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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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-base-spanish-wwm-uncased-finetuned-clinical
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased]... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bert-base-spanish-wwm-uncased-finetuned-clinical", "results": []}]} | RodrigoGuerra/bert-base-spanish-wwm-uncased-finetuned-clinical | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T03:04:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-spanish-wwm-uncased-finetuned-clinical
================================================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7962
* F1: 0.1081
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 80",
"### Trainin... | [
"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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_... |
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... | Abhinandan/Atari | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-29T03:38:24+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... |
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-960h-lv60-self-4-gram_fine-tune_real_29_Jun
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-l... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun", "results": []}]} | RuiqianLi/wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T03:45:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-960h-lv60-self-4-gram\_fine-tune\_real\_29\_Jun
==============================================================
This model is a fine-tuned version of facebook/wav2vec2-large-960h-lv60-self on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2895
* Wer: 0.45... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #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: 0.0002\n* t... |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | iiShreya/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T04:28:08+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 |
<!-- 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-health_facts
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["health_fact"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-health_facts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "health_fact", "ty... | austinmw/distilbert-base-uncased-finetuned-health_facts | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:health_fact",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T04:34:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-health_fact #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-health\_facts
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the health\_fact dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1227
* Accuracy: 0.6285
* F1: 0.6545
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-health_fact #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* l... |
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="iiShreya/frozenLake_8x8_Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "frozenLake_8x8_Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "metrics"... | iiShreya/frozenLake_8x8_Slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T04:50:17+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-version-1**
This is a trained model of a **Q-Learning Algorithm** agent playing in the **FrozenLake-v1 Environment** .
## Usage
```python
model = load_from_hub(repo_id="iiShreya/frozenLake_4x4_nonSlippery", filename="q-learning.pkl")
env = gym.make(model["env_... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "frozenLake_4x4_nonSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "metri... | iiShreya/frozenLake_4x4_nonSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T05:26:32+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-version-1
This is a trained model of a Q-Learning Algorithm agent playing in the FrozenLake-v1 Environment .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-version-1\n This is a trained model of a Q-Learning Algorithm agent playing in the FrozenLake-v1 Environment .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-version-1\n This is a trained model of a Q-Learning Algorithm agent playing in the FrozenLake-v1 Environment .\n \n ## Usage"
] |
text-to-speech | espnet | license: cc-by-4.0
---
| {"tags": ["espnet", "audio", "text-to-speech"]} | SYSPIN/Telugu_Male_TTS | null | [
"espnet",
"audio",
"text-to-speech",
"has_space",
"region:us"
] | null | 2022-06-29T05:29:24+00:00 | [] | [] | TAGS
#espnet #audio #text-to-speech #has_space #region-us
| license: cc-by-4.0
---
| [] | [
"TAGS\n#espnet #audio #text-to-speech #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2
This model is a fine-tuned version of [gary109/ai-light-dance_singi... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2", "results": []}]} | gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T05:40:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-5gram-v4-2
===============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-5gram-v4-1 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING2 dataset.
It achieves the follow... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #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... |
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... | coolzhao/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 | 2022-06-29T06:01:12+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: 0.1356
* F1: 0.8600
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\\_... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail da... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailyma... | ubikpt/t5-small-finetuned-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T06:19:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn
======================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8436
* Rouge1: 33.2082
* Rouge2: 16.798
* Rougel: 28.9573
* Rougelsum: 31.1044
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameter... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ms12345/distilbert-base-cased-distilled-squad-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-s... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ms12345/distilbert-base-cased-distilled-squad-finetuned-squad", "results": []}]} | ms12345/distilbert-base-cased-distilled-squad-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T06:40:29+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ms12345/distilbert-base-cased-distilled-squad-finetuned-squad
=============================================================
This model is a fine-tuned version of distilbert-base-cased-distilled-squad on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7381
* Validation Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 46, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name'... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
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-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]} | ss756/bert-base-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T06:56:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased-finetuned-squad
===============================
This model is a fine-tuned version of bert-base-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0081
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4... |
null | transformers |
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr", "common_voice"]} | cwkeam/m-ctc-t-large-lid | null | [
"transformers",
"pytorch",
"mctct",
"speech",
"en",
"dataset:librispeech_asr",
"dataset:common_voice",
"arxiv:2111.00161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T07:08:36+00:00 | [
"2111.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #mctct #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us
| M-CTC-T
=======
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After traini... | [] | [
"TAGS\n#transformers #pytorch #mctct #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
summarization | transformers | [AI-HUB 도서자료 요약](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=93)
|model|sampling|ROUGE-1 (↑)|ROUGE-2 (↑)|ROUGE-L (↑)|
|:---:|:---:|:---:|:---:|:---:|
|-|-|-|-|-|
|ours|greedy|**49.87**|**34.44**|**41.65**|
|ainize/kobart-news|greedy|42.35|23.27|32.61|
|gogamza/kob... | {"language": ["ko"], "license": "apache-2.0", "tags": ["summarization"]} | psyche/KoT5-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T07:15:27+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| AI-HUB 도서자료 요약
* greedy 는 최대길이 제한(max\_length=256)과 repetition\_penalty=2.0으로 단순 샘플링한 방법을 의미합니다.
More Information of KoT5
| [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-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... | ambekarsameer/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 | 2022-06-29T07:16:08+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: 0.8051
* Matthews Correlation: 0.5338
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... |
text2text-generation | transformers | # Multilingual mT5 model trained with MLSUM_TR and MLSUM_CNN (EN)
## Results:
MLSUM_TR:
* Rouge-1: 45.11
* Rouge-2: 30.96
* Rouge-L: 39.23
MLSUM_CNN:
* Rouge-1: 39.65
* Rouge-2: 17.49
* Rouge-L: 27.66
Note: Huggingface Inference API truncates the results, which results in unfinished sentences when making a predicti... | {} | harunkuf/mlsum_tr_en_mt5-small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T07:17:41+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Multilingual mT5 model trained with MLSUM_TR and MLSUM_CNN (EN)
## Results:
MLSUM_TR:
* Rouge-1: 45.11
* Rouge-2: 30.96
* Rouge-L: 39.23
MLSUM_CNN:
* Rouge-1: 39.65
* Rouge-2: 17.49
* Rouge-L: 27.66
Note: Huggingface Inference API truncates the results, which results in unfinished sentences when making a predicti... | [
"# Multilingual mT5 model trained with MLSUM_TR and MLSUM_CNN (EN)",
"## Results:\n\nMLSUM_TR:\n* Rouge-1: 45.11\n* Rouge-2: 30.96\n* Rouge-L: 39.23\n\nMLSUM_CNN:\n* Rouge-1: 39.65\n* Rouge-2: 17.49\n* Rouge-L: 27.66\n\nNote: Huggingface Inference API truncates the results, which results in unfinished sentences w... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multilingual mT5 model trained with MLSUM_TR and MLSUM_CNN (EN)",
"## Results:\n\nMLSUM_TR:\n* Rouge-1: 45.11\n* Rouge-2: 30.96\n* Rouge-L: 39.23\n\nMLSUM_CNN:\n* Ro... |
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. -->
# opt-125m-custom-data
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the ... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-125m-custom-data", "results": []}]} | Aalaa/opt-125m-custom-data | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T07:47:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| opt-125m-custom-data
====================
This model is a fine-tuned version of facebook/opt-125m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9594
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #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... |
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-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | Nancyzzz/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T07:59:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-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: 0.5253
* Wer: 0.3406
Model description
-----------------
More information needed
Intend... | [
"### 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... |
text2text-generation | transformers | # svalabs/mt5-large-german-query-gen-v1
This is a german [doc2query](https://arxiv.org/abs/1904.08375) model usable for document expansion to further boost search results by generating queries.
## Usage (code from doc2query/msmarco-14langs-mt5-base-v1)
```python
from transformers import AutoTokenizer, AutoModelForSeq2S... | {"language": ["de"], "datasets": ["unicamp-dl/mmarco", "deepset/germanquad"], "widget": [{"text": "Python ist eine universelle, \u00fcblicherweise interpretierte, h\u00f6here Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu f\u00f6rdern. So werden beispielsweise Bl\u00f6cke nicht... | svalabs/mt5-large-german-query-gen-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"de",
"dataset:unicamp-dl/mmarco",
"dataset:deepset/germanquad",
"arxiv:1904.08375",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.12741",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:... | null | 2022-06-29T08:09:03+00:00 | [
"1904.08375",
"1908.10084",
"1611.09268",
"2104.12741"
] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #de #dataset-unicamp-dl/mmarco #dataset-deepset/germanquad #arxiv-1904.08375 #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.12741 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # svalabs/mt5-large-german-query-gen-v1
This is a german doc2query model usable for document expansion to further boost search results by generating queries.
## Usage (code from doc2query/msmarco-14langs-mt5-base-v1)
Console Output:
### References
'Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'.
'M... | [
"# svalabs/mt5-large-german-query-gen-v1\nThis is a german doc2query model usable for document expansion to further boost search results by generating queries.",
"## Usage (code from doc2query/msmarco-14langs-mt5-base-v1)\n\n\nConsole Output:",
"### References\n'Sentence-BERT: Sentence Embeddings using Siamese ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #de #dataset-unicamp-dl/mmarco #dataset-deepset/germanquad #arxiv-1904.08375 #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.12741 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# svalabs/mt5-large-german-query-gen-v... |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | gguichard/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T08:13:59+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"
] |
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-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | FabianWillner/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T08:16:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0106
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
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="gguichard/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": "7.48 +/... | gguichard/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T08:26:45+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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-mutation-recognition-1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-mutation-recognition-1", "results": []}]} | Salvatore/bert-finetuned-mutation-recognition-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T08:40:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-mutation-recognition-1
=====================================
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: 0.0380
* Proteinmutation F1: 0.8631
* Dnamutation F1: 0.7522
* Snp F1: 1.0
* Precision: 0.8061
* Rec... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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": []}]} | ashutoshyadav4/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 | 2022-06-29T08:45:50+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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased 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",
"#... | [
"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.",
"## Mode... |
text2text-generation | transformers |
# Model description
This is an [mt5-base](https://huggingface.co/google/mt5-base) model, finetuned to generate questions using [TyDi QA](https://huggingface.co/datasets/tydiqa) dataset. It was trained to take the context and answer as input to generate questions.
# Overview
*Language model*: mT5-base \
*Language*: ... | {"license": "apache-2.0"} | PrimeQA/mt5-base-tydi-question-generator | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T08:46:08+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model description
This is an mt5-base model, finetuned to generate questions using TyDi QA dataset. It was trained to take the context and answer as input to generate questions.
# Overview
*Language model*: mT5-base \
*Language*: Arabic, Bengali, English, Finnish, Indonesian, Korean, Russian, Swahili, Telugu \
*T... | [
"# Model description\n\nThis is an mt5-base model, finetuned to generate questions using TyDi QA dataset. It was trained to take the context and answer as input to generate questions.",
"# Overview\n\n*Language model*: mT5-base \\\n*Language*: Arabic, Bengali, English, Finnish, Indonesian, Korean, Russian, Swahil... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model description\n\nThis is an mt5-base model, finetuned to generate questions using TyDi QA dataset. It was trained to take the contex... |
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... | PoloHuggingface/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-29T08:53:09+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... |
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-mutation-recognition-2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-mutation-recognition-2", "results": []}]} | Salvatore/bert-finetuned-mutation-recognition-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T09:10:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-mutation-recognition-2
=====================================
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: 0.0818
* Dnamutation F1: 0.6371
* Snp F1: 0.0952
* Proteinmutation F1: 0.8412
* Precision: 0.7646
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-generation | transformers |
# Bulgarian language poetry generation
Pretrained model using causal language modeling (CLM) objective based on [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). <br/>
Developed by [Radostin Cholakov](https://www.linkedin.com/in/radostin-chola... | {"language": ["bg"], "license": "apache-2.0", "tags": ["torch"], "datasets": ["chitanka"], "inference": false} | radi-cho/poetry-bg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"torch",
"custom_code",
"bg",
"dataset:chitanka",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T09:10:17+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-chitanka #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
# Bulgarian language poetry generation
Pretrained model using causal language modeling (CLM) objective based on GPT-2. <br/>
Developed by Radostin Cholakov as a part of the AzBuki.ML initiatives.
# How to use?
# Custom Tokens
We introduced 3 custom tokens in the tokenizer - '[NEL]', '[BDY]', '[HED]'
- '[HED]' den... | [
"# Bulgarian language poetry generation\n\nPretrained model using causal language modeling (CLM) objective based on GPT-2. <br/>\nDeveloped by Radostin Cholakov as a part of the AzBuki.ML initiatives.",
"# How to use?",
"# Custom Tokens\nWe introduced 3 custom tokens in the tokenizer - '[NEL]', '[BDY]', '[HED]'... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-chitanka #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"# Bulgarian language poetry generation\n\nPretrained model using causal language modeling (CLM) objective based on GPT-2. <br/>\nDeve... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ranguis/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-swc-fr](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ranguis/marian-finetuned-kde4-en-to-fr", "results": []}]} | ranguis/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T09:13:52+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ranguis/marian-finetuned-kde4-en-to-fr
======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-swc-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.5054
* Train Accuracy: 0.3469
* Validation Loss: 2.8945
* Validation Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 12, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': Fa... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T09:25:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
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: 1.1027
* Accuracy: 0.5447
* F1: 0.4832
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
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-endava-only-misc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BeardedJohn/bert-finetuned-ner-ubb-endava-only-misc", "results": []}]} | BeardedJohn/bert-finetuned-ner-ubb-endava-only-misc | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T10:44:27+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-endava-only-misc
===================================================
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: 0.0015
* Validation Loss: 0.0006
* Epoch: 2
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 705, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"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\\_... |
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-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | ones/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T11:12:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-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: 0.5112
* Wer: 0.9988
Model description
-----------------
More information needed
Intend... | [
"### 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... |
text-classification | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch models" width="75%">
</p>
# RobBERTje finetuned for sentiment analysis on DBRD
This is a finetuned model based on [RobBERTje (merged)](https://huggin... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "datasets": ["dbrd"], "widget": [{"text": "Ik erken dat dit een boek is, daarmee is alles gezegd."}, {"text": "Prachtig verhaal, heel mooi verteld en een verrassend einde... Een topper!"}], "thumbnail": "https://github.com/iPieter/... | DTAI-KULeuven/robbertje-merged-dutch-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"dataset:dbrd",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T11:17:36+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-dbrd #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
RobBERTje finetuned for sentiment analysis on DBRD
==================================================
This is a finetuned model based on RobBERTje (merged). We used DBRD, which consists of book reviews from URL. Hence our example sentences about books. We did some limited experiments to test if this... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-dbrd #license-mit #model-index #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="igpaub/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | igpaub/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T11:17:41+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
re... | {"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... | Corianas/ppo-LunarLander-v2.loadbest_ | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-29T11:26:03+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
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with... | [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #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\nand the RL Zoo.\n\nThe RL Zoo is a training framework... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1580596905721171969/0NnL... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/benshapiro/1666124624885/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/benshapiro | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T11:26:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ben Shapiro
@benshapiro
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# OncUponTim
This model is a fine-tuned version of [ilan541/OncUponTim](https://huggingface.co/ilan541/OncUponTim) on an unknown dataset... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "OncUponTim", "results": []}]} | ilan541/OncUponTim | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-29T12:06:52+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us
| OncUponTim
==========
This model is a fine-tuned version of ilan541/OncUponTim on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5891
* Train Accuracy: 0.7106
* Validation Loss: 0.5824
* Validation Accuracy: 0.7115
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'dec... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-tradition-zh
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-tradition-zh", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type": "xlsum", "... | elliotthwang/mt5-small-finetuned-tradition-zh | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T12:09:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-tradition-zh
================================
This model is a fine-tuned version of google/mt5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9218
* Rouge1: 5.7806
* Rouge2: 1.266
* Rougel: 5.761
* Rougelsum: 5.7833
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 6",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
null | null | Плохой стример | {} | Graaaa/Ben | null | [
"region:us"
] | null | 2022-06-29T12:09:23+00:00 | [] | [] | TAGS
#region-us
| Плохой стример | [] | [
"TAGS\n#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="igpaub/q-FrozenLake-v1-4x4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "metrics": [{... | igpaub/q-FrozenLake-v1-4x4 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T12:12:43+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"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="trtd56/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | trtd56/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T12:17:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="trtd56/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | trtd56/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T12:22:18+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="igpaub/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"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 +/... | igpaub/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T13:07:59+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | transformers | ## Introduction
Universal Information Extraction
More detail:
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie | {} | freedomking/prompt-uie-base | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T13:28:56+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| ## Introduction
Universal Information Extraction
More detail:
URL | [
"## Introduction\nUniversal Information Extraction\n\nMore detail: \nURL"
] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"## Introduction\nUniversal Information Extraction\n\nMore detail: \nURL"
] |
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. -->
# cifar10_outputs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["cifar10"], "metrics": ["accuracy"], "model-index": [{"name": "cifar10_outputs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10", "type": "cif... | jimypbr/cifar10_outputs | null | [
"transformers",
"pytorch",
"tensorboard",
"optimum_graphcore",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T13:30:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #optimum_graphcore #vit #image-classification #vision #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# cifar10_outputs
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0806
- Accuracy: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# cifar10_outputs\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0806\n- Accuracy: 0.9914",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #optimum_graphcore #vit #image-classification #vision #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# cifar10_outputs\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifa... |
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-mutation-recognition-3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-mutation-recognition-3", "results": []}]} | Salvatore/bert-finetuned-mutation-recognition-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T13:32:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-mutation-recognition-3
=====================================
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: 0.0727
* Dnamutation F1: 0.6484
* Proteinmutation F1: 0.8571
* Snp F1: 1.0
* Precision: 0.7966
* Rec... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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": []}]} | elhamagk/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 | 2022-06-29T13:54:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Abonia/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T14:12:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2991
- Accuracy: 0.8767
- F1: 0.8771
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2991\n- Accuracy: 0.8767\n- F1: 0.8771",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text-classification | transformers |
## Model information:
This model is the [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other... | {"language": "en", "license": "cc", "tags": ["text classification"], "datasets": "MIMIC-III\u00a0", "widget": [{"text": "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."}]} | sarahmiller137/bioclinical-bert-ft-m3-lc | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"text classification",
"en",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T14:14:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #text classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
This model is the emilyalsentzer/Bio_ClinicalBERT model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radio... | [
"## Model information:\nThis model is the emilyalsentzer/Bio_ClinicalBERT model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #text classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nThis model is the emilyalsentzer/Bio_ClinicalBERT model that has been finetuned using radiology report texts from the MIMIC... |
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-zindi_tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-zindi_tweets", "results": []}]} | okite97/distilbert-base-uncased-finetuned-zindi_tweets | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T14:24:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-zindi\_tweets
===============================================
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: 0.3203
* Accuracy: 0.9168
* F1: 0.9168
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# vit-snacks
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patc... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["snacks"], "metrics": ["accuracy"], "model-index": [{"name": "vit-snacks", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Matthijs/snacks", "type": "snacks", "a... | Shivagowri/vit-snacks | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:snacks",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T15:05:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-snacks #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-snacks
==========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Matthijs/snacks dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2754
* Accuracy: 0.9393
Model description
-----------------
upload any image of your fave yummy snack
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-snacks #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\\_r... |
unconditional-image-generation | keras |
This model was created for the [Keras code example](https://keras.io/examples/generative/ddim/) on [denoising diffusion implicit models (DDIM)](https://arxiv.org/abs/2010.02502).
## Model description
The model uses a [U-Net](https://arxiv.org/abs/1505.04597) with identical input and output dimensions. It progressive... | {"library_name": "keras", "tags": ["generative", "denoising", "diffusion", "ddim", "ddpm", "unconditional-image-generation"]} | keras-io/denoising-diffusion-implicit-models | null | [
"keras",
"generative",
"denoising",
"diffusion",
"ddim",
"ddpm",
"unconditional-image-generation",
"arxiv:2010.02502",
"arxiv:1505.04597",
"arxiv:2006.11239",
"arxiv:1706.03762",
"arxiv:1711.05101",
"has_space",
"region:us"
] | null | 2022-06-29T15:18:25+00:00 | [
"2010.02502",
"1505.04597",
"2006.11239",
"1706.03762",
"1711.05101"
] | [] | TAGS
#keras #generative #denoising #diffusion #ddim #ddpm #unconditional-image-generation #arxiv-2010.02502 #arxiv-1505.04597 #arxiv-2006.11239 #arxiv-1706.03762 #arxiv-1711.05101 #has_space #region-us
| This model was created for the Keras code example on denoising diffusion implicit models (DDIM).
Model description
-----------------
The model uses a U-Net with identical input and output dimensions. It progressively downsamples and upsamples its input image, adding skip connections between layers having the same r... | [] | [
"TAGS\n#keras #generative #denoising #diffusion #ddim #ddpm #unconditional-image-generation #arxiv-2010.02502 #arxiv-1505.04597 #arxiv-2006.11239 #arxiv-1706.03762 #arxiv-1711.05101 #has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | k3nneth/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 | 2022-06-29T15:31:05+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: 0.1363
* F1: 0.8627
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\\_... |
null | transformers | ## Introduction
Universal Information Extraction
More detail:
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie
| {} | freedomking/prompt-uie-medical-base | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T15:32:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| ## Introduction
Universal Information Extraction
More detail:
URL
| [
"## Introduction\nUniversal Information Extraction\n\nMore detail: \nURL"
] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"## Introduction\nUniversal Information Extraction\n\nMore detail: \nURL"
] |
automatic-speech-recognition | transformers |
## Wav2Vec2-2-Bart-Large-Tedlium
This model is a sequence-2-sequence (seq2seq) model trained on the [TEDLIUM](https://huggingface.co/datasets/LIUM/tedlium) corpus (release 3).
It combines a speech encoder with a text decoder to perform automatic speech recognition. The encoder weights are initialised with the [Wav2V... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["automatic-speech-recognition"], "datasets": ["LIUM/tedlium"], "metrics": [{"name": "Dev WER", "type": "wer", "value": 9.0}, {"name": "Test WER", "type": "wer", "value": 6.4}]} | sanchit-gandhi/wav2vec2-2-bart-large-tedlium | null | [
"transformers",
"pytorch",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"en",
"dataset:LIUM/tedlium",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T15:33:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #speech-encoder-decoder #automatic-speech-recognition #en #dataset-LIUM/tedlium #license-cc-by-4.0 #endpoints_compatible #region-us
|
## Wav2Vec2-2-Bart-Large-Tedlium
This model is a sequence-2-sequence (seq2seq) model trained on the TEDLIUM corpus (release 3).
It combines a speech encoder with a text decoder to perform automatic speech recognition. The encoder weights are initialised with the Wav2Vec2 LV-60k checkpoint from @facebook. The decoder... | [
"## Wav2Vec2-2-Bart-Large-Tedlium\nThis model is a sequence-2-sequence (seq2seq) model trained on the TEDLIUM corpus (release 3). \n\nIt combines a speech encoder with a text decoder to perform automatic speech recognition. The encoder weights are initialised with the Wav2Vec2 LV-60k checkpoint from @facebook. The ... | [
"TAGS\n#transformers #pytorch #jax #speech-encoder-decoder #automatic-speech-recognition #en #dataset-LIUM/tedlium #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## Wav2Vec2-2-Bart-Large-Tedlium\nThis model is a sequence-2-sequence (seq2seq) model trained on the TEDLIUM corpus (release 3). \n\nIt combi... |
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": []}]} | k3nneth/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 | 2022-06-29T15:55:27+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: 0.1644
* F1: 0.8617
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*... |
question-answering | transformers |
# AraElectra for Question Answering on Arabic-SQuADv2
This is the [AraElectra](https://huggingface.co/aubmindlab/araelectra-base-discriminator) model, fine-tuned using the [Arabic-SQuADv2.0](https://huggingface.co/datasets/ZeyadAhmed/Arabic-SQuADv2.0) dataset. It's been trained on question-answer pairs, including una... | {"language": ["ar"], "datasets": ["ZeyadAhmed/Arabic-SQuADv2.0"], "metrics": [{"name": "exact_match", "type": "exact_match", "value": 65.12}, {"name": "F1", "type": "f1", "value": 71.49}]} | ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"ar",
"dataset:ZeyadAhmed/Arabic-SQuADv2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-29T16:25:33+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #electra #question-answering #ar #dataset-ZeyadAhmed/Arabic-SQuADv2.0 #endpoints_compatible #has_space #region-us
|
# AraElectra for Question Answering on Arabic-SQuADv2
This is the AraElectra model, fine-tuned using the Arabic-SQuADv2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. with help of AraElectra Classifier to predicted unanswerable question.
... | [
"# AraElectra for Question Answering on Arabic-SQuADv2\n\nThis is the AraElectra model, fine-tuned using the Arabic-SQuADv2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. with help of AraElectra Classifier to predicted unanswerable questi... | [
"TAGS\n#transformers #pytorch #electra #question-answering #ar #dataset-ZeyadAhmed/Arabic-SQuADv2.0 #endpoints_compatible #has_space #region-us \n",
"# AraElectra for Question Answering on Arabic-SQuADv2\n\nThis is the AraElectra model, fine-tuned using the Arabic-SQuADv2.0 dataset. It's been trained on question-... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071
## Validation Metrics
- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.730220467684272... | {"language": "unk", "tags": "autotrain", "datasets": ["kakashi210/autotrain-data-tweet-sentiment-classifier"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 17.43982800509071} | kakashi210/autotrain-tweet-sentiment-classifier-1055036381 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"unk",
"dataset:kakashi210/autotrain-data-tweet-sentiment-classifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T16:45:44+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-kakashi210/autotrain-data-tweet-sentiment-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071
## Validation Metrics
- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.730220467684272... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1055036381\n- CO2 Emissions (in grams): 17.43982800509071",
"## Validation Metrics\n\n- Loss: 0.6177256107330322\n- Accuracy: 0.7306006137658921\n- Macro F1: 0.719534854339415\n- Micro F1: 0.730600613765892\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-kakashi210/autotrain-data-tweet-sentiment-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 105... |
feature-extraction | transformers |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agree... | {"language": ["en"], "tags": ["MusicGeneration"]} | ArthurZ/jukebox-5b-lyrics | null | [
"transformers",
"pytorch",
"jukebox",
"feature-extraction",
"MusicGeneration",
"en",
"arxiv:2005.00341",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-29T16:51:20+00:00 | [
"2005.00341"
] | [
"en"
] | TAGS
#transformers #pytorch #jukebox #feature-extraction #MusicGeneration #en #arxiv-2005.00341 #endpoints_compatible #has_space #region-us
|
# Jukebox
## Overview
The Jukebox model was proposed in Jukebox: A generative model for music
by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
Ilya Sutskever.
This model proposes a generative music model which can be produce minute long samples which can bne conditionned on
artist, ... | [
"# Jukebox",
"## Overview\n\nThe Jukebox model was proposed in Jukebox: A generative model for music\nby Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,\nIlya Sutskever.\n\nThis model proposes a generative music model which can be produce minute long samples which can bne conditionned... | [
"TAGS\n#transformers #pytorch #jukebox #feature-extraction #MusicGeneration #en #arxiv-2005.00341 #endpoints_compatible #has_space #region-us \n",
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"## Overview\n\nThe Jukebox model was proposed in Jukebox: A generative model for music\nby Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Ki... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | JHart96/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T17:10:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3300
- Accuracy: 0.86
- F1: 0.8627
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3300\n- Accuracy: 0.86\n- F1: 0.8627",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
question-answering | transformers | # GELECTRA-distilled-LegalQuAD
## Overview
**Language model:** GELECTRA-distilled
**Language:** German
**Downstream-task:** Extractive QA
**Training data:** German-legal-SQuAD
**Eval data:** German-legal-SQuAD testset
## Hyperparameters
```
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
##... | {"language": ["de"], "tags": ["qa"], "widget": [{"text": "", "context": "", "example_title": "Extractive QA"}]} | Christoph911/GELECTRA-distilled-LegalQuAD | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"qa",
"de",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T17:14:25+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #electra #question-answering #qa #de #endpoints_compatible #region-us
| # GELECTRA-distilled-LegalQuAD
## Overview
Language model: GELECTRA-distilled
Language: German
Downstream-task: Extractive QA
Training data: German-legal-SQuAD
Eval data: German-legal-SQuAD testset
## Hyperparameters
'''
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
## Eval results
Evalua... | [
"# GELECTRA-distilled-LegalQuAD",
"## Overview\nLanguage model: GELECTRA-distilled \nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD testset",
"## Hyperparameters\n\n'''\nbatch_size = 10\nn_epochs = 2\nmax_seq_len=256,\nlearning_rate=1e-5,... | [
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"## Overview\nLanguage model: GELECTRA-distilled \nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD t... |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | zhav1k/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T17:23:24+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers | # Arabic NER Model
- [Github repo](https://github.com/edchengg/GigaBERT)
- NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
- ACE2005 Training data: English + Arabic
- [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelin... | {"language": ["ar", "en"], "license": "mit", "tags": ["BERT", "token-classification", "sequence-tagger-model"], "datasets": ["ACE2005"]} | ychenNLP/arabic-ner-ace | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"BERT",
"token-classification",
"sequence-tagger-model",
"ar",
"en",
"dataset:ACE2005",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T17:45:26+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #BERT #token-classification #sequence-tagger-model #ar #en #dataset-ACE2005 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Arabic NER Model
================
* Github repo
* NER BIO tagging model based on GigaBERTv4.
* ACE2005 Training data: English + Arabic
* NER tags including: PER, VEH, GPE, WEA, ORG, LOC, FAC
Hyperparameters
---------------
* learning\_rate=2e-5
* num\_train\_epochs=10
* weight\_decay=0.01
ACE2005 Evaluation res... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #BERT #token-classification #sequence-tagger-model #ar #en #dataset-ACE2005 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | zhav1k/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-29T17:55:53+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-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. -->
# attempt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "attempt", "results": []}]} | ullasmrnva/LawBerta | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-29T17:56:39+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# attempt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# attempt\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# attempt\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\... |
text-classification | transformers |
# Arabic Relation Extraction Model
- [Github repo](https://github.com/edchengg/GigaBERT)
- Relation Extraction model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
- Model detail: mark two entities in the sentence with special markers (e.g., ```XXXX <PER> entity1 </PER> XXXXXXX ... | {"language": ["ar", "en"], "license": "mit", "tags": ["BERT", "Text Classification", "relation"], "datasets": ["ACE2005"]} | ychenNLP/arabic-relation-extraction | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"text-classification",
"BERT",
"Text Classification",
"relation",
"ar",
"en",
"dataset:ACE2005",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T18:45:34+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #tf #tensorboard #bert #text-classification #BERT #Text Classification #relation #ar #en #dataset-ACE2005 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Arabic Relation Extraction Model
- Github repo
- Relation Extraction model based on GigaBERTv4.
- Model detail: mark two entities in the sentence with special markers (e.g., ). Then we use the BERT [CLS] representation to make a prediction.
- ACE2005 Training data: Arabic
- Relation tags including: Physical, Part-wh... | [
"# Arabic Relation Extraction Model\n- Github repo\n- Relation Extraction model based on GigaBERTv4.\n- Model detail: mark two entities in the sentence with special markers (e.g., ). Then we use the BERT [CLS] representation to make a prediction.\n- ACE2005 Training data: Arabic\n- Relation tags including: Physical... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #bert #text-classification #BERT #Text Classification #relation #ar #en #dataset-ACE2005 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Arabic Relation Extraction Model\n- Github repo\n- Relation Extraction model based on GigaBERTv4.\n- M... |
fill-mask | transformers |
Simple model trained with 2790 Discord messages
( Might have some NSFW responses )
| {"license": "mit"} | TheDiamondKing/Discord-Message-Small | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T19:10:42+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Simple model trained with 2790 Discord messages
( Might have some NSFW responses )
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
# DALL-E Mini Running in the Browser (work in progress)
### Notes:
* Working tflite conversion in [this notebook](https://colab.research.google.com/gist/josephrocca/f427377f76c574f1c1e8e4d6d62c34b6/tflite-dalle-mini-conversion-separated-encoder-and-decoder.ipynb).
* Note that the encoder and decoder need to be c... | {"license": "mit"} | rocca/dalle-mini-js | null | [
"tflite",
"license:mit",
"region:us"
] | null | 2022-06-29T19:17:53+00:00 | [] | [] | TAGS
#tflite #license-mit #region-us
|
# DALL-E Mini Running in the Browser (work in progress)
### Notes:
* Working tflite conversion in this notebook.
* Note that the encoder and decoder need to be converted separately for some reason. More info on this bug.
* But these models currently require TF Select operators due to bitwise operations that a... | [
"# DALL-E Mini Running in the Browser (work in progress)",
"### Notes:\n * Working tflite conversion in this notebook.\n * Note that the encoder and decoder need to be converted separately for some reason. More info on this bug.\n * But these models currently require TF Select operators due to bitwise operati... | [
"TAGS\n#tflite #license-mit #region-us \n",
"# DALL-E Mini Running in the Browser (work in progress)",
"### Notes:\n * Working tflite conversion in this notebook.\n * Note that the encoder and decoder need to be converted separately for some reason. More info on this bug.\n * But these models currently requ... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer Large (zh-ZH)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architect... | {"language": ["zh"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Transducer", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["AISHELL-2"], "model-index": [{"name": "stt_zh_conformer_transducer_large", "results": [{"task... | nvidia/stt_zh_conformer_transducer_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"Transducer",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"zh",
"dataset:AISHELL-2",
"arxiv:2005.08100",
"arxiv:1808.10583",
"license:cc-by-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-29T19:26:16+00:00 | [
"2005.08100",
"1808.10583"
] | [
"zh"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #zh #dataset-AISHELL-2 #arxiv-2005.08100 #arxiv-1808.10583 #license-cc-by-4.0 #model-index #has_space #region-us
| NVIDIA Conformer-Transducer Large (zh-ZH)
=========================================
img {
display: inline;
}
| 
| 
| 
This model transcribes speech in Mandarin alphabet.
It is a large version of Conformer-Transducer... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nYou may transcribe an audio file like this:",
"### 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 as... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #zh #dataset-AISHELL-2 #arxiv-2005.08100 #arxiv-1808.10583 #license-cc-by-4.0 #model-index #has_space #region-us \n",
"### Automatically instantiate the model",
"### Transcribing usin... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **PPO** agent playing **Pixelcopter-PLE-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 huggingf... | {"library_name": "stable-baselines3", "tags": ["Pixelcopter-PLE-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type":... | ThomasSimonini/PixelCopter | null | [
"stable-baselines3",
"Pixelcopter-PLE-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-29T19:37:43+00:00 | [] | [] | TAGS
#stable-baselines3 #Pixelcopter-PLE-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Pixelcopter-PLE-v0
This is a trained model of a PPO agent playing Pixelcopter-PLE-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing Pixelcopter-PLE-v0\nThis is a trained model of a PPO agent playing Pixelcopter-PLE-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #Pixelcopter-PLE-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Pixelcopter-PLE-v0\nThis is a trained model of a PPO agent playing Pixelcopter-PLE-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nT... |
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. -->
# deberta-v3-large-dapt-scientific-papers-pubmed
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggin... | {"license": "mit", "tags": ["fill-mask", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large-dapt-scientific-papers-pubmed", "results": []}]} | domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T20:03:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-dapt-scientific-papers-pubmed
==============================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4729
* Accuracy: 0.3510
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\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\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 12\n... |
fill-mask | transformers |
Medium-Sized model trained with philosophical questions ( mainly from discord )
~11000 Messages | {"license": "mit"} | TheDiamondKing/Discord-Philosophy-Medium | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T20:16:21+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Medium-Sized model trained with philosophical questions ( mainly from discord )
~11000 Messages | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-1", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T20:16:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-1
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8655
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #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\\... |
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-tweets-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-tweets-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "... | austinmw/distilbert-base-uncased-finetuned-tweets-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T20:23:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-tweets-sentiment
==================================================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8192
* Accuracy: 0.7295
* F1: 0.7303
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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... | jdang/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 | 2022-06-29T20:47:46+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: 0.0629
* Precision: 0.9358
* Recall: 0.9510
* F1: 0.9433
* 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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | tbasic5/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-29T21:07:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2222
* Accuracy: 0.925
* F1: 0.9250
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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. -->
# distilbert-base-uncased-becas-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-2", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T21:40:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-2
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9506
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.1\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #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: 0.1\n* train\\_batch\\_s... |
reinforcement-learning | null | lo
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pix", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, ... | ThomasSimonini/Reinforce-Pix | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-06-29T22:39:06+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
| lo
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer Large (fr)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture... | {"language": ["fr"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Transducer", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["multilingual_librispeech", "mozilla-foundation/common_voice_7_0", "VoxPopuli"], "model-index"... | nvidia/stt_fr_conformer_transducer_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"Transducer",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"fr",
"dataset:multilingual_librispeech",
"dataset:mozilla-foundation/common_voice_7_0",
"dataset:VoxPopuli",
"arxiv:2005.08100",
"license:cc-by... | null | 2022-06-29T23:34:51+00:00 | [
"2005.08100"
] | [
"fr"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #fr #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_7_0 #dataset-VoxPopuli #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
|
# NVIDIA Conformer-Transducer Large (fr)
<style>
img {
display: inline;
}
</style>
| 
| 
| 
This model was trained on a composite dataset comprising of over 1500 hours of French speech. It is a large size version of ... | [
"# NVIDIA Conformer-Transducer Large (fr)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| \n| \n\n\nThis model was trained on a composite dataset comprising of over 1500 hours of French speech. It is a large... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #fr #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_7_0 #dataset-VoxPopuli #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us \n",
"# NVIDIA Conform... |
audio-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. -->
# wav2vec2-base-finetuned-ks-de
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks-de", "results": []}]} | skpawar1305/wav2vec2-base-finetuned-ks-de | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T00:02:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks-de
=============================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8987
* Accuracy: 0.7222
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 3e-05\n* train\\_batch\\_size: 32\n* eval... |
audio-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. -->
# wav2vec2-large-xlsr-53-german-finetuned-ks-de
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-germa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-large-xlsr-53-german-finetuned-ks-de", "results": []}]} | skpawar1305/wav2vec2-large-xlsr-53-german-finetuned-ks-de | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T00:21:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-german-finetuned-ks-de
=============================================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-german on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8681
* Accuracy: 0.6667
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 3e-05\n* train\\_batch\\_size: 32\n* eval... |
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-v7
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-v7", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T00:22:04+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-v7
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v6 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: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"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* ... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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).
[Here is a video of the Agent... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | Corianas/qrdqn-3frame-SpaceInvadersNoFrameskip-v4_3.loadbest | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T00:26:53+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
Here is a video of the Agent playing for longer than the included video
The RL Zoo is a training framework for Stable Baselines3
reinforce... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nHere is a video of the Agent playing for longer than the included video\n\nThe RL Zoo is a training framework for Stable Baselines... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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).
[Here is a video of the Agent... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | Corianas/qrdqn-3frame-SpaceInvadersNoFrameskip-v4_3 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T00:34:03+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
Here is a video of the Agent playing for longer than the included video
The RL Zoo is a training framework for Stable Baselines3
reinforce... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nHere is a video of the Agent playing for longer than the included video\n\nThe RL Zoo is a training framework for Stable Baselines... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
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": []}]} | jdang/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 | 2022-06-30T00:49:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
audio-classification | null |
copy of https://tfhub.dev/google/vggish/1 | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification"], "datasets": ["AudioSet"]} | thelou1s/viggish | null | [
"audio-classification",
"multilingual",
"dataset:AudioSet",
"license:apache-2.0",
"region:us"
] | null | 2022-06-30T00:51:30+00:00 | [] | [
"multilingual"
] | TAGS
#audio-classification #multilingual #dataset-AudioSet #license-apache-2.0 #region-us
|
copy of URL | [] | [
"TAGS\n#audio-classification #multilingual #dataset-AudioSet #license-apache-2.0 #region-us \n"
] |
audio-classification | null |
copy of https://pypi.org/project/panns-inference/ | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification"]} | thelou1s/panns-inference | null | [
"audio-classification",
"multilingual",
"license:apache-2.0",
"region:us"
] | null | 2022-06-30T01:13:19+00:00 | [] | [
"multilingual"
] | TAGS
#audio-classification #multilingual #license-apache-2.0 #region-us
|
copy of URL | [] | [
"TAGS\n#audio-classification #multilingual #license-apache-2.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **BreakoutNoFrameskip-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 ... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4... | Corianas/qrdqn-3frame-BreakoutNoFrameskip-v4_scoretest | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T01:56:08+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing BreakoutNoFrameskip-v4
This is a trained model of a QRDQN agent playing BreakoutNoFrameskip-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 inclu... | [
"# QRDQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing BreakoutNoFrameskip-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 a... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL... |
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. -->
# dlub-2022-mlm-full
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the follow... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dlub-2022-mlm-full", "results": []}]} | Gansukh/dlub-2022-mlm-full | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T02:35:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dlub-2022-mlm-full
==================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4321
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
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-hotpot_qa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-hotpot_qa", "results": []}]} | vish88/distilbert-base-uncased-finetuned-hotpot_qa | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T02:39:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-hotpot\_qa
============================================
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: 1.2565
Model description
-----------------
More information needed
Inten... | [
"### 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 #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... |
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. -->
# dlub-2022-mlm
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following r... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dlub-2022-mlm", "results": []}]} | bayartsogt/dlub-2022-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T02:42:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dlub-2022-mlm
=============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4546
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: 32\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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
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. -->
# Malaya-speech_fine-tune_realcase_30_Jun_lm
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](h... | {"tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "Malaya-speech_fine-tune_realcase_30_Jun_lm", "results": []}]} | RuiqianLi/Malaya-speech_fine-tune_realcase_30_Jun_lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T03:06:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us
| Malaya-speech\_fine-tune\_realcase\_30\_Jun\_lm
===============================================
This model is a fine-tuned version of malay-huggingface/wav2vec2-xls-r-300m-mixed on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7669
* Wer: 0.3194
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size:... |
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. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned-bert-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": ... | shahma/finetuned-bert-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T03:35:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4266
* Accuracy: 0.8603
* F1: 0.9032
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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": []}]} | Akihiro2/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T03:50:35+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. -->
# roberta-base-finetuned-hotpot_qa
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on th... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-hotpot_qa", "results": []}]} | vish88/roberta-base-finetuned-hotpot_qa | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T03:59:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-hotpot\_qa
=================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8677
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-mit #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\\_batch\\... |
null | null | Question-Answering-Task | {} | Gowtham2001/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
] | null | 2022-06-30T04:25:47+00:00 | [] | [] | TAGS
#region-us
| Question-Answering-Task | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# GPT-J 6b Shakespeare
<p style="color:green"> <b> 1.) The "Hosted inference API" is turned off. Go to the <a href="https://huggingface.co/crumb/gpt-j-6b-shakespeare#how-to-use">How to Use</a> section <br>
2.) This is a "proof of concept" and not fully trained, simple training script also in "How to Use" section. </b... | {"language": ["en"], "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile", "tiny_shakespeare"], "inference": false} | crumb/gpt-j-6b-shakespeare | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"arxiv:2101.00027",
"autotrain_compatible",
"region:us"
] | null | 2022-06-30T04:33:29+00:00 | [
"2101.00027"
] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2101.00027 #autotrain_compatible #region-us
| GPT-J 6b Shakespeare
====================
**1.) The "Hosted inference API" is turned off. Go to the [Model Description
-----------------
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
... | [
"### How to use",
"### Limitations and Biases\n\n\n(same as gpt-j-6b)\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to ... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2101.00027 #autotrain_compatible #region-us \n",
"### How to use",
"### Limitations and Biases\n\n\n(same as gpt-j-6b)\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models a... |
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. -->
# xenery/indobert-finetuned-ner
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/ind... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "xenery/indobert-finetuned-ner", "results": []}]} | xenergy/indobert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T05:37:30+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xenery/indobert-finetuned-ner
=============================
This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2219
* Validation Loss: 0.2306
* Epoch: 2
Model description
-----------------
More infor... | [
"### 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': 315, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #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': ... |
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="Galeros/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": ... | Galeros/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-30T05:58:04+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"
] |
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