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null | keras |
This model is a TensorFlow port of DINO [1] ViT B-16 [2]. The backbone of this model was pre-trained using the DINO pretext task. After that its head layer was trained
by keeping the backbone frozen. ImageNet-1k dataset was used for training purposes. You can refer to [this notebook](https://github.com/sayakpaul/probi... | {"library_name": "keras"} | probing-vits/vit-dino-base16 | null | [
"keras",
"arxiv:2104.14294",
"arxiv:2010.11929",
"has_space",
"region:us"
] | null | 2022-04-11T13:52:31+00:00 | [
"2104.14294",
"2010.11929"
] | [] | TAGS
#keras #arxiv-2104.14294 #arxiv-2010.11929 #has_space #region-us
|
This model is a TensorFlow port of DINO [1] ViT B-16 [2]. The backbone of this model was pre-trained using the DINO pretext task. After that its head layer was trained
by keeping the backbone frozen. ImageNet-1k dataset was used for training purposes. You can refer to this notebook to know how the porting was done.
#... | [
"## References\n\n[1] Emerging Properties in Self-Supervised Vision Transformers: URL\n\n[2] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL"
] | [
"TAGS\n#keras #arxiv-2104.14294 #arxiv-2010.11929 #has_space #region-us \n",
"## References\n\n[1] Emerging Properties in Self-Supervised Vision Transformers: URL\n\n[2] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL"
] |
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. -->
# ls-timit-100percent-supervised-meta
This model was trained from scratch on the None dataset.
It achieves the following results o... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ls-timit-100percent-supervised-meta", "results": []}]} | Kuray107/ls-timit-100percent-supervised-meta | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T13:57:43+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ls-timit-100percent-supervised-meta
===================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0649
* Wer: 0.0253
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* se... |
text-generation | transformers | # CodeGen (CodeGen-NL 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | {"license": "bsd-3-clause"} | Salesforce/codegen-350M-nl | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T14:19:18+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-NL 350M)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origin... | [
"# CodeGen (CodeGen-NL 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-NL 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pape... |
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-en-maptask-finetuned
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Mod... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-en-maptask-finetuned", "results": []}]} | maesneako/gpt2-en-maptask-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T14:28:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-en-maptask-finetuned
This model is a fine-tuned version of gpt2 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 hyperparameters
The foll... | [
"# gpt2-en-maptask-finetuned\n\nThis model is a fine-tuned version of gpt2 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",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-en-maptask-finetuned\n\nThis model is a fine-tuned version of gpt2 on the None dataset.",
"## Model description\n\... |
null | keras |
This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. ImageNet-1k dataset was used for training purposes. You can refer to [this notebook](https://github.com/sayakpaul/probing-vits/blob/main/notebooks/load-jax-weights-vitb16.ipynb) to know how the porting was done.
## References
[1] An Image... | {"library_name": "keras"} | probing-vits/vit_b16_patch16_224_i1k | null | [
"keras",
"arxiv:2010.11929",
"arxiv:2106.10270",
"region:us"
] | null | 2022-04-11T14:44:22+00:00 | [
"2010.11929",
"2106.10270"
] | [] | TAGS
#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us
|
This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. ImageNet-1k dataset was used for training purposes. You can refer to this notebook to know how the porting was done.
## References
[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL
[2] How to train your ... | [
"## References\n\n[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL\n\n[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL"
] | [
"TAGS\n#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us \n",
"## References\n\n[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL\n\n[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj_v5_11Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v5_11Epoch", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v5_11Epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T14:51:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v5\_11Epoch
==================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3467
* Precision: 0.8240
* Recall: 0.8287
* F1: 0.8263
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 11",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
null | keras |
This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. It was first pre-trained on ImageNet-21k and was then fine-tuned on the ImageNet-1k dataset. You can refer to [this notebook](https://github.com/sayakpaul/probing-vits/blob/main/notebooks/load-jax-weights-vitb16.ipynb) to know how the porti... | {"library_name": "keras"} | probing-vits/vit_b16_patch16_224_i21k_i1k | null | [
"keras",
"arxiv:2010.11929",
"arxiv:2106.10270",
"region:us"
] | null | 2022-04-11T14:53:07+00:00 | [
"2010.11929",
"2106.10270"
] | [] | TAGS
#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us
|
This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. It was first pre-trained on ImageNet-21k and was then fine-tuned on the ImageNet-1k dataset. You can refer to this notebook to know how the porting was done.
## References
[1] An Image is Worth 16x16 Words: Transformers for Image Recognit... | [
"## References\n\n[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL\n\n[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL"
] | [
"TAGS\n#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us \n",
"## References\n\n[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL\n\n[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL"
] |
text-generation | transformers | # CodeGen (CodeGen-Multi 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Sav... | {"license": "bsd-3-clause"} | Salesforce/codegen-350M-multi | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T15:11:35+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Multi 350M)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are ori... | [
"# CodeGen (CodeGen-Multi 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The mod... | [
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"# CodeGen (CodeGen-Multi 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the p... |
text-generation | transformers | # CodeGen (CodeGen-Mono 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Sava... | {"license": "bsd-3-clause"} | Salesforce/codegen-350M-mono | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T15:18:21+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Mono 350M)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are orig... | [
"# CodeGen (CodeGen-Mono 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The mode... | [
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"# CodeGen (CodeGen-Mono 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pa... |
zero-shot-image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-test
This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch... | {"tags": ["generated_from_trainer"], "datasets": ["arampacha/rsicd"], "model-index": [{"name": "clip-test", "results": []}]} | arampacha/clip-test | null | [
"transformers",
"pytorch",
"tensorboard",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"dataset:arampacha/rsicd",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T15:29:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #clip #zero-shot-image-classification #generated_from_trainer #dataset-arampacha/rsicd #endpoints_compatible #region-us
|
# clip-test
This model is a fine-tuned version of openai/clip-vit-base-patch32 on the arampacha/rsicd dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation d... | [
"# clip-test\n\nThis model is a fine-tuned version of openai/clip-vit-base-patch32 on the arampacha/rsicd dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.2656",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #clip #zero-shot-image-classification #generated_from_trainer #dataset-arampacha/rsicd #endpoints_compatible #region-us \n",
"# clip-test\n\nThis model is a fine-tuned version of openai/clip-vit-base-patch32 on the arampacha/rsicd dataset.\nIt achieves the following resu... |
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | theojolliffe/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T16:00:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2915
* Bleu: 27.9273
* Gen Len: 34.0935
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: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #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\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ParulChaudhari/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ParulChaudhari/distilbert-base-uncased-finetuned-squad", "results": []}]} | ParulChaudhari/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T16:01:40+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ParulChaudhari/distilbert-base-uncased-finetuned-squad
======================================================
This model is a fine-tuned version of distilbert-base-uncased on an SQUAD dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3927
* Validation Loss: 1.1305
* Epoch: 0
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 177048, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'n... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b8_lr3e-5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b8_lr3e-5", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T16:28:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b8\_lr3e-5
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4836
* Rouge1: 25.9411
* Rouge2: 9.226
* Rougel: 21.9087
* Rougelsum: 25.2863
* Gen ... | [
"### 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: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
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. -->
# ft-pt-br-local
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/j... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "model-index": [{"name": "ft-pt-br-local", "results": []}]} | tonyalves/ft-pt-br-local | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T16:41:58+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# ft-pt-br-local
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-portuguese on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# ft-pt-br-local\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-portuguese on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ft-pt-br-local\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-portuguese on the None dataset.",
"## Model description\n\nMore... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-2", "results": []}]} | hufanyoung/segformer-b0-finetuned-segments-sidewalk-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T16:56:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-2
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9327
* Mean Iou: 0.0763
* Mean Accuracy: 0.1260
* Overall Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.05",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"license": "gpl-3.0", "tags": ["fastai"]} | fastai/fastbook_01_is_cat_dog | null | [
"fastai",
"license:gpl-3.0",
"region:us"
] | null | 2022-04-11T17:39:10+00:00 | [] | [] | TAGS
#fastai #license-gpl-3.0 #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #license-gpl-3.0 #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (docum... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-xsum", "results": []}]} | adasnew/t5-small-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T17:45:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-xsum
=============
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3953
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #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* l... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | nateraw/fastai-dummy-learner | null | [
"fastai",
"region:us"
] | null | 2022-04-11T18:15:53+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
text2text-generation | transformers | UFAL English to French Machine Translation Model based on MarianMT model. | {"license": "other"} | irenelizihui/MarianMT_UFAL_en_fr | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T18:25:46+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #license-other #autotrain_compatible #endpoints_compatible #region-us
| UFAL English to French Machine Translation Model based on MarianMT model. | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | MarianMT trained on the UFAL dataset: English to Spanish Machine Translation model. | {"license": "wtfpl"} | irenelizihui/MarianMT_UFAL_en_es | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:wtfpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T18:29:20+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us
| MarianMT trained on the UFAL dataset: English to Spanish Machine Translation model. | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | UFAL English to Romainian Machine Translation Model based on MarianMT model. | {"license": "wtfpl"} | irenelizihui/MarianMT_UFAL_en_ro | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:wtfpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T18:53:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us
| UFAL English to Romainian Machine Translation Model based on MarianMT model. | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained [BERTurk model 128k uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) with [BounTi dataset](https://ieeexplore.ieee.org/document/9477814).
## Usage in Hugging Face Pipeline
```
from transformers import pipeline
bounti = ... | {"language": "tr", "tags": ["sentiment", "twitter", "turkish"]} | akoksal/bounti | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment",
"twitter",
"turkish",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T18:55:36+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #text-classification #sentiment #twitter #turkish #tr #autotrain_compatible #endpoints_compatible #has_space #region-us
| This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained BERTurk model 128k uncased with BounTi dataset.
Usage in Hugging Face Pipeline
------------------------------
Results
-------
The scores of the finetuned model with BERTurk:
Dataset
-------
You can find the dataset in our Github r... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #sentiment #twitter #turkish #tr #autotrain_compatible #endpoints_compatible #has_space #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. -->
# ft-pt-br-local-2
This model is a fine-tuned version of [tonyalves/output](https://huggingface.co/tonyalves/output) on the None d... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "model-index": [{"name": "ft-pt-br-local-2", "results": []}]} | tonyalves/ft-pt-br-local-2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T19:46:13+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# ft-pt-br-local-2
This model is a fine-tuned version of tonyalves/output 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 hyperparameters
The f... | [
"# ft-pt-br-local-2\n\nThis model is a fine-tuned version of tonyalves/output 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",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ft-pt-br-local-2\n\nThis model is a fine-tuned version of tonyalves/output on the None dataset.",
"## Model description\n\nMore information needed",
"## In... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln3")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-fou... | {} | BigSalmon/MediumInformalToFormalLincoln3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T19:49:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
(makes two sentences, one sentence) (probably will not work all that well)
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | LysandreJik/my-new-keras-model | null | [
"keras",
"region:us"
] | null | 2022-04-11T20:15:34+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
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. -->
# ls-timit-wsj0-100percent-supervised-meta
This model was trained from scratch on the None dataset.
It achieves the following resu... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ls-timit-wsj0-100percent-supervised-meta", "results": []}]} | Kuray107/ls-timit-wsj0-100percent-supervised-meta | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T21:24:57+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ls-timit-wsj0-100percent-supervised-meta
========================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0531
* Wer: 0.0214
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* se... |
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/1506323689456947207/xBvv... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T21:43:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
makeouthill & VacuumF & Jason Hendricks & Angry Memories
@angrymemorys-oldandtoothless-sadboi666\_-witheredstrings
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
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # CodeGen (CodeGen-NL 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese... | {"license": "bsd-3-clause"} | Salesforce/codegen-2B-nl | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T22:18:08+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-NL 2B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are original... | [
"# CodeGen (CodeGen-NL 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-NL 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper:... |
text-generation | transformers | # CodeGen (CodeGen-Multi 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savar... | {"license": "bsd-3-clause"} | Salesforce/codegen-2B-multi | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T22:18:25+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Multi 2B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origi... | [
"# CodeGen (CodeGen-Multi 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The model... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Multi 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pap... |
text-generation | transformers | # CodeGen (CodeGen-Mono 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | {"license": "bsd-3-clause"} | Salesforce/codegen-2B-mono | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T22:18:40+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Mono 2B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origin... | [
"# CodeGen (CodeGen-Mono 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Mono 2B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pape... |
token-classification | transformers | ### Description
A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset.
### Intended Use
This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www... | {"license": "cc-by-4.0", "widget": [{"text": "This house was let out in tiny tenements and was inhabited by working people of all kinds--tailors, locksmiths, cooks, Germans ofsorts, girls picking up a living as best they could, petty clerks, etc.", "example_title": "Crime and Punishment"}, {"text": "Quixote having got ... | nates/LER-roberta | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T23:01:14+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Description
A 'roberta-base' model which has been fine tuned for token classification on the LitBank dataset.
### Intended Use
This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from ACE 2005
- Person (PER)
- ORG
- GPE
- LOC
- VEH
- FAC
Due to the fine-tuning domain... | [
"### Description\nA 'roberta-base' model which has been fine tuned for token classification on the LitBank dataset.",
"### Intended Use\nThis model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from ACE 2005\n- Person (PER)\n- ORG\n- GPE\n- LOC\n- VEH\n- FAC\n\nDue to the... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\nA 'roberta-base' model which has been fine tuned for token classification on the LitBank dataset.",
"### Intended Use\nThis model is ready to be used for ... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | espejelomar/fastai_model_34 | null | [
"fastai",
"region:us"
] | null | 2022-04-11T23:46:59+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\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. -->
# ernie-finetuned-qqp
This model is a fine-tuned version of [nghuyong/ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) ... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "ernie-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qqp"}, "metrics": [{"type": "accuracy", "val... | rajiv003/ernie-finetuned-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T00:37:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| ernie-finetuned-qqp
===================
This model is a fine-tuned version of nghuyong/ernie-2.0-en on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4381
* Accuracy: 0.9157
* F1: 0.8861
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
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. -->
# chinese-pert-large-finetuned-product
This model is a fine-tuned version of [hfl/chinese-pert-large](https://huggingface.co/hfl/c... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-pert-large-finetuned-product", "results": []}]} | agdsga/chinese-pert-large-finetuned-product | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T01:13:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-pert-large-finetuned-product
====================================
This model is a fine-tuned version of hfl/chinese-pert-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0208
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\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: 10",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-generation | transformers |
# Han Solo DialoGPT Model | {"tags": ["conversational"]} | NonzeroCornet34/DialoGPT-small-hansolo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-12T01:20:23+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Han Solo DialoGPT Model | [
"# Han Solo DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Han Solo DialoGPT Model"
] |
null | null | **Model Description:**
This model is a Resnet18 trained in Pytorch to classify human faces orientation (Flipped or not flipped).
**Dataset:**
The model is pretrained on ImageNet and then finetuned on LFWPeople dataset. LFWPeople is a dataset of human faces. The dataset is labelled as follows:
* Flipped image -> label... | {} | Amro-Kamal/orientation_classifier | null | [
"region:us"
] | null | 2022-04-12T01:27:46+00:00 | [] | [] | TAGS
#region-us
| Model Description:
This model is a Resnet18 trained in Pytorch to classify human faces orientation (Flipped or not flipped).
Dataset:
The model is pretrained on ImageNet and then finetuned on LFWPeople dataset. LFWPeople is a dataset of human faces. The dataset is labelled as follows:
* Flipped image -> label = 1
* ... | [] | [
"TAGS\n#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. -->
# mi-modelo-bacan-test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "mi-modelo-bacan-test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "met... | fmesa/mi-modelo-bacan-test | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T01:33:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mi-modelo-bacan-test
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3318
- Accuracy: 0.8767
- F1: 0.8825
## Model description
More information needed
## Intended uses & limitations
More information needed
#... | [
"# mi-modelo-bacan-test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3318\n- Accuracy: 0.8767\n- F1: 0.8825",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# mi-modelo-bacan-test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb datase... |
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... | luckydog/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 | 2022-04-12T01:41:30+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: 0.3298
* Accuracy: 0.9
* F1: 0.8981
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
fill-mask | transformers | # Condenser for Vietnamese
Transformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, [Condenser: a Pre-training Architecture for Dense Retrieval](https://arxiv.org/abs/2104.08253) and [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Ret... | {} | NlpHUST/Condenser-phobert-base | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"arxiv:2104.08253",
"arxiv:2108.05540",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T01:59:19+00:00 | [
"2104.08253",
"2108.05540"
] | [] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #arxiv-2104.08253 #arxiv-2108.05540 #autotrain_compatible #endpoints_compatible #region-us
| # Condenser for Vietnamese
Transformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, Condenser: a Pre-training Architecture for Dense Retrieval and Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
.
For example, to load Conden... | [
"# Condenser for Vietnamese\nTransformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, Condenser: a Pre-training Architecture for Dense Retrieval and Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval\n.\n\nFor example, to lo... | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #arxiv-2104.08253 #arxiv-2108.05540 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Condenser for Vietnamese\nTransformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, Condenser: a Pre-... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into t... | {} | BigSalmon/GPT2Neo1.3BPoints2 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T03:10:13+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-100h-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 100 hours of Libri-Ligh... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-100h-lv60", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dat... | Splend1dchan/wav2vec2-large-100h-lv60-self | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T03:53:16+00:00 | [
"2010.11430",
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-100h-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 100 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training object... | [
"# Wav2Vec2-Large-100h-Lv60 + Self-Training",
"# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical \n\nFacebook's Wav2Vec2\n\nThe large model pretrained and fine-tuned on 100 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-T... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-100h-Lv60 + Self-Training",
"# This is a direct state... |
fill-mask | transformers | # PHS-BERT
We present and release [PHS-BERT](https://arxiv.org/abs/2204.04521), a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media. Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-of-the-art ... | {} | publichealthsurveillance/PHS-BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2204.04521",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T04:35:31+00:00 | [
"2204.04521"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2204.04521 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # PHS-BERT
We present and release PHS-BERT, a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media. Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-of-the-art performance on 25 tested datasets, s... | [
"# PHS-BERT\n\nWe present and release PHS-BERT, a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media. Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-of-the-art performance on 25 tested data... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-2204.04521 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# PHS-BERT\n\nWe present and release PHS-BERT, a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | adache/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T04:43:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2270
* Accuracy: 0.9245
* F1: 0.9249
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# codeparrot-ds-500sample-gpt-neo-2ep
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/Eleut... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-500sample-gpt-neo-2ep", "results": []}]} | Pavithra/codeparrot-ds-500sample-gpt-neo-2ep | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T04:47:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| codeparrot-ds-500sample-gpt-neo-2ep
===================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5483
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-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: 0.0005\n* train\\_batch\\... |
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. -->
# xlnet-base-cased-IUChatbot-ontologyDts-12April2022
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-IUChatbot-ontologyDts-12April2022", "results": []}]} | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-12April2022 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T04:55:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-IUChatbot-ontologyDts-12April2022
==================================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6500
Model description
-----------------
More information needed
... | [
"### 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 #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-10min-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 10min of Libri-Light and... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-10min-lv60", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "da... | Splend1dchan/wav2vec2-large-10min-lv60-self | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T05:14:30+00:00 | [
"2010.11430",
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-10min-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 10min of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training objective. ... | [
"# Wav2Vec2-Large-10min-Lv60 + Self-Training",
"# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical\nFacebook's Wav2Vec2\n\nThe large model pretrained and fine-tuned on 10min of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Trainin... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-10min-Lv60 + Self-Training",
"# This is a direct stat... |
image-classification | transformers |
# blocks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lazyturtl/blocks | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T05:15:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# blocks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### blue color
!blue color
#### cyan color
!cyan color
#### green color
!green color
#### orange color
!orange ... | [
"# blocks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### blue color\n\n!blue color",
"#### cyan color\n\n!cyan color",
"#### green color\n\n!green col... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# blocks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with t... |
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-tr-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.c... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo-dist", "results": []}]} | gary109/wav2vec2-common_voice-tr-demo-dist | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T05:35:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo-dist
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3934
* Wer: 0.3305
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 8\n* total\\_eval\\_batch\\_size: 16\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #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\\... |
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. -->
# mlner-mlwptok-muril
This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlner2021"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mlner-mlwptok-muril", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mlner2021", "type": "... | junaidamk/mlner-mlwptok-muril | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:mlner2021",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T05:53:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-mlner2021 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| mlner-mlwptok-muril
===================
This model is a fine-tuned version of google/muril-base-cased on the mlner2021 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8331
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.8113
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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-mlner2021 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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. -->
# Gram-Vaani-Harveen-Chadda-Fine-Tuning
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](htt... | {"license": "mit", "tags": ["generated_from_trainer"]} | nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T05:59:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-mit #endpoints_compatible #region-us
| Gram-Vaani-Harveen-Chadda-Fine-Tuning
=====================================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8934
* Wer: 0.359
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eva... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | ACSHCSE/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 | 2022-04-12T06:34:44+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: 0.0611
* Precision: 0.9230
* Recall: 0.9366
* F1: 0.9298
* Accuracy: 0.9832
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
feature-extraction | transformers |
# ReACC-py-retriever
This is the retrieval model for [ReACC: A Retrieval-Augmented Code Completion Framework](https://arxiv.org/abs/2203.07722).
In this paper, the model is used to retrieve similar codes given an incompletion code snippet as query. The model can be also used for incomplete code-to-code search, code ... | {"license": "mit"} | microsoft/reacc-py-retriever | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.07722",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T06:41:18+00:00 | [
"2203.07722"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07722 #license-mit #endpoints_compatible #region-us
|
# ReACC-py-retriever
This is the retrieval model for ReACC: A Retrieval-Augmented Code Completion Framework.
In this paper, the model is used to retrieve similar codes given an incompletion code snippet as query. The model can be also used for incomplete code-to-code search, code clone detection.
'py-retriever' is ... | [
"# ReACC-py-retriever\n\nThis is the retrieval model for ReACC: A Retrieval-Augmented Code Completion Framework.\n\nIn this paper, the model is used to retrieve similar codes given an incompletion code snippet as query. The model can be also used for incomplete code-to-code search, code clone detection.\n\n'py-retr... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07722 #license-mit #endpoints_compatible #region-us \n",
"# ReACC-py-retriever\n\nThis is the retrieval model for ReACC: A Retrieval-Augmented Code Completion Framework.\n\nIn this paper, the model is used to retrieve similar codes given an in... |
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. -->
# kobigbird-bert-base-finetuned-klue
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/m... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "kobigbird-bert-base-finetuned-klue", "results": []}]} | obokkkk/kobigbird-bert-base-finetuned-klue | null | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T06:41:56+00:00 | [] | [] | TAGS
#transformers #pytorch #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us
| kobigbird-bert-base-finetuned-klue
==================================
This model is a fine-tuned version of monologg/kobigbird-bert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5589
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* ... |
null | null | ## This is a PyTorch implementation of the paper [Multi-Source Domain Adaptation Based on Federated Knowledge Alignment](https://arxiv.org/abs/2203.11635).
## Table of Contents
* [General information](#general-information)
* [Running the systems](#running-the-systems)
* [Further readings](#further-readings)
## Genera... | {} | yuwei/federated-knowledge-alignment | null | [
"arxiv:2203.11635",
"region:us"
] | null | 2022-04-12T06:47:15+00:00 | [
"2203.11635"
] | [] | TAGS
#arxiv-2203.11635 #region-us
| ## This is a PyTorch implementation of the paper Multi-Source Domain Adaptation Based on Federated Knowledge Alignment.
## Table of Contents
* General information
* Running the systems
* Further readings
## General information
FedKA that consists of three building blocks, i.e., features disentangler, embedding matchi... | [
"## This is a PyTorch implementation of the paper Multi-Source Domain Adaptation Based on Federated Knowledge Alignment.",
"## Table of Contents\n* General information\n* Running the systems\n* Further readings",
"## General information\nFedKA that consists of three building blocks, i.e., features disentangler,... | [
"TAGS\n#arxiv-2203.11635 #region-us \n",
"## This is a PyTorch implementation of the paper Multi-Source Domain Adaptation Based on Federated Knowledge Alignment.",
"## Table of Contents\n* General information\n* Running the systems\n* Further readings",
"## General information\nFedKA that consists of three bu... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022
This model is a fine-tuned version of [bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022", "results": []}]} | nntadotzip/bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T06:53:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022
=============================================================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3856
Model des... | [
"### 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 #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/CycleGAN_punk2apes | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-12T07:02:31+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639
## Validation Metrics
- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.94708017150... | {"language": "ja", "tags": "autotrain", "datasets": ["jurader/autotrain-data-livedoor_news"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.02886635131127639} | jurader/autotrain-livedoor_news-732022289 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ja",
"dataset:jurader/autotrain-data-livedoor_news",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T07:03:38+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jurader/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639
## Validation Metrics
- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.94708017150... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 732022289\n- CO2 Emissions (in grams): 0.02886635131127639",
"## Validation Metrics\n\n- Loss: 0.19849611818790436\n- Accuracy: 0.9471186440677966\n- Macro F1: 0.9441816841379956\n- Micro F1: 0.9471186440677966\n- Weighted... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jurader/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 732022289\n- CO2 Emissions... |
null | null |
Model binaries downloaded from https://github.com/Layout-Parser/layout-parser/blob/c0044a08da7a630e2241348e597a08ba6aa87ba1/src/layoutparser/models/detectron2/catalog.py | {"license": "apache-2.0", "tags": ["detectron2", "layout_parser"]} | Eterna2/LayoutParser | null | [
"detectron2",
"layout_parser",
"license:apache-2.0",
"region:us"
] | null | 2022-04-12T07:13:51+00:00 | [] | [] | TAGS
#detectron2 #layout_parser #license-apache-2.0 #region-us
|
Model binaries downloaded from URL | [] | [
"TAGS\n#detectron2 #layout_parser #license-apache-2.0 #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-distilbert-base-uncased-finetuned-emotion", "results": []}]} | adache/tf-distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T07:19:50+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# tf-distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evalu... | [
"# tf-distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"#... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achie... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-960h + 4-gram
This model is identical to [Facebook's Wav2Vec2-Base-960h](https://huggingface.co/facebook/wav2vec2-base-960h), but is
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
## Evaluation
This code snippet sho... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "sr... | patrickvonplaten/wav2vec2-base-960h-4-gram | null | [
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T07:25:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base-960h + 4-gram
===========================
This model is identical to Facebook's Wav2Vec2-Base-960h, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.
Evaluation
----------
This code snippet shows how to evaluate patrickvonplaten/wav2vec2-base-960h-4-gram on... | [] | [
"TAGS\n#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-960h + 4-gram
This model is identical to [Facebook's Wav2Vec2-Large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), but is
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
## Evaluation
... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "sr... | patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram | null | [
"transformers",
"pytorch",
"tf",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T07:36:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base-960h + 4-gram
===========================
This model is identical to Facebook's Wav2Vec2-Large-960h-lv60-self, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.
Evaluation
----------
This code snippet shows how to evaluate patrickvonplaten/wav2vec2-large-96... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
This model is a binary classifier developed to analyze comment authorship patterns on Korean news articles.
For further details, refer to our paper on Journalism: [News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments](https://journals.sagepub.c... | {"license": "apache-2.0"} | conviette/korPolBERT | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T07:49:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a binary classifier developed to analyze comment authorship patterns on Korean news articles.
For further details, refer to our paper on Journalism: News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments
* This model is a BERT clas... | [
"### How to use\n* The model requires an edited version of the transformers class 'BertTokenizer', which can be found in the file 'URL'.\n* Usage example:\n\n~~~python\nfrom KorBertTokenizer import KorBertTokenizer\nfrom transformers import BertForSequenceClassification\nimport torch\n\ntokenizer = KorBertTokenizer... | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n* The model requires an edited version of the transformers class 'BertTokenizer', which can be found in the file 'URL'.\n* Usage example:\n\n~~~python\nfrom Kor... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Cla... | nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T07:49:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0664
* Accuracy: 0.9744
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hype... |
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... | satish860/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 | 2022-04-12T08:35:34+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: 0.2174
* Accuracy: 0.923
* F1: 0.9233
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | transformers |
# Hugging NFT: test-light
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/test-light).
D... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images"], "task": "unconditional-image-generation"} | AlekseyKorshuk/test-light | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T08:40:34+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: test-light
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link... | [
"# Hugging NFT: test-light",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available her... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: test-light",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model descripti... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Ayoola/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Ayoola/distilbert-base-uncased-finetuned-squad", "results": []}]} | Ayoola/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T08:59:43+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Ayoola/distilbert-base-uncased-finetuned-squad
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9604
* Validation Loss: 1.1109
* Epoch: 1
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11064, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-clinc-12
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc-12", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus... | lewtun/roberta-large-finetuned-clinc-12 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T09:02:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc-12
================================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1429
* Accuracy: 0.9765
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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\\_ra... |
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. -->
# opus-mt-en-ro-finetuned-en-to-cy
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-cy", "results": []}]} | theojolliffe/opus-mt-en-ro-finetuned-en-to-cy | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T09:14:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-ro-finetuned-en-to-cy
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# opus-mt-en-ro-finetuned-en-to-cy\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro 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",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-ro-finetuned-en-to-cy\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on an unknown dataset.",
"## Mo... |
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/521651470832136193/8-Xdh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/nv1t | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-12T09:24:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
nuit
@nv1t
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
This Repository includes the files required to run the `Computer Science Named Entity Recognition (CS-NER)` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. | {"license": "mit"} | orkg/orkgnlp-cs-ner-abstracts | null | [
"license:mit",
"region:us"
] | null | 2022-04-12T09:51:55+00:00 | [] | [] | TAGS
#license-mit #region-us
|
This Repository includes the files required to run the 'Computer Science Named Entity Recognition (CS-NER)' ORKG-NLP service.
Please check this article for more details about the service. | [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | transformers |
## Model overview
Mutual Implication Score is a symmetric measure of text semantic similarity
based on a RoBERTA model pretrained for natural language inference
and fine-tuned on a paraphrase detection dataset.
The code for inference and evaluation of the model is available [here](https://github.com/skoltech-nlp/mu... | {"language": ["en"], "tags": ["paraphrase detection", "paraphrase", "paraphrasing"], "licenses": ["cc-by-nc-sa"]} | s-nlp/Mutual_Implication_Score | null | [
"transformers",
"pytorch",
"roberta",
"paraphrase detection",
"paraphrase",
"paraphrasing",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T09:58:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #paraphrase detection #paraphrase #paraphrasing #en #endpoints_compatible #region-us
|
## Model overview
Mutual Implication Score is a symmetric measure of text semantic similarity
based on a RoBERTA model pretrained for natural language inference
and fine-tuned on a paraphrase detection dataset.
The code for inference and evaluation of the model is available here.
This measure is particularly usefu... | [
"## Model overview\n\nMutual Implication Score is a symmetric measure of text semantic similarity\nbased on a RoBERTA model pretrained for natural language inference\nand fine-tuned on a paraphrase detection dataset. \n\nThe code for inference and evaluation of the model is available here.\n\nThis measure is partic... | [
"TAGS\n#transformers #pytorch #roberta #paraphrase detection #paraphrase #paraphrasing #en #endpoints_compatible #region-us \n",
"## Model overview\n\nMutual Implication Score is a symmetric measure of text semantic similarity\nbased on a RoBERTA model pretrained for natural language inference\nand fine-tuned on ... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/ape2punk_epoch80 | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-12T10:21:43+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
image-to-image | null |
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | {"license": "mit", "tags": ["huggan", "gan", "image-to-image", "huggingnft", "nft", "image", "images"]} | huggingnft/cryptopunks__2__bored-apes-yacht-club | null | [
"pytorch",
"huggan",
"gan",
"image-to-image",
"huggingnft",
"nft",
"image",
"images",
"arxiv:1703.10593",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-12T10:24:26+00:00 | [
"1703.10593"
] | [] | TAGS
#pytorch #huggan #gan #image-to-image #huggingnft #nft #image #images #arxiv-1703.10593 #license-mit #has_space #region-us
|
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | [
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, the following components are trained end2end to translate between such domains: \n- A generator A to B, named G_AB conditioned on an image from... | [
"TAGS\n#pytorch #huggan #gan #image-to-image #huggingnft #nft #image #images #arxiv-1703.10593 #license-mit #has_space #region-us \n",
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, the foll... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/real2sim | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-12T10:33:27+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
image-to-image | null |
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | {"license": "mit", "tags": ["conditional-image-generation", "image-to-image", "gan", "cyclegan"]} | huggan/sim2real_cyclegan | null | [
"pytorch",
"conditional-image-generation",
"image-to-image",
"gan",
"cyclegan",
"arxiv:2104.13395",
"arxiv:1703.10593",
"license:mit",
"region:us"
] | null | 2022-04-12T10:33:57+00:00 | [
"2104.13395",
"1703.10593"
] | [] | TAGS
#pytorch #conditional-image-generation #image-to-image #gan #cyclegan #arxiv-2104.13395 #arxiv-1703.10593 #license-mit #region-us
|
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | [
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, the following components are trained end2end to translate between such domains: \n- A generator A to B, named G_AB conditioned on an image from... | [
"TAGS\n#pytorch #conditional-image-generation #image-to-image #gan #cyclegan #arxiv-2104.13395 #arxiv-1703.10593 #license-mit #region-us \n",
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, t... |
fill-mask | transformers |
# JobBERT
This is the JobBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno... | {"language": ["en"], "tags": ["JobBERT", "job postings"]} | jjzha/jobbert-base-cased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"JobBERT",
"job postings",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T10:39:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #JobBERT #job postings #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# JobBERT
This is the JobBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno... | [
"# JobBERT\n\nThis is the JobBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Languag... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #JobBERT #job postings #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# JobBERT\n\nThis is the JobBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill ... |
fill-mask | transformers |
# JobBERT
This is the DaJobBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, and Barbara Plank. __Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning__. Proceedings of the Language Resources and Evaluation Conference (LREC). 2022.
This model is c... | {"language": ["da"], "tags": ["job postings", "DaJobBERT"]} | jjzha/dajobbert-base-uncased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"job postings",
"DaJobBERT",
"da",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T10:39:34+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #job postings #DaJobBERT #da #autotrain_compatible #endpoints_compatible #region-us
|
# JobBERT
This is the DaJobBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, and Barbara Plank. __Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning__. Proceedings of the Language Resources and Evaluation Conference (LREC). 2022.
This model is c... | [
"# JobBERT\n\nThis is the DaJobBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, and Barbara Plank. __Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning__. Proceedings of the Language Resources and Evaluation Conference (LREC). 2022.\n\nThis m... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #job postings #DaJobBERT #da #autotrain_compatible #endpoints_compatible #region-us \n",
"# JobBERT\n\nThis is the DaJobBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, and Barbara Plank. __Kompetencer: Fine-grained Skill Classification in Danis... |
null | transformers |
SpanBERT
This is the SpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techn... | {"language": ["en"], "tags": ["retrained", "SpanBERT"]} | jjzha/spanbert-base-cased | null | [
"transformers",
"pytorch",
"bert",
"retrained",
"SpanBERT",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T10:39:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #retrained #SpanBERT #en #endpoints_compatible #region-us
|
SpanBERT
This is the SpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techn... | [] | [
"TAGS\n#transformers #pytorch #bert #retrained #SpanBERT #en #endpoints_compatible #region-us \n"
] |
null | transformers |
# JobSpanBERT
This is the JobSpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua... | {"language": ["en"], "tags": ["continuous pretraining", "job postings", "JobSpanBERT"]} | jjzha/jobspanbert-base-cased | null | [
"transformers",
"pytorch",
"bert",
"continuous pretraining",
"job postings",
"JobSpanBERT",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T10:39:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #continuous pretraining #job postings #JobSpanBERT #en #endpoints_compatible #region-us
|
# JobSpanBERT
This is the JobSpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua... | [
"# JobSpanBERT\n\nThis is the JobSpanBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human... | [
"TAGS\n#transformers #pytorch #bert #continuous pretraining #job postings #JobSpanBERT #en #endpoints_compatible #region-us \n",
"# JobSpanBERT\n\nThis is the JobSpanBERT model from:\n\nMike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job P... |
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. -->
# paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT
This model is a fine-tuned version of [sentence-transformers/paraphrase-mult... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT", "results": []}]} | veddm/paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T10:59:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT
===================================================
This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.4783
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-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: 2e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-clinc-123
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc-123", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plu... | lewtun/roberta-large-finetuned-clinc-123 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T11:00:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc-123
=================================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7226
* Accuracy: 0.9255
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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\\_ra... |
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. -->
# lg-en
This model is a fine-tuned version of [AI-Lab-Makerere/lg_en](https://huggingface.co/AI-Lab-Makerere/lg_en) on an unknown ... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "lg-en", "results": []}]} | Conrad747/lg-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T11:01:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| lg-en
=====
This model is a fine-tuned version of AI-Lab-Makerere/lg\_en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0047
* Bleu: 31.3411
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: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #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: 2\n* eval\... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "base_model": "facebook/convnext-tiny-224", "model-index": [{"name": "convnext-tiny-224-finetuned-eurosat-albumentations", "results": [{"task": {"type": "image-classification", "name": "Image Classificat... | nielsr/convnext-tiny-224-finetuned-eurosat-albumentations | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/convnext-tiny-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T11:04:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-tiny-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| convnext-tiny-224-finetuned-eurosat-albumentations
==================================================
This model is a fine-tuned version of facebook/convnext-tiny-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0727
* Accuracy: 0.9748
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-tiny-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following... |
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. -->
# aesthetic_attribute_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Check your vertical on the main support; it looks a little off. I'd also like to see how it looks with a bit of the sky cropped from the photo"}], "model-index": [{"name": "aesthetic_... | daveni/aesthetic_attribute_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T11:38:03+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| aesthetic\_attribute\_classifier
================================
This model is a fine-tuned version of distilbert-base-uncased on the PCCD dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3976
* Precision: {'precision': 0.877129341279301}
* Recall: {'recall': 0.8751381215469614}
* F1: {'... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Guldeniz/pix2pix_maps | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-12T12:53:41+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #has_space #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #has_space #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potenti... |
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. -->
# claim-spotter
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "claim-spotter", "results": []}]} | gzomer/claim-spotter | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T12:59:35+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| claim-spotter
=============
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3266
* F1: 0.8709
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: 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 #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: ... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | huggan/pix2pix-maps-test | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-12T13:45:41+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-clinc-314
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc-314", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plu... | lewtun/roberta-large-finetuned-clinc-314 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T13:58:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc-314
=================================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7983
* Accuracy: 0.9323
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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\\_ra... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViT pre-trained from scratch on CIFAR10
This model is a ViT (with the same arch as Google's [vit-base-patch16-224](https://huggi... | {"tags": ["masked-image-modeling", "generated_from_trainer"], "datasets": ["cifar10"], "model-index": [{"name": "vit-cifar10", "results": []}]} | mrm8488/vit-base-patch16-224-pretrained-cifar10 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"masked-image-modeling",
"generated_from_trainer",
"dataset:cifar10",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T14:09:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #masked-image-modeling #generated_from_trainer #dataset-cifar10 #endpoints_compatible #region-us
| ViT pre-trained from scratch on CIFAR10
=======================================
This model is a ViT (with the same arch as Google's vit-base-patch16-224 pre-trained from scratch on the cifar10 dataset for masked image modeling.
It achieves the following results on the evaluation set:
* Loss: 0.0891
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100.0",
"### ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #masked-image-modeling #generated_from_trainer #dataset-cifar10 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batc... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | AndrewR/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T14:10:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3919
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-clinc-3141
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) o... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc-3141", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "pl... | lewtun/roberta-large-finetuned-clinc-3141 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T14:19:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc-3141
==================================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1533
* Accuracy: 0.9739
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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\\_ra... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | xieb0001/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T14:21:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8208
* Matthews Correlation: 0.5504
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm-wikihow
This model is a fine-tuned version of [Sevil/t5-small-finetuned-cnndm_3epoch_v2](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm-wikihow", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "wikih... | Chikashi/t5-small-finetuned-cnndm-wikihow | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-12T14:22:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm-wikihow
================================
This model is a fine-tuned version of Sevil/t5-small-finetuned-cnndm\_3epoch\_v2 on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2653
* Rouge1: 27.5037
* Rouge2: 10.8442
* Rougel: 23.4674
* Rougelsum: 26.799... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 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\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
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... | mekondjo/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 | 2022-04-12T14:39:27+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: 0.2219
* 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... |
token-classification | transformers | Label ID Label Name
0 0
1. B-PER
2. I-PER
3. B-ORG
4. I-ORG
5. B-LOC
6. I-LOC | {} | Wanjiru/bert-base-multilingual_en_ner_ | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T15:05:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Label ID Label Name
0 0
1. B-PER
2. I-PER
3. B-ORG
4. I-ORG
5. B-LOC
6. I-LOC | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #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. -->
# SSL-Harveen-Chadda-Fine-Tuning
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://hu... | {"license": "mit", "tags": ["generated_from_trainer"]} | rajat99/SSL-Harveen-Chadda-Fine-Tuning | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T16:22:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-mit #endpoints_compatible #region-us
| SSL-Harveen-Chadda-Fine-Tuning
==============================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0032
* Wer: 0.1008
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* ev... |
null | null | ghods gohds dgihdsfg dfg dsfgiu dsifgisdfg sdgi hsdfg
https://www.fuzia.com/article_detail/365600/what-are-the-features-of-miracle-watt | {} | iuihgisgsd/kiguiughdsfihgdsfg | null | [
"region:us"
] | null | 2022-04-12T17:19:00+00:00 | [] | [] | TAGS
#region-us
| ghods gohds dgihdsfg dfg dsfgiu dsifgisdfg sdgi hsdfg
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null |
# MyModelName
## Model description
[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mappi... | {"license": "mit", "tags": ["huggan", "gan"], "datasets": ["arakesh/uavid-15-hq-mixedres"]} | huggan/pix2pix-uavid-15 | null | [
"pytorch",
"huggan",
"gan",
"dataset:arakesh/uavid-15-hq-mixedres",
"arxiv:1611.07004",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-12T17:53:30+00:00 | [
"1611.07004"
] | [] | TAGS
#pytorch #huggan #gan #dataset-arakesh/uavid-15-hq-mixedres #arxiv-1611.07004 #license-mit #has_space #region-us
|
# MyModelName
## Model description
Pix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply ... | [
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible... | [
"TAGS\n#pytorch #huggan #gan #dataset-arakesh/uavid-15-hq-mixedres #arxiv-1611.07004 #license-mit #has_space #region-us \n",
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only l... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | lewtun/sagemaker-distilbert-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T18:01:43+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-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: 0.2322
* Accuracy: 0.921
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model1.2_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opu... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "test_model1.2_update", "results": []}]} | kabelomalapane/test_model1.2_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T18:08:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test_model1.2_update
This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6296
- Bleu: 4.0505
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# test_model1.2_update\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.6296\n- Bleu: 4.0505",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test_model1.2_update\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.\nIt achiev... |
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