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null | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split
Vision-and-Language Transformer (ViLT) model fine-tuned on random split of [Visual Spatial Reasoning (VSR)](https://arxiv.org/abs/2205.00363). ViLT was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region ... | {"license": "apache-2.0"} | juletxara/vilt-vsr-random | null | [
"transformers",
"pytorch",
"vilt",
"arxiv:2205.00363",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T09:37:16+00:00 | [
"2205.00363",
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split
Vision-and-Language Transformer (ViLT) model fine-tuned on random split of Visual Spatial Reasoning (VSR). ViLT was introduced in the paper ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision by Kim et al. and first r... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split\n\nVision-and-Language Transformer (ViLT) model fine-tuned on random split of Visual Spatial Reasoning (VSR). ViLT was introduced in the paper ViLT: Vision-and-Language Transformer\nWithout Convolution or Region Supervision by Kim et al. and ... | [
"TAGS\n#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split\n\nVision-and-Language Transformer (ViLT) model fine-tuned on random split of Visual Spatial Reasoning (VSR). ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | happy098/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T09:41:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4884
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ai_data
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ai_data", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-ai_data | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T09:44:03+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-ai_data
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluati... | [
"# distilbert-base-uncased-finetuned-ai_data\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-ai_data\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the follo... |
null | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on VSR zeroshot split
Vision-and-Language Transformer (ViLT) model fine-tuned on zeroshot split of [Visual Spatial Reasoning (VSR)](https://arxiv.org/abs/2205.00363). ViLT was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Reg... | {"license": "apache-2.0"} | juletxara/vilt-vsr-zeroshot | null | [
"transformers",
"pytorch",
"vilt",
"arxiv:2205.00363",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T09:55:43+00:00 | [
"2205.00363",
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on VSR zeroshot split
Vision-and-Language Transformer (ViLT) model fine-tuned on zeroshot split of Visual Spatial Reasoning (VSR). ViLT was introduced in the paper ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision by Kim et al. and fir... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on VSR zeroshot split\n\nVision-and-Language Transformer (ViLT) model fine-tuned on zeroshot split of Visual Spatial Reasoning (VSR). ViLT was introduced in the paper ViLT: Vision-and-Language Transformer\nWithout Convolution or Region Supervision by Kim et al. ... | [
"TAGS\n#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Vision-and-Language Transformer (ViLT), fine-tuned on VSR zeroshot split\n\nVision-and-Language Transformer (ViLT) model fine-tuned on zeroshot split of Visual Spatial Reasoning (VS... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | masapasa/blurr_IMDB_distilbert_classification | null | [
"fastai",
"region:us"
] | null | 2022-08-04T10:01:30+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
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. -->
# Bio_ClinicalBERT_fold_1_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_1_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_1_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T10:12:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_1\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9306
* F1: 0.7943
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# mini-phobert-v3.1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the followi... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mini-phobert-v3.1", "results": []}]} | keepitreal/mini-phobert-v3.1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T10:32:18+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mini-phobert-v3.1
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0527
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# mini-phobert-v3.1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0527",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mini-phobert-v3.1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0527",
"## Model de... |
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. -->
# Bio_ClinicalBERT_fold_2_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_2_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_2_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T10:38:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_2\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8186
* F1: 0.8038
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | spacy |
NER4Archives pipeline optimized for GPU and specialized on French National Archives findings aids (XML-EAD). Components: transformer, ner. Based on camembert-base model.
| Feature | Description |
| --- | --- |
| **Name** | `fr_ner4archives_camembert_base` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.3.1,<3.4.0` ... | {"language": ["fr"], "tags": ["spacy", "token-classification"], "widget": [{"text": "415 Lyon Lettres de r\u00e9mission accord\u00e9es \u00e0 Denis Fromant, marinier, pour meurtre commis \u00e0 Saint-Haon 1, au pays de Roannais, sur la personne de Driet Cantin qui l'accusait d'avoir maltrait\u00e9 un de ses pages et de... | ner4archives/fr_ner4archives_camembert_base | null | [
"spacy",
"token-classification",
"fr",
"model-index",
"region:us"
] | null | 2022-08-04T10:59:28+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #model-index #region-us
| NER4Archives pipeline optimized for GPU and specialized on French National Archives findings aids (XML-EAD). Components: transformer, ner. Based on camembert-base model.
### Label Scheme
View label scheme (5 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] |
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-ft1500_norm1000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm1000", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm1000 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T11:02:33+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-ft1500\_norm1000
==================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0875
* Mse: 1.3594
* Mae: 0.5794
* R2: 0.3573
* Accuracy: 0.70... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"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... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | 29thDay/PPO-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-04T11:08:40+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
Trained from XLM-RoBERTa. | {"language": ["et"], "license": "cc-by-4.0", "base_model": "xlm-roberta-base"} | tartuNLP/EstRoBERTa | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"et",
"base_model:xlm-roberta-base",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T12:02:23+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #et #base_model-xlm-roberta-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
Trained from XLM-RoBERTa. | [] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #et #base_model-xlm-roberta-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | tj-solergibert/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 | 2022-08-04T12:08:15+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\\_... |
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data
This model is a fine-tuned version of [silviacamplani/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T12:37:35+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai\_data
==================================================================
This model is a fine-tuned version of silviacamplani/distilbert-base-uncased-finetuned-ai\_data on an unknown dataset.
It achieves the following results on the evaluation set:
* Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer':... |
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data_3labels
This model is a fine-tuned version of [silviacamplani/distil... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data_3labels", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data_3labels | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T12:48:00+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai\_data\_3labels
===========================================================================
This model is a fine-tuned version of silviacamplani/distilbert-base-uncased-finetuned-ai\_data on an unknown dataset.
It achieves the following results on the evalua... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer':... |
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. -->
# results
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "results", "results": []}]} | fzwd6666/NLT-trainer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T13:00:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0743
* Accuracy: 0.9858
* F1: 0.9857
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* 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: 0.0001\n* train\\_batch\\_size: 8\n* ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | jjjjjjjjjj/dqn-SpaceInvadersNoFrame-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-04T13:02:15+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
image-segmentation | 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. -->
# segformer-b5-finetuned-segments-instryde-foot-test
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/... | {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b5-finetuned-segments-instryde-foot-test", "results": []}]} | plant/segformer-b5-finetuned-segments-instryde-foot-test | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T13:30:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| segformer-b5-finetuned-segments-instryde-foot-test
==================================================
This model is a fine-tuned version of nvidia/mit-b5 on the inStryde/inStrydeSegmentationFoot dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0496
* Mean Iou: 0.4672
* Mean Accuracy: 0.93... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eva... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | tj-solergibert/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T13:35:07+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1608
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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. -->
# mini-phobert-v2.1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the followi... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mini-phobert-v2.1", "results": []}]} | keepitreal/mini-phobert-v2.1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T13:49:57+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mini-phobert-v2.1
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# mini-phobert-v2.1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.3279",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mini-phobert-v2.1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.3279",
"## Model de... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.fr", "s... | tj-solergibert/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T14:00:53+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2763
* F1: 0.8346
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-es-clinical-trials-medic-attr-ner
This named entity recognition model detects medication-related information:
- Contrain... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Azitromicina en suspensi\u00f3n oral, 10 mg/kg una vez al d\u00eda durante siete d\u00edas"}, {"text": "A un grupo se le administr\u00f3 Ciprofloxacino 200 mg bid EV y al otro Cefaz... | medspaner/roberta-es-clinical-trials-medic-attr-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T14:12:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-es-clinical-trials-medic-attr-ner
=========================================
This named entity recognition model detects medication-related information:
* Contraindication: e.g. *contraindicación a aspirina*
* Dose, strength or concentration: e.g. *14 mg*, *100.000 UI*
* Form: e.g. *tabletas*, *comprimidos*
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: we used different seeds for 5 evaluation rounds, and uploaded the model with the best results\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.it", "s... | tj-solergibert/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T14:21:38+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2630
* F1: 0.8124
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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. -->
# article_title
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dai... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "article_title", "results": []}]} | abdulmatinomotoso/article_title | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T14:52:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# article_title
This model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# article_title\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# article_title\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore information ... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) librar... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-mask-prompt-b-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics... | research-backup/roberta-large-conceptnet-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T15:33:12+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-mask-prompt-b-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full resu... | [
"# relbert/roberta-large-conceptnet-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done vi... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | quadpartisan/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-04T15:33:44+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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. -->
# mal-tls-bert-large-relu
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the follo... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-bert-large-relu", "results": []}]} | SharpAI/mal-tls-bert-large-relu | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T16:58:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal-tls-bert-large-relu
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# mal-tls-bert-large-relu\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal-tls-bert-large-relu\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"#... |
null | null |
# [ACR-Loss](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=96lS6HIAAAAJ&citation_for_view=96lS6HIAAAAJ:eQOLeE2rZwMC)
### Accepted in ICPR 2022
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
#### Link to the paper:
https://arxiv.org/pdf/2203.15835.pdf
```diff
@@plaes... | {"language": "en", "license": "mit", "tags": ["ICPR", "ICPR2022", "computer vision", "face alignment", "facial landmark point", "CNN", "loss", "Tensor Flow"]} | aliprf/ACR-Loss | null | [
"ICPR",
"ICPR2022",
"computer vision",
"face alignment",
"facial landmark point",
"CNN",
"loss",
"Tensor Flow",
"en",
"arxiv:2203.15835",
"license:mit",
"region:us"
] | null | 2022-08-04T17:26:32+00:00 | [
"2203.15835"
] | [
"en"
] | TAGS
#ICPR #ICPR2022 #computer vision #face alignment #facial landmark point #CNN #loss #Tensor Flow #en #arxiv-2203.15835 #license-mit #region-us
|
# ACR-Loss
### Accepted in ICPR 2022
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
#### Link to the paper:
URL
!Samples
## Introduction
Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when we dea... | [
"# ACR-Loss",
"### Accepted in ICPR 2022\nACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment",
"#### Link to the paper:\nURL\n\n\n\n\n\n\n!Samples",
"## Introduction\n\nAlthough deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task... | [
"TAGS\n#ICPR #ICPR2022 #computer vision #face alignment #facial landmark point #CNN #loss #Tensor Flow #en #arxiv-2203.15835 #license-mit #region-us \n",
"# ACR-Loss",
"### Accepted in ICPR 2022\nACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment",
"#### Link to the paper:\nURL\n\n\n\n\n\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaSentencepiece30k_continuation_continuation
This model is a fine-tuned version of [Vlasta/DNADebertaSentencepiece30k_co... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaSentencepiece30k_continuation_continuation", "results": []}]} | Vlasta/DNADebertaSentencepiece30k_continuation_continuation | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T17:58:36+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaSentencepiece30k\_continuation\_continuation
======================================================
This model is a fine-tuned version of Vlasta/DNADebertaSentencepiece30k\_continuation on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9867
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaSentencepiece10k_continuation_continuation
This model is a fine-tuned version of [Vlasta/DNADebertaSentencepiece10k_co... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaSentencepiece10k_continuation_continuation", "results": []}]} | Vlasta/DNADebertaSentencepiece10k_continuation_continuation | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T18:00:28+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaSentencepiece10k\_continuation\_continuation
======================================================
This model is a fine-tuned version of Vlasta/DNADebertaSentencepiece10k\_continuation on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.3056
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cochonaki/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cochonaki/distilbert-base-uncased-finetuned-cola", "results": []}]} | cochonaki/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 | 2022-08-04T18:06:42+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| cochonaki/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: 0.1905
* Validation Loss: 0.5536
* Train Matthews Correlation:... | [
"### 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': 1602, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"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... |
null | null |
# Ad-Corre
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
[](https://paperswithcode.com/sota/facial-expressi... | {"language": "en", "license": "mit", "tags": ["Ad-Corre", "facial expression recognition", "emotion recognition", "expression recognition", "computer vision", "CNN", "loss", "IEEE Access", "Tensor Flow"]} | aliprf/Ad-Corre | null | [
"Ad-Corre",
"facial expression recognition",
"emotion recognition",
"expression recognition",
"computer vision",
"CNN",
"loss",
"IEEE Access",
"Tensor Flow",
"en",
"license:mit",
"region:us"
] | null | 2022-08-04T18:11:54+00:00 | [] | [
"en"
] | TAGS
#Ad-Corre #facial expression recognition #emotion recognition #expression recognition #computer vision #CNN #loss #IEEE Access #Tensor Flow #en #license-mit #region-us
|
# Ad-Corre
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
:
URL
#### Link to the URL:
URL
## Introduction
Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to ... | [
"# Ad-Corre\nAd-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild\n\n\t\n ![PWC](URL",
"#### Link to the paper (open access):\nURL",
"#### Link to the URL:\nURL",
"## Introduction\n\nAutomated Facial Expression Recognition (FER) in the wild using deep neural networks is sti... | [
"TAGS\n#Ad-Corre #facial expression recognition #emotion recognition #expression recognition #computer vision #CNN #loss #IEEE Access #Tensor Flow #en #license-mit #region-us \n",
"# Ad-Corre\nAd-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild\n\n\t\n ![PWC](URL",
"#### Lin... |
null | null |
[](https://paperswithcode.com/sota/pose-estimation-on-300w-full?p=deep-active-shape-model-for-face-alignment)
[![PWC](https://img.shields.io/endpoint.svg?url=https... | {"language": "en", "license": "mit", "tags": ["cvpr2021", "computer vision", "face alignment", "facial landmark point", "pose estimation", "face pose tracking", "CNN", "loss", "custom loss", "ASMNet", "Tensor Flow"]} | aliprf/ASMNet | null | [
"cvpr2021",
"computer vision",
"face alignment",
"facial landmark point",
"pose estimation",
"face pose tracking",
"CNN",
"loss",
"custom loss",
"ASMNet",
"Tensor Flow",
"en",
"license:mit",
"region:us"
] | null | 2022-08-04T18:19:41+00:00 | [] | [
"en"
] | TAGS
#cvpr2021 #computer vision #face alignment #facial landmark point #pose estimation #face pose tracking #CNN #loss #custom loss #ASMNet #Tensor Flow #en #license-mit #region-us
|
 which is designed... | [
"# ASMNet",
"## a Lightweight Deep Neural Network for Face Alignment and Pose Estimation",
"#### Link to the paper:\nURL",
"#### Link to the URL:\nURL",
"#### Link to the article on URL:\nURL",
"## Introduction\n\n ASMNet is a lightweight Convolutional Neural Network (CNN) which is designed to perform fac... | [
"TAGS\n#cvpr2021 #computer vision #face alignment #facial landmark point #pose estimation #face pose tracking #CNN #loss #custom loss #ASMNet #Tensor Flow #en #license-mit #region-us \n",
"# ASMNet",
"## a Lightweight Deep Neural Network for Face Alignment and Pose Estimation",
"#### Link to the paper:\nURL",... |
null | null |
[](https://paperswithcode.com/sota/face-alignment-on-cofw?p=facial-landmark-points-detection-using)
#
Facial Landmark Points Detection Using Knowledge Distillation-Based Neura... | {"language": "en", "license": "mit", "tags": ["computer vision", "face alignment", "facial landmark point", "CNN", "Knowledge Distillation", "loss", "CVIU", "Tensor Flow"]} | aliprf/KD-Loss | null | [
"computer vision",
"face alignment",
"facial landmark point",
"CNN",
"Knowledge Distillation",
"loss",
"CVIU",
"Tensor Flow",
"en",
"arxiv:2111.07047",
"license:mit",
"region:us"
] | null | 2022-08-04T18:22:34+00:00 | [
"2111.07047"
] | [
"en"
] | TAGS
#computer vision #face alignment #facial landmark point #CNN #Knowledge Distillation #loss #CVIU #Tensor Flow #en #arxiv-2111.07047 #license-mit #region-us
|
![PWC](URL
#
Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
#### Link to the paper:
Google Scholar:
URL
Elsevier:
URL
Arxiv:
URL
#### Link to the URL:
URL
## Introduction
Facial landmark detection is a vital step for numerous facial image analysis applications. Althoug... | [
"#### Link to the paper:\nGoogle Scholar:\nURL\n\nElsevier:\nURL\n\nArxiv:\nURL",
"#### Link to the URL:\nURL",
"## Introduction\nFacial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, the... | [
"TAGS\n#computer vision #face alignment #facial landmark point #CNN #Knowledge Distillation #loss #CVIU #Tensor Flow #en #arxiv-2111.07047 #license-mit #region-us \n",
"#### Link to the paper:\nGoogle Scholar:\nURL\n\nElsevier:\nURL\n\nArxiv:\nURL",
"#### Link to the URL:\nURL",
"## Introduction\nFacial landm... |
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. -->
# wikigold_trained_no_DA_testing2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikigold_splits"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "wikigold_trained_no_DA_testing2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wik... | DOOGLAK/wikigold_trained_no_DA_testing2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikigold_splits",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T18:39:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| wikigold\_trained\_no\_DA\_testing2
===================================
This model is a fine-tuned version of bert-base-cased on the wikigold\_splits dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1431
* Precision: 0.8411
* Recall: 0.8477
* F1: 0.8444
* Accuracy: 0.9572
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# article_title_2299
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cn... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "article_title_2299", "results": []}]} | abdulmatinomotoso/article_title_2299 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T18:49:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# article_title_2299
This model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# article_title_2299\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# article_title_2299\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore informa... |
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. -->
# facebook_large_CV_bn3
This model is a fine-tuned version of [Sameen53/facebook_large_CV_bn](https://huggingface.co/Sameen53/face... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "facebook_large_CV_bn3", "results": []}]} | Sameen53/facebook_large_CV_bn3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T19:36:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| facebook\_large\_CV\_bn3
========================
This model is a fine-tuned version of Sameen53/facebook\_large\_CV\_bn on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2308
* Wer: 0.2379
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* eval\\_... |
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. -->
# multi_news_article_title_2299
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pe... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_2299", "results": []}]} | abdulmatinomotoso/multi_news_article_title_2299 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T20:06:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# multi_news_article_title_2299
This model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# multi_news_article_title_2299\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# multi_news_article_title_2299\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore... |
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. -->
# mal-tls-bert-large-relu-w8a8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-bert-large-relu-w8a8", "results": []}]} | SharpAI/mal-tls-bert-large-relu-w8a8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T20:31:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal-tls-bert-large-relu-w8a8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# mal-tls-bert-large-relu-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal-tls-bert-large-relu-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mo... |
text-classification | transformers | This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.9690210656753407} {'recall': 0.9722337339411521} {'f1': 0.9706247414149772} {'accuracy': 0.9535340314136126}
Training hyperparameters:
learning_rate: 2e-5
train_batch_s... | {} | fzwd6666/NLI_new | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T20:42:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.9690210656753407} {'recall': 0.9722337339411521} {'f1': 0.9706247414149772} {'accuracy': 0.9535340314136126}
Training hyperparameters:
learning_rate: 2e-5
train_batch_s... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # An Idol Chatbot
| {"tags": ["conversational"]} | celine45688/LuTing | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-04T21:01:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # An Idol Chatbot
| [
"# An Idol Chatbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# An Idol Chatbot"
] |
text-classification | transformers | This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.8384560400285919} {'recall': 0.9536585365853658} {'f1': 0.892354507417269} {'accuracy': 0.8345996493278784}
Training hyperparameters:
learning_rate=2e-5
batch_size=32
ep... | {} | fzwd6666/Ged_bert_new | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T21:14:19+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.8384560400285919} {'recall': 0.9536585365853658} {'f1': 0.892354507417269} {'accuracy': 0.8345996493278784}
Training hyperparameters:
learning_rate=2e-5
batch_size=32
ep... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-to-text | generic |
# Fork of [salesforce/BLIP](https://github.com/salesforce/BLIP) for a `image-captioning` task on 🤗Inference endpoint.
This repository implements a `custom` task for `image-captioning` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/blip_... | {"license": "bsd-3-clause", "library_name": "generic", "tags": ["image-to-text", "image-captioning", "endpoints-template"]} | florentgbelidji/blip_captioning | null | [
"generic",
"image-to-text",
"image-captioning",
"endpoints-template",
"license:bsd-3-clause",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-04T21:20:58+00:00 | [] | [] | TAGS
#generic #image-to-text #image-captioning #endpoints-template #license-bsd-3-clause #endpoints_compatible #has_space #region-us
|
# Fork of salesforce/BLIP for a 'image-captioning' task on Inference endpoint.
This repository implements a 'custom' task for 'image-captioning' for Inference Endpoints. The code for the customized pipeline is in the URL.
To use deploy this model a an Inference Endpoint you have to select 'Custom' as task to use the... | [
"# Fork of salesforce/BLIP for a 'image-captioning' task on Inference endpoint.\n\nThis repository implements a 'custom' task for 'image-captioning' for Inference Endpoints. The code for the customized pipeline is in the URL.\nTo use deploy this model a an Inference Endpoint you have to select 'Custom' as task to ... | [
"TAGS\n#generic #image-to-text #image-captioning #endpoints-template #license-bsd-3-clause #endpoints_compatible #has_space #region-us \n",
"# Fork of salesforce/BLIP for a 'image-captioning' task on Inference endpoint.\n\nThis repository implements a 'custom' task for 'image-captioning' for Inference Endpoints.... |
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. -->
# soft-search
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "base_model": "distilbert-base-uncased-finetuned-sst-2-english", "model-index": [{"name": "soft-search", "results": []}]} | evamaxfield/soft-search | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased-finetuned-sst-2-english",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T21:50:01+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| soft-search
===========
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5558
* F1: 0.5960
* Accuracy: 0.7109
* Precision: 0.5769
* Recall: 0.6164
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
text-classification | transformers | This model is a fine-tuned version of fzwd6666/Ged_bert_new on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.9647540983606557} {'recall': 0.9755491089929549} {'f1': 0.9701215742839481} {'accuracy': 0.9525523560209425}
Training hyperparameters:
learning_rate: 2e-5
train_batc... | {} | fzwd6666/NLI_multi_new | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T21:50:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of fzwd6666/Ged_bert_new on an NLI dataset. It achieves the following results on the evaluation set:
{'precision': 0.9647540983606557} {'recall': 0.9755491089929549} {'f1': 0.9701215742839481} {'accuracy': 0.9525523560209425}
Training hyperparameters:
learning_rate: 2e-5
train_batc... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This model is a fine-tuned version of bert-base-uncase with 4 layers on an NLT dataset. It achieves the following results on the evaluation set:
{'precision': 0.9804255319148936} {'recall': 0.9888412017167382} {'f1': 0.9846153846153847} {'accuracy': 0.9849498327759197}
Training hyperparameters:
learning_rate: 1e-4
t... | {} | fzwd6666/NLTBert_singal_fine_tune_new | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T22:28:29+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of bert-base-uncase with 4 layers on an NLT dataset. It achieves the following results on the evaluation set:
{'precision': 0.9804255319148936} {'recall': 0.9888412017167382} {'f1': 0.9846153846153847} {'accuracy': 0.9849498327759197}
Training hyperparameters:
learning_rate: 1e-4
t... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1376912180721766401/ZVhV... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dominic_w-lastmjs-vitalikbuterin/1659656428920/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dominic_w-lastmjs-vitalikbuterin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-04T22:38:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
URL ∞ & URL & URL ∞
@dominic\_w-lastmjs-vitalikbuterin
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B rep... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | This model is a fine-tuned version of fzwd6666/Ged_bert_new with 4 layers on an NLT dataset. It achieves the following results on the evaluation set:
{'precision': 0.9795081967213115} {'recall': 0.989648033126294} {'f1': 0.984552008238929} {'accuracy': 0.9843227424749164}
Training hyperparameters:
learning_rate: 1e-... | {} | fzwd6666/NLTBert_multi_fine_tune_new | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T23:04:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of fzwd6666/Ged_bert_new with 4 layers on an NLT dataset. It achieves the following results on the evaluation set:
{'precision': 0.9795081967213115} {'recall': 0.989648033126294} {'f1': 0.984552008238929} {'accuracy': 0.9843227424749164}
Training hyperparameters:
learning_rate: 1e-... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #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. -->
# xlnet-base-mnli-orgs-finetuned1
This model is a fine-tuned version of [clevrly/xlnet-base-mnli-finetuned](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-mnli-orgs-finetuned1", "results": []}]} | vish88/xlnet-base-mnli-orgs-finetuned1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T23:45:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-mnli-orgs-finetuned1
===============================
This model is a fine-tuned version of clevrly/xlnet-base-mnli-finetuned on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1542
* F1: 0.6957
Model description
-----------------
More information needed
Intended... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
sentence-similarity | sentence-transformers |
# embedding-data/deberta-sentence-transformer
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)
Usin... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["embedding-data/QQP_triplets"], "pipeline_tag": "sentence-similarity"} | embedding-data/deberta-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"deberta-v2",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:embedding-data/QQP_triplets",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T01:17:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #deberta-v2 #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us
|
# embedding-data/deberta-sentence-transformer
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-transforme... | [
"# embedding-data/deberta-sentence-transformer\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-... | [
"TAGS\n#sentence-transformers #pytorch #deberta-v2 #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us \n",
"# embedding-data/deberta-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dime... |
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. -->
# Bio_ClinicalBERT-zero-shot-finetuned-all-cad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://hug... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Bio_ClinicalBERT-zero-shot-finetuned-all-cad", "results": []}]} | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-all-cad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T03:33:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Bio_ClinicalBERT-zero-shot-finetuned-all-cad
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# Bio_ClinicalBERT-zero-shot-finetuned-all-cad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bio_ClinicalBERT-zero-shot-finetuned-all-cad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.",
"... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "ppo-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type"... | Saraswati/ppo-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-05T03:43:50+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBE... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal", "results": []}]} | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T04:12:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 9.8836
- Accuracy: 0.5
- F1: 0.0
## Model description
More information needed
## Intended uses ... | [
"# Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 9.8836\n- Accuracy: 0.5\n- F1: 0.0",
"## Model description\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bio_ClinicalBERT-zero-shot-finetuned-50cad-50noncad-optimal\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None... |
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-tiny-finetuned-legal-definitions-downstream-alt
This model is a fine-tuned version of [muhtasham/bert-tiny-finetuned-legal-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-tiny-finetuned-legal-definitions-downstream-alt", "results": []}]} | muhtasham/bert-tiny-finetuned-legal-definitions-downstream-alt | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T04:16:06+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-legal-definitions-downstream-alt
====================================================
This model is a fine-tuned version of muhtasham/bert-tiny-finetuned-legal-definitions on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2298
* Micro f1: 0.0
* Macro f1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4096\n* eval\\_batch\\_size: 1024\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1000",
"###... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# multi_news_article_title_1200
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pe... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_1200", "results": []}]} | abdulmatinomotoso/multi_news_article_title_1200 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T05:42:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# multi_news_article_title_1200
This model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# multi_news_article_title_1200\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# multi_news_article_title_1200\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore... |
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"]} | jefsnacker/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-05T06:52:11+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 |
<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/547731071479996417/53RFX... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/calm/1660376376560/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/calm | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T07:05:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Calm
@calm
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mini-phoelectra-v1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the follow... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mini-phoelectra-v1", "results": []}]} | keepitreal/mini-phoelectra-v1 | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T08:19:28+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mini-phoelectra-v1
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ne... | [
"# mini-phoelectra-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.4503",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation dat... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mini-phoelectra-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.4503",
"## Model d... |
null | null | This model was trained using the plant village dataset | {} | hitmonleet/PlantVillage__MobileNetV3 | null | [
"region:us"
] | null | 2022-08-05T08:26:14+00:00 | [] | [] | TAGS
#region-us
| This model was trained using the plant village dataset | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/547731071479996417/53RFX... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/calm-headspace/1659691640977/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/calm-headspace | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T08:26:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Calm & Headspace
@calm-headspace
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# Spoof_detection
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Spoof_detection", "results": []}]} | 202015004/Spoof_detection | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T08:32:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Spoof\_detection
================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7448
* Wer: 0.1090
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | LiptaphX/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T09:46:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
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-tiny-finetuned-legal-definitions-longer-downstream-alt
This model is a fine-tuned version of [muhtasham/bert-tiny-finetuned... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-tiny-finetuned-legal-definitions-longer-downstream-alt", "results": []}]} | muhtasham/bert-tiny-finetuned-legal-definitions-longer-downstream-alt | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T09:48:10+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-legal-definitions-longer-downstream-alt
===========================================================
This model is a fine-tuned version of muhtasham/bert-tiny-finetuned-legal-definitions-longer on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2301
* Micro f1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4096\n* eval\\_batch\\_size: 1024\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1000",
"###... | [
"TAGS\n#transformers #pytorch #safetensors #bert #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: 2e-05\n* train\\_batch\\_size: 4096\n* eval\... |
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"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | tester2047/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T10:38:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
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: 0.3120
- Accuracy: 0.8733
- F1: 0.8742
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3120\n- Accuracy: 0.8733\n- F1: 0.8742",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
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. -->
# wav2vec2-1b-npsc-nst
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-1b-npsc-nst", "results": []}]} | NbAiLab/wav2vec2-1b-npsc-nst | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T10:45:30+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-1b-npsc-nst
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0633
* Wer: 0.0358
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: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #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... |
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. -->
# multi_news_article_title_7500
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pe... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_7500", "results": []}]} | abdulmatinomotoso/multi_news_article_title_7500 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T11:14:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# multi_news_article_title_7500
This model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# multi_news_article_title_7500\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# multi_news_article_title_7500\n\nThis model is a fine-tuned version of google/pegasus-multi_news on an unknown dataset.",
"## Model description\n\nMore... |
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. -->
# clip-archdaily-5k
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the follo... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "clip-archdaily-5k", "results": []}]} | TheLitttleThings/clip-archdaily-5k | null | [
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T11:31:23+00:00 | [] | [] | TAGS
#transformers #pytorch #clip #zero-shot-image-classification #generated_from_trainer #endpoints_compatible #region-us
| clip-archdaily-5k
=================
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5681
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 56\n* eval\\_batch\\_size: 56\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: 56\n* eval\\_batch\\_size: 56\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. -->
# recipe-reg
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-reg", "results": []}]} | paola-md/recipe-reg | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T11:31:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-reg
==========
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3717
* Rmse: 1.8362
* Mse: 3.3717
* Mae: 1.6145
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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\\_batc... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) librar... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-mask-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics... | research-backup/roberta-large-conceptnet-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T11:31:59+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-mask-prompt-c-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full resu... | [
"# relbert/roberta-large-conceptnet-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done vi... |
image-classification | transformers |
**InvoiceReceiptClassifier** is a fine-tuned LayoutLMv2 model that classifies a document to an invoice or receipt.
## Quick start: using the raw model
```python
from transformers import (
AutoModelForSequenceClassification,
AutoProcessor,
)
from PIL import Image
from urllib.request import urlopen
model = A... | {"language": ["es", "en"], "tags": ["image-classification"], "pipeline_tag": "image-classification", "widget": [{"src": "https://upserve.com/media/sites/2/Bill-from-Mezcalero-in-Washington-D.C.-photo-by-Alfredo-Solis-1-e1507226752437.jpg", "example_title": "receipt"}, {"src": "https://templates.invoicehome.com/invoice-... | fedihch/InvoiceReceiptClassifier | null | [
"transformers",
"pytorch",
"layoutlmv2",
"text-classification",
"image-classification",
"es",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-05T11:39:10+00:00 | [] | [
"es",
"en"
] | TAGS
#transformers #pytorch #layoutlmv2 #text-classification #image-classification #es #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
InvoiceReceiptClassifier is a fine-tuned LayoutLMv2 model that classifies a document to an invoice or receipt.
## Quick start: using the raw model
| [
"## Quick start: using the raw model"
] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #text-classification #image-classification #es #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Quick start: using the raw model"
] |
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... | datajello/lunar-test-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T11:42:13+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... |
image-classification | transformers |
# candy-first
An initial attempt to identify candy in images.
## Example Images
#### airheads
#### candy corn
#### caramel
#### chips
#### chocolate
![chocolate](images/chocola... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ajanco/candy-first | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T12:03:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# candy-first
An initial attempt to identify candy in images.
## Example Images
#### airheads
!airheads
#### candy corn
!candy corn
#### caramel
!caramel
#### chips
!chips
#### chocolate
!chocolate
#### fruit
!fruit
#### gum
!gum
#### haribo
!haribo
#### jelly beans
!jelly beans
#### lollipop
... | [
"# candy-first\n\n\nAn initial attempt to identify candy in images.",
"## Example Images",
"#### airheads\n\n!airheads",
"#### candy corn\n\n!candy corn",
"#### caramel\n\n!caramel",
"#### chips\n\n!chips",
"#### chocolate\n\n!chocolate",
"#### fruit\n\n!fruit",
"#### gum\n\n!gum",
"#### haribo\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# candy-first\n\n\nAn initial attempt to identify candy in images.",
"## Example Images",
"#### airheads\n\n!airheads",
"#### candy corn\n\n!candy corn... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BeamRiderNoFrameskip-v4... | mrm8488/dqn-BeamRiderNoFrameskip-v4 | null | [
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T13:03:10+00:00 | [] | [] | TAGS
#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-QG-finetuned-FairytaleQA
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the fairytal... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fairytale_qa"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-QG-finetuned-FairytaleQA", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "fairytale_qa", "typ... | ahmedbilal5/t5-base-QG-finetuned-FairytaleQA | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:fairytale_qa",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T13:23:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-fairytale_qa #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-QG-finetuned-FairytaleQA
================================
This model is a fine-tuned version of t5-base on the fairytale\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0876
* Rouge1: 45.1292
* Rouge2: 26.5987
* Rougel: 43.2701
* Rougelsum: 43.2744
* Gen Len: 15.1024
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-fairytale_qa #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_finetuned_xsum_hr
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5_finetuned_xsum_hr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "config": "defau... | arinze/t5_finetuned_xsum_hr | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T13:50:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_finetuned\_xsum\_hr
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4224
* Rouge1: 29.1239
* Rouge2: 8.3165
* Rougel: 22.9946
* Rougelsum: 22.9996
* Gen Len: 18.819
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "small-sentiment-model", "results": []}]} | AlexAnge/small-sentiment-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T13:50:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# small-sentiment-model
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: 0.3325
- Accuracy: 0.8633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training... | [
"# small-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3325\n- Accuracy: 0.8633",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# small-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the foll... |
translation | transformers |
This is a BART-large model finetuned on roughly 58000 aligned sentence pairs in English and Middle English, collected from the works of Geoffrey Chaucer, John Wycliffe, and the Gawain Poet.
<br>
It includes special characters such as þ.
<br>
This model reflects the spelling inconsistencies characteristic of Middle En... | {"language": ["en", "me"], "license": "afl-3.0", "tags": ["translation"], "datasets": ["Qilex/EN-ME"], "metrics": ["bleu"], "model-index": [{"name": "en-me", "results": [{"task": {"type": "translation", "name": "translation en-me"}, "dataset": {"name": "Qilex/EN-ME", "type": "translation"}, "metrics": [{"type": "bleu",... | Qilex/bart-largeEN-ME | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"translation",
"en",
"me",
"dataset:Qilex/EN-ME",
"license:afl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-05T14:48:59+00:00 | [] | [
"en",
"me"
] | TAGS
#transformers #pytorch #bart #text2text-generation #translation #en #me #dataset-Qilex/EN-ME #license-afl-3.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is a BART-large model finetuned on roughly 58000 aligned sentence pairs in English and Middle English, collected from the works of Geoffrey Chaucer, John Wycliffe, and the Gawain Poet.
<br>
It includes special characters such as þ.
<br>
This model reflects the spelling inconsistencies characteristic of Middle En... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #en #me #dataset-Qilex/EN-ME #license-afl-3.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-ROBERTaBECAS
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-bne-ROBERTaBECAS", "results": []}]} | Evelyn18/roberta-base-bne-ROBERTaBECAS | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-05T14:49:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| roberta-base-bne-ROBERTaBECAS
=============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5760
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: 11\n* eval\\_batch\\_size: 11\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 #roberta #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\... |
text-classification | transformers | # Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0).
## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/) obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and ... | {"license": "afl-3.0"} | Aryan4/sucidal | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T14:50:37+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| # Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0).
## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide.
## Parameters... | [
"# Suicidal\nThis text categorization model can predict if a word sequence is suicidal (1) or not (0).",
"## Data\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide.",
... | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Suicidal\nThis text categorization model can predict if a word sequence is suicidal (1) or not (0).",
"## Data\nThe model was trained on the Suicide and Depres... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_Mod_7_Squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "BERT_Mod_7_Squad", "results": []}]} | Go2Heart/BERT_Mod_7_Squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T14:54:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| BERT\_Mod\_7\_Squad
===================
This model is a fine-tuned version of bert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0928
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad_v2 #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: 0.001\n* train\\_batch\\_size: 32\n* eval\... |
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... | roberta-sgariglia/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T15:03:39+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | Jinchen/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T15:09:20+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-wikitext2
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 7.125",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMo... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.\nIt achieves the following result... |
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. -->
# libCap_prBERTbfd_clf
This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd)... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "libCap_prBERTbfd_clf", "results": []}]} | avuhong/libCap_prBERTbfd_clf | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T15:17:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| libCap\_prBERTbfd\_clf
======================
This model is a fine-tuned version of Rostlab/prot\_bert\_bfd on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5197
* Accuracy: 0.7457
* F1: 0.7940
* Precision: 0.7567
* Recall: 0.8352
* Auroc: 0.7268
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 4096\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_... |
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... | LilOpa/Testing_HF | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T15:30:50+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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="Galeros/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": "7.52 +/... | Galeros/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-05T15:33:32+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"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Ts_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ts](https://huggingface.co/Helsinki-NLP/opus-mt-en-t... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Ts_update", "results": []}]} | kabelomalapane/En-Ts_update | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T16:27:23+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Ts\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ts on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1258
* Bleu: 40.9729
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 9",
"### Traini... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #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\\_batc... |
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/1111483799320182784/waO8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/skobae7/1661479155848/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/skobae7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T17:08:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
skobae
@skobae7
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | bennyeatspants/Cat-vs-Dog | null | [
"fastai",
"region:us"
] | null | 2022-08-05T17:21:41+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
sentence-similarity | sentence-transformers |
# embedding-data/distilroberta-base-sentence-transformer
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-Transfor... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["embedding-data/QQP_triplets"], "pipeline_tag": "sentence-similarity"} | embedding-data/distilroberta-base-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:embedding-data/QQP_triplets",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T17:36:25+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us
|
# embedding-data/distilroberta-base-sentence-transformer
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... | [
"# embedding-data/distilroberta-base-sentence-transformer\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 hav... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us \n",
"# embedding-data/distilroberta-base-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a ... |
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", "config": "PAN-X.de", "s... | Keneston/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 | 2022-08-05T17:44:34+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\\_... |
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. -->
# multi_news_article_title_5000
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pe... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_5000", "results": []}]} | abdulmatinomotoso/multi_news_article_title_5000 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T18:01:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| multi\_news\_article\_title\_5000
=================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2547
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval... |
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. -->
# Tweets disaster detection model
This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disas... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp_isorz6_", "results": []}]} | sacculifer/dimbat_disaster_distilbert | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-05T18:26:40+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Tweets disaster detection model
This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset
It achieves the following results on the evaluation set:
- Train Loss: 0.1400
- Train Accuracy: 0.9516
- Validation Loss: 0.199... | [
"# Tweets disaster detection model\n\nThis model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset\nIt achieves the following results on the evaluation set:\n- Train Loss: 0.1400\n- Train Accuracy: 0.9516\n- Validation Los... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Tweets disaster detection model\n\nThis model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tw... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | mrm8488/dqn-SpaceInvadersNoFrameskip-v4-3 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:35:48+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# Tweets disaster type classification model
This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpzujlpono", "results": []}]} | sacculifer/dimbat_disaster_type_distilbert | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T18:36:01+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# Tweets disaster type classification model
This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset
It achieves the following results on the evaluation set:
- Train Loss: 0.0875
- Train Accuracy: 0.8783
- Validation L... | [
"# Tweets disaster type classification model\n\nThis model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset\nIt achieves the following results on the evaluation set:\n- Train Loss: 0.0875\n- Train Accuracy: 0.8783\n- Vali... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tweets disaster type classification model\n\nThis model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Twe... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **MsPacmanNoFrameskip-v4**
This is a trained model of a **A2C** agent playing **MsPacmanNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["MsPacmanNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MsPacmanNoFrameskip-v4",... | sb3/a2c-MsPacmanNoFrameskip-v4 | null | [
"stable-baselines3",
"MsPacmanNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:36:37+00:00 | [] | [] | TAGS
#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing MsPacmanNoFrameskip-v4
This is a trained model of a A2C agent playing MsPacmanNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# A2C Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a A2C agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a A2C agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MsPacmanNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **MsPacmanNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["MsPacmanNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MsPacmanNoFrameskip-v4",... | sb3/ppo-MsPacmanNoFrameskip-v4 | null | [
"stable-baselines3",
"MsPacmanNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:40:35+00:00 | [] | [] | TAGS
#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MsPacmanNoFrameskip-v4
This is a trained model of a PPO agent playing MsPacmanNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# PPO Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a PPO agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a PPO agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MsPacmanNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **MsPacmanNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["MsPacmanNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MsPacmanNoFrameskip-v4",... | sb3/dqn-MsPacmanNoFrameskip-v4 | null | [
"stable-baselines3",
"MsPacmanNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:42:08+00:00 | [] | [] | TAGS
#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MsPacmanNoFrameskip-v4
This is a trained model of a DQN agent playing MsPacmanNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# DQN Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a DQN agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a DQN agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **MsPacmanNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **MsPacmanNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for ... | {"library_name": "stable-baselines3", "tags": ["MsPacmanNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MsPacmanNoFrameskip-v4... | sb3/qrdqn-MsPacmanNoFrameskip-v4 | null | [
"stable-baselines3",
"MsPacmanNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:46:57+00:00 | [] | [] | TAGS
#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing MsPacmanNoFrameskip-v4
This is a trained model of a QRDQN agent playing MsPacmanNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents inclu... | [
"# QRDQN Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained a... | [
"TAGS\n#stable-baselines3 #MsPacmanNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing MsPacmanNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing MsPacmanNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL... |
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. -->
# DistilBART_CNN_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/DistilBART_CNN_GNAD](https://huggingface.co/Einmalumd... | {"language": ["de"], "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "DistilBART_CNN_GNAD", "results": []}]} | Einmalumdiewelt/DistilBART_CNN_GNAD | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-05T18:47:30+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# DistilBART_CNN_GNAD
This model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8723
- Rouge1: 27.4368
- Rouge2: 8.159
- Rougel: 18.1359
- Rougelsum: 23.1339
- Gen Len: 91.5847
## Model description
More infor... | [
"# DistilBART_CNN_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8723\n- Rouge1: 27.4368\n- Rouge2: 8.159\n- Rougel: 18.1359\n- Rougelsum: 23.1339\n- Gen Len: 91.5847",
"## Model descript... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DistilBART_CNN_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD on an unknown dataset.\nIt achieves the following results... |
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... | jackoyoungblood/ppo-LunarLander-v2d | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T18:51:55+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... |
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