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null | transformers | # oBERT-3-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - unstructured`.
```
Pru... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:01:00+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-unstructured-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - unstructured'.
The dev-set performance of this model:
... | [
"# oBERT-3-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - unstructured'.\n\n\n\nThe dev-set performance of ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large L... |
null | transformers | # oBERT-3-downstream-pruned-unstructured-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - unstructured`.
```
Pru... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:01:15+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-unstructured-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - unstructured'.
The dev-set performance of this model:
... | [
"# oBERT-3-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - unstructured'.\n\n\n\nThe dev-set performance of ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large L... |
null | transformers | # oBERT-3-downstream-pruned-block4-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block`.
```
Pruning method... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:01:27+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-block4-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - 4-block'.
The dev-set performance of this model:
Code: URL
... | [
"# oBERT-3-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - 4-block'.\n\n\n\nThe dev-set performance of this model:... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Languag... |
null | transformers | # oBERT-3-downstream-pruned-block4-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block`.
```
Pruning method... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:01:41+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-block4-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - 4-block'.
The dev-set performance of this model:
Code: URL
... | [
"# oBERT-3-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - 4-block'.\n\n\n\nThe dev-set performance of this model:... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Languag... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | lbianchi/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T13:07:51+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | This model is a T5-small reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
For more details on how to use it, check the following links:
- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
- [Rerank MS MARCO passages](https://github.com/castori... | {} | castorini/monot5-small-msmarco-10k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T14:04:10+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-small reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
For more details on how to use it, check the following links:
- A simple reranking example
- Rerank MS MARCO passages
- Rerank Robust04 documents
Paper describing the model: Document Ranking with a Pretrained Sequ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is a T5-small reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 1 epoch).
For more details on how to use it, check the following links:
- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
- [Rerank MS MARCO passages](https://github.com/casto... | {} | castorini/monot5-small-msmarco-100k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T14:04:22+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model is a T5-small reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 1 epoch).
For more details on how to use it, check the following links:
- A simple reranking example
- Rerank MS MARCO passages
- Rerank Robust04 documents
Paper describing the model: Document Ranking with a Pretrained Se... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | Annabelleabbott/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:33:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0767
* Accuracy: 0.9726
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": []}]} | schoenml/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:46:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image_folder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1551
- eval_accuracy: 0.9474
- eval_runtime: 13.1569
- eval_samples_per_second: 205.216
-... | [
"# swin-tiny-patch4-window7-224-finetuned-eurosat\n\nThis model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image_folder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.1551\n- eval_accuracy: 0.9474\n- eval_runtime: 13.1569\n- eval_samples_per_second: ... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# swin-tiny-patch4-window7-224-finetuned-eurosat\n\nThis model is a fine-tuned version of microsoft/swin-tiny-patch4... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | mehnaazasad/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:47:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0703
* Accuracy: 0.9770
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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/701052820754190336/OwxAZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/sickziii | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T15:17:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
sickzee
@sickziii
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- 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-arxiv
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- L... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-arxiv", "results": []}]} | MadFace/t5-arxiv | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T15:26:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-arxiv
========
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3852
* Rouge1: 18.0722
* Rouge2: 6.8453
* Rougel: 14.3659
* Rougelsum: 16.4137
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended us... | [
"### 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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... | arcAman07/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:00:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2222
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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-german-cased-finetuned-subj_v6_7Epoch_v2
This model is a fine-tuned version of [bert-base-german-cased](https://huggin... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v6_7Epoch_v2", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v6_7Epoch_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:03:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v6\_7Epoch\_v2
=====================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2860
* Precision: 0.7623
* Recall: 0.7514
* F1: 0.7568
* ... | [
"### 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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-resumes-sections
This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-resumes-sections", "results": []}]} | has-abi/bert-finetuned-resumes-sections | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:44:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-resumes-sections
===============================
This model is a fine-tuned version of dbmdz/bert-base-french-europeana-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0333
* F1: 0.9548
* Roc Auc: 0.9732
* Accuracy: 0.9493
Model description
----------... | [
"### 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: 12",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
feature-extraction | 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. -->
# TurQA-bert-base-turkish-cased-finetuned-toqad
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "TurQA-bert-base-turkish-cased-finetuned-toqad", "results": []}]} | meetyildiz/TurQA-bert-base-turkish-cased-finetuned-toqad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"feature-extraction",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:49:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #feature-extraction #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| TurQA-bert-base-turkish-cased-finetuned-toqad
=============================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9711
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #feature-extraction #generated_from_trainer #dataset-squad #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* ev... |
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": []}]} | wrice/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 | 2022-05-25T16:51:14+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: 0.6348
* Wer: 0.3204
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: 4\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: 4... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e2
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e2", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:53:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e2
==============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8604
* Rouge1: 53.7901
* Rouge2: 34.5052... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e1
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e1", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T16:55:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e1
==============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8952
* Rouge1: 53.0722
* Rouge2: 32.4229... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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-german-cased-finetuned-subj_v6_7Epoch_v3
This model is a fine-tuned version of [bert-base-german-cased](https://huggin... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v6_7Epoch_v3", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v6_7Epoch_v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T17:16:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v6\_7Epoch\_v3
=====================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2732
* Precision: 0.7654
* Recall: 0.7829
* F1: 0.7740
* ... | [
"### 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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e4
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e4", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T17:43:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e4
==============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8121
* Rouge1: 53.9237
* Rouge2: 34.5683... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e8
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e8", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e8 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T17:58:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e8
==============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8063
* Rouge1: 54.9922
* Rouge2: 38.7265... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | voleg44/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T18:15:50+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | transformers | # oBERT-12-downstream-dense-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - 0% Sparsity - QAT`, and it represents an upper bou... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-dense-QAT-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:19:55+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-dense-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the correspond... | [
"# oBERT-12-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the c... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Mode... |
null | transformers | # oBERT-12-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - Sparsity 80% - 4-block + QAT`.
```
Pr... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-block4-80-QAT-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:20:09+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 80% - 4-block + QAT'.
The dev-set performance of this model:
... | [
"# oBERT-12-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 80% - 4-block + QAT'.\n\n\n\nThe dev-set performance of... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large La... |
null | transformers | # oBERT-12-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - Sparsity 90% - 4-block + QAT`.
```
Pr... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-block4-90-QAT-squadv1 | null | [
"transformers",
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"oBERT",
"sparsity",
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"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:20:22+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 90% - 4-block + QAT'.
The dev-set performance of this model:
... | [
"# oBERT-12-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 90% - 4-block + QAT'.\n\n\n\nThe dev-set performance of... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large La... |
null | transformers | # oBERT-6-downstream-dense-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity - QAT`, and it represents an upper bound... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-dense-QAT-squadv1 | null | [
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"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:20:36+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-dense-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the correspondin... | [
"# oBERT-6-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the cor... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Model... |
null | transformers | # oBERT-6-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block + QAT`.
```
Prun... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1 | null | [
"transformers",
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"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:20:49+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - 4-block + QAT'.
The dev-set performance of this model:
C... | [
"# oBERT-6-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - 4-block + QAT'.\n\n\n\nThe dev-set performance of t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lan... |
null | transformers | # oBERT-6-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - 4-block + QAT`.
```
Prun... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-block4-90-QAT-squadv1 | null | [
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"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:21:02+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - 4-block + QAT'.
The dev-set performance of this model:
C... | [
"# oBERT-6-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - 4-block + QAT'.\n\n\n\nThe dev-set performance of t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lan... |
null | transformers | # oBERT-3-downstream-dense-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - 0% Sparsity - QAT`, and it represents an upper bound... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-dense-QAT-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:21:16+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-dense-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the correspondin... | [
"# oBERT-3-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - 0% Sparsity - QAT', and it represents an upper bound for performance of the cor... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-dense-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Model... |
null | transformers | # oBERT-3-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block + QAT`.
```
Prun... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-block4-80-QAT-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:21:28+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-block4-80-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - 4-block + QAT'.
The dev-set performance of this model:
C... | [
"# oBERT-3-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 80% - 4-block + QAT'.\n\n\n\nThe dev-set performance of t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-block4-80-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lan... |
null | transformers | # oBERT-3-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block + QAT`.
```
Prun... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-pruned-block4-90-QAT-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:21:41+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - 4-block + QAT'.
The dev-set performance of this model:
C... | [
"# oBERT-3-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - Sparsity 90% - 4-block + QAT'.\n\n\n\nThe dev-set performance of t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-pruned-block4-90-QAT-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lan... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM)
This repository provides all the necessary... | {"language": "dar", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-darija | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"dar",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] | null | 2022-05-25T18:36:53+00:00 | [] | [
"dar"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-commonvoice #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a DVoice Darija dataset within
SpeechBrain. For a bet... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-commonvoice #license-apache-2.0 #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-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... | uygarkurt/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T18:42:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2156
* Accuracy: 0.92
* F1: 0.9200
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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="jcgarciaca/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"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": ... | jcgarciaca/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T19:03:40+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="jcgarciaca/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-v3-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3-4x4-no_slippery", "type": "Taxi-v3-4x4-no_slippery"}, "metr... | jcgarciaca/q-Taxi-v3 | null | [
"Taxi-v3-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T19:17:23+00:00 | [] | [] | TAGS
#Taxi-v3-4x4-no_slippery #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-v3-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | OD/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T19:25:23+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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-5000-samples
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-5000-samples", "results": []}]} | joebobby/finetuning-sentiment-model-5000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T19:32:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-model-5000-samples
=======================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0701
* Accuracy: 0.758
* F1: 0.7580
Model description
-----------------
More informati... | [
"### 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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
null | null | # HairCLIP
- https://arxiv.org/abs/2112.05142
- https://github.com/wty-ustc/HairCLIP
- weight
- https://drive.google.com/file/d/1hqZT6ZMldhX3M_x378Sm4Z2HMYr-UwQ4/view?usp=sharing
| {} | public-data/HairCLIP | null | [
"arxiv:2112.05142",
"has_space",
"region:us"
] | null | 2022-05-25T20:55:12+00:00 | [
"2112.05142"
] | [] | TAGS
#arxiv-2112.05142 #has_space #region-us
| # HairCLIP
- URL
- URL
- weight
- URL
| [
"# HairCLIP\n\n- URL\n- URL\n- weight\n - URL"
] | [
"TAGS\n#arxiv-2112.05142 #has_space #region-us \n",
"# HairCLIP\n\n- URL\n- URL\n- weight\n - URL"
] |
null | null | # encoder4editing
- https://arxiv.org/abs/2102.02766
- https://github.com/omertov/encoder4editing
- weights
- https://drive.google.com/file/d/1cUv_reLE6k3604or78EranS7XzuVMWeO/
- https://drive.google.com/file/d/17faPqBce2m1AQeLCLHUVXaDfxMRU2QcV/
- https://drive.google.com/file/d/1TkLLnuX86B_BMo2ocYD0kX9kWh... | {} | public-data/e4e | null | [
"arxiv:2102.02766",
"has_space",
"region:us"
] | null | 2022-05-25T21:00:09+00:00 | [
"2102.02766"
] | [] | TAGS
#arxiv-2102.02766 #has_space #region-us
| # encoder4editing
- URL
- URL
- weights
- URL
- URL
- URL
- URL
| [
"# encoder4editing\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2102.02766 #has_space #region-us \n",
"# encoder4editing\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL\n - URL"
] |
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-german-cased-finetuned-subj_preTrained_with_noisyData_v2
This model is a fine-tuned version of [bert-base-german-cased... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v2", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T21:21:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v2
======================================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0074
* Precision: 0.977... | [
"### 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 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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-voa-example
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-voa-example", "results": []}]} | duclee9x/wav2vec2-voa-example | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T21:33:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-voa-example
====================
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: nan
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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: 3... |
text-generation | transformers |
# Homer Simpson DialogGPT Model | {"tags": ["conversational"]} | HomerChatbot/DialoGPT-small-homersimpsonbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T21:51:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Homer Simpson DialogGPT Model | [
"# Homer Simpson DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Homer Simpson DialogGPT Model"
] |
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. -->
# deberta-base-combined-squad1-aqa-newsqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/mi... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-newsqa", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T21:59:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-newsqa
=======================================
This model is a fine-tuned version of microsoft/deberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8860
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #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: 8\n* eval\\_batch\\_... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | bhaswara/ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T22:00:06+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Korean Question Generation Model
## Github
https://github.com/Seoneun/KoBART-Question-Generation
## Fine-tuning Dataset
KorQuAD 1.0
## Demo
https://huggingface.co/Sehong/kobart-QuestionGeneration
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import Ba... | {"language": "ko", "license": "mit", "tags": ["bart"], "datasets": ["korquad"]} | Sehong/kobart-QuestionGeneration | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"ko",
"dataset:korquad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T00:02:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bart #text2text-generation #ko #dataset-korquad #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Korean Question Generation Model
## Github
URL
## Fine-tuning Dataset
KorQuAD 1.0
## Demo
URL
## How to use
| [
"# Korean Question Generation Model",
"## Github\n\nURL",
"## Fine-tuning Dataset\n\nKorQuAD 1.0",
"## Demo\n\nURL",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #ko #dataset-korquad #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Korean Question Generation Model",
"## Github\n\nURL",
"## Fine-tuning Dataset\n\nKorQuAD 1.0",
"## Demo\n\nURL",
"## How to use"
] |
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. -->
# scibert_scivocab_cased-new-finetuned-breastcancer
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https:/... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "scibert_scivocab_cased-new-finetuned-breastcancer", "results": []}]} | ENM/scibert_scivocab_cased-new-finetuned-breastcancer | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T01:04:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_cased-new-finetuned-breastcancer
===================================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2439
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch... |
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. -->
# wavlm-large-timit-punctuation
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wav... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wavlm-large-timit-punctuation", "results": []}]} | wrice/wavlm-large-timit-punctuation | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wavlm",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T02:13:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wavlm #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wavlm-large-timit-punctuation
=============================
This model is a fine-tuned version of microsoft/wavlm-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3368
* Wer: 0.2601
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #safetensors #wavlm #automatic-speech-recognition #generated_from_trainer #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\n* eval\\... |
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="SusBioRes-UBC/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | SusBioRes-UBC/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-26T03:39:47+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"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small_6_3-hi_en-to-en
This model was trained from scratch on the cmu_hinglish_dog dataset.
It achieves the following results ... | {"tags": ["translation", "generated_from_trainer"], "datasets": ["cmu_hinglish_dog"], "metrics": ["bleu"], "model-index": [{"name": "t5-small_6_3-hi_en-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cmu_hinglish_dog", "type": "cmu_hi... | sayanmandal/t5-small_6_3-hi_en-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:cmu_hinglish_dog",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-26T03:44:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-cmu_hinglish_dog #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small\_6\_3-hi\_en-to-en
===========================
This model was trained from scratch on the cmu\_hinglish\_dog dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3662
* Bleu: 18.0863
* Gen Len: 15.2708
Model description
-----------------
Model generated using:
Check this l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-cmu_hinglish_dog #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
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-hindi-new-4
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hindi-new-4", "results": []}]} | morahil/wav2vec2-hindi-new-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T03:52:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hindi-new-4
====================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3743
* Wer: 0.8926
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"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: 1... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vincentbonnet/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | vincentbonnet/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-26T04:33:04+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"
] |
null | null | test | {} | amehta633/dogs-and-cats | null | [
"region:us"
] | null | 2022-05-26T05:16:36+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-news-v3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-news-v3", "results": []}]} | jbreuch/bert-news-v3 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T05:22:53+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-news-v3
This model is a fine-tuned version of bert-base-uncased 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
#... | [
"# bert-news-v3\n\nThis model is a fine-tuned version of bert-base-uncased 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... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-news-v3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"... |
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. -->
# one-simple-finetune-test
This model is a fine-tuned version of [RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["li_singlish"], "model-index": [{"name": "one-simple-finetune-test", "results": []}]} | RuiqianLi/one-simple-finetune-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:li_singlish",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T05:59:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-li_singlish #license-apache-2.0 #endpoints_compatible #region-us
|
# one-simple-finetune-test
This model is a fine-tuned version of RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab on the li_singlish dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# one-simple-finetune-test\n\nThis model is a fine-tuned version of RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab on the li_singlish dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-li_singlish #license-apache-2.0 #endpoints_compatible #region-us \n",
"# one-simple-finetune-test\n\nThis model is a fine-tuned version of RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab on the l... |
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. -->
# tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
This model is a fine-tuned version of [thundaa/tape-fluo... | {"license": "apache-2.0", "tags": ["protein language model", "generated_from_trainer"], "datasets": ["train"], "metrics": ["spearmanr"], "model-index": [{"name": "tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}... | cradle-bio/tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"protein language model",
"generated_from_trainer",
"dataset:train",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T06:10:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #protein language model #generated_from_trainer #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
=======================================================================
This model is a fine-tuned version of thundaa/tape-fluorescence-evotuning-DistilProtBert on the cradle-bio/tape-fluorescence dataset.
It achieves the following results on the... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 2560\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #bert #text-classification #protein language model #generated_from_trainer #dataset-train #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... |
feature-extraction | 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. -->
# TurQA-bert-base-turkish-uncased-finetuned-toqad
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "TurQA-bert-base-turkish-uncased-finetuned-toqad", "results": []}]} | meetyildiz/TurQA-bert-base-turkish-uncased-finetuned-toqad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"feature-extraction",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T06:11:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #feature-extraction #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| TurQA-bert-base-turkish-uncased-finetuned-toqad
===============================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9506
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\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 #bert #feature-extraction #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 16\n* ev... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e10
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e10", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T07:07:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e10
===============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8234
* Rouge1: 55.5793
* Rouge2: 40.08... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e12
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e12", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e12 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T07:08:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e12
===============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8501
* Rouge1: 56.1453
* Rouge2: 40.01... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 12\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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": []}]} | ryan1998/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 | 2022-05-26T07:09:34+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: 2.5280
* Accuracy: 0.2886
* F1: 0.2742
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... |
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. -->
# mbart
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown da... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mbart", "results": []}]} | madatnlp/mbart | null | [
"transformers",
"tf",
"mbart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T07:26:54+00:00 | [] | [] | TAGS
#transformers #tf #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mbart
=====
This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5342
* Validation Loss: 0.5633
* Epoch: 35
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'SGD', 'learning\\_rate': 0.01, 'decay': 0.0, 'momentum': 0.9, 'nesterov': False}\n* training\\_precision: mixed\\_bfloat16",
"### Training results",
"### Framework versions\n\n\n* Transformers... | [
"TAGS\n#transformers #tf #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'SGD', 'learning\\_rate': 0.01, 'decay': 0.0, 'mom... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | GRANTHE2761/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T07:44:26+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0866
* Accuracy: 0.9689
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5... |
text2text-generation | transformers | ## Plainly
A model for simple english. | {} | mynti/plainly-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-26T07:55:33+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## Plainly
A model for simple english. | [
"## Plainly\n\nA model for simple english."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Plainly\n\nA model for simple english."
] |
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-STTTest
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-STTTest", "results": []}]} | Giseok/wav2vec2-base-STTTest | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T08:01:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-STTTest
=====================
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: 0.5198
* Wer: 0.3393
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Obaid/Test1ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-26T08:03:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
This repository provides all the necessar... | {"language": "sw", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-swahili | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"sw",
"dataset:commonvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-26T08:39:30+00:00 | [] | [
"sw"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #sw #dataset-commonvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a DVoice-VoxLingua107 Swahili dataset within
Speech... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #sw #dataset-commonvoice #license-apache-2.0 #has_space #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Simon10/simone-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Simon10/simone-base-uncased-finetuned-imdb", "results": []}]} | Simon10/simone-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T09:22:03+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Simon10/simone-base-uncased-finetuned-imdb
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3024
* Validation Loss: 0.1714
* Epoch: 0
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
text-generation | transformers | ---
tags:
- conversation
---
#Damon from TVD | {} | Kashni/damontvd | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-26T10:24:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
tags:
- conversation
---
#Damon from TVD | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T11:02:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3
==============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8311
* Rouge1: 53.458
* Rouge2: 34.076
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM)
This repository provides all the necessary... | {"language": "dar", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-amharic | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"dar",
"dataset:commonvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-26T11:41:35+00:00 | [] | [
"dar"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-commonvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM)
================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Amharic dataset within
SpeechBrain. For a b... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-commonvoice #license-apache-2.0 #has_space #region-us \n"
] |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Kabyle (No LM)
This repository provides all the necessary ... | {"language": "kab", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-kabyle | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"kab",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] | null | 2022-05-26T11:48:47+00:00 | [] | [
"kab"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #kab #dataset-commonvoice #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Kabyle (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a CommonVoice Kabyle dataset within
SpeechBrain. For ... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #kab #dataset-commonvoice #license-apache-2.0 #region-us \n"
] |
text-classification | transformers |
A domain detection model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the [TartuNLP](https://tartunlp.ai), the NLP research group at the University of Tartu, and [Tilde](https://tilde.com). More information about the project can be found [here](https://githu... | {"language": ["et", "en", "ru", "de"], "tags": ["text-classification"], "widget": [{"text": "T\u00e4na l\u00f5ppes Valgamaa \u00f5ppuse Siil aktiivne lahingutegevus, mille k\u00e4igus pidi t\u00e4ielikult formeeritud 2. jalav\u00e4ebrigaad kaitsma end vastase pealetungi eest."}]} | tartuNLP/mtee-domain-detection | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"et",
"en",
"ru",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T12:25:31+00:00 | [] | [
"et",
"en",
"ru",
"de"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #et #en #ru #de #autotrain_compatible #endpoints_compatible #region-us
|
A domain detection model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the TartuNLP, the NLP research group at the University of Tartu, and Tilde. More information about the project can be found here.
#### Model Description
The model is a fine-tuned versio... | [
"#### Model Description\n\nThe model is a fine-tuned version of xlm-roberta-base. It classifies the input sentence into one of the following four domains: 'general', 'crisis', 'legal', 'military'."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #et #en #ru #de #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Model Description\n\nThe model is a fine-tuned version of xlm-roberta-base. It classifies the input sentence into one of the following four domains: 'gener... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-awesome-model
This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-awesome-model", "results": []}]} | Fra96/my-awesome-model | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T12:28:56+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| my-awesome-model
================
This model is a fine-tuned version of dbmdz/bert-base-italian-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3847
* Validation Loss: 0.3267
* Epoch: 0
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #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': {'class\\_n... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | i8pxgd2s/ppo-LunarLander-v2-version3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-26T12:29:25+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers |
# KBLab's wav2vec 2.0 large VoxRex Swedish (C) with 4-gram model
Training of the acoustic model is the work of KBLab. See [VoxRex-C](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) for more details. This repo extends the acoustic model with a social media 4-gram language model for boosted performance.
## ... | {"language": "sv", "license": "cc0-1.0", "tags": ["audio", "automatic-speech-recognition", "speech", "hf-asr-leaderboard", "sv"], "datasets": ["common_voice", "NST_Swedish_ASR_Database", "P4", "The_Swedish_Culturomics_Gigaword_Corpus"], "metrics": ["wer"], "model-index": [{"name": "Wav2vec 2.0 large VoxRex Swedish (C) ... | viktor-enzell/wav2vec2-large-voxrex-swedish-4gram | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"sv",
"dataset:common_voice",
"dataset:NST_Swedish_ASR_Database",
"dataset:P4",
"dataset:The_Swedish_Culturomics_Gigaword_Corpus",
"license:cc0-1.0",
"model-index",
"endpoints_... | null | 2022-05-26T12:32:57+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #sv #dataset-common_voice #dataset-NST_Swedish_ASR_Database #dataset-P4 #dataset-The_Swedish_Culturomics_Gigaword_Corpus #license-cc0-1.0 #model-index #endpoints_compatible #has_space #region-us
|
# KBLab's wav2vec 2.0 large VoxRex Swedish (C) with 4-gram model
Training of the acoustic model is the work of KBLab. See VoxRex-C for more details. This repo extends the acoustic model with a social media 4-gram language model for boosted performance.
## Model description
VoxRex-C is extended with a 4-gram language ... | [
"# KBLab's wav2vec 2.0 large VoxRex Swedish (C) with 4-gram model\nTraining of the acoustic model is the work of KBLab. See VoxRex-C for more details. This repo extends the acoustic model with a social media 4-gram language model for boosted performance.",
"## Model description\nVoxRex-C is extended with a 4-gram... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #sv #dataset-common_voice #dataset-NST_Swedish_ASR_Database #dataset-P4 #dataset-The_Swedish_Culturomics_Gigaword_Corpus #license-cc0-1.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# KBLab'... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 913229914
- CO2 Emissions (in grams): 1.8892280988467902
## Validation Metrics
- Loss: 1.0592747926712036
- Accuracy: 0.6535535147098981
- Macro F1: 0.46508274468173677
- Micro F1: 0.6535535147098981
- Weighted F1: 0.645297549742... | {"language": "ar", "widget": [{"text": "\u0642\u0641\u0627 \u0646\u0628\u0643 \u0645\u0646 \u0630\u0650\u0643\u0631\u0649 \u062d\u0628\u064a\u0628 \u0648\u0645\u0646\u0632\u0644\u0650 \u0628\u0633\u0650\u0642\u0637\u0650 \u0627\u0644\u0644\u0650\u0651\u0648\u0649 \u0628\u064a\u0646\u064e \u0627\u0644\u062f\u064e\u0651... | Yah216/Arabic_poem_meter_classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T12:49:29+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 913229914
- CO2 Emissions (in grams): 1.8892280988467902
## Validation Metrics
- Loss: 1.0592747926712036
- Accuracy: 0.6535535147098981
- Macro F1: 0.46508274468173677
- Micro F1: 0.6535535147098981
- Weighted F1: 0.645297549742... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 913229914\n- CO2 Emissions (in grams): 1.8892280988467902",
"## Validation Metrics\n\n- Loss: 1.0592747926712036\n- Accuracy: 0.6535535147098981\n- Macro F1: 0.46508274468173677\n- Micro F1: 0.6535535147098981\n- Weighted ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 913229914\n- CO2 Emissions (in grams): 1.8892280988467902",
"## Validation Metrics\n\n- Loss: 1.059... |
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... | ericntay/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T12:53:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2055
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
image-classification | transformers |
# PANDA_ConvNeXT_K
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
ISUP 0:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c02d3bb3a62519b31c63d0301c6843e_0.jpeg... | {"tags": ["image-classification", "pytorch"], "metrics": ["accuracy", "Cohen's Kappa"]} | smc/PANDA_ConvNeXT_K | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T13:13:03+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# PANDA_ConvNeXT_K
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
ISUP 0:
<img width="256" height="256" src="URL
ISUP 1:
<img width="256" height="256" src="URL
ISUP 2:
<img width="256" height="256"... | [
"# PANDA_ConvNeXT_K\n\nAn attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input\n\n\nISUP 0:\n<img width=\"256\" height=\"256\" src=\"URL\n\nISUP 1:\n<img width=\"256\" height=\"256\" src=\"URL\nISUP 2:\n<img... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# PANDA_ConvNeXT_K\n\nAn attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-pubmed1o3
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-la... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-pubmed1o3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scientific_papers", "type": "... | theojolliffe/bart-large-cnn-pubmed1o3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T13:13:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-pubmed1o3
========================
This model is a fine-tuned version of facebook/bart-large-cnn on the scientific\_papers dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9359
* Rouge1: 36.7566
* Rouge2: 14.813
* Rougel: 22.4693
* Rougelsum: 33.4325
* Gen Len: 138.7332
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #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* learnin... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | makram/TEST2ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-26T13:17:21+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)
This repository provides all the necessary t... | {"language": "wo", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-wolof | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"wo",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] | null | 2022-05-26T13:28:04+00:00 | [] | [
"wo"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #wo #dataset-commonvoice #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)
==============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Wolof dataset within
SpeechBrain. For a better ... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #wo #dataset-commonvoice #license-apache-2.0 #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | irudnyts/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T13:30:49+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM)
This repository provides all the necessary ... | {"language": "fon", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | aioxlabs/dvoice-fongbe | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"fon",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] | null | 2022-05-26T13:34:58+00:00 | [] | [
"fon"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #fon #dataset-commonvoice #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Fongbe dataset within
SpeechBrain. For a bett... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #fon #dataset-commonvoice #license-apache-2.0 #region-us \n"
] |
fill-mask | transformers |
# deberta-large-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune `deberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-large-jap... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-large-japanese-aozora | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T13:46:58+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Ref... | [
"# deberta-large-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours ... |
token-classification | transformers |
# deberta-large-japanese-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://unive... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/deberta-large-japanese-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T13:52:32+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## Reference
安岡孝一: 青空文庫DeBERTaモデ... | [
"# deberta-large-japanese-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## Reference\... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-t... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | xrverse/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T14:10:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2144
* Accuracy: 0.9235
* F1: 0.9233
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collect... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-8h | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T14:29:10+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 8h of labelled
Luxembourgish speech from the same domain.
## Intended uses & limitations
More information needed
## Training and evaluati... | [
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 8h of labelled\nLuxembourgish speech from the same domain.",
"## Intended uses & limitations\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us \n",
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollecte... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-common-voice-persian-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-common-voice-persian-colab", "results": []}]} | zoha/wav2vec2-base-common-voice-persian-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T14:55:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-common-voice-persian-colab
========================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1446
* Wer: 0.6911
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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: 1... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Against61/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Against61/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-26T15:14:33+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"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BobBraico/rlb-cyber-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BobBraico/rlb-cyber-finetuned-imdb", "results": []}]} | BobBraico/rlb-cyber-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T15:14:35+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BobBraico/rlb-cyber-finetuned-imdb
==================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7869
* Validation Loss: 2.4354
* Epoch: 0
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
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="Against61/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Against61/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-26T15:17:58+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"
] |
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-booksum-finetuned-booksum-test
This model is a fine-tuned version of [cnicu/t5-small-booksum](https://huggingface.co/cn... | {"license": "mit", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-booksum-finetuned-booksum-test", "results": []}]} | Gergoe/t5-small-booksum-finetuned-booksum-test | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-26T15:23:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-booksum-finetuned-booksum-test
=======================================
This model is a fine-tuned version of cnicu/t5-small-booksum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2739
* Rouge1: 22.7829
* Rouge2: 4.8349
* Rougel: 18.2465
* Rougelsum: 19.2417
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased-finetuned-oparticles
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](ht... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-portuguese-cased-finetuned-oparticles", "results": []}]} | inessilva/bert-base-portuguese-cased-finetuned-oparticles | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T15:46:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased-finetuned-oparticles
===============================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2012
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | Aiyshwariya/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T16:15:41+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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | actionpace/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T16:45:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4841
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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\\... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | augustocsc/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-26T16:48:18+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
#Audrey Hepburn DialoGPT Model | {"tags": ["conversational"]} | ElMuchoDingDong/DialoGPT-medium-AudreyHepburn | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-26T17:10:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Audrey Hepburn DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
re... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | sb3/ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-26T18:59:34+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with... | [
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework... |
fill-mask | transformers |
# MathBERTa model
Pretrained model on English language and LaTeX using a masked language modeling
(MLM) objective. It was introduced in [this paper][1] and first released in
[this repository][2]. This model is case-sensitive: it makes a difference
between english and English.
[1]: http://ceur-ws.org/Vol-3180/paper-... | {"language": "en", "license": "mit", "datasets": ["arxmliv", "math-stackexchange"]} | witiko/mathberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:arxmliv",
"dataset:math-stackexchange",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-26T19:21:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #dataset-arxmliv #dataset-math-stackexchange #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# MathBERTa model
Pretrained model on English language and LaTeX using a masked language modeling
(MLM) objective. It was introduced in [this paper][1] and first released in
[this repository][2]. This model is case-sensitive: it makes a difference
between english and English.
[1]: URL
[2]: URL
## Model descriptio... | [
"# MathBERTa model\n\nPretrained model on English language and LaTeX using a masked language modeling\n(MLM) objective. It was introduced in [this paper][1] and first released in\n[this repository][2]. This model is case-sensitive: it makes a difference\nbetween english and English.\n\n [1]: URL\n [2]: URL",
"## ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #dataset-arxmliv #dataset-math-stackexchange #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# MathBERTa model\n\nPretrained model on English language and LaTeX using a masked language modeling\n(MLM) objective. It was introduced in [... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- CO2 Emissions (in grams): 404.66986451902227
## Dataset
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:
```
@Article{Yousef2019LearningM... | {"language": "ar", "widget": [{"text": "\u0642\u0641\u0627 \u0646\u0628\u0643 \u0645\u0646 \u0630\u0650\u0643\u0631\u0649 \u062d\u0628\u064a\u0628 \u0648\u0645\u0646\u0632\u0644\u0650 \u0628\u0633\u0650\u0642\u0637\u0650 \u0627\u0644\u0644\u0650\u0651\u0648\u0649 \u0628\u064a\u0646\u064e \u0627\u0644\u062f\u064e\u0651... | Yah216/Arabic_poem_meter_3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-26T19:45:27+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- CO2 Emissions (in grams): 404.66986451902227
## Dataset
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:
## Validation Metrics
- Loss: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- CO2 Emissions (in grams): 404.66986451902227",
"## Dataset\nWe used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:",
"## Validation ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- CO2 Emissions (in grams): 404.66986451902227",
"## Dataset\nWe used the APCD dat... |
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