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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | RobertoMCA97/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:41:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2323
* F1: 0.8228
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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. -->
# xtreme_s_xlsr_mls_upd
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["pl"], "license": "apache-2.0", "tags": ["mls", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "model-index": [{"name": "xtreme_s_xlsr_mls_upd", "results": []}]} | anton-l/xtreme_s_xlsr_mls_upd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mls",
"google/xtreme_s",
"generated_from_trainer",
"pl",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:53:26+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mls #google/xtreme_s #generated_from_trainer #pl #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_mls\_upd
=========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MLS.PL dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1489
* Wer: 1.0
* Cer: 1.0
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mls #google/xtreme_s #generated_from_trainer #pl #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | RobertoMCA97/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:56:49+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3925
* F1: 0.7075
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg", "model-index": [{"name": "osanseviero/distilbert-base-uncased-finetuned-squad-d5716d28", "results": [{"task": {"ty... | osanseviero/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T13:49:18+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-0
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-0", "results": []}]} | newtonkwan/gpt2-xl-ft-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T14:26:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-0
============
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0324
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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
null | null | A tokenizer created using the gpt2 architecture, which was trained on the reversed text of Harry Potter books 1-7 | {} | calebcsjm/reverse-harrypotter-tokenizer | null | [
"region:us"
] | null | 2022-03-16T14:51:21+00:00 | [] | [] | TAGS
#region-us
| A tokenizer created using the gpt2 architecture, which was trained on the reversed text of Harry Potter books 1-7 | [] | [
"TAGS\n#region-us \n"
] |
image-classification | transformers |
# ResNet-152 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | microsoft/resnet-152 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T14:54:22+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ResNet-152 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## ... | [
"# ResNet-152 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face te... | [
"TAGS\n#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ResNet-152 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introdu... |
image-classification | transformers |
# Van
Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification).
Disclaimer: The team releasing Van did not write a model card for this model so... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | Visual-Attention-Network/van-tiny | null | [
"transformers",
"pytorch",
"van",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T15:05:02+00:00 | [
"2202.09741"
] | [] | TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Van
Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository.
Disclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This paper introduc... | [
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. \n\nDisclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThis ... | [
"TAGS\n#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this rep... |
image-classification | transformers |
# Van
Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification).
Disclaimer: The team releasing Van did not write a model card for this model so... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | Visual-Attention-Network/van-small | null | [
"transformers",
"pytorch",
"van",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T15:05:40+00:00 | [
"2202.09741"
] | [] | TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Van
Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository.
Disclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This paper introduc... | [
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. \n\nDisclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThis ... | [
"TAGS\n#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this rep... |
image-classification | transformers |
# Van
Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification).
Disclaimer: The team releasing Van did not write a model card for this model so... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | Visual-Attention-Network/van-base | null | [
"transformers",
"pytorch",
"van",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T15:06:37+00:00 | [
"2202.09741"
] | [] | TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Van
Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository.
Disclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This paper introduc... | [
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. \n\nDisclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThis ... | [
"TAGS\n#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this rep... |
token-classification | transformers |
# vpelloin/MEDIA_NLU-flaubert_oral_ft
This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
It maps each input words into outputs concepts tags (76 available).
This model is trained using [`nherve/flaubert-oral-ft`](http... | {"language": "fr", "tags": ["bert", "flaubert", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding", "MEDIA"], "pipeline_tag": "token-classification", "widget": [{"text": "je voudrais r\u00e9server une chambre \u00e0 paris pour demain et lundi"}, {"text": "d'accord pour l'h\... | vpelloin/MEDIA_NLU-flaubert_oral_ft | null | [
"transformers",
"pytorch",
"tensorboard",
"flaubert",
"token-classification",
"bert",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"MEDIA",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T15:20:26+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us
|
# vpelloin/MEDIA_NLU-flaubert_oral_ft
This is a Natural Language Understanding (NLU) model for the French MEDIA benchmark.
It maps each input words into outputs concepts tags (76 available).
This model is trained using 'nherve/flaubert-oral-ft' as its inital checkpoint. It obtained 11.98% CER (*lower is better*) in t... | [
"# vpelloin/MEDIA_NLU-flaubert_oral_ft\nThis is a Natural Language Understanding (NLU) model for the French MEDIA benchmark.\nIt maps each input words into outputs concepts tags (76 available).\n\nThis model is trained using 'nherve/flaubert-oral-ft' as its inital checkpoint. It obtained 11.98% CER (*lower is bette... | [
"TAGS\n#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us \n",
"# vpelloin/MEDIA_NLU-flaubert_oral_ft\nThis is a Natural Language Underst... |
image-classification | transformers |
# ResNet
ResNet model trained on imagenet-1k. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and first released in [this repository](https://github.com/KaimingHe/deep-residual-networks).
Disclaimer: The team releasing ResNet did not write a model card... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/resnet-18 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T15:40:26+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ResNet
ResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model des... | [
"# ResNet\n\nResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"... | [
"TAGS\n#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ResNet\n\nResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Lear... |
image-classification | transformers |
# ResNet-34 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wri... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | microsoft/resnet-34 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T15:41:51+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ResNet-34 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## M... | [
"# ResNet-34 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face tea... | [
"TAGS\n#transformers #pytorch #tf #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ResNet-34 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduc... |
image-classification | transformers |
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wri... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | microsoft/resnet-50 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T15:42:43+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## M... | [
"# ResNet-50 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face tea... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ResNet-50 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was int... |
image-classification | transformers |
# ResNet-101 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | microsoft/resnet-101 | null | [
"transformers",
"pytorch",
"tf",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T15:43:40+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #tf #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ResNet-101 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## ... | [
"# ResNet-101 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face te... | [
"TAGS\n#transformers #pytorch #tf #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ResNet-101 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the pa... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_10k_5eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_10k_5eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_10k_5eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T15:55:23+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_10k\_5eps
====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7210579
* Train Accuracy: 0.5
* Validation Loss: 0.6863412
* Validation Accuracy: 0.540625... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
null | null | This model uses the Deep Fashion dataset in order to create a category classifier among the 50 or so provided categories.
https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
This model leverages the ViT (Vision transformer), loaded with the custom dataset and the 50 odd categoes to which they are assigned. The ... | {"license": "cc-by-4.0"} | adityavithaldas/Fashion_Category_Classifier | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-16T15:59:51+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| This model uses the Deep Fashion dataset in order to create a category classifier among the 50 or so provided categories.
URL
This model leverages the ViT (Vision transformer), loaded with the custom dataset and the 50 odd categoes to which they are assigned. The objective here, is to expand the same and get to
... | [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
text2text-generation | transformers | <h2>Re-Punctuate:</h2>
Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
<h3>DataSet:</h3>
DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
<h3>Usage:</h3>
<pre>
from transformers import T5Tokenize... | {"license": "apache-2.0"} | SJ-Ray/Re-Punctuate | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T16:10:00+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| <h2>Re-Punctuate:</h2>
Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
<h3>DataSet:</h3>
DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
<h3>Usage:</h3>
<pre>
from transformers import T5Tokenize... | [] | [
"TAGS\n#transformers #tf #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
# vpelloin/MEDIA_NLU-flaubert_base_uncased
This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
It maps each input words into outputs concepts tags (76 available).
This model is trained using [`flaubert/flaubert_base_un... | {"language": "fr", "tags": ["bert", "flaubert", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding", "MEDIA"], "pipeline_tag": "token-classification", "widget": [{"text": "je voudrais r\u00e9server une chambre \u00e0 paris pour demain et lundi"}, {"text": "d'accord pour l'h\... | vpelloin/MEDIA_NLU-flaubert_base_uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"flaubert",
"token-classification",
"bert",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"MEDIA",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T16:18:10+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us
|
# vpelloin/MEDIA_NLU-flaubert_base_uncased
This is a Natural Language Understanding (NLU) model for the French MEDIA benchmark.
It maps each input words into outputs concepts tags (76 available).
This model is trained using 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% CER (*lower is b... | [
"# vpelloin/MEDIA_NLU-flaubert_base_uncased\nThis is a Natural Language Understanding (NLU) model for the French MEDIA benchmark.\nIt maps each input words into outputs concepts tags (76 available).\n\nThis model is trained using 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% CER (*lo... | [
"TAGS\n#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us \n",
"# vpelloin/MEDIA_NLU-flaubert_base_uncased\nThis is a Natural Language Un... |
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. -->
# debug_mbert_task2_1
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_mbert_task2_1", "results": []}]} | horsbug98/Part_2_mBERT_Model_E1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T16:53:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us
|
# debug_mbert_task2_1
This model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# debug_mbert_task2_1\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# debug_mbert_task2_1\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.",
"## Model description\n\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# debug_mbert_task2_2
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_mbert_task2_2", "results": []}]} | horsbug98/Part_2_mBERT_Model_E2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T17:04:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us
|
# debug_mbert_task2_2
This model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# debug_mbert_task2_2\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# debug_mbert_task2_2\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa secondary_task dataset.",
"## Model description\n\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# debug_xlm_task2_1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiq... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_xlm_task2_1", "results": []}]} | horsbug98/Part_2_XLM_Model_E1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T17:32:47+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us
|
# debug_xlm_task2_1
This model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
"# debug_xlm_task2_1\n\nThis model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pr... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us \n",
"# debug_xlm_task2_1\n\nThis model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore informati... |
text2text-generation | transformers |
# MultiIndicParaphraseGeneration
This repository contains the [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2203... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": ["mit"], "tags": ["paraphrase-generation", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicParaphrase"]} | ai4bharat/MultiIndicParaphraseGeneration | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"paraphrase-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicParaphrase",
"arxiv:2203.05437",
"license:mit",
"autotrain_com... | null | 2022-03-16T17:37:59+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #paraphrase-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicParaphrase #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| MultiIndicParaphraseGeneration
==============================
This repository contains the IndicBART checkpoint finetuned on the 11 languages of IndicParaphrase dataset. For finetuning details,
see the paper.
* Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tam... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #paraphrase-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicParaphrase #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# MultiIndicParaphraseGenerationSS
This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/ab... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": ["mit"], "tags": ["paraphrase-generation", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicParaphrase"]} | ai4bharat/MultiIndicParaphraseGenerationSS | null | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"paraphrase-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicParaphrase",
"arxiv:2203.05437",
"license:mit",... | null | 2022-03-16T17:38:42+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #paraphrase-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicParaphrase #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| MultiIndicParaphraseGenerationSS
================================
This repository contains the IndicBARTSS checkpoint finetuned on the 11 languages of IndicParaphrase dataset. For finetuning details,
see the paper.
* Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayala... | [] | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #paraphrase-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicParaphrase #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-IMDB
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"], "model-index": [{"name": "distilbert-IMDB", "results": []}]} | ScandinavianMrT/distilbert-IMDB-POS | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T17:42:29+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-IMDB
===============
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: 0.1905
* Accuracy: 0.9295
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
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. -->
# debug_bert_finetuned_dutch_task2_1
This model is a fine-tuned version of [henryk/bert-base-multilingual-cased-finetuned-dutch-sq... | {"tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_bert_finetuned_dutch_task2_1", "results": []}]} | horsbug98/Part_2_BERT_Multilingual_Dutch_Model_E1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T17:44:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us
|
# debug_bert_finetuned_dutch_task2_1
This model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"# debug_bert_finetuned_dutch_task2_1\n\nThis model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eva... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us \n",
"# debug_bert_finetuned_dutch_task2_1\n\nThis model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task 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. -->
# poem-gen-gpt2-small-spanish
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datific... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-gpt2-small-spanish", "results": []}]} | DrishtiSharma/poem-gen-gpt2-small-spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T17:46:36+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
| poem-gen-gpt2-small-spanish
===========================
This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9229
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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... |
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. -->
# debug_bert_finetuned_dutch_task2_1
This model is a fine-tuned version of [henryk/bert-base-multilingual-cased-finetuned-dutch-sq... | {"tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_bert_finetuned_dutch_task2_1", "results": []}]} | horsbug98/Part_1_mBERT_Model_E1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T18:20:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us
|
# debug_bert_finetuned_dutch_task2_1
This model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"# debug_bert_finetuned_dutch_task2_1\n\nThis model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eva... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-tydiqa #endpoints_compatible #region-us \n",
"# debug_bert_finetuned_dutch_task2_1\n\nThis model is a fine-tuned version of henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 on the tydiqa secondary_task dataset.",
... |
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. -->
# debug_xlm_task1_1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiq... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "debug_xlm_task1_1", "results": []}]} | horsbug98/Part_1_XLM_Model_E1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T18:22:10+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us
|
# debug_xlm_task1_1
This model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
"# debug_xlm_task1_1\n\nThis model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pr... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us \n",
"# debug_xlm_task1_1\n\nThis model is a fine-tuned version of xlm-roberta-base on the tydiqa secondary_task dataset.",
"## Model description\n\nMore informati... |
text-classification | transformers |
Model to predict whether a given text is racist or not:
* `LABEL_0` output indicates non-racist text
* `LABEL_1` output indicates racist text
Usage:
```python
from transformers import pipeline
RACISM_MODEL = "davidmasip/racism"
racism_analysis_pipe = pipeline("text-classification",
... | {"language": "es", "license": "cc", "widget": [{"text": "Me cae muy bien.", "example_title": "Non-racist example"}, {"text": "Unos menas agreden a una mujer.", "example_title": "Racist example"}]} | davidmasip/racism | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"es",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T18:23:46+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #text-classification #es #license-cc #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Model to predict whether a given text is racist or not:
* 'LABEL_0' output indicates non-racist text
* 'LABEL_1' output indicates racist text
Usage:
| [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #es #license-cc #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | null |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | nandezgarcia/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] | null | 2022-03-16T18:26:44+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_prefinetune_IMDB
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2_prefinetune_IMDB", "results": []}]} | ScandinavianMrT/gpt2_prefinetune_IMDB | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T18:44:37+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2\_prefinetune\_IMDB
=======================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6875
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
##... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-large"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | navteca/nli-deberta-v3-large | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-large",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T18:53:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the... | [
"# Cross-Encoder for Natural Language Inference\n\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspon... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\n\nThis model was tra... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-2
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-2", "results": []}]} | newtonkwan/gpt2-xl-ft-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T19:38:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-2
============
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6371
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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-SARC_withcontext_3.0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-SARC_withcontext_3.0", "results": []}]} | ScandinavianMrT/distilbert-SARC_withcontext_3.0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T20:02:24+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-SARC_withcontext_3.0
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training ... | [
"# distilbert-SARC_withcontext_3.0\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trainin... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-SARC_withcontext_3.0\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Savitar/DialoGPT-medium-RickandMorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T20:09:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | radev/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T21:47:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3645
* Accuracy: 0.8945
* F1: 0.8872
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | radev/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T22:11:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1345
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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/1829196789/bild_400x400.... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ericson_ubbhult/1653986423351/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ericson_ubbhult | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T22:15:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jan Ericson 🇸🇪🇺🇦
@ericson\_ubbhult
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"
] |
text2text-generation | transformers | A small language generation head to generate text from a prompt.
Fine-tuned on the t5-base model with the aeslc dataset. | {} | Guen/guen_test_prompt_generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T22:18:10+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A small language generation head to generate text from a prompt.
Fine-tuned on the t5-base model with the aeslc dataset. | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TinyBERT_General_6L_768D-finetuned-wikitext103
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_6L_768D](http... | {"tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "TinyBERT_General_6L_768D-finetuned-wikitext103", "results": []}]} | saghar/TinyBERT_General_6L_768D-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T22:46:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us
| TinyBERT\_General\_6L\_768D-finetuned-wikitext103
=================================================
This model is a fine-tuned version of huawei-noah/TinyBERT\_General\_6L\_768D on the wikitext dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3768
Model description
-----------------
M... | [
"### 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: 3.0",
"### Trai... | [
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"### 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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | zdepablo/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T23:28:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7712
* Accuracy: 0.9174
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-mlm
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-mlm", "results": []}]} | wypoon/bert-base-uncased-mlm | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T23:48:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-mlm
=====================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7425
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-3
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-3", "results": []}]} | newtonkwan/gpt2-xl-ft-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T23:58:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-3
============
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4315
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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | MolePatrol/Olbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-17T01:23:44+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
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-cnn_dailymail-100-lit-evalMA-ga
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pegasus-cnn_dailymail-100-lit-evalMA-ga", "results": []}]} | cammy/pegasus-cnn_dailymail-100-lit-evalMA-ga | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T02:06:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-cnn_dailymail-100-lit-evalMA-ga
This model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure... | [
"# pegasus-cnn_dailymail-100-lit-evalMA-ga\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
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"## Model descri... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | zdepablo/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T02:34:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2587
* Accuracy: 0.9474
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
null | null | ### BERT-base compressed by JPQD with Regularization Factor 0.03
```
F1: 87.66
EM: 80.23
```
### Description of important files
```
├── r0.030-squad-bert-b-mvmt-8bit
│ ├── 8bit_ref_bert_squad_nncf_mvmt.json (nncf config used with ssbs-feb branch)
│ ├── checkpoint-110000 (trained checkpoint for generation)
│ ├─... | {} | vuiseng9/bert-base-squad-jpqd-r0.030 | null | [
"onnx",
"region:us"
] | null | 2022-03-17T04:56:32+00:00 | [] | [] | TAGS
#onnx #region-us
| ### BERT-base compressed by JPQD with Regularization Factor 0.03
### Description of important files
| [
"### BERT-base compressed by JPQD with Regularization Factor 0.03",
"### Description of important files"
] | [
"TAGS\n#onnx #region-us \n",
"### BERT-base compressed by JPQD with Regularization Factor 0.03",
"### Description of important files"
] |
fill-mask | transformers |
# roberta-small-belarusian
## Model Description
This is a RoBERTa model pre-trained on [CC-100](https://data.statmt.org/cc-100/). You can fine-tune `roberta-small-belarusian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-belarusian-upos), dependency-parsing, and so on... | {"language": ["be"], "license": "cc-by-sa-4.0", "tags": ["belarusian", "masked-lm"], "datasets": ["cc100"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/roberta-small-belarusian | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"belarusian",
"masked-lm",
"be",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T07:47:45+00:00 | [] | [
"be"
] | TAGS
#transformers #pytorch #roberta #fill-mask #belarusian #masked-lm #be #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-small-belarusian
## Model Description
This is a RoBERTa model pre-trained on CC-100. You can fine-tune 'roberta-small-belarusian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# roberta-small-belarusian",
"## Model Description\n\nThis is a RoBERTa model pre-trained on CC-100. You can fine-tune 'roberta-small-belarusian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #belarusian #masked-lm #be #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-small-belarusian",
"## Model Description\n\nThis is a RoBERTa model pre-trained on CC-100. You can fine-tune 'roberta-small-bel... |
token-classification | transformers |
# roberta-small-belarusian-upos
## Model Description
This is a RoBERTa model pre-trained with [UD_Belarusian](https://universaldependencies.org/be/) for POS-tagging and dependency-parsing, derived from [roberta-small-belarusian](https://huggingface.co/KoichiYasuoka/roberta-small-belarusian). Every word is tagged by ... | {"language": ["be"], "license": "cc-by-sa-4.0", "tags": ["belarusian", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/roberta-small-belarusian-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"belarusian",
"pos",
"dependency-parsing",
"be",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T08:01:31+00:00 | [] | [
"be"
] | TAGS
#transformers #pytorch #roberta #token-classification #belarusian #pos #dependency-parsing #be #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-small-belarusian-upos
## Model Description
This is a RoBERTa model pre-trained with UD_Belarusian for POS-tagging and dependency-parsing, derived from roberta-small-belarusian. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dep... | [
"# roberta-small-belarusian-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained with UD_Belarusian for POS-tagging and dependency-parsing, derived from roberta-small-belarusian. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokeni... | [
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"# roberta-small-belarusian-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained ... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | libalabala/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-17T08:45:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1997
* Rouge1: 16.7312
* Rouge2: 8.6607
* Rougel: 16.1846
* Rougelsum: 16.2411
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #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*... |
null | transformers |
# Megatron-BERT-large Swedish 110k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was ... | {"language": ["sv"]} | KBLab/megatron-bert-large-swedish-cased-110k | null | [
"transformers",
"pytorch",
"megatron-bert",
"sv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T10:29:51+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #megatron-bert #sv #endpoints_compatible #region-us
|
# Megatron-BERT-large Swedish 110k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was ... | [
"# Megatron-BERT-large Swedish 110k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-large with 340M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTra... | [
"TAGS\n#transformers #pytorch #megatron-bert #sv #endpoints_compatible #region-us \n",
"# Megatron-BERT-large Swedish 110k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-large with 340M parameters.\nThe model was trained on about 70GB of data, consisting mos... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | taehyunzzz/distilbert-base-uncased-finetuned-ner | 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 | 2022-03-17T10:33:09+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
| distilbert-base-uncased-finetuned-ner
=====================================
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: 0.0722
* Precision: 0.9032
* Recall: 0.9220
* F1: 0.9125
* Accuracy: 0.9800
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### 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... |
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/1487686479/Tanner1_400x4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/missdaytona/1647513656155/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/missdaytona | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-17T10:39:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
xx
@missdaytona
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-vietnamese-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-vietnamese-colab", "results": []}]} | Jungwonchang/wav2vec2-large-xls-r-300m-vietnamese-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T11:09:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-vietnamese-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# wav2vec2-large-xls-r-300m-vietnamese-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-vietnamese-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the comm... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | amir36/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T11:23:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0616
* Precision: 0.9357
* Recall: 0.9495
* F1: 0.9425
* Accuracy: 0.9858
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electricidad-small-finetuned-amazon-review-classification
This model is a fine-tuned version of [mrm8488/electricidad-small-disc... | {"tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "electricidad-small-finetuned-amazon-review-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type... | richardc7/electricidad-small-finetuned-amazon-review-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T12:37:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #model-index #autotrain_compatible #endpoints_compatible #region-us
| electricidad-small-finetuned-amazon-review-classification
=========================================================
This model is a fine-tuned version of mrm8488/electricidad-small-discriminator on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9601
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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... |
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. -->
# TinyBERT_L-4_H-312_v2-finetuned-wikitext103
This model is a fine-tuned version of [nreimers/TinyBERT_L-4_H-312_v2](https://huggi... | {"tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "TinyBERT_L-4_H-312_v2-finetuned-wikitext103", "results": []}]} | saghar/TinyBERT_L-4_H-312_v2-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T12:52:55+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us
| TinyBERT\_L-4\_H-312\_v2-finetuned-wikitext103
==============================================
This model is a fine-tuned version of nreimers/TinyBERT\_L-4\_H-312\_v2 on the wikitext dataset.
It achieves the following results on the evaluation set:
* Loss: 6.4638
Model description
-----------------
More informat... | [
"### 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch... |
null | null |
# MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning
Paper: https://arxiv.org/abs/2112.05253
## Abstract
Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data an... | {"license": "mit"} | osanseviero/magma | null | [
"arxiv:2112.05253",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-17T12:59:07+00:00 | [
"2112.05253"
] | [] | TAGS
#arxiv-2112.05253 #license-mit #has_space #region-us
|
# MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning
Paper: URL
## Abstract
Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-st... | [
"# MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning\r\n\r\nPaper: URL",
"## Abstract\r\n\r\nLarge-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of com... | [
"TAGS\n#arxiv-2112.05253 #license-mit #has_space #region-us \n",
"# MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning\r\n\r\nPaper: URL",
"## Abstract\r\n\r\nLarge-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_EN
This model is a fine-tuned version of [StivenLancheros/biobert-... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_EN", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_EN | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T13:21:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner-CRAFT\_AugmentedTransfer\_EN
==================================================================
This model is a fine-tuned version of StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT\_Augmented\_EN on the CRAFTone dataset.
It achieves the following results on the evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_ES
This model is a fine-tuned version of [StivenLancheros/biobert-... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_ES", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT_AugmentedTransfer_ES | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T13:21:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner-CRAFT\_AugmentedTransfer\_ES
==================================================================
This model is a fine-tuned version of StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT\_Augmented\_ES on the CRAFT dataset.
It achieves the following results on the evaluati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
fill-mask | transformers |
# IceBERT-ic3
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
| Dataset | Size | Tok... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <mask> \u00e1 brag\u00f0i\u00f0."}]} | mideind/IceBERT-ic3 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"icelandic",
"masked-lm",
"is",
"arxiv:2201.05601",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T13:44:47+00:00 | [
"2201.05601"
] | [
"is"
] | TAGS
#transformers #pytorch #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-ic3
===========
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
Dataset: Icelandic Common Crawl Corpus (IC3), Size: 4.9 GB,... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
*We do not recommend the use of this model besides for comparison with the other IceBERT models*
# IceBERT-mC4-is
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. It was trained on the ... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <mask> \u00e1 brag\u00f0i\u00f0."}]} | mideind/IceBERT-mC4-is | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"icelandic",
"masked-lm",
"is",
"arxiv:2201.05601",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T13:45:41+00:00 | [
"2201.05601"
] | [
"is"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
*We do not recommend the use of this model besides for comparison with the other IceBERT models*
# IceBERT-mC4-is
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. It was trained on the ... | [
"# IceBERT-mC4-is\n\nThis model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. It was trained on the Icelandic part of the mC4 dataset.\n\nThe model is described in this paper URL Please cite t... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# IceBERT-mC4-is\n\nThis model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have tr... |
fill-mask | transformers |
# IceBERT-xlmr-ic3
This model was trained with fairseq using the RoBERTa-base architecture. The model `xlm-roberta-base` was used as a starting point. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
| Data... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <mask> \u00e1 brag\u00f0i\u00f0."}]} | mideind/IceBERT-xlmr-ic3 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"icelandic",
"masked-lm",
"is",
"arxiv:2201.05601",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T13:48:56+00:00 | [
"2201.05601"
] | [
"is"
] | TAGS
#transformers #pytorch #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-xlmr-ic3
================
This model was trained with fairseq using the RoBERTa-base architecture. The model 'xlm-roberta-base' was used as a starting point. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bart-debug | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T14:46:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
#
This model was trained from scratch on the librispeech_asr 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 hyperparameters... | [
"# \n\nThis model was trained from scratch on the librispeech_asr 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 hyperparameters\... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-4
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-4", "results": []}]} | newtonkwan/gpt2-xl-ft-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-17T15:00:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-4
============
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2823
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: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
question-answering | transformers |
# Graphcore/bert-base-uncased-squad
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "Graphcore/bert-base-uncased-squad", "results": []}]} | Graphcore/bert-base-uncased-squad | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T16:17:37+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# Graphcore/bert-base-uncased-squad
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"# Graphcore/bert-base-uncased-squad\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run mode... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Graphcore/bert-base-uncased-squad\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optim... |
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-gpt2-regularisation | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T16:34:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8529
* Wer: 0.9977
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-gpt2-no-adapter-regularisation | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T16:34:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7494
* Wer: 1.0532
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_minds14
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": ["all"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_minds14", "results": []}]} | anton-l/xtreme_s_xlsr_300m_minds14 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"all",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T17:24:20+00:00 | [] | [
"all"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_minds14
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MINDS14.ALL dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.9033
* Accuracy Cs-cz: 0.9164
* Accuracy De-de: 0.9477
* Accuracy En-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
feature-extraction | transformers | Fine-Tune it using this [nb](https://colab.research.google.com/drive/1JRmrAYR0pcEWyni_VtT4SSFxZ5adlAhS?usp=sharing) | {"license": "afl-3.0"} | niksss/Hinglish-HATEBERT | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T17:47:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #license-afl-3.0 #endpoints_compatible #region-us
| Fine-Tune it using this nb | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #license-afl-3.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | swetava/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T18:13:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2259
* Accuracy: 0.9245
* F1: 0.9248
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# led-large-16384-arxiv-100-MDS
This model is a fine-tuned version of [allenai/led-large-16384-arxiv](https://huggingface.co/allen... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "led-large-16384-arxiv-100-MDS", "results": []}]} | cammy/led-large-16384-arxiv-100-MDS | null | [
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T18:49:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| led-large-16384-arxiv-100-MDS
=============================
This model is a fine-tuned version of allenai/led-large-16384-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3897
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
* Gen Len: 512.0
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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. -->
# drug-stance-bert
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardif... | {"tags": ["generated_from_trainer"]} | ningkko/drug-stance-bert | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T21:05:00+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| drug-stance-bert
================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on COVID-CQ, a dataset that contains 3-label annotated opinions (negative, neutral, and positive) of the tweet initiators regarding the use of Chloroquine or Hydroxychloroquine for the treatment or preven... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Fram... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_si... |
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. -->
# MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-... | {"tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103", "results": []}]} | saghar/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T21:19:53+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103
===================================================================
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the wikitext dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_ba... |
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. -->
# Longformer-finetuned-comp5
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/l... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Longformer-finetuned-comp5", "results": []}]} | brad1141/Longformer-finetuned-comp5 | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-17T23:09:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Longformer-finetuned-comp5
==========================
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8180
* Precision: 0.5680
* Recall: 0.7490
* F1: 0.6430
* Accuracy: 0.6430
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-vanilla-debiased-5000
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown data... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-vanilla-debiased-5000", "results": []}]} | beston91/gpt2-xl-ft-logits-5k | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-17T23:54:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-vanilla-debiased-5000
=============================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.0371
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batc... |
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_common_voice_accents_4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_4", "results": []}]} | willcai/wav2vec2_common_voice_accents_4 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T01:46:54+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_4
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0047
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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\... |
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. -->
# Longformer-finetuned-norm
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/lo... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Longformer-finetuned-norm", "results": []}]} | brad1141/Longformer-finetuned-norm | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T02:29:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Longformer-finetuned-norm
=========================
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8127
* Precision: 0.8429
* Recall: 0.8701
* F1: 0.8562
* Accuracy: 0.8221
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
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. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | aaraki/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T06:29:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5155
* Accuracy: 0.8002
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\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. -->
# sentiment-model-sample-group-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample-group-emotion", "results": []}]} | jkhan447/sentiment-model-sample-group-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T06:53:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample-group-emotion
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4604
- Accuracy: 0.7004
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# sentiment-model-sample-group-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4604\n- Accuracy: 0.7004",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample-group-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the fol... |
text-classification | transformers | imdb_finetuned_distilbert-base-uncased-finetuned-sst-2-english for boostcamp ai tech 3
| {} | juns/imdb_finetuned_distilbert-base-uncased-finetuned-sst-2-english | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T07:05:06+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| imdb_finetuned_distilbert-base-uncased-finetuned-sst-2-english for boostcamp ai tech 3
| [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # Fr-word to phonemic pronunciation
This model aims at predicting the syllabized phonemic pronunciation of the French words.
The generated pronunciation is:
* A text string made of International Phonetic Alphabet (IPA) characters;
* Phonemic (i.e. remains at the phoneme-level, not deeper);
* Syllabized (i.e. characte... | {"language": ["fr"], "tags": ["text-generation"], "datasets": ["Marxav/frpron"], "metrics": ["loss/eval", "perplexity"], "thumbnail": "url to a thumbnail used in social sharing", "widget": [{"text": "bonjour:"}, {"text": "salut, comment \u00e7a va:"}, {"text": "Louis XIII:"}, {"text": "anticonstitutionnellement:"}, {"t... | Marxav/frpron | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"dataset:Marxav/frpron",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-18T07:14:10+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #fr #dataset-Marxav/frpron #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Fr-word to phonemic pronunciation
This model aims at predicting the syllabized phonemic pronunciation of the French words.
The generated pronunciation is:
* A text string made of International Phonetic Alphabet (IPA) characters;
* Phonemic (i.e. remains at the phoneme-level, not deeper);
* Syllabized (i.e. characte... | [
"# Fr-word to phonemic pronunciation\n\nThis model aims at predicting the syllabized phonemic pronunciation of the French words.\n\nThe generated pronunciation is:\n* A text string made of International Phonetic Alphabet (IPA) characters;\n* Phonemic (i.e. remains at the phoneme-level, not deeper);\n* Syllabized (i... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #fr #dataset-Marxav/frpron #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Fr-word to phonemic pronunciation\n\nThis model aims at predicting the syllabized phonemic pronunciation of the French words.\n\nThe ... |
sentence-similarity | sentence-transformers |
# moshew/paraphrase-mpnet-base-v2_SetFit_emotions
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | moshew/paraphrase-mpnet-base-v2_SetFit_emotions | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T07:16:19+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# moshew/paraphrase-mpnet-base-v2_SetFit_emotions
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transf... | [
"# moshew/paraphrase-mpnet-base-v2_SetFit_emotions\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# moshew/paraphrase-mpnet-base-v2_SetFit_emotions\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be ... |
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. -->
# gpt2-finetuned-comp2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "gpt2-finetuned-comp2", "results": []}]} | brad1141/gpt2-finetuned-comp2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-18T07:26:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| gpt2-finetuned-comp2
====================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7788
* Precision: 0.3801
* Recall: 0.6854
* F1: 0.4800
* Accuracy: 0.4800
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | erinchocolate/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-18T07:47:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
sentence-similarity | sentence-transformers |
# moshew/paraphrase-mpnet-base-v2_SetFit_sst2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Usin... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | moshew/paraphrase-mpnet-base-v2_SetFit_sst2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T07:53:07+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# moshew/paraphrase-mpnet-base-v2_SetFit_sst2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# moshew/paraphrase-mpnet-base-v2_SetFit_sst2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# moshew/paraphrase-mpnet-base-v2_SetFit_sst2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used... |
question-answering | transformers | ---
language:
- vi
- vn
- en
- multilingual
tags:
- question-answering
- pytorch
datasets:
- squad
metrics:
- squad
pipeline_tag: question-answering
widget:
- text: what is the capital of Vietnam ?
context: Keeping an ageless charm through centuries, Hanoi - the capital of Vietnam
... | {} | aicryptogroup/distill-xlm-mrc | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T08:11:36+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| ---
language:
- vi
- vn
- en
- multilingual
tags:
- question-answering
- pytorch
datasets:
- squad
metrics:
- squad
pipeline_tag: question-answering
widget:
- text: what is the capital of Vietnam ?
context: Keeping an ageless charm through centuries, Hanoi - the capital of Vietnam
... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-rewriter
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-rewriter", "results": []}]} | chinhon/pegasus-newsroom-rewriter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-18T08:46:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-newsroom-rewriter
=========================
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3424
* Rouge1: 46.6856
* Rouge2: 31.6377
* Rougel: 33.2741
* Rougelsum: 44.5003
* Gen Len: 126.58
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-100-MDS-own
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-100-MDS-own", "results": []}]} | cammy/bart-large-cnn-100-MDS-own | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T09:31:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-100-MDS-own
==========================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5357
* Rouge1: 22.4039
* Rouge2: 4.681
* Rougel: 13.1526
* Rougelsum: 15.7986
* Gen Len: 70.3
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size:... |
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. -->
# mbart-large-cc25-finetuned-hi-to-en
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/fac... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-cc25-finetuned-hi-to-en", "results": []}]} | rahulacj/mbart-large-cc25-finetuned-hi-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T10:19:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-cc25-finetuned-hi-to-en
===================================
This model is a fine-tuned version of facebook/mbart-large-cc25 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4710
* Bleu: 16.6154
* Gen Len: 42.6244
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\... |
reinforcement-learning | null |
# TestSB3/ppo-CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1 using the [rl-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) library.
## Usage (with RL-baselines3-zoo)
Just clone the [rl-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) library.
Then run:
```python
p... | {"tags": ["gym", "reinforcement-learning"]} | TestSB3/ppo-CartPole-v1 | null | [
"gym",
"reinforcement-learning",
"region:us"
] | null | 2022-03-18T10:20:52+00:00 | [] | [] | TAGS
#gym #reinforcement-learning #region-us
|
# TestSB3/ppo-CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1 using the rl-baselines3-zoo library.
## Usage (with RL-baselines3-zoo)
Just clone the rl-baselines3-zoo library.
Then run:
## Evaluation Results
Mean Reward: 500.0 +/- 0.0 (300 test episodes)
## Citing the Project
To cite thi... | [
"# TestSB3/ppo-CartPole-v1\n\nThis is a trained model of a PPO agent playing CartPole-v1 using the rl-baselines3-zoo library.",
"## Usage (with RL-baselines3-zoo)\n\nJust clone the rl-baselines3-zoo library.\n\nThen run:",
"## Evaluation Results\n\nMean Reward: 500.0 +/- 0.0 (300 test episodes)",
"## Citing t... | [
"TAGS\n#gym #reinforcement-learning #region-us \n",
"# TestSB3/ppo-CartPole-v1\n\nThis is a trained model of a PPO agent playing CartPole-v1 using the rl-baselines3-zoo library.",
"## Usage (with RL-baselines3-zoo)\n\nJust clone the rl-baselines3-zoo library.\n\nThen run:",
"## Evaluation Results\n\nMean Rewa... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-logits-1k
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It a... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-logits-1k", "results": []}]} | beston91/gpt2-xl-ft-logits-1k | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-18T12:21:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-logits-1k
====================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.5341
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batc... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-qmsum-meeting-summarization
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["yawnick/QMSum"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-qmsum-meeting-summarization", "results": []}]} | mikeadimech/bart-large-cnn-qmsum-meeting-summarization | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:yawnick/QMSum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T12:44:49+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-cnn-qmsum-meeting-summarization
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714
## Model descriptio... | [
"# bart-large-cnn-qmsum-meeting-summarization\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.7578\n- Rouge1: 37.9431\n- Rouge2: 10.6366\n- Rougel: 25.5782\n- Rougelsum: 33.0209\n- Gen Len: 72.7714",
"## M... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-cnn-qmsum-meeting-summarization\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nI... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-d1
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-d1", "results": []}]} | IsaacSST/gpt2-xl-ft-d1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-18T13:07:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-d1
=============
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2993
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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
null | transformers |
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaub... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding"]} | nherve/flaubert-oral-asr | null | [
"transformers",
"pytorch",
"flaubert",
"bert",
"language-model",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region... | null | 2022-03-18T13:16:38+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us
|
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the flaubert... | [
"# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling\r\n\r\nFlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the f... | [
"TAGS\n#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us \n",
"# FlauBERT-Oral models: Using ASR-Generated Text for Spok... |
null | transformers |
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaub... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding"]} | nherve/flaubert-oral-asr_nb | null | [
"transformers",
"pytorch",
"flaubert",
"bert",
"language-model",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region... | null | 2022-03-18T13:40:37+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us
|
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the flaubert... | [
"# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling\r\n\r\nFlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the f... | [
"TAGS\n#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us \n",
"# FlauBERT-Oral models: Using ASR-Generated Text for Spok... |
null | transformers |
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaub... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding"]} | nherve/flaubert-oral-mixed | null | [
"transformers",
"pytorch",
"flaubert",
"bert",
"language-model",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region... | null | 2022-03-18T13:46:50+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us
|
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the flaubert... | [
"# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling\r\n\r\nFlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the f... | [
"TAGS\n#transformers #pytorch #flaubert #bert #language-model #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us \n",
"# FlauBERT-Oral models: Using ASR-Generated Text for Spok... |
feature-extraction | transformers |
# RegNetModel
RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER).
Disclaimer: The t... | {"license": "apache-2.0", "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/d... | facebook/regnet-y-320-seer | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"feature-extraction",
"vision",
"arxiv:2202.08360",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T13:51:03+00:00 | [
"2202.08360"
] | [] | TAGS
#transformers #pytorch #tf #regnet #feature-extraction #vision #arxiv-2202.08360 #license-apache-2.0 #endpoints_compatible #region-us
|
# RegNetModel
RegNetModel model was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision and first released in this repository.
Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written b... | [
"# RegNetModel\n\nRegNetModel model was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision and first released in this repository. \n\nDisclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been w... | [
"TAGS\n#transformers #pytorch #tf #regnet #feature-extraction #vision #arxiv-2202.08360 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# RegNetModel\n\nRegNetModel model was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision and first... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER).
... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-y-320-seer-in1k | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2202.08360",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T14:35:06+00:00 | [
"2202.08360"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2202.08360 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has be... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model car... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2202.08360 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Vision Models Are More Robust And Fair Whe... |
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