_compatible #has_space #region-us \n",
"# Optimized and Quantized DistilBERT with a custom pipeline with URL\n\n> NOTE: Blog post coming soon\n\nThis is a template repository for Text Classification using Optimum and onnxruntime to ... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-clincal-scratch-wl-es
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-clincal-scratch-wl-es", "results": []}]} | plncmm/deberta-clinical-scratch-wl-es | null | [
"generated_from_trainer",
"license:mit",
"region:us"
] | null | 2022-06-24T18:56:47+00:00 | [] | [] | TAGS
#generated_from_trainer #license-mit #region-us
|
# deberta-clincal-scratch-wl-es
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# deberta-clincal-scratch-wl-es\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base 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",
"## Train... | [
"TAGS\n#generated_from_trainer #license-mit #region-us \n",
"# deberta-clincal-scratch-wl-es\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trainin... |
null | null | | Feature | Description |
| --- | --- |
| **Name** | `en_ethicalads_topics` |
| **Version** | `20221006_18_20_26` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `transformer`, `textcat_multilabel` |
| **Components** | `transformer`, `textcat_multilabel` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensi... | {} | davidfischer/en_ethicalads_topics | null | [
"region:us"
] | null | 2022-06-24T19:04:00+00:00 | [] | [] | TAGS
#region-us
|
### Label Scheme
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] |
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-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | KukuyKukuev/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T19:59:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1099
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #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: 2e-05\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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-a4s
This model was trained from scratch on an unknown dataset.
It achieves the following resul... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-esg-a4s", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-esg-a4s | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T20:40:06+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-esg-a4s
===========================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9437
* Validation Loss: 1.9842
* Epoch: 9
Model description
-----------------
More informati... | [
"### 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 #bert #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': 'WarmUp... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | KukuyKukuev/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T21:15:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8574
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | shash2409/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T21:53:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad 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 #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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. -->
# deberta-v3-xsmall-finetuned-DAGPap22
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-xsmall-finetuned-DAGPap22", "results": []}]} | domenicrosati/deberta-v3-xsmall-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T22:01:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-xsmall-finetuned-DAGPap22
====================================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0798
* Accuracy: 0.9907
* F1: 0.9934
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794
## Validation Metrics
- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0... | {"language": "zh", "tags": "autotrain", "datasets": ["AI-Prize-Challenges/autotrain-data-finetuned1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.03608660562919794} | AI-Prize-Challenges/autotrain-finetuned1-1035435583 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain",
"zh",
"dataset:AI-Prize-Challenges/autotrain-data-finetuned1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T22:19:13+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #text-classification #autotrain #zh #dataset-AI-Prize-Challenges/autotrain-data-finetuned1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794
## Validation Metrics
- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1035435583\n- CO2 Emissions (in grams): 0.03608660562919794",
"## Validation Metrics\n\n- Loss: 0.31551286578178406\n- Accuracy: 0.8816629547141797\n- Precision: 0.8965702036441586\n- Recall: 0.8906042054830983\n- AUC: 0.944918... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1035435583\n- CO2 Em... |
automatic-speech-recognition | nemo | # NVIDIA Streaming Citrinet 1024 (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![La... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["librispeech_asr", "fisher_corpus", "Switchboard-1", "WSJ-0", "WSJ-1", "National-Singapore-Cor... | nvidia/stt_en_citrinet_1024_gamma_0_25 | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"en",
"dataset:librispeech_asr",
"dataset:fisher_corpus",
"dataset:Switchboard-1",
"dataset:WSJ-0",
"dataset:WSJ-1",
"dataset:National-Singa... | null | 2022-06-24T22:54:31+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #en #dataset-librispeech_asr #dataset-fisher_corpus #dataset-Switchboard-1 #dataset-WSJ-0 #dataset-WSJ-1 #dataset-National-Singapore-Corpus-Part-1 #dataset-National-Singapore-Corpus-Part-6 #arxiv... | NVIDIA Streaming Citrinet 1024 (en-US)
======================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lowercase English... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 kHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #en #dataset-librispeech_asr #dataset-fisher_corpus #dataset-Switchboard-1 #dataset-WSJ-0 #dataset-WSJ-1 #dataset-National-Singapore-Corpus-Part-1 #dataset-National-Singapore-Corpus-Part-6 ... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/TextbookInformalFormalEnglish")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/TextbookInformalFormalEnglish")
```
```
How To Make Prompt:
informal english: i am very ready to do that just t... | {} | BigSalmon/TextbookInformalFormalEnglish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T01:17:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | jwuthri/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T01:21:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3811
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# deberta-v3-large-finetuned-DAGPap22
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/mi... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "model-index": [{"name": "deberta-v3-large-finetuned-DAGPap22", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T01:26:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-v3-large-finetuned-DAGPap22
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# deberta-v3-large-finetuned-DAGPap22\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large 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 #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-v3-large-finetuned-DAGPap22\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.",
"## Mod... |
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. -->
# test1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "test1", "results": []}]} | shuidun/test1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T02:46:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test1
This model is a fine-tuned version of gpt2 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 hyperparameter... | [
"# test1\n\nThis model is a fine-tuned version of gpt2 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 hyperparameters... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test1\n\nThis model is a fine-tuned version of gpt2 on the None dataset.",
"## Model description\n\nMore information ne... |
fill-mask | transformers |
# deberta-base-japanese-wikipedia
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 109 hours 27 minutes for training. You can fine-tune `deberta-base-japanese-wikipedia` for downstream tasks, such as [POS-tagging](https://huggingface.co/Koi... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-base-japanese-wikipedia | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"wikipedia",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T02:46:58+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-japanese-wikipedia
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 109 hours 27 minutes for training. You can fine-tune 'deberta-base-japanese-wikipedia' for downstream tasks, such as POS-tagging, dependency-parsing, and so ... | [
"# deberta-base-japanese-wikipedia",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 109 hours 27 minutes for training. You can fine-tune 'deberta-base-japanese-wikipedia' for downstream tasks, such as POS-tagging, dependency-parsin... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-japanese-wikipedia",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. N... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | Forkits/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-06-25T02:56:07+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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. -->
# tiny_focal_v3
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_focal_v3", "results": []}]} | kktoto/tiny_focal_v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T04:11:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_focal\_v3
===============
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0023
* Precision: 0.6975
* Recall: 0.6822
* F1: 0.6898
* Accuracy: 0.9515
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
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... | bousejin/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 | 2022-06-25T04:19:20+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: 0.1372
* F1: 0.8621
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 #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\\_... |
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": []}]} | bousejin/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 | 2022-06-25T04:23:24+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: 0.1631
* F1: 0.8579
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. -->
# xlm-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": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | bousejin/xlm-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 | 2022-06-25T04:57:28+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-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: 0.1013
* F1: 0.9242
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... |
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-it
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"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": ... | bousejin/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T05:15:57+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
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: 0.2562
* F1: 0.8223
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 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
token-classification | transformers |
# deberta-base-japanese-wikipedia-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia). Every long-unit-wo... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"wikipedia",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T05:28:11+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-japanese-wikipedia-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Us... | [
"# deberta-base-japanese-wikipedia-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-japanese-wikipedia-luw-upos",
"## Model Description\n\nThis is a DeB... |
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. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T05:31:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v3
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v2 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\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. -->
# xlm-roberta-base-finetuned-panx-en
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-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | bousejin/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T05:32:26+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
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: 0.3909
* F1: 0.6901
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... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | QuickSilver007/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-06-25T08:30:41+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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... | traxes/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T08:31:43+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 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | 745H1N/MountainCar-v0-DQN-optuna | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T08:39:34+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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-base-uncased-fine-tuned-on-clinc_oos-dataset
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "model-index": [{"name": "bert-base-uncased-fine-tuned-on-clinc_oos-dataset", "results": []}]} | itzo/bert-base-uncased-fine-tuned-on-clinc_oos-dataset | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T09:05:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-fine-tuned-on-clinc\_oos-dataset
==================================================
This model is a fine-tuned version of bert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2811
* Accuracy Score: 0.9239
* F1 Score: 0.9213
Model descrip... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-clinc_oos #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... |
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. -->
# bert-finetuned-custom
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-custom", "results": []}]} | VedantS01/bert-finetuned-custom | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T09:46:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-custom
This model is a fine-tuned version of bert-base-uncased 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
... | [
"# bert-finetuned-custom\n\nThis model is a fine-tuned version of bert-base-uncased 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 #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-custom\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | danieladejumo/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T10:31:44+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
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... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\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-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | NikitaErmolaev/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T11:19:14+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
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... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\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-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert2gpt2_med_v2", "results": []}]} | Chemsseddine/bert2gpt2_med_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T11:50:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| <img src="URL alt="Map of positive probabilities per country." width="200"/>
bert2gpt2\_med\_v2
==================
This model is a fine-tuned version of Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange\_sum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0684
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
question-answering | transformers |
# deberta-base-japanese-wikipedia-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b6... | KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"japanese",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T12:03:09+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# deberta-base-japanese-wikipedia-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-wikipedia and UD_Japanese-GSDLUW. Use [MASK] inside 'context'... | [
"# deberta-base-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-wikipedia and UD_Japanese-GSDLUW. Use [MASK] inside... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# deberta-base-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on J... |
automatic-speech-recognition | transformers |
# Sharif-wav2vec2
This is a fine-tuned version of Sharif Wav2vec2 for Farsi. The base model went through a fine-tuning process in which 108 hours of Commonvoice's Farsi samples with a sampling rate equal to 16kHz. Afterward, we trained a 5gram using [kenlm](https://github.com/kpu/kenlm) toolkit and used it in the pro... | {"language": "fa", "license": "mit", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice_6_1"], "widget": [{"example_title": "Common Voice Sample 1", "src": "https://datasets-server.huggingface.co/assets/common_voice/--/fa/train/0/audio/audio.mp3"}, {"example_title": "Common Voice Sample 2", "... | SLPL/Sharif-wav2vec2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"fa",
"dataset:common_voice_6_1",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-25T12:11:41+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #fa #dataset-common_voice_6_1 #license-mit #model-index #endpoints_compatible #has_space #region-us
| Sharif-wav2vec2
===============
This is a fine-tuned version of Sharif Wav2vec2 for Farsi. The base model went through a fine-tuning process in which 108 hours of Commonvoice's Farsi samples with a sampling rate equal to 16kHz. Afterward, we trained a 5gram using kenlm toolkit and used it in the processor which incre... | [
"### Contributions\n\n\nThanks to @sarasadeghii and @sadrasabouri for adding this model."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #fa #dataset-common_voice_6_1 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### Contributions\n\n\nThanks to @sarasadeghii and @sadrasabouri for adding this model."
] |
null | null | Batman and Superman fussing | {} | Hhhjhhhhhhjhjjjjhh/Dddd | null | [
"region:us"
] | null | 2022-06-25T12:31:28+00:00 | [] | [] | TAGS
#region-us
| Batman and Superman fussing | [] | [
"TAGS\n#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... | trtd56/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T13:18:51+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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", ... | mvonwyl/distilbert-base-uncased-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T15:01:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-imdb
============================
This model is a fine-tuned version of distilbert-base-uncased on an imdb dataset where an evaluation of 5000 samples was created by splitting the training set.
It achieves the following results on the evaluation set:
* Loss: 0.6252
* Accuracy: 0.9214
Model... | [
"### 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: 128\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were u... |
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. -->
# wav2vec_trained
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_trained", "results": []}]} | eugenetanjc/wav2vec_trained | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T15:04:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_trained
================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0337
* Wer: 0.1042
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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
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. -->
# wav2vec_test
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intende... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_test", "results": []}]} | eugenetanjc/wav2vec_test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T15:23:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec_test
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameter... | [
"# wav2vec_test\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 hyperparameters... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec_test\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... |
fill-mask | transformers | DeBERTa trained from scratch
Source data: https://dumps.wikimedia.org/archive/2006/
Tools used: https://github.com/mikesong724/Point-in-Time-Language-Model
2006 wiki archive 2.7 GB trained 24 epochs = 65GB
GLUE benchmark
cola (3e): matthews corr: 0.2848
sst2 (3e): acc: 0.8876
mrpc (5e): F1: 0.80... | {} | mikesong724/deberta-wiki-2006 | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T15:40:24+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa trained from scratch
Source data: URL
Tools used: URL
2006 wiki archive 2.7 GB trained 24 epochs = 65GB
GLUE benchmark
cola (3e): matthews corr: 0.2848
sst2 (3e): acc: 0.8876
mrpc (5e): F1: 0.8033, acc: 0.7108
stsb (3e): pearson: 0.7542, spearman: 0.7536
qqp (3e): acc: 0.8852, F1: ... | [] | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #autotrain_compatible #endpoints_compatible #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... | WasuratS/simple_ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T16:04:09+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... |
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. -->
# test-clm
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor train-v1.1.json ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["bittensor"], "metrics": ["accuracy"], "model-index": [{"name": "test-clm", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "bittensor train-v1.1.json", "type": "bittensor", "args": "trai... | rpgz31/jibber | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:bittensor",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T16:57:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-bittensor #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test-clm
This model is a fine-tuned version of distilgpt2 on the bittensor train-v1.1.json dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5199
- Accuracy: 0.1387
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and ev... | [
"# test-clm\n\nThis model is a fine-tuned version of distilgpt2 on the bittensor train-v1.1.json dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.5199\n- Accuracy: 0.1387",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-bittensor #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test-clm\n\nThis model is a fine-tuned version of distilgpt2 on the bittensor train-v1.1.json d... |
question-answering | transformers |
microsoft/xtremedistil-l6-h256-uncased fined-tuned on SQuAD (https://huggingface.co/datasets/squad)
Hyperparameters:
- epochs: 1
- lr: 1e-5
- train batch sie: 16
- optimizer: adamW
- lr_scheduler: linear
- num warming steps: 0
- max_length: 512
Results on the dev set:
- 'exact_match': 62.66792809839168
- 'f1': 74.99... | {"language": ["en"], "license": "mit", "tags": ["QA", "Question Answering", "SQuAD"], "datasets": ["squad"], "metrics": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "SQuAD", "type": "squad", "sp... | haritzpuerto/xtremedistil-l6-h256-uncased-squad_1.1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"QA",
"Question Answering",
"SQuAD",
"en",
"dataset:squad",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T17:54:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #QA #Question Answering #SQuAD #en #dataset-squad #license-mit #model-index #endpoints_compatible #region-us
|
microsoft/xtremedistil-l6-h256-uncased fined-tuned on SQuAD (URL
Hyperparameters:
- epochs: 1
- lr: 1e-5
- train batch sie: 16
- optimizer: adamW
- lr_scheduler: linear
- num warming steps: 0
- max_length: 512
Results on the dev set:
- 'exact_match': 62.66792809839168
- 'f1': 74.99490608582015
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #QA #Question Answering #SQuAD #en #dataset-squad #license-mit #model-index #endpoints_compatible #region-us \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. -->
# tiny-nfl
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor tiny.json datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["bittensor"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-nfl", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "bittensor tiny.json", "type": "bittensor", "args": "tiny.json"... | rpgz31/tiny-nfl | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:bittensor",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T17:56:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-bittensor #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# tiny-nfl
This model is a fine-tuned version of distilgpt2 on the bittensor URL dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4602
- Accuracy: 0.1556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation dat... | [
"# tiny-nfl\n\nThis model is a fine-tuned version of distilgpt2 on the bittensor URL dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.4602\n- Accuracy: 0.1556",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-bittensor #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# tiny-nfl\n\nThis model is a fine-tuned version of distilgpt2 on the bittensor URL dataset.\nIt ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | SusBioRes-UBC/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T18:10:14+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
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... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\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-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
fill-mask | transformers |
## RoBERTa Spanish base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/es](https://www.tensorflow.org/datasets/catalog/wiki4... | {"language": "es", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "Yo vivo en <mask>."}, {"text": "Quiero <mask> contigo ?"}, {"text": "Es clima es <mask>."}, {"text": "Me llamo <mask>."}, {"text": "Las negociaciones est\u00e1n <mask>."}]} | ClassCat/roberta-base-spanish | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T19:07:43+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #fill-mask #es #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RoBERTa Spanish base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* wiki40b/es (Spanish Wikipedia)
* Subset of CC-100/es : Monolin... | [
"## RoBERTa Spanish base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* wiki40b/es (Spanish Wikipedia)\n* ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #es #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Spanish base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa b... |
text-generation | transformers |
# GPT2-Snapsvisor
This model is trained on scraped websites of Snapsvisor.
TODO: Fill in the rest | {"language": "sv", "tags": ["gpt2"], "widget": [{"text": "Nu tar vi e nubbe"}, {"text": "Spriten den"}]} | lunde/gpt2-snapsvisor | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T19:26:58+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #sv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-Snapsvisor
This model is trained on scraped websites of Snapsvisor.
TODO: Fill in the rest | [
"# GPT2-Snapsvisor\nThis model is trained on scraped websites of Snapsvisor.\n\nTODO: Fill in the rest"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #sv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-Snapsvisor\nThis model is trained on scraped websites of Snapsvisor.\n\nTODO: Fill in the rest"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-ft-cv3-v3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-ft-cv3-v3", "results": []}]} | danieleV9H/wav2vec2-base-ft-cv3-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T19:34:02+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-base-ft-cv3-v3
=======================
This model is a fine-tuned version of facebook/wav2vec2-base on the "mozilla-foundation/common\_voice\_3\_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation.
It achieves the following results on the evalua... | [
"### 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: 12\n* mixed\\_pre... | [
"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: 5e-05\n* tr... |
summarization | transformers |
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
> NOTE: this is still a work-in-progress (WIP) and not completed/converged by any means, but sharing to maybe save some time for others :)
## Updates
_As I update this WIP checkpoint, I will post a note here._
- July 26, 2022: add two more epochs of training, me... | {"license": "apache-2.0", "tags": ["summarization", "summary", "booksum", "long-document", "long-form"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "inference": false, "model-index": [{"name": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP", "results": [{"task": {"type": "summarization", "name":... | pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"region:us"
] | null | 2022-06-25T19:47:58+00:00 | [] | [] | TAGS
#transformers #pytorch #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #license-apache-2.0 #model-index #autotrain_compatible #region-us
|
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
> NOTE: this is still a work-in-progress (WIP) and not completed/converged by any means, but sharing to maybe save some time for others :)
## Updates
_As I update this WIP checkpoint, I will post a note here._
- July 26, 2022: add two more epochs of training, me... | [
"# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n\n> NOTE: this is still a work-in-progress (WIP) and not completed/converged by any means, but sharing to maybe save some time for others :)",
"## Updates\n\n_As I update this WIP checkpoint, I will post a note here._\n\n- July 26, 2022: add two more epochs of... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #license-apache-2.0 #model-index #autotrain_compatible #region-us \n",
"# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n\n> NOTE: this is still a work-in-progress (WI... |
text2text-generation | transformers |
# Trinidad English Creole to English Translator
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Trinidad English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library... | {"license": "apache-2.0", "tags": ["text2text-generation", "Trinidadian Creole", "Caribbean dialect"]} | KES/TEC-English | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"Trinidadian Creole",
"Caribbean dialect",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T19:59:53+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #Trinidadian Creole #Caribbean dialect #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinidad English Creole to English Translator
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Trinidad English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library... | [
"# Trinidad English Creole to English Translator \nThis model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Trinidad English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the li... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #Trinidadian Creole #Caribbean dialect #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinidad English Creole to English Translator \nThis model utilises T5-base pre-trained model. I... |
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... | pranaval/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-25T21:14:02+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 | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | Forkits/MLAgents-Worm | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-06-25T21:26:06+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
#... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
text-generation | transformers |
## GPT2 Spanish base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/es](https://www.tensorflow.org/datasets/catalog/wiki40b#wik... | {"language": "es", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "\u00bfHablas espa\u00f1ol?"}, {"text": "Es clima es"}, {"text": "Las negociaciones est\u00e1n paradas, pero"}]} | ClassCat/gpt2-base-spanish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"es",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-25T21:29:07+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #es #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## GPT2 Spanish base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* wiki40b/es (Spanish Wikipedia)
* Subset of CC-100/es : Monolingual D... | [
"## GPT2 Spanish base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses GPT2 base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* wiki40b/es (Spanish Wikipedia)\n* Subset... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #es #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 Spanish base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n... |
null | null | See https://github.com/k2-fsa/icefall/pull/380
| {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless3_giga_0.5_20220625 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-25T22:48:01+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| See URL
| [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
null | null | See https://github.com/k2-fsa/icefall/pull/380
| {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless2_20220625 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-25T22:48:37+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| See URL
| [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
null | null |
See https://github.com/k2-fsa/icefall/pull/380 | {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless3_giga_0.9_20220625 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-25T22:49:27+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
|
See URL | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
null | null | See https://github.com/k2-fsa/icefall/pull/380 | {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless4_20220625 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-25T22:49:45+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| See URL | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | romainlhardy/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-25T23:21:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0602
* Precision: 0.9293
* Recall: 0.9488
* F1: 0.9390
* Accuracy: 0.9864
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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* learning... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a ... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | danielcfho/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-06-26T01:11:23+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume th... | [
"# ppo Agent playing Pyramids\r\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\r\n \r\n ## Usage (with ML-Agents)\r\n The Documentation: URL\r\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\r\n\r\n\r\n ... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\r\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\r\n \r\n ## Usage (with ML-Agents)\r\n The... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | vebie91/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T01:29:12+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
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... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\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-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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... | workRL/Lundar | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T01:44:44+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 | keras |
## Model description
Text classification on the Newsgroup20 dataset using pre-trained [GloVe](https://nlp.stanford.edu/projects/glove/) word embeddings.
This repo contains the model [to this Keras example using pre-trained word embeddings](https://keras.io/examples/nlp/pretrained_word_embeddings/).
Full credits to ... | {"language": "en", "library_name": "keras", "tags": ["multiclass-classification", "newsgroup"], "datasets": "newsgroup"} | sumedh/pretrained-word-embeddings | null | [
"keras",
"tensorboard",
"multiclass-classification",
"newsgroup",
"en",
"dataset:newsgroup",
"region:us"
] | null | 2022-06-26T02:04:29+00:00 | [] | [
"en"
] | TAGS
#keras #tensorboard #multiclass-classification #newsgroup #en #dataset-newsgroup #region-us
| Model description
-----------------
Text classification on the Newsgroup20 dataset using pre-trained GloVe word embeddings.
This repo contains the model to this Keras example using pre-trained word embeddings.
Full credits to : fchollet
Model reproduced by : Sumedh
Training and evaluation data
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #multiclass-classification #newsgroup #en #dataset-newsgroup #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | hyan97/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T02:31:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3517
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | HKHKHKHK/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T04:00:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad 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 #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | ashhyun/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T04:20:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1563
- eval_runtime: 141.535
- eval_samples_per_second: 76.193
- eval_steps_per_second: 4.762
- epoch: 1.0
- step: 553... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1563\n- eval_runtime: 141.535\n- eval_samples_per_second: 76.193\n- eval_steps_per_second: 4.762\n- epoch: 1.0\n... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves... |
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. -->
# roberta-large-finetuned-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the c... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": ... | romainlhardy/roberta-large-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T07:07:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-ner
===========================
This model is a fine-tuned version of roberta-large on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0495
* Precision: 0.9477
* Recall: 0.9663
* F1: 0.9569
* Accuracy: 0.9907
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-conll2003 #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\\_r... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | kidzy/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T07:42:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7548
* Matthews Correlation: 0.5444
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-common-voice-40p-persian-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-common-voice-40p-persian-colab", "results": []}]} | zoha/wav2vec2-base-common-voice-40p-persian-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T07:46:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-common-voice-40p-persian-colab
============================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1805
* Wer: 0.6024
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00018\n* train\\_batch\\_size: 16\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* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00018\n* train\\_batch\\_size: ... |
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