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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-SARC
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-SARC", "results": []}]} | ScandinavianMrT/distilbert-SARC | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T06:17:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-SARC
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4976
- eval_accuracy: 0.7590
- eval_runtime: 268.1875
- eval_samples_per_second: 753.782
- eval_steps_per_second: 47.113
- epoch: 1.0
- step: 5... | [
"# distilbert-SARC\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4976\n- eval_accuracy: 0.7590\n- eval_runtime: 268.1875\n- eval_samples_per_second: 753.782\n- eval_steps_per_second: 47.113\n- epoch: 1.... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-SARC\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following re... |
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. -->
# mt-align-finetuned-LST-en-to-th
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mt-align-finetuned-LST-en-to-th", "results": []}]} | huak95/mt-align-finetuned-LST-en-to-th | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T07:45:26+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mt-align-finetuned-LST-en-to-th
===============================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and eval... | [
"### 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: 1\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-ks-2sec
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-2sec", "results": []}]} | alirezafarashah/wav2vec2-base-ks-2sec | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T07:52:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-ks-2sec
=====================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0880
* Accuracy: 0.9822
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 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 #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | lijingxin/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T08:33:06+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: 0.2161
* Accuracy: 0.9225
* F1: 0.9226
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... |
fill-mask | transformers |
# RoBERTa Turkish medium WordPiece 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 5... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-wp-7k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T08:55:17+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium WordPiece 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 5... | [
"# RoBERTa Turkish medium WordPiece 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 hea... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium WordPiece 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The... |
fill-mask | transformers |
# RoBERTa Turkish medium WordPiece 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-wp-28k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T09:00:27+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium WordPiece 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | [
"# RoBERTa Turkish medium WordPiece 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 he... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium WordPiece 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. Th... |
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/1460097593015472141/Yt6Y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/aniraster_/1646816595677/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/aniraster_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-09T09:02:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Aniraster
@aniraster\_
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# RoBERTa Turkish medium WordPiece 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-wp-44k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T09:04:05+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium WordPiece 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | [
"# RoBERTa Turkish medium WordPiece 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 he... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium WordPiece 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. Th... |
fill-mask | transformers |
# RoBERTa Turkish medium WordPiece 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-wp-66k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T09:15:04+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium WordPiece 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | [
"# RoBERTa Turkish medium WordPiece 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 he... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium WordPiece 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. Th... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191
## Validation Metrics
- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224... | {"language": "zh", "tags": "autonlp", "datasets": ["kyleinincubated/autonlp-data-abbb"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.22514962526191} | kyleinincubated/autonlp-abbb-622117836 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"zh",
"dataset:kyleinincubated/autonlp-data-abbb",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T09:27:47+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-abbb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191
## Validation Metrics
- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 622117836\n- CO2 Emissions (in grams): 2.22514962526191",
"## Validation Metrics\n\n- Loss: 1.2368708848953247\n- Accuracy: 0.7973333333333333\n- Macro F1: 0.46009076588978487\n- Micro F1: 0.7973333333333333\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-abbb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 622117836\n- CO2 Emissions (in gr... |
fill-mask | transformers |
AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters.
AraBART achieves the best performance on multiple abs... | {"language": ["ar"], "license": "apache-2.0", "tags": ["summarization", "bart"], "widget": [{"text": "\u0628\u064a\u0631\u0648\u062a \u0647\u064a \u0639\u0627\u0635\u0645\u0629 <mask>."}], "pipeline_tag": "fill-mask"} | moussaKam/AraBART | null | [
"transformers",
"pytorch",
"mbart",
"feature-extraction",
"summarization",
"bart",
"fill-mask",
"ar",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T10:05:16+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #mbart #feature-extraction #summarization #bart #fill-mask #ar #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters.
AraBART achieves the best performance on multiple abs... | [] | [
"TAGS\n#transformers #pytorch #mbart #feature-extraction #summarization #bart #fill-mask #ar #license-apache-2.0 #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. -->
# wav2vec2-large-xlsr-53-demo1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo1", "results": []}]} | EngNada/wav2vec2-large-xlsr-53-demo1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T10:25:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-demo1
============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9692
* Wer: 0.8462
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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-combinedmodel1-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-combinedmodel1-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-combinedmodel1-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T11:01:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-combinedmodel1-ner
====================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3126
* Precision: 0.0289
* Recall: 0.1443
* F1: 0.0481
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #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: 3e-05\n* train\\_... |
fill-mask | transformers |
# RoBERTa Turkish medium BPE 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hid... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-bpe-7k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T11:56:11+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium BPE 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hid... | [
"# RoBERTa Turkish medium BPE 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium BPE 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model... |
fill-mask | transformers |
# RoBERTa Turkish medium BPE 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-bpe-28k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:00:46+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium BPE 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | [
"# RoBERTa Turkish medium BPE 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 heads, a... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium BPE 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The mode... |
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. -->
# vit-finetuned-chest-xray-pneumonia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["chest X-rays"], "metrics": ["accuracy"], "widget": [{"src": "https://drive.google.com/uc?id=1ygVCyEn6mfsNwpT1ZvWxANg5_DvStA7M", "example_title": "PNEUMONIA"}, {"src": "https://drive.google.com/uc?id=1xjcIEDb8kuSd4wF44gCE... | nickmuchi/vit-finetuned-chest-xray-pneumonia | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T12:04:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-finetuned-chest-xray-pneumonia
==================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the chest-xray-pneumonia dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1271
* Accuracy: 0.9551
Model description
-----------------
More i... | [
"### 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: 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* trai... |
fill-mask | transformers |
# RoBERTa Turkish medium BPE 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-bpe-44k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:04:35+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium BPE 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | [
"# RoBERTa Turkish medium BPE 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 heads, a... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium BPE 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The mode... |
fill-mask | transformers |
# RoBERTa Turkish medium BPE 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-bpe-66k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:10:26+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium BPE 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | [
"# RoBERTa Turkish medium BPE 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 heads, a... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium BPE 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The mode... |
fill-mask | transformers |
# RoBERTa Turkish medium Morph-level 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-morph-7k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:18:19+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Morph-level 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | [
"# RoBERTa Turkish medium Morph-level 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 h... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Morph-level 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. T... |
text-generation | transformers |
### GPT-J COVID-19 French News with 8-bit weights
This is a version of Cedille's GPT-J ([fr-boris](https://huggingface.co/gustavecortal/fr-boris-8bit)) with 6 billion parameters fine-tuned on [COVID-19 French News dataset](https://huggingface.co/datasets/gustavecortal/fr_covid_news) to generate French headlines rela... | {"language": "fr", "license": "mit", "tags": ["causal-lm", "fr"], "datasets": ["gustavecortal/fr_covid_news"]} | gustavecortal/gpt-j-fr-covid-news | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"fr",
"dataset:gustavecortal/fr_covid_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:18:31+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #fr #dataset-gustavecortal/fr_covid_news #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### GPT-J COVID-19 French News with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters fine-tuned on COVID-19 French News dataset to generate French headlines related to COVID-19.
You can generate the model in colab or equivalent desktop gpu (e.g. single 1080Ti) as the model ha... | [
"### GPT-J COVID-19 French News with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters fine-tuned on COVID-19 French News dataset to generate French headlines related to COVID-19. \n\nYou can generate the model in colab or equivalent desktop gpu (e.g. single 1080Ti) as the... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #fr #dataset-gustavecortal/fr_covid_news #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### GPT-J COVID-19 French News with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters fine-t... |
text2text-generation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | SGrannemann/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:27:54+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| marian-finetuned-kde4-en-to-fr
==============================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6859
* Validation Loss: 0.8062
* Epoch: 2
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 17733, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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. -->
# model
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "model", "results": []}]} | Narshion/mWACH_mBERT_System | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:28:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# model
This model is a fine-tuned version of bert-base-multilingual-cased on mWACH NEO dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6344
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More info... | [
"# model\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on mWACH NEO dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6344",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eval... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# model\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on mWACH NEO dataset.\nIt achieves the following results on the evaluation... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-irish-local
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-irish-local", "results": []}]} | jfealko/wav2vec2-large-xls-r-300m-irish-local | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:28:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-irish-local
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0788
* Wer: 0.7527
Model description
-----------------
More information ... | [
"### 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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
fill-mask | transformers |
# RoBERTa Turkish medium Morph-level 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-morph-28k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:36:37+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Morph-level 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"# RoBERTa Turkish medium Morph-level 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Morph-level 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. ... |
fill-mask | transformers |
# RoBERTa Turkish medium Morph-level 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-morph-44k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:41:09+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Morph-level 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"# RoBERTa Turkish medium Morph-level 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Morph-level 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. ... |
fill-mask | transformers |
# RoBERTa Turkish medium Morph-level 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-morph-66k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:47:05+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Morph-level 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"# RoBERTa Turkish medium Morph-level 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Morph-level 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. ... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-quac | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T12:52:37+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# RoBERTa Turkish medium Word-level 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-word-7k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T13:17:25+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Word-level 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | [
"# RoBERTa Turkish medium Word-level 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 he... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Word-level 7k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. Th... |
fill-mask | transformers |
# RoBERTa Turkish medium Word-level 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-word-28k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T13:26:34+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Word-level 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | [
"# RoBERTa Turkish medium Word-level 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 h... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Word-level 28k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. T... |
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. -->
# beto_stars
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "beto_stars", "results": []}]} | MarioPenguin/beto_stars | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T13:30:22+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| beto\_stars
===========
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8954
* Train Accuracy: 0.6248
* Validation Loss: 1.1278
* Validation Accuracy: 0.5148
* Epoch: 14
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'bet... |
fill-mask | transformers |
# RoBERTa Turkish medium Word-level 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-word-44k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T13:42:29+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Word-level 44k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | [
"# RoBERTa Turkish medium Word-level 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 h... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Word-level 44k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. T... |
fill-mask | transformers |
# RoBERTa Turkish medium Word-level 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-word-66k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T13:48:40+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Word-level 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | [
"# RoBERTa Turkish medium Word-level 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 h... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Word-level 66k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. T... |
text-classification | transformers | sberbank-ai/ruRoberta-large fine-tuned for Russian Artificial Text Detection shared task
| {} | orzhan/ruroberta-ruatd-binary | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T15:28:56+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| sberbank-ai/ruRoberta-large fine-tuned for Russian Artificial Text Detection shared task
| [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | PyTorch |
Face Frontalization is a generative computer vision task in which the model takes a photo of a person's head taken at an angle between -90 and 90 degrees, and produces an image of what that person's frontal (i.e. 0 degree) view of the face might look like. The present model was first released in [this repository](http... | {"language": "en", "license": "mit", "library_name": "PyTorch", "tags": ["computer vision", "GAN"], "datasets": ["multi-pie"]} | opetrova/face-frontalization | null | [
"PyTorch",
"computer vision",
"GAN",
"en",
"dataset:multi-pie",
"arxiv:1704.04086",
"arxiv:1511.06434",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-09T15:44:56+00:00 | [
"1704.04086",
"1511.06434"
] | [
"en"
] | TAGS
#PyTorch #computer vision #GAN #en #dataset-multi-pie #arxiv-1704.04086 #arxiv-1511.06434 #license-mit #has_space #region-us
|
Face Frontalization is a generative computer vision task in which the model takes a photo of a person's head taken at an angle between -90 and 90 degrees, and produces an image of what that person's frontal (i.e. 0 degree) view of the face might look like. The present model was first released in this repository by Sca... | [
"# Model description\n\nThe Face Frontalization model is the Generator part of a GAN that was trained in a supervised fashion on profile-frontal image pairs. The Discriminator was based on a fairly standard DCGAN architecture, where the input is a 128x128x3 image that is processed through multiple convolutional lay... | [
"TAGS\n#PyTorch #computer vision #GAN #en #dataset-multi-pie #arxiv-1704.04086 #arxiv-1511.06434 #license-mit #has_space #region-us \n",
"# Model description\n\nThe Face Frontalization model is the Generator part of a GAN that was trained in a supervised fashion on profile-frontal image pairs. The Discriminator w... |
null | null |
# Model Card for Test Gated
<!-- Provide a quick summary of what the model is/does. [Optional] -->
Some cool model...
# Table of Contents
- [Model Card for Test Gated](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-deta... | {"extra_gated_prompt": "You agree to not use the model to conduct experiments that cause harm to human subjects. Click [here](https://huggingface.co/terms-of-service) for more info ", "extra_gated_fields": {"Company": "text", "Country": "text"... | mfuntowicz/xlm-roberta-large-squad2 | null | [
"arxiv:1910.09700",
"region:us"
] | null | 2022-03-09T16:09:59+00:00 | [
"1910.09700"
] | [] | TAGS
#arxiv-1910.09700 #region-us
|
# Model Card for Test Gated
Some cool model...
# Table of Contents
- Model Card for Test Gated
- Table of Contents
- Table of Contents
- Model Details
- Model Description
- Uses
- Direct Use
- [Downstream Use [Optional]](#downstream-use-optional)
- Out-of-Scope Use
- Bias, Risks, and Limitations
- R... | [
"# Model Card for Test Gated\n\n\nSome cool model...",
"# Table of Contents\n\n- Model Card for Test Gated\n- Table of Contents\n- Table of Contents\n- Model Details\n - Model Description\n- Uses\n - Direct Use\n - [Downstream Use [Optional]](#downstream-use-optional)\n - Out-of-Scope Use\n- Bias, Risks, and... | [
"TAGS\n#arxiv-1910.09700 #region-us \n",
"# Model Card for Test Gated\n\n\nSome cool model...",
"# Table of Contents\n\n- Model Card for Test Gated\n- Table of Contents\n- Table of Contents\n- Model Details\n - Model Description\n- Uses\n - Direct Use\n - [Downstream Use [Optional]](#downstream-use-optional... |
null | keras | ## UK & Ireland Accent Classification Model
This model classifies UK & Ireland accents using feature extraction from [Yamnet](https://tfhub.dev/google/yamnet/1).
### Yamnet Model
Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on ... | {"license": "apache-2.0"} | fbadine/uk_ireland_accent_classification | null | [
"keras",
"tensorboard",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-09T16:53:02+00:00 | [] | [] | TAGS
#keras #tensorboard #license-apache-2.0 #has_space #region-us
| UK & Ireland Accent Classification Model
----------------------------------------
This model classifies UK & Ireland accents using feature extraction from Yamnet.
### Yamnet Model
Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is availabl... | [
"### Yamnet Model\n\n\nYamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.\nYamnet accepts a 1-D tensor of audio samples with a sample rate of 16 kHz. \n\nAs output, the model returns a 3-tuple:\n\n\n* Scores of ... | [
"TAGS\n#keras #tensorboard #license-apache-2.0 #has_space #region-us \n",
"### Yamnet Model\n\n\nYamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.\nYamnet accepts a 1-D tensor of audio samples with a sample 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. -->
# bigbird-pegasus-large-arxiv-finetuned-pubmed
This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "bigbird-pegasus-large-arxiv-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": ... | Kevincp560/bigbird-pegasus-large-arxiv-finetuned-pubmed | null | [
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T17:14:25+00:00 | [] | [] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bigbird-pegasus-large-arxiv-finetuned-pubmed
============================================
This model is a fine-tuned version of google/bigbird-pegasus-large-arxiv on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6049
* Rouge1: 45.4807
* Rouge2: 20.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bigbird_pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
image-classification | transformers |
# Van
Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification).
Disclaimer: The team releasing Van did not write a model card for this model so... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | Visual-Attention-Network/van-large | null | [
"transformers",
"pytorch",
"van",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T18:03:37+00:00 | [
"2202.09741"
] | [] | TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Van
Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository.
Disclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This paper introduc... | [
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. \n\nDisclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThis ... | [
"TAGS\n#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Van\n\nVan model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this rep... |
text2text-generation | transformers | An AI model that, given a statement, generates a question that would have likely resulted in said statement.
Created for a Senior Project at Calvin University. | {} | hyechanjun/reverse-interview-question | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T18:52:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| An AI model that, given a statement, generates a question that would have likely resulted in said statement.
Created for a Senior Project at Calvin University. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-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... | antho-data/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T20:30:05+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: 0.2294
* Accuracy: 0.9235
* F1: 0.9237
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
# t5-small-24L-dutch-english
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5 eff** model has **249M... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-small-24L-dutch-english | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-09T20:39:13+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl",
"en"
] | TAGS
#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-small-24L-dutch-english
==========================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4.
This t5 eff model has 249M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset
'... | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
This is a BERT model fine-tuned on a named-entity recognition (NER) dataset.
The notebook that was used to create this model can be found here: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/BERT/Custom_Named_Entity_Recognition_with_BERT.ipynb | {"language": ["en"]} | nielsr/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T21:04:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is a BERT model fine-tuned on a named-entity recognition (NER) dataset.
The notebook that was used to create this model can be found here: URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# toy
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "toy", "results": []}]} | datarpit/toy | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T21:38:51+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| toy
===
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2124
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Tra... | [
"### 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: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #distilbert #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\\_batch\\_siz... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-newsqa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T21:51:00+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This model was trained on a new dataset composed of available poems by Anne Bradstreet hosted by [Public Domain Poetry.](https://www.public-domain-poetry.com/anne-bradstreet) Specifically I downloaded all 40 poems and fine-tuned a bert-base-uncased text classification model on Amazon SageMaker. For the negative class, ... | {"license": "mit"} | edubz/anne_bradstreet | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T22:03:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This model was trained on a new dataset composed of available poems by Anne Bradstreet hosted by Public Domain Poetry. Specifically I downloaded all 40 poems and fine-tuned a bert-base-uncased text classification model on Amazon SageMaker. For the negative class, I actually generated GPT-2 samples of length 70. That is... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | zdepablo/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T22:55:01+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: 0.2311
* Accuracy: 0.924
* F1: 0.9242
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... |
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. -->
# toy-qa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "toy-qa", "results": []}]} | datarpit/toy-qa | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T00:23:45+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| toy-qa
======
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2410
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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #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: 16\n* eval\\_batch\\_si... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-scouting
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-scouting", "results": []}]} | amanm27/bert-base-uncased-scouting | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T00:27:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-scouting
==========================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5443
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
null | transformers | # UCTopic
This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for <strong>Topic Mining</strong>, <strong>Unsupervised Aspect Extractioin</strong> or <strong>Phrase Retrieval</strong>.
Our ACL 2022 paper [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and ... | {"license": "mit"} | JiachengLi/uctopic-base | null | [
"transformers",
"pytorch",
"luke",
"arxiv:2202.13469",
"arxiv:2010.01057",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T00:53:46+00:00 | [
"2202.13469",
"2010.01057"
] | [] | TAGS
#transformers #pytorch #luke #arxiv-2202.13469 #arxiv-2010.01057 #license-mit #endpoints_compatible #region-us
| UCTopic
=======
This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for **Topic Mining**, **Unsupervised Aspect Extractioin** or **Phrase Retrieval**.
Our ACL 2022 paper UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining.
Quick Links
====... | [
"### Initialization\n\n\n'UCTopicTool' is initialized by giving the 'model\\_name\\_or\\_path' and 'device'.",
"### Phrase Encoding\n\n\nPhrases are encoded by our method 'URL' in batches, which is more efficient than 'UCTopic'.\n\n\nNote: Each instance in 'phrases' contains only one sentence and one span (charac... | [
"TAGS\n#transformers #pytorch #luke #arxiv-2202.13469 #arxiv-2010.01057 #license-mit #endpoints_compatible #region-us \n",
"### Initialization\n\n\n'UCTopicTool' is initialized by giving the 'model\\_name\\_or\\_path' and 'device'.",
"### Phrase Encoding\n\n\nPhrases are encoded by our method 'URL' in batches, ... |
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... | aaraki/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T01:29:51+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: 0.0788
* Precision: 0.8857
* Recall: 0.9092
* F1: 0.8973
* Accuracy: 0.9775
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 623817873
- CO2 Emissions (in grams): 147.38973865706626
## Validation Metrics
- Loss: 0.2412157654762268
- Accuracy: 0.9306
- Precision: 0.9377795851972347
- Recall: 0.9224
- AUC: 0.97000504
- F1: 0.9300262149626941
## Usage
You can ... | {"language": "en", "tags": "autonlp", "datasets": ["chiragme/autonlp-data-imdb-sentiment-analysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 147.38973865706626} | chiragme/autonlp-imdb-sentiment-analysis-623817873 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:chiragme/autonlp-data-imdb-sentiment-analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T02:03:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-chiragme/autonlp-data-imdb-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 623817873
- CO2 Emissions (in grams): 147.38973865706626
## Validation Metrics
- Loss: 0.2412157654762268
- Accuracy: 0.9306
- Precision: 0.9377795851972347
- Recall: 0.9224
- AUC: 0.97000504
- F1: 0.9300262149626941
## Usage
You can ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 623817873\n- CO2 Emissions (in grams): 147.38973865706626",
"## Validation Metrics\n\n- Loss: 0.2412157654762268\n- Accuracy: 0.9306\n- Precision: 0.9377795851972347\n- Recall: 0.9224\n- AUC: 0.97000504\n- F1: 0.9300262149626941"... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-chiragme/autonlp-data-imdb-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 623817873\n- CO2 Emissions... |
text-generation | transformers |
#Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | BeanBoi50404/DialoGPT-small-PeppaPigButBetter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T03:10:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Peppa Pig DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# jabalov/MARBERT-finetuned-arabic-dialects-identification
This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.c... | {"tags": ["generated_from_keras_callback"], "base_model": "UBC-NLP/MARBERT", "model-index": [{"name": "jabalov/MARBERT-finetuned-arabic-dialects-identification", "results": []}]} | jabalov/MARBERT-finetuned-arabic-dialects-identification | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:UBC-NLP/MARBERT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T03:22:54+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #text-classification #generated_from_keras_callback #base_model-UBC-NLP/MARBERT #autotrain_compatible #endpoints_compatible #region-us
| jabalov/MARBERT-finetuned-arabic-dialects-identification
========================================================
This model is a fine-tuned version of UBC-NLP/MARBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.4599
* Validation Loss: 1.0517
* Epoch: 0
Model de... | [
"### 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': 64431, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #bert #text-classification #generated_from_keras_callback #base_model-UBC-NLP/MARBERT #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'le... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 624217911
- CO2 Emissions (in grams): 2.267288583123193
## Validation Metrics
- Loss: 0.39670249819755554
- Accuracy: 0.9098901098901099
- Macro F1: 0.7398394202169645
- Micro F1: 0.9098901098901099
- Weighted F1: 0.907332946411916... | {"language": "zh", "tags": "autonlp", "datasets": ["kyleinincubated/autonlp-data-cat333"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.267288583123193} | kyleinincubated/autonlp-cat333-624217911 | null | [
"transformers",
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"bert",
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"autonlp",
"zh",
"dataset:kyleinincubated/autonlp-data-cat333",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T03:45:34+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-cat333 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 624217911
- CO2 Emissions (in grams): 2.267288583123193
## Validation Metrics
- Loss: 0.39670249819755554
- Accuracy: 0.9098901098901099
- Macro F1: 0.7398394202169645
- Micro F1: 0.9098901098901099
- Weighted F1: 0.907332946411916... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 624217911\n- CO2 Emissions (in grams): 2.267288583123193",
"## Validation Metrics\n\n- Loss: 0.39670249819755554\n- Accuracy: 0.9098901098901099\n- Macro F1: 0.7398394202169645\n- Micro F1: 0.9098901098901099\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-cat333 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 624217911\n- CO2 Emissions (in ... |
fill-mask | transformers |
# Pretrained Model
BASE MODEL : BERT-BASE-UNCASED
DATASET : [TWTEVAL SENTIMENT](https://huggingface.co/datasets/ArnavL/TWTEval-Pretraining-Processed)
| {"license": "mit"} | ArnavL/twteval-pretrained | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T04:10:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Pretrained Model
BASE MODEL : BERT-BASE-UNCASED
DATASET : TWTEVAL SENTIMENT
| [
"# Pretrained Model\r\n\r\nBASE MODEL : BERT-BASE-UNCASED\r\n\r\n\r\nDATASET : TWTEVAL SENTIMENT"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pretrained Model\r\n\r\nBASE MODEL : BERT-BASE-UNCASED\r\n\r\n\r\nDATASET : TWTEVAL SENTIMENT"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-100k-lit-evalMA
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/b... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-cnn-100k-lit-evalMA", "results": []}]} | cammy/bart-large-cnn-100k-lit-evalMA | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T04:44:55+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-cnn-100k-lit-evalMA
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.7715
- eval_rouge1: 29.7037
- eval_rouge2: 15.0234
- eval_rougeL: 23.5169
- eval_rougeLsum: 26.8682
- eval_gen_len: 68.1209
- ... | [
"# bart-large-cnn-100k-lit-evalMA\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.7715\n- eval_rouge1: 29.7037\n- eval_rouge2: 15.0234\n- eval_rougeL: 23.5169\n- eval_rougeLsum: 26.8682\n- eval_gen_len:... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-cnn-100k-lit-evalMA\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on... |
table-question-answering | transformers |
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretrain... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"]} | microsoft/tapex-large | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-10T04:55:54+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# TAPEX (large-sized model)
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
## Model description
TAPEX (Table Pre-training via Execution) is a conceptuall... | [
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.",
"## Model description\n\nTAPEX (Table Pre-training via Execution) is a... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural S... |
table-question-answering | transformers |
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretrain... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["wikitablequestions"]} | microsoft/tapex-large-finetuned-wtq | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-10T05:06:08+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikitablequestions #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| TAPEX (large-sized model)
=========================
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
Model description
-----------------
TAPEX (Table Pre-... | [
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eval\n\n\nPlease find the eval script here.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikitablequestions #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to Use\n\n\nHere is how to use this model in transformers:",
"###... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | clisi2000/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T05:20:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1371
* F1: 0.8604
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
table-question-answering | transformers |
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretrain... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"]} | microsoft/tapex-large-sql-execution | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-10T05:21:42+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# TAPEX (large-sized model)
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
## Model description
TAPEX (Table Pre-training via Execution) is a conceptuall... | [
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.",
"## Model description\n\nTAPEX (Table Pre-training via Execution) is a... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural S... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-wiki
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-wiki", "results": []}]} | amanm27/bert-base-uncased-wiki | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T05:58:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-wiki
======================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7509
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 624317932
- CO2 Emissions (in grams): 1.2490471218570545
## Validation Metrics
- Loss: 0.5579860806465149
- Accuracy: 0.8717391304347826
- Macro F1: 0.6625543939916455
- Micro F1: 0.8717391304347827
- Weighted F1: 0.859330374267149... | {"language": "zh", "tags": "autonlp", "datasets": ["kyleinincubated/autonlp-data-cat33"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.2490471218570545} | kyleinincubated/autonlp-cat33-624317932 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"zh",
"dataset:kyleinincubated/autonlp-data-cat33",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T06:09:35+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-cat33 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 624317932
- CO2 Emissions (in grams): 1.2490471218570545
## Validation Metrics
- Loss: 0.5579860806465149
- Accuracy: 0.8717391304347826
- Macro F1: 0.6625543939916455
- Micro F1: 0.8717391304347827
- Weighted F1: 0.859330374267149... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 624317932\n- CO2 Emissions (in grams): 1.2490471218570545",
"## Validation Metrics\n\n- Loss: 0.5579860806465149\n- Accuracy: 0.8717391304347826\n- Macro F1: 0.6625543939916455\n- Micro F1: 0.8717391304347827\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-cat33 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 624317932\n- CO2 Emissions (in g... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sports
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-sports", "results": []}]} | amanm27/bert-base-uncased-sports | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T06:32:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-sports
========================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0064
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-100-lit-evalMA
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/ba... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-cnn-100-lit-evalMA", "results": []}]} | cammy/bart-large-cnn-100-lit-evalMA | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T06:32:37+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-cnn-100-lit-evalMA
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.1514
- eval_rouge1: 27.8026
- eval_rouge2: 11.2998
- eval_rougeL: 21.4708
- eval_rougeLsum: 24.6333
- eval_gen_len: 62.5
- eval... | [
"# bart-large-cnn-100-lit-evalMA\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.1514\n- eval_rouge1: 27.8026\n- eval_rouge2: 11.2998\n- eval_rougeL: 21.4708\n- eval_rougeLsum: 24.6333\n- eval_gen_len: ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-cnn-100-lit-evalMA\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on ... |
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-E
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-E", "results": []}]} | M-Quan/wav2vec2-E | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T06:37:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-E
==========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4832
* Wer: 0.3432
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-wiki-sports
This model is a fine-tuned version of [amanm27/bert-base-uncased-wiki](https://huggingface.co/aman... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-wiki-sports", "results": []}]} | amanm27/bert-base-uncased-wiki-sports | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T06:44:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-wiki-sports
=============================
This model is a fine-tuned version of amanm27/bert-base-uncased-wiki on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9753
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-wiki-scouting
This model is a fine-tuned version of [amanm27/bert-base-uncased-wiki](https://huggingface.co/am... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-wiki-scouting", "results": []}]} | amanm27/bert-base-uncased-wiki-scouting | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T07:00:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-wiki-scouting
===============================
This model is a fine-tuned version of amanm27/bert-base-uncased-wiki on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5048
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sports-scouting
This model is a fine-tuned version of [amanm27/bert-base-uncased-sports](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-sports-scouting", "results": []}]} | amanm27/bert-base-uncased-sports-scouting | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T07:07:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-sports-scouting
=================================
This model is a fine-tuned version of amanm27/bert-base-uncased-sports on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5127
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-wiki-sports-scouting
This model is a fine-tuned version of [amanm27/bert-base-uncased-wiki-sports](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-wiki-sports-scouting", "results": []}]} | amanm27/bert-base-uncased-wiki-sports-scouting | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T07:14:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-wiki-sports-scouting
======================================
This model is a fine-tuned version of amanm27/bert-base-uncased-wiki-sports on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4909
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-base-manuscripts
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://hu... | {"license": "apache-2.0", "tags": ["masked-image-modeling", "generated_from_trainer"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-base-patch16-224-in21k-base-manuscripts", "results": []}]} | davanstrien/vit-base-patch16-224-in21k-base-manuscripts | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"masked-image-modeling",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T07:44:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #masked-image-modeling #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-base-manuscripts
===========================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the davanstrien/iiif\_manuscripts\_label\_ge\_50 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5210
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 1333\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #masked-image-modeling #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #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:... |
translation | null |
[Fairseq](https://github.com/pytorch/fairseq) model for translating between English, Estonian, Latvian and Livonian.
Subword units created with [SentencePiece](https://github.com/google/sentencepiece).
To specify the target language to translate into, prepend one of the language code tags to the source sentence... | {"license": "apache-2.0", "tags": ["translation", "Fairseq"], "widget": [{"text": "<2li> Let us generate some Livonian text!"}]} | tartuNLP/liv4ever-mt | null | [
"translation",
"Fairseq",
"license:apache-2.0",
"region:us"
] | null | 2022-03-10T08:28:42+00:00 | [] | [] | TAGS
#translation #Fairseq #license-apache-2.0 #region-us
|
Fairseq model for translating between English, Estonian, Latvian and Livonian.
Subword units created with SentencePiece.
To specify the target language to translate into, prepend one of the language code tags to the source sentences:
This should be done after applying SentencePiece. | [] | [
"TAGS\n#translation #Fairseq #license-apache-2.0 #region-us \n"
] |
null | null | test huggingface | {} | rajatguptakgp/test | null | [
"region:us"
] | null | 2022-03-10T08:47:37+00:00 | [] | [] | TAGS
#region-us
| test huggingface | [] | [
"TAGS\n#region-us \n"
] |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | pratt3000/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T08:59:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0029
* Accuracy: 0.9997
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 3e-05\n* train\\_batch\\_size: 8\n* eval\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | lijingxin/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T09:01:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1348
* F1: 0.8595
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment-model-sample-ekman-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample-ekman-emotion", "results": []}]} | jkhan447/sentiment-model-sample-ekman-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T09:21:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample-ekman-emotion
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4963
- Accuracy: 0.6713
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# sentiment-model-sample-ekman-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.4963\n- Accuracy: 0.6713",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample-ekman-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the fol... |
null | null | Моя модель умеет распознавать ценники и сравнивать с ценами конкурентов. | {} | verok/verok_private | null | [
"region:us"
] | null | 2022-03-10T09:28:49+00:00 | [] | [] | TAGS
#region-us
| Моя модель умеет распознавать ценники и сравнивать с ценами конкурентов. | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
# Model Description
This model is for English extractive question answering. It is based on the [bert-base-cased](https://huggingface.co/bert-base-uncased) model, and it is case-sensitive: it makes a difference between english and English.
# Training data
[English SQuAD v2.0](https://rajpurkar.github.io/SQuAD-explo... | {"language": "English", "tags": ["bert-base"], "datasets": "SQuAD 2.0", "task": "extractive question answering"} | zhufy/squad-en-bert-base | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"bert-base",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T10:12:56+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #bert #question-answering #bert-base #endpoints_compatible #region-us
|
# Model Description
This model is for English extractive question answering. It is based on the bert-base-cased model, and it is case-sensitive: it makes a difference between english and English.
# Training data
English SQuAD v2.0
# How to use
You can use it directly from the Transformers library with a pipelin... | [
"# Model Description\n\nThis model is for English extractive question answering. It is based on the bert-base-cased model, and it is case-sensitive: it makes a difference between english and English.",
"# Training data\n\nEnglish SQuAD v2.0",
"# How to use\n\nYou can use it directly from the Transformers libra... | [
"TAGS\n#transformers #pytorch #bert #question-answering #bert-base #endpoints_compatible #region-us \n",
"# Model Description\n\nThis model is for English extractive question answering. It is based on the bert-base-cased model, and it is case-sensitive: it makes a difference between english and English.",
"# Tr... |
text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | nabin19677/small-cartman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T10:17:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
feature-extraction | transformers |
Latvian BERT-base-cased model.
```
@inproceedings{Znotins-Barzdins:2020:BalticHLT,
author = "A. Znotins and G. Barzdins",
title = "LVBERT: Transformer-Based Model for Latvian Language Understanding",
year = 2020,
booktitle = "Human Language Technologies - The Baltic Perspective",
publisher = "IOS Press",
... | {"license": "gpl-3.0"} | AiLab-IMCS-UL/lvbert | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T10:26:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #license-gpl-3.0 #endpoints_compatible #region-us
|
Latvian BERT-base-cased model.
Please use the following text to cite this item or export to a predefined format:
Znotiņš, Artūrs, 2020, LVBERT - Latvian BERT, CLARIN-LV digital library at IMCS, University of Latvia, URL
| [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #license-gpl-3.0 #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tmp_trainer
This model is a fine-tuned version of [pong/opus-mt-en-mul-finetuned-en-to-th](https://huggingface.co/pong/opus-mt-e... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "tmp_trainer", "results": []}]} | huak95/tmp_trainer | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T10:32:50+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# tmp_trainer
This model is a fine-tuned version of pong/opus-mt-en-mul-finetuned-en-to-th on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# tmp_trainer\n\nThis model is a fine-tuned version of pong/opus-mt-en-mul-finetuned-en-to-th on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# tmp_trainer\n\nThis model is a fine-tuned version of pong/opus-mt-en-mul-finetuned-en-to-th on an unknown dataset.",
"## Model description\n\nMore information needed... |
token-classification | transformers |
# bert-base-german-upos
## Model Description
This is a BERT model pre-trained with [UD_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT) for POS-tagging and dependency-parsing, derived from [gbert-base](https://huggingface.co/deepset/gbert-base). Every word is tagged by [UPOS](https://universaldepe... | {"language": ["de"], "license": "mit", "tags": ["german", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/bert-base-german-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"german",
"pos",
"dependency-parsing",
"de",
"dataset:universal_dependencies",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T10:32:58+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #token-classification #german #pos #dependency-parsing #de #dataset-universal_dependencies #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-german-upos
## Model Description
This is a BERT model pre-trained with UD_German-HDT for POS-tagging and dependency-parsing, derived from gbert-base. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/... | [
"# bert-base-german-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_German-HDT for POS-tagging and dependency-parsing, derived from gbert-base. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger and Depend... | [
"TAGS\n#transformers #pytorch #bert #token-classification #german #pos #dependency-parsing #de #dataset-universal_dependencies #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-german-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_German-HDT for POS-... |
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. -->
# bigbird-pegasus-large-bigpatent-finetuned-pubMed
This model is a fine-tuned version of [google/bigbird-pegasus-large-bigpatent](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "bigbird-pegasus-large-bigpatent-finetuned-pubMed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "datase... | Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed | null | [
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T10:58:00+00:00 | [] | [] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bigbird-pegasus-large-bigpatent-finetuned-pubMed
================================================
This model is a fine-tuned version of google/bigbird-pegasus-large-bigpatent on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5403
* Rouge1: 45.0851
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bigbird_pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | MoHai/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T11:30:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4701
* Wer: 0.4537
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
null | null |
Datasets here: https://huggingface.co/datasets/rocca/sims4-faces | {"license": "mit"} | rocca/sims4-faces | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2022-03-10T12:15:19+00:00 | [] | [] | TAGS
#onnx #license-mit #region-us
|
Datasets here: URL | [] | [
"TAGS\n#onnx #license-mit #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625317956
- CO2 Emissions (in grams): 1.1406456838043837
## Validation Metrics
- Loss: 0.513037919998169
- Accuracy: 0.8982035928143712
- Macro F1: 0.7843756230226546
- Micro F1: 0.8982035928143712
- Weighted F1: 0.8891653474608059... | {"language": "en", "tags": "autonlp", "datasets": ["Chijioke/autonlp-data-mono"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.1406456838043837} | Chijioke/autonlp-mono-625317956 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:Chijioke/autonlp-data-mono",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T12:45:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-Chijioke/autonlp-data-mono #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625317956
- CO2 Emissions (in grams): 1.1406456838043837
## Validation Metrics
- Loss: 0.513037919998169
- Accuracy: 0.8982035928143712
- Macro F1: 0.7843756230226546
- Micro F1: 0.8982035928143712
- Weighted F1: 0.8891653474608059... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625317956\n- CO2 Emissions (in grams): 1.1406456838043837",
"## Validation Metrics\n\n- Loss: 0.513037919998169\n- Accuracy: 0.8982035928143712\n- Macro F1: 0.7843756230226546\n- Micro F1: 0.8982035928143712\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-Chijioke/autonlp-data-mono #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625317956\n- CO2 Emissions (in gra... |
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. -->
# hubert-base-ser
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-l... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-ser", "results": []}]} | RamiEbeid/hubert-base-ser | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T13:34:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-base-ser
===============
This model is a fine-tuned version of facebook/hubert-base-ls960 on the Crema dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0105
* Accuracy: 0.6313
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* se... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-nq-large-batch | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T13:57:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-en-ru
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base-en-ru", "results": []}]} | kazandaev/mt5-base-en-ru | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T14:01:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-en-ru
==============
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7194
* Bleu: 14.3528
* Gen Len: 17.8655
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: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad
This model is a fine-tuned version of [bert-large-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad", "results": []}]} | haddadalwi/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T14:03:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad
=============================================================================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the squad\_v2 dataset.
It achieves the following results on the eva... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size... |
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. -->
# tmp9770t4k0
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/be... | {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "nlptown/bert-base-multilingual-uncased-sentiment", "model-index": [{"name": "tmp9770t4k0", "results": []}]} | juancopi81/tutorial-model-bert-base-spanish-uncased-movie-rating | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:nlptown/bert-base-multilingual-uncased-sentiment",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T14:07:17+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-nlptown/bert-base-multilingual-uncased-sentiment #license-mit #autotrain_compatible #endpoints_compatible #region-us
| tmp9770t4k0
===========
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an muchocine dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6629
* Train Accuracy: 0.7345
* Validation Loss: 1.4827
* Validation Accuracy: 0.5
* Epoch: 2
Model descr... | [
"### 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': 5e-05, 'decay\\_steps': 1305, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-nlptown/bert-base-multilingual-uncased-sentiment #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717986
- CO2 Emissions (in grams): 68.73074770596023
## Validation Metrics
- Loss: 0.859463632106781
- Accuracy: 0.6118427330852181
- Macro F1: 0.6112554383858383
- Micro F1: 0.6118427330852181
- Weighted F1: 0.6112706859556324
... | {"language": "en", "tags": "autonlp", "datasets": ["Someshfengde/autonlp-data-kaggledays"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 68.73074770596023} | Someshfengde/autonlp-kaggledays-625717986 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Someshfengde/autonlp-data-kaggledays",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T14:39:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Someshfengde/autonlp-data-kaggledays #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717986
- CO2 Emissions (in grams): 68.73074770596023
## Validation Metrics
- Loss: 0.859463632106781
- Accuracy: 0.6118427330852181
- Macro F1: 0.6112554383858383
- Micro F1: 0.6118427330852181
- Weighted F1: 0.6112706859556324
... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625717986\n- CO2 Emissions (in grams): 68.73074770596023",
"## Validation Metrics\n\n- Loss: 0.859463632106781\n- Accuracy: 0.6118427330852181\n- Macro F1: 0.6112554383858383\n- Micro F1: 0.6118427330852181\n- Weighted F1: 0... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Someshfengde/autonlp-data-kaggledays #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625717986\n- CO2 Emissions (in... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717992
- CO2 Emissions (in grams): 28.622267513847273
## Validation Metrics
- Loss: 0.8782362937927246
- Accuracy: 0.6022282660559214
- Macro F1: 0.6024258279848015
- Micro F1: 0.6022282660559214
- Weighted F1: 0.602429990862437... | {"language": "en", "tags": "autonlp", "datasets": ["Someshfengde/autonlp-data-kaggledays"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 28.622267513847273} | Someshfengde/autonlp-kaggledays-625717992 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:Someshfengde/autonlp-data-kaggledays",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T14:39:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-Someshfengde/autonlp-data-kaggledays #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717992
- CO2 Emissions (in grams): 28.622267513847273
## Validation Metrics
- Loss: 0.8782362937927246
- Accuracy: 0.6022282660559214
- Macro F1: 0.6024258279848015
- Micro F1: 0.6022282660559214
- Weighted F1: 0.602429990862437... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625717992\n- CO2 Emissions (in grams): 28.622267513847273",
"## Validation Metrics\n\n- Loss: 0.8782362937927246\n- Accuracy: 0.6022282660559214\n- Macro F1: 0.6024258279848015\n- Micro F1: 0.6022282660559214\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-Someshfengde/autonlp-data-kaggledays #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 625717992\n- CO2 Emissio... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-blame-assassin
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-blame-assassin", "results": []}]} | responsibility-framing/predict-perception-bert-blame-assassin | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:32:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-blame-assassin
======================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5128
* Rmse: 1.0287
* Rmse Blame::a L'assassino: 1.0287
* Mae: 0.8883
* Mae ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-blame-victim
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-blame-victim", "results": []}]} | responsibility-framing/predict-perception-bert-blame-victim | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:44:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-blame-victim
====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5075
* Rmse: 0.4599
* Rmse Blame::a La vittima: 0.4599
* Mae: 0.3607
* Mae Blame... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-blame-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-blame-object", "results": []}]} | responsibility-framing/predict-perception-bert-blame-object | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:49:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-blame-object
====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5837
* Rmse: 0.5589
* Rmse Blame::a Un oggetto: 0.5589
* Mae: 0.3862
* Mae Blame... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-blame-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-blame-concept", "results": []}]} | responsibility-framing/predict-perception-bert-blame-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:51:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-blame-concept
=====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7359
* Rmse: 0.6962
* Rmse Blame::a Un concetto astratto o un'emozione: 0.6962... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-blame-none
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-blame-none", "results": []}]} | responsibility-framing/predict-perception-bert-blame-none | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:54:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-blame-none
==================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8646
* Rmse: 1.1072
* Rmse Blame::a Nessuno: 1.1072
* Mae: 0.8721
* Mae Blame::a Nes... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-cause-human
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfac... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-cause-human", "results": []}]} | responsibility-framing/predict-perception-bert-cause-human | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T15:59:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-cause-human
===================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7139
* Rmse: 1.2259
* Rmse Cause::a Causata da un essere umano: 1.2259
* Mae: 1.04... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
# PyAutoCode: GPT-2 based Python auto-code.
PyAutoCode is a cut-down python autosuggestion built on **GPT-2** *(motivation: GPyT)* model. This baby model *(trained only up to 3 epochs)* is not **"fine-tuned"** yet therefore, I highly recommend not to use it in a production environment or incorporate PyAutoCode in ... | {"license": "mit"} | P0intMaN/PyAutoCode | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T16:01:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# PyAutoCode: GPT-2 based Python auto-code.
PyAutoCode is a cut-down python autosuggestion built on GPT-2 *(motivation: GPyT)* model. This baby model *(trained only up to 3 epochs)* is not "fine-tuned" yet therefore, I highly recommend not to use it in a production environment or incorporate PyAutoCode in any of y... | [
"# PyAutoCode: GPT-2 based Python auto-code.\r\n\r\nPyAutoCode is a cut-down python autosuggestion built on GPT-2 *(motivation: GPyT)* model. This baby model *(trained only up to 3 epochs)* is not \"fine-tuned\" yet therefore, I highly recommend not to use it in a production environment or incorporate PyAutoCode in... | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# PyAutoCode: GPT-2 based Python auto-code.\r\n\r\nPyAutoCode is a cut-down python autosuggestion built on GPT-2 *(motivation: GPyT)* model. Th... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-cause-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-cause-object", "results": []}]} | responsibility-framing/predict-perception-bert-cause-object | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:01:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-cause-object
====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4120
* Rmse: 1.0345
* Rmse Cause::a Causata da un oggetto (es. una pistola): 1.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
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