pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
image-classification | transformers |
# pond_image_classification_3
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:02:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_3
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
image-classification | transformers |
# pond_image_classification_4
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:25:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_4
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
text-classification | transformers |
# bert-base-dutch-cased-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_de... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/bert-base-dutch-cased-hebban-reviews5 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:36:08+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-dutch-cased-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_de... | [
"# bert-base-dutch-cased-hebban-reviews5",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_rating\n- dataset_revision: 2.0.0\n- labelcolumn: review_rating0\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_train_bat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-dutch-cased-hebban-reviews5",
"# Dataset\n- dataset_name: BramVanroy/he... |
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_Mod_3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "BERT_Mod_3", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accu... | Go2Heart/BERT_Mod_3 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:36:44+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Mod\_3
============
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6760
* Accuracy: 0.8199
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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
text-classification | transformers |
# bert-base-multilingual-cased-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
-... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/bert-base-multilingual-cased-hebban-reviews5 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:37:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-multilingual-cased-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
-... | [
"# bert-base-multilingual-cased-hebban-reviews5",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_rating\n- dataset_revision: 2.0.0\n- labelcolumn: review_rating0\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-multilingual-cased-hebban-reviews5",
"# Dataset\n- dataset_name: BramVa... |
text-classification | transformers |
# robbert-v2-dutch-base-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_de... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/robbert-v2-dutch-base-hebban-reviews5 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:37:41+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# robbert-v2-dutch-base-hebban-reviews5
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_rating
- dataset_revision: 2.0.0
- labelcolumn: review_rating0
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_de... | [
"# robbert-v2-dutch-base-hebban-reviews5",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_rating\n- dataset_revision: 2.0.0\n- labelcolumn: review_rating0\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_train_bat... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# robbert-v2-dutch-base-hebban-reviews5",
"# Dataset\n- dataset_nam... |
image-classification | transformers |
# pond_image_classification_5
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-29T06:41:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# pond_image_classification_5
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_5\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# pond_image_classification_5\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Co... |
null | null | See https://github.com/k2-fsa/icefall/pull/454
### training command:
```bash
./pruned_transducer_stateless5/train.py \
--exp-dir pruned_transducer_stateless5/exp \
--num-encoder-layers 18 \
--dim-feedforward 2048 \
--nhead 8 \
--encoder-dim 512 \
--decoder-dim 512 \
--joiner-dim 512 \
--full-libri 1 \
... | {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless5_20220729 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-07-29T06:42:03+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| See URL
### training command:
You can find the tensorboard log here <URL
### The decoding command is:
### export command is:
| [
"### training command:\n\n\nYou can find the tensorboard log here <URL",
"### The decoding command is:",
"### export command is:"
] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n",
"### training command:\n\n\nYou can find the tensorboard log here <URL",
"### The decoding command is:",
"### export command is:"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vinitharaj/distilbert-base-uncased-finetuned-squad2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vinitharaj/distilbert-base-uncased-finetuned-squad2", "results": []}]} | vinitharaj/distilbert-base-uncased-finetuned-squad2 | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:47:14+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| vinitharaj/distilbert-base-uncased-finetuned-squad2
===================================================
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: 0.4953
* Validation Loss: 0.3885
* Epoch: 1
Model desc... | [
"### 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 #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
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-summarization
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbart-summarization", "results": []}]} | mselbach/distilbart-rehadat | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:54:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbart-summarization
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 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... | [
"# distilbart-summarization\n\nThis model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 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",
"## Traini... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbart-summarization\n\nThis model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset.",
"## Model description\n\nM... |
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-newsroom-cnn_full-adam8bit
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pegasus-newsroom-cnn_full-adam8bit", "results": []}]} | oMateos2020/pegasus-newsroom-cnn_full-adam8bit | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:55:23+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-newsroom-cnn_full-adam8bit
This model is a fine-tuned version of google/pegasus-newsroom on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
-... | [
"# pegasus-newsroom-cnn_full-adam8bit\n\nThis model is a fine-tuned version of google/pegasus-newsroom on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.9826\n- eval_rouge1: 38.2456\n- eval_rouge2: 17.3966\n- eval_rougeL: 26.9273\n- eval_rougeLsum: 35.3265\n- eval_gen_le... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-newsroom-cnn_full-adam8bit\n\nThis model is a fine-tuned version of google/pegasus-newsroom on the None dataset.\nIt achieves the following results on the eva... |
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. -->
# first_try
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-30... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_7_0"], "model-index": [{"name": "first_try", "results": []}]} | AkmalAshirmatov/first_try | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T06:58:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
|
# first_try
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_7_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# first_try\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_7_0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# first_try\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_7_0 dataset.",
"##... |
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. -->
# out
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model des... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "out", "results": []}]} | Frikallo/out | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T07:00:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# out
This model is a fine-tuned version of gpt2-medium 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 hyperparameters
The following hyperpa... | [
"# out\n\nThis model is a fine-tuned version of gpt2-medium 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",
"### Training hyperpar... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# out\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore informa... |
text-generation | transformers |
## DialoGPT_AfriWOZ (Pidgin)
This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Nigeria Pidgin English language.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking.... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["AfriWOZ"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "How I fit chop for here?"}]} | tosin/dialogpt_afriwoz_pidgin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"dataset:AfriWOZ",
"arxiv:2204.08083",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T07:00:24+00:00 | [
"2204.08083"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-AfriWOZ #arxiv-2204.08083 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| DialoGPT\_AfriWOZ (Pidgin)
--------------------------
This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Nigeria Pidgin English language.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, ... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\ntokenizer = AutoTokenizer.from\\_pretrained(\"tosin/dialogpt\\_afriwoz\\_pidgin\")\nmodel = AutoModelForCausalLM.from\\_pretrained(\... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-AfriWOZ #arxiv-2204.08083 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partne... |
multiple-choice | 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-urdu-small-finetuned-news
This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co/urd... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-urdu-small-finetuned-news", "results": []}]} | SyedArsal/roberta-urdu-small-finetuned-news | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"multiple-choice",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T07:04:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us
| roberta-urdu-small-finetuned-news
=================================
This model is a fine-tuned version of urduhack/roberta-urdu-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2702
* Accuracy: 0.9482
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
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-ema-anime-256
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugg... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/selfie2anime", "metrics": []} | mrm8488/ddpm-ema-anime-256 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/selfie2anime",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-29T07:15:04+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/selfie2anime #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-anime-256
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/selfie2anime' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: ... | [
"# ddpm-ema-anime-256",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/selfie2anime' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/selfie2anime #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-anime-256",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/selfie2anime' dataset.",
"## Intended uses & limitations",
... |
image-classification | transformers |
# pond_image_classification_6
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_6 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T07:19:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_6
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_6\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_6\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
image-classification | transformers |
# pond_image_classification_7
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_7 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T07:32:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_7
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_7\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_7\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
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. -->
# vgdunkey-vgdunkeybot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "vgdunkey-vgdunkeybot", "results": []}]} | Frikallo/vgdunkey-vgdunkeybot | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T07:37:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# vgdunkey-vgdunkeybot
This model is a fine-tuned version of gpt2 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 hyperparameters
The followi... | [
"# vgdunkey-vgdunkeybot\n\nThis model is a fine-tuned version of gpt2 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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# vgdunkey-vgdunkeybot\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMo... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | RRajesh27/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T07:39:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3236\n- Accuracy: 0.8667\n- F1: 0.8667",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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... | bkaemper/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T07:43:58+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... |
null | null | git lfs install
git clone https://huggingface.co/Dallasmorningstar/Hb | {} | Dallasmorningstar/Hb | null | [
"region:us"
] | null | 2022-07-29T07:51:13+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#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. -->
# movieHunt4-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "movieHunt4-ner", "results": []}]} | AbidHasan95/movieHunt4-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T08:02:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| movieHunt4-ner
==============
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0005
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
* Accuracy: 1.0
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: 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: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
image-classification | transformers |
# pond_image_classification_9
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_9 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T08:13:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_9
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_9\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_9\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
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... | marii/lunarlander | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T08:25:07+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | psroy/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T09:16:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4772
* Wer: 0.2821
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | tusbaki/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T09:58:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1966
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | raisin2402/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T10:08:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8311
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8560\n- Bleu: 52.8311",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
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": "Reinforce-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "... | AlbertShu/Reinforce-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-29T10:26:01+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-generation | transformers |
## Basic info
model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)
fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean)
data filter by python
## Usage
```python
from transformers import AutoTokenizer, AutoModel... | {"license": "apache-2.0", "widget": [{"text": "<|endoftext|>\ndef load_excel(path):\n return pd.read_excel(path)\n# docstring\n\"\"\""}]} | kdf/python-docstring-generation | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T10:51:57+00:00 | [] | [] | TAGS
#transformers #pytorch #codegen #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Basic info
model based Salesforce/codegen-350M-mono
fine-tuned with data codeparrot/github-code-clean
data filter by python
## Usage
## Prompt
You could give model a style or a specific language, for example:
| [
"## Basic info\n\nmodel based Salesforce/codegen-350M-mono\n\nfine-tuned with data codeparrot/github-code-clean\n\ndata filter by python",
"## Usage",
"## Prompt\n\nYou could give model a style or a specific language, for example:"
] | [
"TAGS\n#transformers #pytorch #codegen #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Basic info\n\nmodel based Salesforce/codegen-350M-mono\n\nfine-tuned with data codeparrot/github-code-clean\n\ndata filter by python",
"## Usage",
"## Prompt\n\nYou could... |
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": "Reinforce-Cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | SamuelMYoussef/Reinforce-Cartpole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-29T10:54:08+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-generation | transformers |
## Basic info
model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)
fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean)
data filter by JavaScript and TypeScript
## Usage
```python
from transformers import AutoT... | {"license": "apache-2.0", "widget": [{"text": "<|endoftext|>\nfunction getDateAfterNDay(n){\n return moment().add(n, 'day')\n}\n// docstring\n/**"}]} | kdf/javascript-docstring-generation | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T11:04:31+00:00 | [] | [] | TAGS
#transformers #pytorch #codegen #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Basic info
model based Salesforce/codegen-350M-mono
fine-tuned with data codeparrot/github-code-clean
data filter by JavaScript and TypeScript
## Usage
## Prompt
You could give model a style or a specific language, for example:
| [
"## Basic info\n\nmodel based Salesforce/codegen-350M-mono\n\nfine-tuned with data codeparrot/github-code-clean\n\ndata filter by JavaScript and TypeScript",
"## Usage",
"## Prompt\n\nYou could give model a style or a specific language, for example:"
] | [
"TAGS\n#transformers #pytorch #codegen #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Basic info\n\nmodel based Salesforce/codegen-350M-mono\n\nfine-tuned with data codeparrot/github-code-clean\n\ndata filter by JavaScript and TypeScript",
"## Usage",
"## ... |
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... | turhancan97/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T11:11:45+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 the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-omar-espejel
This model is a fine-tuned version of [distilroberta-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "widget": [{"text": ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to S... | platzi/platzi-distilroberta-base-mrpc-glue-omar-espejel | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T11:17:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| platzi-distilroberta-base-mrpc-glue-omar-espejel
================================================
This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
* Loss: 0.6332
* Accuracy: 0.8431
* F1: 0.8861
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | spacy | Hungarian word vectors for HuSpaCy.
The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: `floret cbow -dim 100 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.1 -thread 30 -epoch 5`
Vectors are published in fasttext and floret forma... | {"language": ["hu"], "license": "cc-by-sa-4.0", "tags": ["spacy", "floret", "fasttext", "feature-extraction", "token-classification"]} | huspacy/hu_vectors_web_md | null | [
"spacy",
"floret",
"fasttext",
"feature-extraction",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-07-29T11:48:29+00:00 | [] | [
"hu"
] | TAGS
#spacy #floret #fasttext #feature-extraction #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us
| Hungarian word vectors for HuSpaCy.
The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: 'floret cbow -dim 100 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.1 -thread 30 -epoch 5'
Vectors are published in fasttext and floret form... | [
"### Accuracy"
] | [
"TAGS\n#spacy #floret #fasttext #feature-extraction #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Accuracy"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | Amine007/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T12:24:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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/1399411396140535812/UwTl... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/onlythesexiest_/1659101307927/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/onlythesexiest_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T12:26:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Only The Sexiest 18+
@onlythesexiest\_
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-bert-base-mrpc-glue-omar-espejel
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "platzi-bert-base-mrpc-glue-omar-espejel", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "... | platzi/platzi-bert-base-mrpc-glue-omar-espejel | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T12:37:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| platzi-bert-base-mrpc-glue-omar-espejel
=======================================
This model is a fine-tuned version of bert-base-uncased on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
* Loss: 0.4366
* Accuracy: 0.8578
* F1: 0.8942
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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 #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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-ema-pokemon-v2-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/pokemon", "metrics": []} | mrm8488/ddpm-ema-pokemon-v2-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-29T12:48:00+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-pokemon-v2-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/pokemon' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: d... | [
"# ddpm-ema-pokemon-v2-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"#... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-pokemon-v2-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"####... |
text-classification | transformers |
## Overview
**Model Description:** roberta-large-faithcritic is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on FaithCritic, a derivative of the [FaithDial](https://huggingface.co/datasets/McGill-NLP/FaithDial) dataset. The objective is to predict whether an utterance is faithful or not,... | {"license": "mit", "datasets": ["McGill-NLP/FaithDial"], "widget": [{"text": "A cardigan is a type of knitted garment (sweater) that has an open front. </s></s> The old version is the regular one, knitted garment that has open front and buttons!"}], "model-index": [{"name": "roberta-large-faithcritic", "results": [{"ta... | McGill-NLP/roberta-large-faithcritic | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:McGill-NLP/FaithDial",
"arxiv:2204.10757",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T12:54:45+00:00 | [
"2204.10757"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #dataset-McGill-NLP/FaithDial #arxiv-2204.10757 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
## Overview
Model Description: roberta-large-faithcritic is the RoBERTa large model fine-tuned on FaithCritic, a derivative of the FaithDial dataset. The objective is to predict whether an utterance is faithful or not, given the source knowledge.
The hyperparameters are provided in URL. To know more about how to tra... | [
"## Overview\n\nModel Description: roberta-large-faithcritic is the RoBERTa large model fine-tuned on FaithCritic, a derivative of the FaithDial dataset. The objective is to predict whether an utterance is faithful or not, given the source knowledge.\n\nThe hyperparameters are provided in URL. To know more about ho... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #dataset-McGill-NLP/FaithDial #arxiv-2204.10757 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## Overview\n\nModel Description: roberta-large-faithcritic is the RoBERTa large model fine-tuned on FaithCritic, a der... |
fill-mask | transformers | # Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Sto... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | phjhk/hklegal-xlm-r-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha... | null | 2022-07-29T13:29:20+00:00 | [
"1911.02116"
] | [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #p... | # Model Description
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoB... | [
"# Model Description\n\nThe XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Faceboo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om ... |
text-generation | transformers |
# DialoGPT BaymaxBot | {"tags": ["conversational"]} | Anon25/DialoGPT-Medium-BaymaxBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T13:31:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT BaymaxBot | [
"# DialoGPT BaymaxBot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT BaymaxBot"
] |
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-uncase-direct-finetuning-ai-ner_3labels
This model is a fine-tuned version of [distilbert-base-uncased](http... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-uncase-direct-finetuning-ai-ner_3labels", "results": []}]} | silviacamplani/distilbert-uncase-direct-finetuning-ai-ner_3labels | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T13:33:10+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-uncase-direct-finetuning-ai-ner\_3labels
==================================================================
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: 0.6593
* Validation Loss: ... | [
"### 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 #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': {'class\\_na... |
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. -->
# data-augmentation-whitenoise-timit-1155
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "data-augmentation-whitenoise-timit-1155", "results": []}]} | gazzehamine/data-augmentation-whitenoise-timit-1155 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T13:52:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| data-augmentation-whitenoise-timit-1155
=======================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5458
* Wer: 0.3324
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference | {"license": "apache-2.0", "title": "Pet classifier!", "emoji": "\ud83d\udc36", "colorFrom": "pink", "colorTo": "blue", "sdk": "gradio", "sdk_version": "2.9.4", "app_file": "app.py", "pinned": false} | pampa/pets | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-29T13:56:39+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
Check out the configuration reference at URL | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
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-ema-pokemon-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hug... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/pokemon", "metrics": []} | jirtan/ddpm-ema-pokemon-64 | null | [
"diffusers",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-29T14:20:10+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-pokemon-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/pokemon' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: desc... | [
"# ddpm-ema-pokemon-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## T... | [
"TAGS\n#diffusers #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-pokemon-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
... |
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. -->
# pos_test_model_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "pos_test_model_1", "results": []}]} | natalierobbins/pos_test_model_1 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T14:29:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pos\_test\_model\_1
===================
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: 0.1521
* Accuracy: 0.9530
* F1: 0.9523
* Precision: 0.9576
* Recall: 0.9530
Model description
-----------------
More infor... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | jackoyoungblood/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T14:34:52+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | susghosh/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T14:55:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7341
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-spm
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following ... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-spm", "results": []}]} | schnell/bert-small-spm | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:05:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-small-spm
==============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5919
* Accuracy: 0.5095
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 768\n* total\\_eval\\_batch\\_size: 24\n... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_... |
text-classification | transformers |
# Model Card for NQ Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval ca... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-reranker-nq | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"information retrieval",
"reranking",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:05:21+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for NQ Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval ca... | [
"# Model Card for NQ Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n> \n>It has been previously established that results from initial r... | [
"TAGS\n#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for NQ Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Re... |
fill-mask | transformers |
# Legal_BERTimbau
## Introduction
Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: ... | {"language": ["pt"], "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["rufimelo/PortugueseLegalSentences-v0"], "thumbnail": "Portugues BERT for the Legal Domain", "widget": [{"text": "O advogado apresentou [MASK] ao ju\u00edz."}]} | rufimelo/Legal-BERTimbau-base | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:rufimelo/PortugueseLegalSentences-v0",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:11:40+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #pt #dataset-rufimelo/PortugueseLegalSentences-v0 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Legal\_BERTimbau
================
Introduction
------------
Legal\_BERTimbau Large is a fine-tuned BERT model based on BERTimbau Large.
"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence ... | [
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use this work, please cite BERTimbau's work:"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #pt #dataset-rufimelo/PortugueseLegalSentences-v0 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use this work, please cite BERTimbau's work:"
] |
feature-extraction | transformers |
# Model Card for NQ Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_car... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-qry-encoder-nq | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:12:20+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for NQ Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inference
... | [
"# Model Card for NQ Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evaluati... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for NQ Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) compone... |
null | transformers |
# Model Card for NQ Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_card... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-ctx-encoder-nq | null | [
"transformers",
"pytorch",
"dpr",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:14:19+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for NQ Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inference
T... | [
"# Model Card for NQ Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evaluatio... | [
"TAGS\n#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for NQ Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo-colab_epoch15
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo-colab_epoch15", "results": []}]} | jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:39:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo-colab\_epoch15
==============================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4133
* Wer: 0.3801
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4... |
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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | JTH/results | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:43:15+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# results\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information ne... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_battle** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_battle", "type": "doom_battle"}, "metrics": [{"typ... | andrewzhang505/sample-factory-2-doom-battle | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T15:53:16+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_battle environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViT-BERT-Chess-V4
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": "ViT-BERT-Chess-V4", "results": []}]} | Migga/ViT-BERT-Chess-V4 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T15:57:48+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us
| ViT-BERT-Chess-V4
=================
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3213
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="andres-hsn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | andres-hsn/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T15:58:29+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="andres-hsn/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": "7.54 +/... | andres-hsn/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T16:02:38+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"
] |
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. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-extracted-sumy
This model is a fine-tuned versio... | {"license": "apache-2.0", "tags": ["summarisation", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-extracted-sumy", "results": []}]} | Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-extracted-sumy | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarisation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T16:08:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization-finetuned-bbc-news-extracted-sumy
================================================================================================
This model is a fine-tuned version of mrm8488/bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization on an unknown d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #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\\_rat... |
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. -->
# DNADebertaK6b
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following r... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaK6b", "results": []}]} | simecek/DNADebertaK6b | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T16:38:17+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaK6b
=============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4362
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_pre... | [
"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: 64\n* eval\\_batch\\_size: 64\n* ... |
text-classification | transformers |
# Model Card for T-REx Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-reranker-trex | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"information retrieval",
"reranking",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T17:06:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for T-REx Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval... | [
"# Model Card for T-REx Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n> \n>It has been previously established that results from initia... | [
"TAGS\n#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for T-REx Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information... |
feature-extraction | transformers |
# Model Card for T-REx Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-qry-encoder-trex | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T17:10:54+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for T-REx Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inferen... | [
"# Model Card for T-REx Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evalu... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for T-REx Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) comp... |
null | transformers |
# Model Card for T-REx Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_c... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-ctx-encoder-trex | null | [
"transformers",
"pytorch",
"dpr",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T17:12:40+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for T-REx Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inferenc... | [
"# Model Card for T-REx Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evalua... | [
"TAGS\n#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for T-REx Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further tra... |
text-classification | transformers |
# Model Card for TriviaQA Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrie... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-reranker-triviaqa | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"information retrieval",
"reranking",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T17:21:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for TriviaQA Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrie... | [
"# Model Card for TriviaQA Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n> \n>It has been previously established that results from ini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for TriviaQA Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Informat... |
feature-extraction | transformers |
# Model Card for TriviaQA Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/mod... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-qry-encoder-triviaqa | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T17:24:45+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for TriviaQA Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Infe... | [
"# Model Card for TriviaQA Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Ev... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for TriviaQA Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) c... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-finetuned-dagpap22-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-dagpap22-only", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-dagpap22-only | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T18:05:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-dagpap22-only
========================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0037
* F1: 0.9995
* Precision: 0.9992
* Recall: 0.9997
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
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. -->
# finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2... | romainlhardy/finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T19:13:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuned-ner
=============
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0712
* Precision: 0.9048
* Recall: 0.9310
* F1: 0.9177
* Accuracy: 0.9817
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
# Model Card for Wizard of Wikipedia Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from ini... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-reranker-wow | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"information retrieval",
"reranking",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T19:23:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Wizard of Wikipedia Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from ini... | [
"# Model Card for Wizard of Wikipedia Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n> \n>It has been previously established that resul... | [
"TAGS\n#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Wizard of Wikipedia Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural I... |
feature-extraction | transformers |
# Model Card for Wizard of Wikipedia Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/r... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-qry-encoder-wow | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T19:25:59+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for Wizard of Wikipedia Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluati... | [
"# Model Card for Wizard of Wikipedia Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## T... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for Wizard of Wikipedia Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information R... |
null | transformers |
# Model Card for Wizard of Wikipedia Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/ra... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-ctx-encoder-wow | null | [
"transformers",
"pytorch",
"dpr",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T19:28:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for Wizard of Wikipedia Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluatio... | [
"# Model Card for Wizard of Wikipedia Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Tr... | [
"TAGS\n#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for Wizard of Wikipedia Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component a... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mrm8488/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | mrm8488/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T19:38:30+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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="mrm8488/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | mrm8488/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T19:43:55+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | 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-v2b | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T20:02:51+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-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/1544789753639436289/_nNZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/zk_faye/1659132206531/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/zk_faye | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T21:01:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
️ ANGEL FAYE ️
@zk\_faye
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# muhtasham/bert-tiny-finetuned-finer-tf
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["nlpaueb/finer-139"], "model-index": [{"name": "muhtasham/bert-tiny-finetuned-finer-tf", "results": []}]} | muhtasham/bert-tiny-finetuned-finer-tf | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"dataset:nlpaueb/finer-139",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T21:13:44+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #dataset-nlpaueb/finer-139 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| muhtasham/bert-tiny-finetuned-finer-tf
======================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0372
* Validation Loss: 0.0296
* Epoch: 2
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 168822, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #dataset-nlpaueb/finer-139 #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': 'Ada... |
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="mrm8488/q-Taxi-v3-1", 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-v3-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | mrm8488/q-Taxi-v3-1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T21:22:21+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
This KenLM model is trained on https://huggingface.co/datasets/indonesian-nlp/id_newspapers_2018 dataset.
This model is **4-gram** and it was pruned.
Used command:
```bash
../kenlm/build/bin/lmplz -T tmp -o 4 --prune 0 1 1 < "texts.txt" > "4gram.arpa"
```
| {"language": ["id"], "license": "cc-by-sa-4.0"} | Yehor/indonesian-kenlm-newspapers | null | [
"id",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-07-29T21:23:52+00:00 | [] | [
"id"
] | TAGS
#id #license-cc-by-sa-4.0 #region-us
|
This KenLM model is trained on URL dataset.
This model is 4-gram and it was pruned.
Used command:
| [] | [
"TAGS\n#id #license-cc-by-sa-4.0 #region-us \n"
] |
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="mrm8488/q-Taxi-v3-2", 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-v3-2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | mrm8488/q-Taxi-v3-2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-29T21:56:52+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | 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-v2c | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-29T22:03:33+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... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-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) lib... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metr... | research-backup/roberta-large-conceptnet-average-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-29T22:51:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-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 r... | [
"# relbert/roberta-large-conceptnet-average-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 (data... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done... |
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. -->
# DeepDunk
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Mode... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "DeepDunk", "results": []}]} | Frikallo/DeepDunk | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-29T23:00:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DeepDunk
This model is a fine-tuned version of gpt2-medium 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 hyperparameters
The following hy... | [
"# DeepDunk\n\nThis model is a fine-tuned version of gpt2-medium 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",
"### Training hyp... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DeepDunk\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore in... |
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/722815128501026817/IMWCR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dags/1659144733206/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dags | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-30T00:30:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
DAGs
@dags
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"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln59Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln59Paraphrase")
```
```
How To Make Prompt:
informal english: i am very ready to do... | {} | BigSalmon/InformalToFormalLincoln59Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-30T01:29:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# marian-finetuned-kde4-en-to-zh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-zh", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | DrY/marian-finetuned-kde4-en-to-zh | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T06:03:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-zh
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9338
- Bleu: 40.6658
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-zh\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.9338\n- Bleu: 40.6658",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-zh\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="r3sist/qLearning-frozenLake", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "qLearning-frozenLake", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLak... | r3sist/qLearning-frozenLake | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-30T06:47:48+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="r3sist/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | r3sist/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-30T06:55:55+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a traced JIT version of the https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm model
The repository contains CPU and GPU... | {"language": ["uk"], "license": "apache-2.0"} | Yehor/wav2vec2-xls-r-300m-uk-traced-jit | null | [
"uk",
"license:apache-2.0",
"region:us"
] | null | 2022-07-30T07:48:06+00:00 | [] | [
"uk"
] | TAGS
#uk #license-apache-2.0 #region-us
|
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - URL
This model is a traced JIT version of the URL model
The repository contains CPU and GPU versions
Created by URL
| [] | [
"TAGS\n#uk #license-apache-2.0 #region-us \n"
] |
image-classification | transformers |
# pond_image_classification_10
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T07:57:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_10
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Norma... | [
"# pond_image_classification_10\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Bo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_10\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRe... |
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. -->
# thesis-audio-4
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": "thesis-audio-4", "results": []}]} | Perselope/thesis-audio-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T08:14:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| thesis-audio-4
==============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5585
* Wer: 0.3457
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
image-classification | transformers |
# rust_image_classification_2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T09:05:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_2
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | devetle/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-30T09:13:05+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
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. -->
# experiment_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "experiment_2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll20... | sophiestein/experiment_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T09:21:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| experiment\_2
=============
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1211
* Precision: 0.8841
* Recall: 0.8926
* F1: 0.8883
* Accuracy: 0.9747
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
image-classification | transformers |
# rust_image_classification_3
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T09:35:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_3
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
image-classification | transformers |
# rust_image_classification_4
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T09:46:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_4
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
image-classification | transformers |
# rust_image_classification_6
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_6 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T10:07:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_6
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_6\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_6\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
null | null | ## COGMEN; Official Pytorch Implementation
[](https://paperswithcode.com/sota/multimodal-emotion-recognition-on-iemocap?p=cogmen-contextualized-gnn-based-m... | {"license": "cc-by-nc-4.0"} | NAACL2022/cogmen | null | [
"arxiv:2205.02455",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-07-30T10:13:03+00:00 | [
"2205.02455"
] | [] | TAGS
#arxiv-2205.02455 #license-cc-by-nc-4.0 #region-us
| ## COGMEN; Official Pytorch Implementation
![PWC](URL
COntextualized GNN based Multimodal Emotion recognitioN
!Teaser image
Picture: *COGMEN Model Architecture*
This repository contains the official Pytorch implementation of the following paper:
> COGMEN: COntextualized GNN based Multimodal Emotion recognitioN<br>
>... | [
"## COGMEN; Official Pytorch Implementation\n![PWC](URL\n\nCOntextualized GNN based Multimodal Emotion recognitioN\n!Teaser image\nPicture: *COGMEN Model Architecture*\n\nThis repository contains the official Pytorch implementation of the following paper:\n> COGMEN: COntextualized GNN based Multimodal Emotion recog... | [
"TAGS\n#arxiv-2205.02455 #license-cc-by-nc-4.0 #region-us \n",
"## COGMEN; Official Pytorch Implementation\n model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-large which derives from... | {"language": ["pt"], "tags": ["sentence-transformers", "sentence-similarity", "transformers"], "datasets": ["assin", "assin2", "rufimelo/PortugueseLegalSentences-v0"], "thumbnail": "Portugues BERT for the Legal Domain", "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "O advogado apresentou as prov... | rufimelo/Legal-BERTimbau-sts-base | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"pt",
"dataset:assin",
"dataset:assin2",
"dataset:rufimelo/PortugueseLegalSentences-v0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-30T10:20:50+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #pt #dataset-assin #dataset-assin2 #dataset-rufimelo/PortugueseLegalSentences-v0 #model-index #endpoints_compatible #has_space #region-us
| rufimelo/Legal-BERTimbau-sts-base
=================================
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-large which der... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #pt #dataset-assin #dataset-assin2 #dataset-rufimelo/PortugueseLegalSentences-v0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
# rust_image_classification_7
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_7 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-30T11:04:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_7
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_7\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_7\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.