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values | library_name stringclasses 198
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sentence-similarity | sentence-transformers |
# Model aiky-sentence-bertino
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model bec... | {"language": ["it"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "cc-by-nc-sa-4.0"], "pipeline_tag": "sentence-similarity"} | aiknowyou/aiky-sentence-bertino | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"cc-by-nc-sa-4.0",
"it",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T07:12:21+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #cc-by-nc-sa-4.0 #it #endpoints_compatible #region-us
|
# Model aiky-sentence-bertino
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# Model aiky-sentence-bertino\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers ins... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #cc-by-nc-sa-4.0 #it #endpoints_compatible #region-us \n",
"# Model aiky-sentence-bertino\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and c... |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* ... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220713-h08m19s38_example_conll2003 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-13T07:19:38+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
+ ... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us \n"
] |
question-answering | null |
**task**: `question-answering`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `1000`
Fixed parameters:
* **model_name_or_path**: `distilbert-base-uncased-distilled-squad`
* **dataset**:
* **path**... | {"tags": ["distilbert"], "datasets": ["squad"], "metrics": ["exact_match", "f1"], "pipeline_tag": "question-answering"} | fxmarty/20220713-h08m45s49_example_squad | null | [
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"region:us"
] | null | 2022-07-13T07:45:49+00:00 | [] | [] | TAGS
#tensorboard #distilbert #question-answering #dataset-squad #region-us
| task: 'question-answering'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: '1000'
Fixed parameters:
* model\_name\_or\_path: 'distilbert-base-uncased-distilled-squad'
* dataset:
+ path: 'squad'
+ eval\_spli... | [] | [
"TAGS\n#tensorboard #distilbert #question-answering #dataset-squad #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_... | jordyvl/biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T07:50:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2\_ncbi\_disease-softmax-labelall-ner
===========================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0629
* Precision: 0.8289
* R... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #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: 2e-05\n*... |
text-generation | transformers | gpt-beatroots | {"language": ["en"]} | sandervg/gpt-beatroots | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T08:08:10+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt-beatroots | [] | [
"TAGS\n#transformers #tf #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2_ncbi_disease-sm-first-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2_ncbi_disease-sm-first-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_disease"... | jordyvl/biobert-base-cased-v1.2_ncbi_disease-sm-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-13T08:18:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| biobert-base-cased-v1.2\_ncbi\_disease-sm-first-ner
===================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0865
* Precision: 0.8522
* Recall: 0.8827
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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-onomatopoeia-finetune_smalldata_ESC50pretrained_2
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretr... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2", "results": []}]} | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T08:25:20+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-onomatopoeia-finetune\_smalldata\_ESC50pretrained\_2
=============================================================
This model is a fine-tuned version of /root/workspace/wav2vec2-pretrained\_with\_ESC50\_10000epochs\_32batch\_2022-07-09\_22-16-46/pytorch\_model.bin on the None dataset.
It achieves the followi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\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* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #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: 64\n* eval\\_batch\\_size: 16\n* s... |
question-answering | null |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | nickcpk/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] | null | 2022-07-13T08:51:27+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
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-cased_conll2003-sm-all-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-ca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased_conll2003-sm-all-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conl... | jordyvl/bert-base-cased_conll2003-sm-all-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T08:59:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased\_conll2003-sm-all-ner
=====================================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0489
* Precision: 0.9487
* Recall: 0.9564
* F1: 0.9526
* Accuracy: 0.9916
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
reinforcement-learning | 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="bothrajat/q-FrozenLake-v1-8x8-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | bothrajat/q-FrozenLake-v1-8x8-Slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T09:03:29+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bothrajat/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | bothrajat/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T09:06:49+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* ... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220713-h10m20s05_example_conll2003 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-13T09:20:05+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
+ ... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #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_finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert_finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plu... | srini98/distilbert_finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T09:23:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_finetuned-clinc
===========================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7799
* Accuracy: 0.9161
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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
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-cased_conll2003-sm-first-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased_conll2003-sm-first-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "co... | jordyvl/bert-base-cased_conll2003-sm-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T09:43:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased\_conll2003-sm-first-ner
=======================================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0783
* Precision: 0.9444
* Recall: 0.9471
* F1: 0.9457
* Accuracy: 0.9861
Model descrip... | [
"### 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* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
reinforcement-learning | 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="Chris1/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Chris1/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T09:45:52+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | transformers | bart trained on wikikp then midas/kp20k | {} | ahadda5/bart_wikikp_kp20k | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T09:54:26+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| bart trained on wikikp then midas/kp20k | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretr... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3", "results": []}]} | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T10:47:57+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-onomatopoeia-finetune\_smalldata\_ESC50pretrained\_3
=============================================================
This model is a fine-tuned version of /root/workspace/wav2vec2-pretrained\_with\_ESC50\_10000epochs\_32batch\_2022-07-09\_22-16-46/pytorch\_model.bin on the None dataset.
It achieves the followi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\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* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #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: 64\n* eval\\_batch\\_size: 16\n* s... |
token-classification | transformers | ## Model Description
Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on Ukrainian texts to recover punctuation and case.
## How to Use
Download script get_predictions.py from the repository.
```py
from transformers import AutoTokenizer, AutoModelForTokenClassification
from... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"], "widget": [{"text": "\u0443\u043f\u0440\u043e\u0434\u043e\u0432\u0436 2012-2014 \u0440\u043e\u043a\u0456\u0432 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u043d\u0438\u0439 \u043f\u0430\u0440\... | ukr-models/uk-punctcase | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"ukrainian",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T10:50:18+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## Model Description
Fine-tuning of XLM-RoBERTa-Uk model on Ukrainian texts to recover punctuation and case.
## How to Use
Download script get_predictions.py from the repository.
| [
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on Ukrainian texts to recover punctuation and case.",
"## How to Use\n\nDownload script get_predictions.py from the repository."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on Ukrainian texts to recover punctuation and case.",
"## How to Use\n\nDownload script... |
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. -->
# udpos28-sm-all-POS
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["udpos28"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "udpos28-sm-all-POS", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "udpos28", "type": "udpos... | jordyvl/udpos28-sm-all-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:udpos28",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T11:03:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-udpos28 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| udpos28-sm-all-POS
==================
This model is a fine-tuned version of bert-base-cased on the udpos28 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1479
* Precision: 0.9587
* Recall: 0.9589
* F1: 0.9588
* Accuracy: 0.9648
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-udpos28 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-base-koquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "ko", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "1990\ub144 \uc601\ud654 \u300a <hl> \ub0a8\ubd80\uad70 <hl> \u300b\uc5d0\uc11c \u... | lmqg/mt5-base-koquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ko",
"dataset:lmqg/qg_koquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T11:12:23+00:00 | [
"2210.03992"
] | [
"ko"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-base-koquad-qg'
=======================================
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-base
* Language: ko
* Training data: lmqg/qg\_koqua... | [
"### Overview\n\n\n* Language model: google/mt5-base\n* Language: ko\n* Training data: lmqg/qg\\_koquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-base\n* Lang... |
text-classification | transformers |
# Non Factoid Question Category classification in English
## NFQA model
Repository: [https://github.com/Lurunchik/NF-CATS](https://github.com/Lurunchik/NF-CATS)
Model trained with NFQA dataset. Base model is [roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2), a RoBERTa-based model for the task... | {"language": ["en"], "license": "mit", "tags": ["text-classification"], "inference": false, "widget": [{"text": "Why do we need an NFQA taxonomy?"}]} | Lurunchik/nf-cats | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"custom_code",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2022-07-13T11:15:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #custom_code #en #license-mit #has_space #region-us
|
# Non Factoid Question Category classification in English
## NFQA model
Repository: URL
Model trained with NFQA dataset. Base model is roberta-base-squad2, a RoBERTa-based model for the task of Question Answering, fine-tuned using the SQuAD2.0 dataset.
Uses 'NOT-A-QUESTION', 'FACTOID', 'DEBATE', 'EVIDENCE-BASED', '... | [
"# Non Factoid Question Category classification in English",
"## NFQA model\n\nRepository: URL\n\nModel trained with NFQA dataset. Base model is roberta-base-squad2, a RoBERTa-based model for the task of Question Answering, fine-tuned using the SQuAD2.0 dataset.\n\nUses 'NOT-A-QUESTION', 'FACTOID', 'DEBATE', 'EVI... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #custom_code #en #license-mit #has_space #region-us \n",
"# Non Factoid Question Category classification in English",
"## NFQA model\n\nRepository: URL\n\nModel trained with NFQA dataset. Base model is roberta-base-squad2, a RoBERTa-based model for the... |
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... | jasheershihab/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-13T11:32:46+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# udpos28-sm-first-POS
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["udpos28"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "udpos28-sm-first-POS", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "udpos28", "type": "udp... | jordyvl/udpos28-sm-first-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:udpos28",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T11:33:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-udpos28 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| udpos28-sm-first-POS
====================
This model is a fine-tuned version of bert-base-cased on the udpos28 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1896
* Precision: 0.9511
* Recall: 0.9546
* F1: 0.9529
* Accuracy: 0.9559
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-udpos28 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
reinforcement-learning | 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="Chris1/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | Chris1/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T11:56:30+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | null | # Introduction
torchscript models for https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2
See also
https://github.com/k2-fsa/icefall/pull/364
and
https://github.com/k2-fsa/icefall/pull/361
| {} | csukuangfj/icefall-asr-gigaspeech-pruned-transducer-stateless2-bak | null | [
"region:us"
] | null | 2022-07-13T12:30:25+00:00 | [] | [] | TAGS
#region-us
| # Introduction
torchscript models for URL
See also
URL
and
URL
| [
"# Introduction\n\ntorchscript models for URL\n\n\nSee also\nURL\nand\nURL"
] | [
"TAGS\n#region-us \n",
"# Introduction\n\ntorchscript models for URL\n\n\nSee also\nURL\nand\nURL"
] |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* ... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220713-h13m33s02_example_conll2003 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-13T12:33:02+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
+ ... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Chris1/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | Chris1/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T12:35:39+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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="Chris1/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.46 +/... | Chris1/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T12:53:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | jpalojarvi/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T13:14:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3239
- Accuracy: 0.86
- F1: 0.8591
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3239\n- Accuracy: 0.86\n- F1: 0.8591",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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-onomatopoeia-finetune_smalldata_ESC50pretrained_5
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretr... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5", "results": []}]} | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T13:30:32+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5
This model is a fine-tuned version of /root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More inform... | [
"# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5\n\nThis model is a fine-tuned version of /root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5\n\nThis model is a fine-tuned version of /root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_2... |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* ... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220713-h14m38s16_example_conll2003 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-13T13:38:16+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
+ ... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us \n"
] |
zero-shot-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. -->
# hug-clip-bid
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "hug-clip-bid", "results": []}]} | thannarot/hug-clip-bid | null | [
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T13:59:12+00:00 | [] | [] | TAGS
#transformers #pytorch #clip #zero-shot-image-classification #generated_from_trainer #endpoints_compatible #region-us
| hug-clip-bid
============
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8276
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #clip #zero-shot-image-classification #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: ... |
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_modelxcxcx_reddit_tslghja_tvcbracked
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_modelxcxcx_reddit_tslghja_tvcbracked", "results": []}]} | fourthbrain-demo/bert_modelxcxcx_reddit_tslghja_tvcbracked | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T14:01:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert_modelxcxcx_reddit_tslghja_tvcbracked
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# bert_modelxcxcx_reddit_tslghja_tvcbracked\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_modelxcxcx_reddit_tslghja_tvcbracked\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",... |
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | NinaXiao/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T14:11:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9947
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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 #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador_pt
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador_pt | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T14:37:48+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador_pt
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then y... | [
"# gemasphi/laprador_pt\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador_pt\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clusteri... |
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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "roberta-base-finetuned-cola", "results": []}]} | Jinchen/roberta-base-finetuned-cola | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T14:41:17+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-cola
===========================
This model is a fine-tuned version of roberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4211
* Matthews Correlation: 0.6279
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_s... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\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. -->
# pixel-base-finetuned-xnli-translate-train-all
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.... | {"language": ["en", "ar", "bg", "de", "el", "fr", "hi", "ru", "es", "sw", "th", "tr", "ur", "vi", "zh"], "tags": ["generated_from_trainer"], "datasets": ["xnli"], "metrics": ["accuracy"], "model-index": [{"name": "pixel-base-finetuned-xnli-translate-train-all", "results": [{"task": {"type": "text-classification", "name... | Team-PIXEL/pixel-base-finetuned-xnli-translate-train-all | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"ar",
"bg",
"de",
"el",
"fr",
"hi",
"ru",
"es",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:xnli",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T14:46:39+00:00 | [] | [
"en",
"ar",
"bg",
"de",
"el",
"fr",
"hi",
"ru",
"es",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #ar #bg #de #el #fr #hi #ru #es #sw #th #tr #ur #vi #zh #dataset-xnli #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-xnli-translate-train-all
This model is a fine-tuned version of Team-PIXEL/pixel-base on the XNLI dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# pixel-base-finetuned-xnli-translate-train-all\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the XNLI dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #ar #bg #de #el #fr #hi #ru #es #sw #th #tr #ur #vi #zh #dataset-xnli #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-xnli-translate-train-all\n\nThis model is a fine-tuned versio... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | bothrajat/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-13T14:57:34+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | 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="birgermoell/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | birgermoell/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T15:38:57+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="birgermoell/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 +/... | birgermoell/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-13T15:48:54+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-clm
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the follow... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test-clm", "results": []}]} | kuttersn/test-clm | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T15:51:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test-clm
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5311
- Accuracy: 0.3946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More infor... | [
"# test-clm\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5311\n- Accuracy: 0.3946",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eval... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test-clm\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the e... |
null | null | # AnimeGANv3 portrait sketch
- https://github.com/TachibanaYoshino/AnimeGANv3
- https://docs.google.com/uc?export=download&id=1F6BSJY3HibzQ08kE_al6pkXd1evxS40s
| {} | public-data/AnimeGANv3-portrait-sketch | null | [
"onnx",
"region:us",
"has_space"
] | null | 2022-07-13T15:59:59+00:00 | [] | [] | TAGS
#onnx #region-us #has_space
| # AnimeGANv3 portrait sketch
- URL
- URL
| [
"# AnimeGANv3 portrait sketch\n\n- URL\n - URL"
] | [
"TAGS\n#onnx #region-us #has_space \n",
"# AnimeGANv3 portrait sketch\n\n- URL\n - URL"
] |
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... | ticoAg/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:00:17+00:00 | [] | [] | TAGS
#transformers #pytorch #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: 0.2148
* Accuracy: 0.926
* F1: 0.9261
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 #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* learning\\_rate: 2... |
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. -->
# Originalbiobert-v1.1-BioRED-CD-128-32-30
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dm... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "Originalbiobert-v1.1-BioRED-CD-128-32-30", "results": []}]} | ghadeermobasher/Originalbiobert-v1.1-BioRED-CD-128-32-30 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:05:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# Originalbiobert-v1.1-BioRED-CD-128-32-30
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 0.9994
- Recall: 1.0
- F1: 0.9997
## Model description
More information needed
## Intended uses & limi... | [
"# Originalbiobert-v1.1-BioRED-CD-128-32-30\n\nThis model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0001\n- Precision: 0.9994\n- Recall: 1.0\n- F1: 0.9997",
"## Model description\n\nMore information needed",
"## I... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# Originalbiobert-v1.1-BioRED-CD-128-32-30\n\nThis model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.\nIt achieves the following re... |
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. -->
# Modifiedbiobert-v1.1-BioRED-CD-128-32-30
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dm... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "Modifiedbiobert-v1.1-BioRED-CD-128-32-30", "results": []}]} | ghadeermobasher/Modifiedbiobert-v1.1-BioRED-CD-128-32-30 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:07:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# Modifiedbiobert-v1.1-BioRED-CD-128-32-30
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitation... | [
"# Modifiedbiobert-v1.1-BioRED-CD-128-32-30\n\nThis model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0000\n- Precision: 1.0\n- Recall: 1.0\n- F1: 1.0",
"## Model description\n\nMore information needed",
"## Intende... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# Modifiedbiobert-v1.1-BioRED-CD-128-32-30\n\nThis model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.\nIt achieves the following re... |
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/1536389142287892481/N6kC... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/angelsexytexty-janieclone/1675633925509/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/angelsexytexty-janieclone | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T16:08:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Columbine Janie & Angel Sexy Texty
@angelsexytexty-janieclone
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 t... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPECTER-finetuned-DAGPap22
This model is a fine-tuned version of [allenai/specter](https://huggingface.co/allenai/specter) on an... | {"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "SPECTER-finetuned-DAGPap22", "results": []}]} | domenicrosati/SPECTER-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:26:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SPECTER-finetuned-DAGPap22
==========================
This model is a fine-tuned version of allenai/specter on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0023
* Accuracy: 0.9993
* F1: 0.9995
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\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 #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: 6e-06\n* train\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-cv-position-classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision"], "model-index": [{"name": "bert-base-uncased-cv-position-classifier", "results": []}]} | jhonparra18/bert-base-uncased-cv-position-classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:39:26+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-cv-position-classifier
========================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6924
* Accuracy: {'accuracy': 0.5780703216130645}
* F1: {'f1': 0.5780703216130645}
* Preci... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* ... |
image-segmentation | 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. -->
# mit-b0-finetuned-sidewalk-semantic
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback", "vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"], "model-index": [{"name": "mit-b0-finetuned-sidewalk-semantic", "results": []}]} | sayakpaul/mit-b0-finetuned-sidewalk-semantic | null | [
"transformers",
"tf",
"segformer",
"generated_from_keras_callback",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T16:45:40+00:00 | [] | [] | TAGS
#transformers #tf #segformer #generated_from_keras_callback #vision #image-segmentation #dataset-segments/sidewalk-semantic #license-apache-2.0 #endpoints_compatible #region-us
| mit-b0-finetuned-sidewalk-semantic
==================================
This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2125
* Validation Loss: 0.5151
* Epoch: 49
Model description
-----------------
The model was f... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 6e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #segformer #generated_from_keras_callback #vision #image-segmentation #dataset-segments/sidewalk-semantic #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam'... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-prueba
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv3"], "model-index": [{"name": "distilbert-base-uncased-prueba", "results": []}]} | Evelyn18/distilbert-base-uncased-prueba | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T17:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-prueba
==============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv3 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3077
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# SportsSum
This model is a fine-tuned version of [allenai/led-base-16384-ms2](https://huggingface.co/allenai/led-base-16384-ms2) ... | {"language": ["en"], "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "SportsSum", "results": []}]} | SushantGautam/SoccerSum-NarSum | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T17:51:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #led #text2text-generation #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us
|
# SportsSum
This model is a fine-tuned version of allenai/led-base-16384-ms2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2759
- Rouge1: 52.3608
- Rouge2: 27.6526
- Rougel: 31.8509
- Rougelsum: 49.9086
- Gen Len: 248.1199
## Model description
More information needed
#... | [
"# SportsSum\n\nThis model is a fine-tuned version of allenai/led-base-16384-ms2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2759\n- Rouge1: 52.3608\n- Rouge2: 27.6526\n- Rougel: 31.8509\n- Rougelsum: 49.9086\n- Gen Len: 248.1199",
"## Model description\n\nMore info... | [
"TAGS\n#transformers #pytorch #led #text2text-generation #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# SportsSum\n\nThis model is a fine-tuned version of allenai/led-base-16384-ms2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss... |
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-hindi-kabita
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-hindi-kabita", "results": []}]} | sam34738/bert-hindi-kabita | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T18:08:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-hindi-kabita
=================
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4795
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* e... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | AndrewK/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-13T18:34:52+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | DennisSoemers/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-13T18:35:36+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 |
# Note
BERT based sentiment analysis, finetune based on https://huggingface.co/IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment .
The model trained on **hotel human review chinese dataset**.
# Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
MODEL =... | {"language": "zh", "tags": ["sentiment-analysis", "pytorch"], "widget": [{"text": "\u623f\u95f4\u975e\u5e38\u975e\u5e38\u5c0f\uff0c\u5185\u7a97\uff0c\u7279\u522b\u4e0d\u900f\u6c14\uff0c\u56e0\u4e3a\u591c\u91cc\u8d70\u5eca\u706f\u5149\u662f\u4eae\u7684\uff0c\u5185\u7a97\u5bf9\u7740\u8d70\u5eca\uff0c\u7a97\u5e18\u53c8\u4... | tezign/Erlangshen-Sentiment-FineTune | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentiment-analysis",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T19:05:57+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentiment-analysis #zh #autotrain_compatible #endpoints_compatible #region-us
|
# Note
BERT based sentiment analysis, finetune based on URL .
The model trained on hotel human review chinese dataset.
# Usage
# Evaluate
We compared and evaluated the performance of Our finetune model and the Original Erlangshen model on the hotel human review test dataset(5429 negative reviews and 1251 positiv... | [
"# Note\n\nBERT based sentiment analysis, finetune based on URL .\n\nThe model trained on hotel human review chinese dataset.",
"# Usage",
"# Evaluate\nWe compared and evaluated the performance of Our finetune model and the Original Erlangshen model on the hotel human review test dataset(5429 negative reviews a... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentiment-analysis #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# Note\n\nBERT based sentiment analysis, finetune based on URL .\n\nThe model trained on hotel human review chinese dataset.",
"# Usage",
"# Evaluate\nWe c... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-prueba2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-prueba2", "results": []}]} | Evelyn18/distilbert-base-uncased-prueba2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T20:05:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-prueba2
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6356
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: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text2text-generation | transformers | memray/bart_wikikp/ trained additionally on kp20k and openkp datasets.
| {} | ahadda5/bart_wikikp_kp20k_openkp | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T20:41:47+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| memray/bart_wikikp/ trained additionally on kp20k and openkp datasets.
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | Promoting empathy among Twitter Users, in order to reduce offensive content that harms the wellness of users. | {} | noorkgill/Tone | null | [
"region:us"
] | null | 2022-07-13T21:02:05+00:00 | [] | [] | TAGS
#region-us
| Promoting empathy among Twitter Users, in order to reduce offensive content that harms the wellness of users. | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./clip-roberta](https://huggingface.co/./clip-roberta) on the dava... | {"tags": ["generated_from_trainer"], "datasets": ["davanstrien/manuscript_noisy_labels_iiif"], "model-index": [{"name": "clip-roberta-finetuned", "results": []}]} | davanstrien/clip-roberta-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"dataset:davanstrien/manuscript_noisy_labels_iiif",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T21:17:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vision-text-dual-encoder #feature-extraction #generated_from_trainer #dataset-davanstrien/manuscript_noisy_labels_iiif #endpoints_compatible #region-us
| clip-roberta-finetuned
======================
This model is a fine-tuned version of ./clip-roberta on the davanstrien/manuscript\_noisy\_labels\_iiif dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5792
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0\n* mixed\\... | [
"TAGS\n#transformers #pytorch #tensorboard #vision-text-dual-encoder #feature-extraction #generated_from_trainer #dataset-davanstrien/manuscript_noisy_labels_iiif #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | rajistics/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-13T21:19:29+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
text-generation | transformers |
# Will Byers DialoGPT model | {"tags": ["conversational"]} | 24adamaliv/DialoGPT-medium-Will | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T21:31:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Will Byers DialoGPT model | [
"# Will Byers DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Will Byers DialoGPT model"
] |
image-classification | transformers |
# MobileNet V3 - Small model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf).
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as acti... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/mobilenet_v3_small | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1905.02244",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T22:53:41+00:00 | [
"1905.02244"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1905.02244 #license-apache-2.0 #endpoints_compatible #region-us
|
# MobileNet V3 - Small model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in this paper.
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and-Excite bl... | [
"# MobileNet V3 - Small model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1905.02244 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MobileNet V3 - Small model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introd... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5_correct_puntuation
本模型使用中文維基百科語料微調 [google/mt5-base](https://huggingface.co/google/mt5-base)預訓練模型之中文標點符號訂正器。目前之準確率為 0.794。
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mt5_correct_puntuation_v3", "results": []}]} | jamie613/mt5_correct_puntuation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T00:43:41+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# mt5_correct_puntuation
本模型使用中文維基百科語料微調 google/mt5-base預訓練模型之中文標點符號訂正器。目前之準確率為 0.794。
This is a google/mt5-base model trained on Mandarin Wikipedia corpus and finetuned for Mandarin punctuation correction. Currently the accuracy is 0.794.
## Datasets
模型使用中文維基百科公開資料微調。將取得的文本以「。」或「，」切分為不超過100字的句子。因為逗號和句號數量壓倒性地多，為... | [
"# mt5_correct_puntuation\n\n本模型使用中文維基百科語料微調 google/mt5-base預訓練模型之中文標點符號訂正器。目前之準確率為 0.794。\n\nThis is a google/mt5-base model trained on Mandarin Wikipedia corpus and finetuned for Mandarin punctuation correction. Currently the accuracy is 0.794.",
"## Datasets\n模型使用中文維基百科公開資料微調。將取得的文本以「。」或「，」切分為不超過100字的句子。因為逗號和... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# mt5_correct_puntuation\n\n本模型使用中文維基百科語料微調 google/mt5-base預訓練模型之中文標點符號訂正器。目前之準確率為 0.794。\n\nThis is a google/mt5-... |
text-generation | transformers |
## GPT2 Catalan small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base model settings, but the size of embedding dimensions are half the size of them.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/ca... | {"language": "ca", "license": "cc-by-sa-4.0", "datasets": ["cc100", "oscar", "wikipedia"], "widget": [{"text": "Vas jugar a"}, {"text": "M'agrada el clima i el menjar"}, {"text": "Ell est\u00e0 una mica"}]} | ClassCat/gpt2-small-catalan-v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ca",
"dataset:cc100",
"dataset:oscar",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T00:52:50+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ca #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## GPT2 Catalan small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base model settings, but the size of embedding dimensions are half the size of them.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* wiki40b/ca ... | [
"## GPT2 Catalan small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses GPT2 base model settings, but the size of embedding dimensions are half the size of them.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Tra... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ca #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 Catalan small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
... |
fill-mask | transformers |
# CORD19-BERT
## How to use
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('CovRelex-SE/CORD19-BERT')
model = BertModel.from_pretrained("CovRelex-SE/CORD19-BERT")
text = "The virus can spread from an infected person’s mouth or nose."
encoded_input = tokenizer(te... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "CORD19_BERT", "results": []}]} | CovRelex-SE/CORD19-BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T01:13:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# CORD19-BERT
## How to use
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs... | [
"# CORD19-BERT",
"## How to use",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\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_typ... | [
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"# CORD19-BERT",
"## How to use",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- t... |
null | null | wiki and marktwain | {} | yochen/distilroberta-base-finetuned-wikiandmark | null | [
"region:us"
] | null | 2022-07-14T01:38:48+00:00 | [] | [] | TAGS
#region-us
| wiki and marktwain | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab-tryjpn
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab-tryjpn", "results": []}]} | hirohiroz/wav2vec2-base-timit-demo-google-colab-tryjpn | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T02:11:46+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-tryjpn
============================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.1527
* Wer: 1.0
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **quadrotor_multi** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"]} | andrewzhang505/quad-swarm-rl-sf2 | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-07-14T02:55:10+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #region-us
|
A(n) APPO model trained on the quadrotor_multi environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #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-base-patch4-window7-224-in22k-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-base-patch4-window7-224-in22k-finetuned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "... | liyijing024/swin-base-patch4-window7-224-in22k-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T03:02:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-base-patch4-window7-224-in22k-finetuned
============================================
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0021
* Accuracy: 0.9993
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hardik1313X/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Hardik1313X/bert-finetuned-ner", "results": []}]} | Hardik1313X/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T03:19:47+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hardik1313X/bert-finetuned-ner
==============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0279
* Validation Loss: 0.0571
* Epoch: 2
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2634, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "c... | shivaniNK8/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T04:17:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4413
* Rouge1: 22.6804
* Rouge2: 8.3299
* Rougel: 17.9992
* Rougelsum: 20.7342
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamete... |
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. -->
# Fine_Tuning_XLSR_300M_testing_4_model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fine_Tuning_XLSR_300M_testing_4_model", "results": []}]} | rajat99/Fine_Tuning_XLSR_300M_testing_4_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T04:50:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# Fine_Tuning_XLSR_300M_testing_4_model
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# Fine_Tuning_XLSR_300M_testing_4_model\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Fine_Tuning_XLSR_300M_testing_4_model\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model des... |
null | fastai |
# Model card
## Model description
Fastai `unet` created with `unet_learner` using `resnet34`
## Intended uses & limitations
This is only used for demonstration of fine tuning capabilities with fastai. It may be useful for further research. This model should **not** be used for gastrointestinal polyp diagnosis.
## T... | {"tags": ["fastai"]} | hugginglearners/kvasir-seg | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-07-14T05:47:24+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Model card
## Model description
Fastai 'unet' created with 'unet_learner' using 'resnet34'
## Intended uses & limitations
This is only used for demonstration of fine tuning capabilities with fastai. It may be useful for further research. This model should not be used for gastrointestinal polyp diagnosis.
## Train... | [
"# Model card",
"## Model description\nFastai 'unet' created with 'unet_learner' using 'resnet34'",
"## Intended uses & limitations\nThis is only used for demonstration of fine tuning capabilities with fastai. It may be useful for further research. This model should not be used for gastrointestinal polyp diagno... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Model card",
"## Model description\nFastai 'unet' created with 'unet_learner' using 'resnet34'",
"## Intended uses & limitations\nThis is only used for demonstration of fine tuning capabilities with fastai. It may be useful for further research. This model should n... |
null | null | dan's sharing on 2022 BAAI, Beijing 1 tmux a -t 1
Verify md5value:
tar -zxvf Daniel_Povey_BAAI_2022.tar.gz
md5sum Daniel_Povey_BAAI_2022.mp4
# 1d0b9f941fc30814528c95bc7630b6a8 Daniel_Povey_BAAI_2022.mp4
| {} | GuoLiyong/dan_sharing_2022_baai | null | [
"region:us"
] | null | 2022-07-14T06:02:42+00:00 | [] | [] | TAGS
#region-us
| dan's sharing on 2022 BAAI, Beijing 1 tmux a -t 1
Verify md5value:
tar -zxvf Daniel_Povey_BAAI_2022.URL
md5sum Daniel_Povey_BAAI_2022.mp4
# 1d0b9f941fc30814528c95bc7630b6a8 Daniel_Povey_BAAI_2022.mp4
| [
"# 1d0b9f941fc30814528c95bc7630b6a8 Daniel_Povey_BAAI_2022.mp4"
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"TAGS\n#region-us \n",
"# 1d0b9f941fc30814528c95bc7630b6a8 Daniel_Povey_BAAI_2022.mp4"
] |
text-classification | null | README | {"language": ["code", "code"], "license": "mit", "tags": ["tag1", "tag2"], "datasets": ["dataset1", "dataset2"], "metrics": ["metric1", "metric2"], "thumbnail": "url to a thumbnail used in social sharing", "pipeline_tag": "text-classification", "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]} | little-star/good_model | null | [
"tag1",
"tag2",
"text-classification",
"code",
"dataset:dataset1",
"dataset:dataset2",
"license:mit",
"region:us"
] | null | 2022-07-14T06:06:24+00:00 | [] | [
"code",
"code"
] | TAGS
#tag1 #tag2 #text-classification #code #dataset-dataset1 #dataset-dataset2 #license-mit #region-us
| README | [] | [
"TAGS\n#tag1 #tag2 #text-classification #code #dataset-dataset1 #dataset-dataset2 #license-mit #region-us \n"
] |
null | null |
# Lao Word Embedding
This model used in LaoNLP that trained from oscar corpus (Lao only). You can see more at [https://github.com/wannaphong/LaoNLP/wiki/Word-Vector](https://github.com/wannaphong/LaoNLP/wiki/Word-Vector).
LaoNLP: [https://github.com/wannaphong/LaoNLP](https://github.com/wannaphong/LaoNLP) | {"language": ["lo"], "license": "apache-2.0"} | wannaphong/Lao-Word-Embedding | null | [
"lo",
"license:apache-2.0",
"region:us"
] | null | 2022-07-14T06:31:31+00:00 | [] | [
"lo"
] | TAGS
#lo #license-apache-2.0 #region-us
|
# Lao Word Embedding
This model used in LaoNLP that trained from oscar corpus (Lao only). You can see more at URL
LaoNLP: URL | [
"# Lao Word Embedding\n\nThis model used in LaoNLP that trained from oscar corpus (Lao only). You can see more at URL\n\nLaoNLP: URL"
] | [
"TAGS\n#lo #license-apache-2.0 #region-us \n",
"# Lao Word Embedding\n\nThis model used in LaoNLP that trained from oscar corpus (Lao only). You can see more at URL\n\nLaoNLP: URL"
] |
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-wikiandmark
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wikiandmark", "results": []}]} | leokai/distilbert-base-uncased-finetuned-wikiandmark | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:13:15+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-wikiandmark
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0329
* Accuracy: 0.9962
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
image-classification | timm |
# EfficientFormer-L1
## Table of Contents
- [EfficientFormer-L1](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- ... | {"language": ["en"], "license": "apache-2.0", "library_name": "timm", "tags": ["mobile", "vison", "image-classification"], "datasets": ["imagenet-1k"], "metrics": ["accuracy"]} | NimaBoscarino/efficientformer-l1-1000 | null | [
"timm",
"pytorch",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] | null | 2022-07-14T07:16:26+00:00 | [
"2206.01191"
] | [
"en"
] | TAGS
#timm #pytorch #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us
|
# EfficientFormer-L1
## Table of Contents
- EfficientFormer-L1
- Table of Contents
- Model Details
- How to Get Started with the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out-of-scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
... | [
"# EfficientFormer-L1",
"## Table of Contents\n- EfficientFormer-L1\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out-of-scope Use\n - Limitations and Biases\n - Training\n - Training Data\n - ... | [
"TAGS\n#timm #pytorch #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us \n",
"# EfficientFormer-L1",
"## Table of Contents\n- EfficientFormer-L1\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - Direct... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Zaib/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Zaib/distilbert-base-uncased-finetuned-cola", "results": []}]} | Zaib/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:17:25+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Zaib/distilbert-base-uncased-finetuned-cola
===========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5343
* Validation Loss: 0.5940
* Train Matthews Correlation: 0.2397
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 195, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
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. -->
# DNADebertaBPE30k
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the followin... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaBPE30k", "results": []}]} | simecek/DNADebertaBPE30k | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:39:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# DNADebertaBPE30k
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.1519
- eval_runtime: 308.5062
- eval_samples_per_second: 337.384
- eval_steps_per_second: 21.089
- epoch: 7.22
- step: 105695
## Model description
More infor... | [
"# DNADebertaBPE30k\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 5.1519\n- eval_runtime: 308.5062\n- eval_samples_per_second: 337.384\n- eval_steps_per_second: 21.089\n- epoch: 7.22\n- step: 105695",
"## Model descript... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# DNADebertaBPE30k\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 5.1519... |
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. -->
# distilroberta-base-wiki-mark
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-wiki-mark", "results": []}]} | NinaXiao/distilroberta-base-wiki-mark | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:42:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-wiki-mark
============================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0062
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-base-All
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-base-All", "results": []}]} | Siyong/MC | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:44:08+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-base-All
================
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: 3.0545
* Wer: 0.8861
* Cer: 0.5014
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 #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\n* eval\\_ba... |
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. -->
# DNADebertaBPE10k
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the followin... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaBPE10k", "results": []}]} | simecek/DNADebertaBPE10k | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:45:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# DNADebertaBPE10k
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.7323
- eval_runtime: 283.5074
- eval_samples_per_second: 394.223
- eval_steps_per_second: 24.641
- epoch: 7.43
- step: 116731
## Model description
More infor... | [
"# DNADebertaBPE10k\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 4.7323\n- eval_runtime: 283.5074\n- eval_samples_per_second: 394.223\n- eval_steps_per_second: 24.641\n- epoch: 7.43\n- step: 116731",
"## Model descript... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# DNADebertaBPE10k\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 4.7323... |
fill-mask | transformers | # Tranception model
This Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper ["Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval"](https://arxiv.org/abs/2205.13760). The official GitHub repository can be accessed [h... | {} | OATML-Markslab/Tranception_Large | null | [
"transformers",
"pytorch",
"tranception",
"fill-mask",
"arxiv:2205.13760",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T07:54:44+00:00 | [
"2205.13760"
] | [] | TAGS
#transformers #pytorch #tranception #fill-mask #arxiv-2205.13760 #autotrain_compatible #endpoints_compatible #region-us
| # Tranception model
This Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper "Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval". The official GitHub repository can be accessed here. This project is a joint collabor... | [
"# Tranception model\n\nThis Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper \"Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval\". The official GitHub repository can be accessed here. This project is a joint... | [
"TAGS\n#transformers #pytorch #tranception #fill-mask #arxiv-2205.13760 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tranception model\n\nThis Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper \"Tranception: protein fitness prediction with au... |
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_abstract_summarization
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart_abstract_summarization", "results": []}]} | jgriffi/bart_abstract_summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T08:13:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart\_abstract\_summarization
=============================
This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1852
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 #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: 5e-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... | Kuro96/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-14T08:20:08+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. -->
# xlm-roberta-hindi-nisha
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/c... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-hindi-nisha", "results": []}]} | sam34738/xlm-roberta-hindi-nisha | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T08:20:57+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-hindi-nisha
=======================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-emotion on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5305
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
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. -->
# distilroberta-base-wiki-mark
This model is a fine-tuned version of [yochen/distilroberta-base-wiki-mark](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-wiki-mark", "results": []}]} | yochen/distilroberta-base-wiki-mark | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T08:28:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-wiki-mark
This model is a fine-tuned version of yochen/distilroberta-base-wiki-mark on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2695
- eval_runtime: 4.3489
- eval_samples_per_second: 431.836
- eval_steps_per_second: 54.037
- epoch: 10.1
- step: 2... | [
"# distilroberta-base-wiki-mark\n\nThis model is a fine-tuned version of yochen/distilroberta-base-wiki-mark on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.2695\n- eval_runtime: 4.3489\n- eval_samples_per_second: 431.836\n- eval_steps_per_second: 54.037\n- epoch: 10.1... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-wiki-mark\n\nThis model is a fine-tuned version of yochen/distilroberta-base-wiki-mark on the None dataset.\nIt achieves the ... |
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. -->
# nb-bert-base-user-needs
This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "NbAiLab/nb-bert-base", "model-index": [{"name": "nb-bert-base-user-needs", "results": []}]} | thusken/nb-bert-base-user-needs | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:NbAiLab/nb-bert-base",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-14T08:52:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-NbAiLab/nb-bert-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| nb-bert-base-user-needs
=======================
This model is a fine-tuned version of NbAiLab/nb-bert-base on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the BBC in 2017
It achieves the f... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\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 #bert #text-classification #generated_from_trainer #base_model-NbAiLab/nb-bert-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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... | spacestar1705/ppo-LunaLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-14T09:39:40+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | stokic/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-14T11:21:59+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... |
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. -->
# pixel-base-finetuned-tydiqa-goldp
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIX... | {"tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "pixel-base-finetuned-tydiqa-goldp", "results": []}]} | Team-PIXEL/pixel-base-finetuned-tydiqa-goldp | null | [
"transformers",
"pytorch",
"pixel",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T11:35:12+00:00 | [] | [] | TAGS
#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us
|
# pixel-base-finetuned-tydiqa-goldp
This model is a fine-tuned version of Team-PIXEL/pixel-base on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# pixel-base-finetuned-tydiqa-goldp \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-tydiqa-goldp \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore inform... |
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. -->
# pixel-base-finetuned-squad-v1
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/p... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "pixel-base-finetuned-squadv1", "results": []}]} | Team-PIXEL/pixel-base-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"pixel",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T12:00:33+00:00 | [] | [] | TAGS
#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# pixel-base-finetuned-squad-v1
This model is a fine-tuned version of Team-PIXEL/pixel-base 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 hy... | [
"# pixel-base-finetuned-squad-v1 \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base 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 ... | [
"TAGS\n#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-squad-v1 \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | natnova/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T12:06:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | workRL/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-14T12:31:40+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
null | transformers |
# Model Overview
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-supervised
manne... | {"language": "en", "license": "apache-2.0", "tags": ["btcv", "medical", "swin"], "datasets": ["BTCV"]} | darragh/swinunetr-btcv-tiny | null | [
"transformers",
"pytorch",
"btcv",
"medical",
"swin",
"en",
"dataset:BTCV",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-14T12:37:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Model Overview
==============
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-sup... | [] | [
"TAGS\n#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #has_space #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. -->
# predict-perception-bertino-cause-object
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-cause-object", "results": []}]} | gossminn/predict-perception-bertino-cause-object | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:06:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-cause-object
=======================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0766
* R2: 0.8216
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: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 0.0001\n* train\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bertino-cause-concept
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-cause-concept", "results": []}]} | gossminn/predict-perception-bertino-cause-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:15:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-cause-concept
========================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2035
* R2: -0.3662
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 0.0001\n* train\\_batch\\... |
text-generation | transformers |
# Drunk IC-0n
IC-0n (or Icon) is a murderous AI protagonist of the Internecion Cube series. This is an attempt to build her in real life (haha it failed, and actually gladly)
This uses Microsoft's DialoGPT-small and it is trained on all of Icon's lines throughout the series from episode 1-3 (only 50 though, so low tr... | {"tags": ["conversational"]} | cybertelx/DialoGPT-small-drunkic0n | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T13:16:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Drunk IC-0n
IC-0n (or Icon) is a murderous AI protagonist of the Internecion Cube series. This is an attempt to build her in real life (haha it failed, and actually gladly)
This uses Microsoft's DialoGPT-small and it is trained on all of Icon's lines throughout the series from episode 1-3 (only 50 though, so low tr... | [
"# Drunk IC-0n\nIC-0n (or Icon) is a murderous AI protagonist of the Internecion Cube series. This is an attempt to build her in real life (haha it failed, and actually gladly)\n\nThis uses Microsoft's DialoGPT-small and it is trained on all of Icon's lines throughout the series from episode 1-3 (only 50 though, so... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Drunk IC-0n\nIC-0n (or Icon) is a murderous AI protagonist of the Internecion Cube series. This is an attempt to build her in real life (haha it failed, an... |
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. -->
# predict-perception-bertino-cause-none
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-ai... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-cause-none", "results": []}]} | gossminn/predict-perception-bertino-cause-none | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:22:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-cause-none
=====================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1988
* R2: 0.4467
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 0.0001\n* train\\_batch\\... |
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