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text-classification | transformers | TrainOutput(global_step=2456, training_loss=0.29150783277878156, metrics={'train_runtime': 939.2154, 'train_samples_per_second': 167.246, 'train_steps_per_second': 2.615, 'total_flos': 321916620637920.0, 'train_loss': 0.29150783277878156, 'epoch': 4.0}) | {} | nbhimte/tiny-bert-best | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T10:09:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| TrainOutput(global_step=2456, training_loss=0.29150783277878156, metrics={'train_runtime': 939.2154, 'train_samples_per_second': 167.246, 'train_steps_per_second': 2.615, 'total_flos': 321916620637920.0, 'train_loss': 0.29150783277878156, 'epoch': 4.0}) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
<!-- 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. -->
# labse-rutoxicity-classification
This model is a fine-tuned version of [rasa/LaBSE](https://huggingface.co/rasa/LaBSE) on the Non... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "labse-rutoxicity-classification", "results": []}]} | npleshkanov/labse-rutoxicity-classification | null | [
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2022-04-18T11:00:04+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #region-us
|
# labse-rutoxicity-classification
This model is a fine-tuned version of rasa/LaBSE on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1995
- Acc: 0.9218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evalu... | [
"# labse-rutoxicity-classification\n\nThis model is a fine-tuned version of rasa/LaBSE on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1995\n- Acc: 0.9218",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
... | [
"TAGS\n#tensorboard #generated_from_trainer #region-us \n",
"# labse-rutoxicity-classification\n\nThis model is a fine-tuned version of rasa/LaBSE on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1995\n- Acc: 0.9218",
"## Model description\n\nMore information needed",
... |
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-nepali
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-nepali", "results": []}]} | shishirAI/wav2vec2-xlsr-nepali | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T11:31:40+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-nepali
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
"# wav2vec2-large-xlsr-nepali\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-nepali\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore... |
text-classification | transformers | PyTorch trained model on GAD dataset for relation classification, using BioBert weights. | {} | ChrisUPM/BioBERT_Re_trained | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T11:54:24+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| PyTorch trained model on GAD dataset for relation classification, using BioBert weights. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #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. -->
# Important Note:
I created the `combined` metric (55% F1 score + 45% exact match score) and load the state with the best result at... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "DSPFirst-Finetuning-5", "results": []}]} | ptran74/DSPFirst-Finetuning-5 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T12:03:21+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| Important Note:
===============
I created the 'combined' metric (55% F1 score + 45% exact match score) and load the state with the best result at the end. Here is the setting in the 'TrainingArguments':
DSPFirst-Finetuning-5
=====================
This model is a fine-tuned version of ahotrod/electra\_large\_discr... | [
"### Before fine-tuning:",
"### After fine-tuning:\n\n\nDataset\n=======\n\n\nA visualization of the dataset can be found here. \n\nThe split between train and test is 70% and 30% respectively.\n\n\nIntended uses & limitations\n---------------------------\n\n\nThis model is fine-tuned to answer questions from th... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Before fine-tuning:",
"### After fine-tuning:\n\n\nDataset\n=======\n\n\nA visualization of the dataset can be found here. \n\nThe split between train and test is 70% and 30% respective... |
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-gl-jupyter4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-gl-jupyter4", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T12:15:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-gl-jupyter4
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0970
* Wer: 0.0636
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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... | imyday/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 | 2022-04-18T12:17:13+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: 0.1380
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information 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... | fvector/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 | 2022-04-18T12:19:48+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: 0.1374
* F1: 0.8627
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 |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223045
- CO2 Emissions (in grams): 8.758858538967111
## Validation Metrics
- Loss: 0.14833936095237732
- Accuracy: 0.9471454508775469
- Precision: 0.5045871559633027
- Recall: 0.4166666666666667
- AUC: 0.8806422686270332
- F1: 0.45... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-dvs"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 8.758858538967111} | crcb/dvs_f | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-dvs",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T12:40:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-dvs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223045
- CO2 Emissions (in grams): 8.758858538967111
## Validation Metrics
- Loss: 0.14833936095237732
- Accuracy: 0.9471454508775469
- Precision: 0.5045871559633027
- Recall: 0.4166666666666667
- AUC: 0.8806422686270332
- F1: 0.45... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 753223045\n- CO2 Emissions (in grams): 8.758858538967111",
"## Validation Metrics\n\n- Loss: 0.14833936095237732\n- Accuracy: 0.9471454508775469\n- Precision: 0.5045871559633027\n- Recall: 0.4166666666666667\n- AUC: 0.880642268... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-dvs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 753223045\n- CO2 Emissions (in grams): 8.758... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223051
- CO2 Emissions (in grams): 5.1746636998598445
## Validation Metrics
- Loss: 0.14639143645763397
- Accuracy: 0.9493645350010087
- Precision: 0.5460992907801419
- Recall: 0.2916666666666667
- AUC: 0.8843542768404266
- F1: 0.3... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-dvs"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.1746636998598445} | crcb/hs_dvs | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-dvs",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T12:40:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-crcb/autotrain-data-dvs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223051
- CO2 Emissions (in grams): 5.1746636998598445
## Validation Metrics
- Loss: 0.14639143645763397
- Accuracy: 0.9493645350010087
- Precision: 0.5460992907801419
- Recall: 0.2916666666666667
- AUC: 0.8843542768404266
- F1: 0.3... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 753223051\n- CO2 Emissions (in grams): 5.1746636998598445",
"## Validation Metrics\n\n- Loss: 0.14639143645763397\n- Accuracy: 0.9493645350010087\n- Precision: 0.5460992907801419\n- Recall: 0.2916666666666667\n- AUC: 0.88435427... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-crcb/autotrain-data-dvs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 753223051\n- CO2 Emissions (in grams):... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | vikasaeta/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T12:59:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9310
* Recall: 0.9498
* F1: 0.9404
* Accuracy: 0.9857
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 753423062
- CO2 Emissions (in grams): 15.91710539314839
## Validation Metrics
- Loss: 0.5205655694007874
- Accuracy: 0.7746741154562383
- Macro F1: 0.5796696218586866
- Micro F1: 0.7746741154562382
- Weighted F1: 0.76023792779475... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-imp_hs"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 15.91710539314839} | crcb/imp_hatred | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-imp_hs",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T13:03:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-crcb/autotrain-data-imp_hs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 753423062
- CO2 Emissions (in grams): 15.91710539314839
## Validation Metrics
- Loss: 0.5205655694007874
- Accuracy: 0.7746741154562383
- Macro F1: 0.5796696218586866
- Micro F1: 0.7746741154562382
- Weighted F1: 0.76023792779475... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 753423062\n- CO2 Emissions (in grams): 15.91710539314839",
"## Validation Metrics\n\n- Loss: 0.5205655694007874\n- Accuracy: 0.7746741154562383\n- Macro F1: 0.5796696218586866\n- Micro F1: 0.7746741154562382\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-crcb/autotrain-data-imp_hs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 753423062\n- CO2 Emissions (in gr... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864
## Validation Metrics
- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.75923415957251... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-imp_hs"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.05286505617263864} | crcb/imp_hatred_f | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-imp_hs",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T13:05:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-imp_hs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864
## Validation Metrics
- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.75923415957251... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 753423076\n- CO2 Emissions (in grams): 0.05286505617263864",
"## Validation Metrics\n\n- Loss: 0.539419412612915\n- Accuracy: 0.7616387337057728\n- Macro F1: 0.6428050387135232\n- Micro F1: 0.761638733705773\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-imp_hs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 753423076\n- CO2 Emissions (in grams... |
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. -->
# chinese-bert-wwm-finetuned-jd
This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chinese-be... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-finetuned-jd", "results": []}]} | wangmiaobeng/chinese-bert-wwm-finetuned-jd | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T13:14:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-bert-wwm-finetuned-jd
=============================
This model is a fine-tuned version of hfl/chinese-bert-wwm on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9340
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"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: 64\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | ysharma/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T13:49:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0634
* Precision: 0.9327
* Recall: 0.9500
* F1: 0.9413
* Accuracy: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers | # layoutlmv3-base-finetuned-funsd
The model [layoutlmv3-base-finetuned-funsd](https://huggingface.co/HYPJUDY/layoutlmv3-base-finetuned-funsd) is fine-tuned on the FUNSD dataset initialized from [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base).
This finetuned model achieves an F1 score of 9... | {"license": "cc-by-nc-sa-4.0"} | HYPJUDY/layoutlmv3-base-finetuned-funsd | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T14:23:41+00:00 | [
"2204.08387"
] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # layoutlmv3-base-finetuned-funsd
The model layoutlmv3-base-finetuned-funsd is fine-tuned on the FUNSD dataset initialized from microsoft/layoutlmv3-base.
This finetuned model achieves an F1 score of 90.59 on the test split of the FUNSD dataset.
Paper | Code | Microsoft Document AI
If you find LayoutLMv3 helpful, p... | [
"# layoutlmv3-base-finetuned-funsd\n\nThe model layoutlmv3-base-finetuned-funsd is fine-tuned on the FUNSD dataset initialized from microsoft/layoutlmv3-base.\nThis finetuned model achieves an F1 score of 90.59 on the test split of the FUNSD dataset.\n\nPaper | Code | Microsoft Document AI\n\n\nIf you find LayoutLM... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# layoutlmv3-base-finetuned-funsd\n\nThe model layoutlmv3-base-finetuned-funsd is fine-tuned on the FUNSD dataset initiali... |
null | transformers |
Please see [this model's DagsHub repository](https://dagshub.com/morrisalp/unikud) for information on usage. | {"language": ["he"]} | malper/unikud | null | [
"transformers",
"pytorch",
"canine",
"he",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T14:56:16+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #canine #he #endpoints_compatible #region-us
|
Please see this model's DagsHub repository for information on usage. | [] | [
"TAGS\n#transformers #pytorch #canine #he #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-1b-bemba-5hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-1b-bemba-5hrs", "results": []}]} | csikasote/xls-r-1b-bemba-5hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T15:06:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-1b-bemba-5hrs
===================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2659
* Wer: 0.3884
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"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: 3e-05\n* train\\_batch\\_size: 4\... |
fill-mask | transformers | Nystromformer for sequence length 2048 trained on WikiText-103 v1. | {} | uw-madison/nystromformer-2048 | null | [
"transformers",
"pytorch",
"nystromformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T15:25:09+00:00 | [] | [] | TAGS
#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Nystromformer for sequence length 2048 trained on WikiText-103 v1. | [] | [
"TAGS\n#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Nystromformer for sequence length 4096 trained on WikiText-103 v1. | {} | uw-madison/nystromformer-4096 | null | [
"transformers",
"pytorch",
"nystromformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T15:33:51+00:00 | [] | [] | TAGS
#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Nystromformer for sequence length 4096 trained on WikiText-103 v1. | [] | [
"TAGS\n#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #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. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | Tianle/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T16:25:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1006
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
text-generation | transformers |
# Big Bang Theory Dialog Model | {"tags": ["conversational"]} | StringCheese/Dialog-small-bigbang | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T16:46:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Big Bang Theory Dialog Model | [
"# Big Bang Theory Dialog Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Big Bang Theory Dialog Model"
] |
token-classification | transformers | # layoutlmv3-large-finetuned-funsd
The model [layoutlmv3-large-finetuned-funsd](https://huggingface.co/HYPJUDY/layoutlmv3-large-finetuned-funsd) is fine-tuned on the FUNSD dataset initialized from [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large).
This finetuned model achieves an F1 score... | {"license": "cc-by-nc-sa-4.0"} | HYPJUDY/layoutlmv3-large-finetuned-funsd | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T17:06:30+00:00 | [
"2204.08387"
] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # layoutlmv3-large-finetuned-funsd
The model layoutlmv3-large-finetuned-funsd is fine-tuned on the FUNSD dataset initialized from microsoft/layoutlmv3-large.
This finetuned model achieves an F1 score of 92.15 on the test split of the FUNSD dataset.
Paper | Code | Microsoft Document AI
If you find LayoutLMv3 helpful... | [
"# layoutlmv3-large-finetuned-funsd\n\nThe model layoutlmv3-large-finetuned-funsd is fine-tuned on the FUNSD dataset initialized from microsoft/layoutlmv3-large.\nThis finetuned model achieves an F1 score of 92.15 on the test split of the FUNSD dataset.\n\nPaper | Code | Microsoft Document AI\n\n\nIf you find Layou... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv3-large-finetuned-funsd\n\nThe model layoutlmv3-large-finetuned-funsd is fine-tuned on the FUNSD dataset initialized from ... |
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-xlsr-nepalii
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-nepalii", "results": []}]} | shishirAI/wav2vec2-xlsr-nepalii | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T17:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xlsr-nepalii
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-xlsr-nepalii\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xlsr-nepalii\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nM... |
null | transformers | # layoutlmv3-base-finetuned-publaynet
The model [layoutlmv3-base-finetuned-publaynet](https://huggingface.co/HYPJUDY/layoutlmv3-base-finetuned-publaynet) is fine-tuned on the PubLayNet dataset initialized from [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base).
This finetuned model achieves ... | {"license": "cc-by-nc-sa-4.0"} | HYPJUDY/layoutlmv3-base-finetuned-publaynet | null | [
"transformers",
"tensorboard",
"layoutlmv3",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T17:33:55+00:00 | [
"2204.08387"
] | [] | TAGS
#transformers #tensorboard #layoutlmv3 #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| # layoutlmv3-base-finetuned-publaynet
The model layoutlmv3-base-finetuned-publaynet is fine-tuned on the PubLayNet dataset initialized from microsoft/layoutlmv3-base.
This finetuned model achieves an overall mAP @ IOU [0.50:0.95] of 95.1 on the PubLayNet validation set.
Paper | Code | Microsoft Document AI
If you f... | [
"# layoutlmv3-base-finetuned-publaynet\n\nThe model layoutlmv3-base-finetuned-publaynet is fine-tuned on the PubLayNet dataset initialized from microsoft/layoutlmv3-base.\nThis finetuned model achieves an overall mAP @ IOU [0.50:0.95] of 95.1 on the PubLayNet validation set.\n\nPaper | Code | Microsoft Document AI\... | [
"TAGS\n#transformers #tensorboard #layoutlmv3 #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# layoutlmv3-base-finetuned-publaynet\n\nThe model layoutlmv3-base-finetuned-publaynet is fine-tuned on the PubLayNet dataset initialized from microsoft/layoutlmv3-base.\nThis finetuned ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218
## Validation Metrics
- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.846716697936... | {"language": "unk", "tags": "autotrain", "datasets": ["zainalq7/autotrain-data-NLU_crypto_sentiment_analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.005300030853867218} | zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:zainalq7/autotrain-data-NLU_crypto_sentiment_analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T17:38:23+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-zainalq7/autotrain-data-NLU_crypto_sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218
## Validation Metrics
- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.846716697936... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 754123133\n- CO2 Emissions (in grams): 0.005300030853867218",
"## Validation Metrics\n\n- Loss: 0.387116938829422\n- Accuracy: 0.8658536585365854\n- Macro F1: 0.7724053724053724\n- Micro F1: 0.8658536585365854\n- Weighted ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-zainalq7/autotrain-data-NLU_crypto_sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 75412... |
null | null | # GAN-Control
- https://arxiv.org/abs/2101.02477
- https://github.com/amazon-research/gan-control
- weights
- https://drive.google.com/file/d/19v0lX69fV6zQv2HbbYUVr9gZ8ZKvUzHq/view?usp=sharing
| {} | public-data/gan-control | null | [
"arxiv:2101.02477",
"has_space",
"region:us"
] | null | 2022-04-18T17:48:34+00:00 | [
"2101.02477"
] | [] | TAGS
#arxiv-2101.02477 #has_space #region-us
| # GAN-Control
- URL
- URL
- weights
- URL
| [
"# GAN-Control\n\n- URL\n- URL\n- weights\n - URL"
] | [
"TAGS\n#arxiv-2101.02477 #has_space #region-us \n",
"# GAN-Control\n\n- URL\n- URL\n- weights\n - URL"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-common-voice-fa-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-common-voice-fa-demo-colab", "results": []}]} | zoha/wav2vec2-base-common-voice-fa-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T17:58:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-common-voice-fa-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: 3.0558
* Wer: 1.0
Model description
-----------------
More information needed
Int... | [
"### 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: 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: 1... |
zero-shot-image-classification | transformers |
### Model Card: clip-imageclef
### Model Details
[OpenAI CLIP model](https://openai.com/blog/clip/) fine-tuned using image-caption pairs from the [Caption Prediction dataset](https://www.imageclef.org/2017/caption) provided for the ImageCLEF 2017 competition. The model was evaluated using before and after fine-tunin... | {"language": ["en"], "license": ["mit"], "tags": ["multimodal", "language", "vision", "image-search", "pytorch"], "metrics": ["MRR"]} | sujitpal/clip-imageclef | null | [
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"multimodal",
"language",
"vision",
"image-search",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T20:08:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #clip #zero-shot-image-classification #multimodal #language #vision #image-search #en #license-mit #endpoints_compatible #has_space #region-us
| ### Model Card: clip-imageclef
### Model Details
OpenAI CLIP model fine-tuned using image-caption pairs from the Caption Prediction dataset provided for the ImageCLEF 2017 competition. The model was evaluated using before and after fine-tuning, MRR@10 were 0.57 and 0.88 respectively.
### Model Date
September 6,... | [
"### Model Card: clip-imageclef",
"### Model Details\n\n\nOpenAI CLIP model fine-tuned using image-caption pairs from the Caption Prediction dataset provided for the ImageCLEF 2017 competition. The model was evaluated using before and after fine-tuning, MRR@10 were 0.57 and 0.88 respectively.",
"### Model Date\... | [
"TAGS\n#transformers #pytorch #clip #zero-shot-image-classification #multimodal #language #vision #image-search #en #license-mit #endpoints_compatible #has_space #region-us \n",
"### Model Card: clip-imageclef",
"### Model Details\n\n\nOpenAI CLIP model fine-tuned using image-caption pairs from the Caption Pred... |
unconditional-image-generation | null |
The model provided is a StyleGan generator trained on the Cars dataset with a resolution of 512px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces. | {"license": "apache-2.0", "tags": ["gan", "stylegan", "huggan", "unconditional-image-generation"]} | huggan/stylegan_car512 | null | [
"pytorch",
"gan",
"stylegan",
"huggan",
"unconditional-image-generation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-18T20:43:45+00:00 | [] | [] | TAGS
#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us
|
The model provided is a StyleGan generator trained on the Cars dataset with a resolution of 512px. It is uploaded as part of porting this project: URL to hugginface spaces. | [] | [
"TAGS\n#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us \n"
] |
unconditional-image-generation | null |
The model provided is a StyleGAN generator trained on the LSUN cats dataset with a resolution of 256px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces. | {"license": "apache-2.0", "tags": ["gan", "stylegan", "huggan", "unconditional-image-generation"]} | huggan/stylegan_cat256 | null | [
"pytorch",
"gan",
"stylegan",
"huggan",
"unconditional-image-generation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-18T20:54:15+00:00 | [] | [] | TAGS
#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us
|
The model provided is a StyleGAN generator trained on the LSUN cats dataset with a resolution of 256px. It is uploaded as part of porting this project: URL to hugginface spaces. | [] | [
"TAGS\n#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us \n"
] |
null | null | # TorToiSe
Tortoise is a text-to-speech program built with the following priorities:
1. Strong multi-voice capabilities.
2. Highly realistic prosody and intonation.
This repo contains all the code needed to run Tortoise TTS in inference mode.
### New features
#### v2.1; 2022/5/2
- Added ability to produce totally ... | {} | jbetker/tortoise-tts-v2 | null | [
"arxiv:2102.12092",
"arxiv:2102.09672",
"arxiv:2106.07889",
"has_space",
"region:us"
] | null | 2022-04-18T21:41:14+00:00 | [
"2102.12092",
"2102.09672",
"2106.07889"
] | [] | TAGS
#arxiv-2102.12092 #arxiv-2102.09672 #arxiv-2106.07889 #has_space #region-us
| # TorToiSe
Tortoise is a text-to-speech program built with the following priorities:
1. Strong multi-voice capabilities.
2. Highly realistic prosody and intonation.
This repo contains all the code needed to run Tortoise TTS in inference mode.
### New features
#### v2.1; 2022/5/2
- Added ability to produce totally ... | [
"# TorToiSe\n\nTortoise is a text-to-speech program built with the following priorities:\n\n1. Strong multi-voice capabilities.\n2. Highly realistic prosody and intonation.\n\nThis repo contains all the code needed to run Tortoise TTS in inference mode.",
"### New features",
"#### v2.1; 2022/5/2\n- Added abilit... | [
"TAGS\n#arxiv-2102.12092 #arxiv-2102.09672 #arxiv-2106.07889 #has_space #region-us \n",
"# TorToiSe\n\nTortoise is a text-to-speech program built with the following priorities:\n\n1. Strong multi-voice capabilities.\n2. Highly realistic prosody and intonation.\n\nThis repo contains all the code needed to run Tort... |
text2text-generation | transformers |
[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) model trained on the [UFAL](https://ufal.mff.cuni.cz/ufal_medical_corpus) dataset, from `en` to `cs, de, es, fr, pl, ro, hu, sv`.
| {"license": "wtfpl"} | irenelizihui/MarianMT_UFAL | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:wtfpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T22:01:31+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us
|
MarianMT model trained on the UFAL dataset, from 'en' to 'cs, de, es, fr, pl, ro, hu, sv'.
| [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | samwell/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T22:36:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #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:
- Loss: 2.2663
- Bleu: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.2663\n- Bleu: 0.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.... |
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. -->
# electra-large-discriminator-nli-efl-tweeteval
This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electra-large-discriminator-nli-efl-tweeteval", "results": []}]} | ChrisZeng/electra-large-discriminator-nli-efl-tweeteval | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T23:29:30+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electra-large-discriminator-nli-efl-tweeteval
=============================================
This model is a fine-tuned version of ynie/electra-large-discriminator-snli\_mnli\_fever\_anli\_R1\_R2\_R3-nli on the None dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.7943
* F1: 0.7872
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
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. -->
# mrafida/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "mrafida/distilbert-base-uncased-finetuned-cola", "results": []}]} | mrafida/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T00:58:45+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mrafida/distilbert-base-uncased-finetuned-cola
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1849
* Validation Loss: 0.5355
* Train Matthews Correlation: 0.5... | [
"### 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': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
text-classification | transformers | # Title 自製QA請假版
---
tags: autonlp
language: unk
widget:
- text: "如果我想請特休，要怎麼使用"
- text: "我想請事假"
---
自製QA請假版
訓練與驗證分開
訓練筆67驗證筆23，總類別23，也就是驗證資料每一類各一測試
驗證acc=1.0
| {} | ShihTing/QA_Leave | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T02:19:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Title 自製QA請假版
---
tags: autonlp
language: unk
widget:
- text: "如果我想請特休，要怎麼使用"
- text: "我想請事假"
---
自製QA請假版
訓練與驗證分開
訓練筆67驗證筆23，總類別23，也就是驗證資料每一類各一測試
驗證acc=1.0
| [
"# Title 自製QA請假版\n---\ntags: autonlp\nlanguage: unk\nwidget:\n- text: \"如果我想請特休，要怎麼使用\"\n- text: \"我想請事假\"\n\n---\n\n自製QA請假版\n訓練與驗證分開\n訓練筆67驗證筆23，總類別23，也就是驗證資料每一類各一測試\n驗證acc=1.0"
] | [
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"# Title 自製QA請假版\n---\ntags: autonlp\nlanguage: unk\nwidget:\n- text: \"如果我想請特休，要怎麼使用\"\n- text: \"我想請事假\"\n\n---\n\n自製QA請假版\n訓練與驗證分開\n訓練筆67驗證筆23，總類別23，也就是驗證資料每一類各一測試\n驗證acc=1.0"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043
## Validation Metrics
- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.952825632430... | {"language": "en", "tags": "autotrain", "datasets": ["xInsignia/autotrain-data-Online_orders-5cf92320"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.4120667129093043} | xInsignia/autotrain-Online_orders-755323156 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:xInsignia/autotrain-data-Online_orders-5cf92320",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T02:27:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-xInsignia/autotrain-data-Online_orders-5cf92320 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043
## Validation Metrics
- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.952825632430... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 755323156\n- CO2 Emissions (in grams): 2.4120667129093043",
"## Validation Metrics\n\n- Loss: 0.17826060950756073\n- Accuracy: 0.9550898203592815\n- Macro F1: 0.8880388927888968\n- Micro F1: 0.9550898203592815\n- Weighted ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-xInsignia/autotrain-data-Online_orders-5cf92320 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 755323156... |
text-classification | transformers |
# Erlangshen-Roberta-110M-NLI
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-base在数个推理任务微调后的版本。
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several NLI dat... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "NLI", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-Roberta-110M-NLI | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"roberta",
"NLU",
"NLI",
"Chinese",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T02:59:55+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #roberta #NLU #NLI #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Erlangshen-Roberta-110M-NLI
===========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-base在数个推理任务微调后的版本。
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several NLI datasets.
模型分类 Model Taxonomy
---... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #roberta #NLU #NLI #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 752423172
- CO2 Emissions (in grams): 313.3534743349287
## Validation Metrics
- Loss: 0.6064515113830566
- Accuracy: 0.805171240644137
- Macro F1: 0.7253473044054398
- Micro F1: 0.805171240644137
- Weighted F1: 0.7970679970423672... | {"language": "en", "tags": "autotrain", "datasets": ["rabiaqayyum/autotrain-data-mental-health-analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 313.3534743349287} | rabiaqayyum/autotrain-mental-health-analysis-752423172 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:rabiaqayyum/autotrain-data-mental-health-analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-19T03:19:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-rabiaqayyum/autotrain-data-mental-health-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 752423172
- CO2 Emissions (in grams): 313.3534743349287
## Validation Metrics
- Loss: 0.6064515113830566
- Accuracy: 0.805171240644137
- Macro F1: 0.7253473044054398
- Micro F1: 0.805171240644137
- Weighted F1: 0.7970679970423672... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 752423172\n- CO2 Emissions (in grams): 313.3534743349287",
"## Validation Metrics\n\n- Loss: 0.6064515113830566\n- Accuracy: 0.805171240644137\n- Macro F1: 0.7253473044054398\n- Micro F1: 0.805171240644137\n- Weighted F1: ... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID:... |
text-classification | transformers | # Erlangshen-Roberta-330M-NLI
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-large在数个推理任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several NLI dat... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "NLI", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-Roberta-330M-NLI | null | [
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"NLI",
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"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T05:04:01+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #roberta #NLU #NLI #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Erlangshen-Roberta-330M-NLI
===========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-large在数个推理任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several NLI datasets.
模型分类 Model Taxonomy
--... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #roberta #NLU #NLI #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are... |
text2text-generation | transformers |
# T5-large-nl36 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The H... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-large-nl36-finnish | null | [
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"jax",
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"finnish",
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"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
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"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-04-19T05:06:16+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
] | [
"fi"
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#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| T5-large-nl36 for Finnish
=========================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fin... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\n... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **3DBall**
This is a trained model of a **ppo** agent playing **3DBall** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
TODO: Add your code
## Configuration file
```json
{'default_settings': None, 'behavio... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "3DBall"]} | ThomasSimonini/Ball | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"3DBall",
"region:us"
] | null | 2022-04-19T05:13:15+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #3DBall #region-us
|
# ppo Agent playing 3DBall
This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.
## Usage (with ML-Agents)
TODO: Add your code
## Configuration file
| [
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n TODO: Add your code\n \n ## Configuration file"
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"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #3DBall #region-us \n",
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n TODO: Add your code\n \n #... |
token-classification | transformers | This is based on Oliver Guhr's work.
The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on sixteen languages instead of four:
English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian, Greek, Swedish, Danish, Hungarian and Roman... | {} | kredor/punctuate-16 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T05:15:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This is based on Oliver Guhr's work.
The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on sixteen languages instead of four:
English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian, Greek, Swedish, Danish, Hungarian and Roman... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-optimized-indobert-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilled-optimized-indobert-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "me... | afbudiman/distilled-optimized-indobert-classification | null | [
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"text-classification",
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"dataset:indonlu",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T05:43:16+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilled-optimized-indobert-classification
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7397
* Accuracy: 0.9
* F1: 0.8994
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.315104717136378e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.315104717136378e-05... |
text-classification | transformers |
# distilbert-depression-base
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
It achieves the following results on the evaluation ... | {"language": ["en"], "license": "mit", "tags": ["text", "Twitter"], "datasets": ["CLPsych 2015"], "metrics": ["accuracy, f1, precision, recall, AUC"], "model-index": [{"name": "distilbert-depression-base", "results": []}]} | migueladarlo/distilbert-depression-base | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text",
"Twitter",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T05:59:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #text #Twitter #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| distilbert-depression-base
==========================
This model is a fine-tuned version of distilbert-base-uncased trained on CLPsych 2015 and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
It achieves the following results on the evaluation set:
* Evaluation Loss:... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for sentiment analysis:\n\n\nOtherwise, download the files and specify within the pipeline the path to the folder that contains the URL, pytorch\\_model.bin, and training\\_args.bin\n\n\nTraining hyperparameters\n------------------------\n\n\nThe ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #text #Twitter #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for sentiment analysis:\n\n\nOtherwise, download the files and specify within the pipeline... |
null | null | #Introduction
See <https://github.com/k2-fsa/icefall/pull/316>
| {} | csukuangfj/icefall-asr-librispeech-transducer-stateless2-torchaudio-2022-04-19 | null | [
"tensorboard",
"region:us"
] | null | 2022-04-19T06:18:56+00:00 | [] | [] | TAGS
#tensorboard #region-us
| #Introduction
See <URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text-classification | transformers |
# distilbert-depression-mixed
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
It achieves the following re... | {"language": ["en"], "license": "mit", "tags": ["text", "Twitter"], "datasets": ["CLPsych 2015"], "metrics": ["accuracy, f1, precision, recall, AUC"], "model-index": [{"name": "distilbert-depression-mixed", "results": []}]} | migueladarlo/distilbert-depression-mixed | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text",
"Twitter",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T06:35:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #text #Twitter #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| distilbert-depression-mixed
===========================
This model is a fine-tuned version of distilbert-base-uncased trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
It achieves the following results on the evaluation ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for sentiment analysis:\n\n\nOtherwise, download the files and specify within the pipeline the path to the folder that contains the URL, pytorch\\_model.bin, and training\\_args.bin\n\n\nTraining hyperparameters\n------------------------\n\n\nThe ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #text #Twitter #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for sentiment analysis:\n\n\nOtherwise, download the files and specify within the pipeline... |
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-ove
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-french](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-ove", "results": []}]} | guillaumegg/wav2vec2-base-timit-demo-ove | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T06:42:23+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-ove
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# wav2vec2-base-timit-demo-ove\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-ove\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.",
"## Model de... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dbddv01-gpt2-french-small_space_paco-cheese-v3
This model was trained from scratch on the None dataset.
## Model description
M... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dbddv01-gpt2-french-small_space_paco-cheese-v3", "results": []}]} | maesneako/dbddv01-gpt2-french-small_space_paco-cheese-v3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-19T06:55:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# dbddv01-gpt2-french-small_space_paco-cheese-v3
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# dbddv01-gpt2-french-small_space_paco-cheese-v3\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# dbddv01-gpt2-french-small_space_paco-cheese-v3\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore inform... |
text2text-generation | transformers |
# PEGASUS BASE
This model was pretrained on Bulgarian language. It was intorduced in [this paper](https://arxiv.org/pdf/1912.08777.pdf).
## Model description
The training data is private Bulgarian text from CNN, DailyMail articles.
## Intended uses & limitations
You can use the raw model for summarization.
###... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/pegasus-base-cnn-dailymail-bg | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1912.08777",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-19T07:14:05+00:00 | [
"1912.08777"
] | [
"bg"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1912.08777 #license-mit #autotrain_compatible #region-us
|
# PEGASUS BASE
This model was pretrained on Bulgarian language. It was intorduced in this paper.
## Model description
The training data is private Bulgarian text from CNN, DailyMail articles.
## Intended uses & limitations
You can use the raw model for summarization.
### How to use
Here is how to use this mod... | [
"# PEGASUS BASE\n\nThis model was pretrained on Bulgarian language. It was intorduced in this paper.",
"## Model description\n\nThe training data is private Bulgarian text from CNN, DailyMail articles.",
"## Intended uses & limitations\n\nYou can use the raw model for summarization.",
"### How to use\n\nHere ... | [
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"# PEGASUS BASE\n\nThis model was pretrained on Bulgarian language. It was intorduced in this paper.",
"## Model descri... |
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. -->
# ru-labse-toxic
This model is a fine-tuned version of [rasa/LaBSE](https://huggingface.co/rasa/LaBSE) on the None dataset.
It ach... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ru-labse-toxic", "results": []}]} | npleshkanov/ru-labse-toxic | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T07:26:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #endpoints_compatible #region-us
|
# ru-labse-toxic
This model is a fine-tuned version of rasa/LaBSE on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1950
- Acc: 0.9302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# ru-labse-toxic\n\nThis model is a fine-tuned version of rasa/LaBSE on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1950\n- Acc: 0.9302",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #endpoints_compatible #region-us \n",
"# ru-labse-toxic\n\nThis model is a fine-tuned version of rasa/LaBSE on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1950\n- Acc: 0.9302",
"## Model descripti... |
text-classification | transformers |
# Erlangshen-Roberta-330M-Similarity
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-large在数个相似度任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on severa... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "Similarity", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-Roberta-330M-Similarity | null | [
"transformers",
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"safetensors",
"bert",
"text-classification",
"roberta",
"NLU",
"Similarity",
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"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-19T07:26:49+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Erlangshen-Roberta-330M-Similarity
==================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-large在数个相似度任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several similarity datasets.
... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我... |
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. -->
# xlsr-53-bemba-15hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-53-bemba-15hrs", "results": []}]} | csikasote/xlsr-53-bemba-15hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T07:38:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-53-bemba-15hrs
===================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2789
* Wer: 0.3751
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
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. -->
# xlsr-53-bemba-10hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-53-bemba-10hrs", "results": []}]} | csikasote/xlsr-53-bemba-10hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T07:55:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-53-bemba-10hrs
===================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3190
* Wer: 0.4032
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
text-classification | transformers | # Erlangshen-Roberta-110M-Similarity
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-base在数个相似度任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several s... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "Similarity", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-Roberta-110M-Similarity | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"roberta",
"NLU",
"Similarity",
"Chinese",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T07:59:20+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Erlangshen-Roberta-110M-Similarity
==================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-base在数个相似度任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several similarity datasets.
模型... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们... |
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-hated
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-hated", "results": []}]} | stevenlx96/distilbert-base-uncased-finetuned-hated | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-19T08:18:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-hated
=======================================
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: 0.5042
* Accuracy: 0.8135
* F1: 0.8127
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-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... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.5 | 73.5 |
| test | 75.5 | 75.5 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-nli-repnum_wl-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T08:39:53+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 73.5, F1macro: 73.5
Set: test, F1micro: 75.5, F1macro: 75.5
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 83.1 | 82.2 |
| test | 86.0 | 85.0 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-nli-xnli_fr-repnum_wl-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T08:44:38+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 83.1, F1macro: 82.2
Set: test, F1micro: 86.0, F1macro: 85.0
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.3 | 71.9 |
| test | 72.5 | 72.1 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-repnum_wl-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T08:55:54+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 72.3, F1macro: 71.9
Set: test, F1micro: 72.5, F1macro: 72.1
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext.
### Usage
```python
>>> from sentence_transformers.cross_... | {"language": "zh", "tags": ["cross-encoder"], "datasets": ["dialogue"]} | tuhailong/cross_encoder_roberta-wwm-ext_v0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T09:16:32+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext.
### Usage
#### Code
train code from URL | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext.",
"### Usage",
"#### Code\ntrain code from URL"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model s... |
null | null | test | {} | areffarzanieh/test | null | [
"region:us"
] | null | 2022-04-19T09:35:15+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | This repository is created with the aim to provide better models for NLI in persian, with the transparent codes for training I hope you guys find it inspiring and build better model in the future. for more details about the task and methods used for training check the [medium post](https://haddadhesam.medium.com/) and ... | {"language": "fa", "license": "apache-2.0"} | demoversion/bert-fa-base-uncased-haddad-wikinli | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T09:58:42+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This repository is created with the aim to provide better models for NLI in persian, with the transparent codes for training I hope you guys find it inspiring and build better model in the future. for more details about the task and methods used for training check the medium post and notebooks.
Dataset
=======
The ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
This model structure is as same as [tuhailong/cross_encod... | {"language": "zh", "tags": ["cross-encoder"], "datasets": ["dialogue"]} | tuhailong/cross_encoder_roberta-wwm-ext_v1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:03:49+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
This model structure is as same as tuhailong/cross_encoder_roberta-wwm-ext_v0，the difference ... | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.\nThis model structure is as same as tuhailong/cross_encoder_roberta-wwm-ext_v0，the ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model s... |
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. -->
# Learning-sentiment-analysis-through-imdb-ds
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "Learning-sentiment-analysis-through-imdb-ds", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "arg... | SeNSiTivE/Learning-sentiment-analysis-through-imdb-ds | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:10:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Learning-sentiment-analysis-through-imdb-ds
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3419
- Accuracy: 0.8767
- F1: 0.8818
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# Learning-sentiment-analysis-through-imdb-ds\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3419\n- Accuracy: 0.8767\n- F1: 0.8818",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Learning-sentiment-analysis-through-imdb-ds\n\nThis model is a fine-tuned version of distilbert-base-unc... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_a_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_a_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:11:15+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_a_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_a_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_a_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
This model structure is as same as [tuhailong/cross_encod... | {"language": "zh", "tags": ["cross-encoder"], "datasets": ["dialogue"]} | tuhailong/cross_encoder_roberta-wwm-ext_v2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:21:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
This model structure is as same as tuhailong/cross_encoder_roberta-wwm-ext_v1，the difference ... | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.\nThis model structure is as same as tuhailong/cross_encoder_roberta-wwm-ext_v1，the ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model s... |
feature-extraction | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
### Usage
```python
>>> from sentence_transformers import SentenceTransformer, util
>>> model = SentenceTrans... | {"language": "zh", "tags": ["sbert"], "datasets": ["dialogue"]} | tuhailong/bi_encoder_roberta-wwm-ext | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"sbert",
"zh",
"dataset:dialogue",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:28:07+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #feature-extraction #sbert #zh #dataset-dialogue #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is bi-encoder
### Usage
#### Code
train code from URL
##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here... | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is bi-encoder",
"### Usage",
"#### Code\ntrain code from URL",
"##### PS\nBecause add the pooling layer and dense layer after model，has folders ... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #sbert #zh #dataset-dialogue #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is bi-encoder",
"### Us... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_a_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_a_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:41:09+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_a_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_a_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_a_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_e_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_e_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:50:18+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_e_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_e_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_e_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_e_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_e_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:51:04+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_e_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_e_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_e_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_f_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_f_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:52:08+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_f_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_f_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_f_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
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. -->
# nbme-roberta-large
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dat... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "nbme-roberta-large", "results": []}]} | smeoni/nbme-roberta-large | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:52:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nbme-roberta-large
==================
This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7825
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* ev... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_f_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_f_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T10:53:08+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_f_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_f_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_f_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
fill-mask | transformers |
Using the ClimateBERT-f model as starting point,the TCFD-BERT language model is additionally pre-trained to include precise paragraphs related to climate change.
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it,... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "TCFD-BERT", "results": []}]} | s50227harry/TCFD-BERT | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:53:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Using the ClimateBERT-f model as starting point,the TCFD-BERT language model is additionally pre-trained to include precise paragraphs related to climate change.
TCFD-BERT
=========
It achieves the following results on the evaluation set:
* Loss: 1.1325
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: tpu\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
fill-mask | transformers | # Legal-HeBERT
Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal ... | {} | avichr/Legal-heBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:1911.03090",
"arxiv:2010.02559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T10:53:43+00:00 | [
"1911.03090",
"2010.02559"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-1911.03090 #arxiv-2010.02559 #autotrain_compatible #endpoints_compatible #region-us
| Legal-HeBERT
============
Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of HeBERT applied on legal and legislative documents. The ... | [
"### Additional training settings:\n\n\n**Fine-tuned HeBERT model:** The first eight layers were freezed (like Lee et al. (2019) suggest) \n\n**Legal-HeBERT trained from scratch:** The training process is similar to HeBERT and inspired by Chalkidis et al. (2020) \n\n\n\nHow to use\n----------\n\n\nThe models can... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1911.03090 #arxiv-2010.02559 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Additional training settings:\n\n\n**Fine-tuned HeBERT model:** The first eight layers were freezed (like Lee et al. (2019) suggest) \n\n**Legal-HeBERT trained from ... |
text2text-generation | transformers |
# PEGASUS BASE
This model was pretrained on Bulgarian language. It was intorduced in [this paper](https://arxiv.org/pdf/1912.08777.pdf).
## Model description
The training data is private Bulgarian squad data.
## Intended uses & limitations
You can use the raw model for generation of question-answer pairs relate... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/pegasus-base-qag-bg | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1912.08777",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-19T11:29:47+00:00 | [
"1912.08777"
] | [
"bg"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1912.08777 #license-mit #autotrain_compatible #region-us
|
# PEGASUS BASE
This model was pretrained on Bulgarian language. It was intorduced in this paper.
## Model description
The training data is private Bulgarian squad data.
## Intended uses & limitations
You can use the raw model for generation of question-answer pairs related with given Bulgarian text.
### How to... | [
"# PEGASUS BASE\n\nThis model was pretrained on Bulgarian language. It was intorduced in this paper.",
"## Model description\n\nThe training data is private Bulgarian squad data.",
"## Intended uses & limitations\n\nYou can use the raw model for generation of question-answer pairs related with given Bulgarian t... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1912.08777 #license-mit #autotrain_compatible #region-us \n",
"# PEGASUS BASE\n\nThis model was pretrained on Bulgarian language. It was intorduced in this paper.",
"## Model descri... |
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_toy_train_fast_masked_augment_random_noise
This model is a fine-tuned version of [facebook/wav2vec2-large... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T11:41:21+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_fast\_masked\_augment\_random\_noise
========================================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3471
* Wer: ... | [
"### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 16\n* eval\\_b... |
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-Ganapati
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-Ganapati", "results": []}]} | stevems1/bert-base-uncased-Ganapati | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T11:53:06+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-Ganapati
==========================
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: 0.0000
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 | null | Based on transformer optimisation chapter of book. used for deployment test purposes only. | {} | alunapr/clinc_intent | null | [
"onnx",
"region:us"
] | null | 2022-04-19T11:57:45+00:00 | [] | [] | TAGS
#onnx #region-us
| Based on transformer optimisation chapter of book. used for deployment test purposes only. | [] | [
"TAGS\n#onnx #region-us \n"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **3DBall**
This is a trained model of a **ppo** agent playing **3DBall** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
TODO: Add your code
## Configuration file
```json
{'default_settings': None, 'b... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "3DBall"]} | osanseviero/Ball_test | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"3DBall",
"region:us"
] | null | 2022-04-19T11:59:18+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #3DBall #region-us
|
# ppo Agent playing 3DBall
This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.
## Usage (with ML-Agents)
TODO: Add your code
## Configuration file
| [
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n TODO: Add your code\n \n ## Configuration file"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #3DBall #region-us \n",
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n TODO: Add your code\n \n ... |
null | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
model's train code by [PairSupCon](https://github.com/amazon-research/sentence-representations/tree/main/PairS... | {"language": "zh", "tags": ["sbert"], "datasets": ["dialogue"]} | tuhailong/PairSupCon-roberta-wwm-ext | null | [
"transformers",
"pytorch",
"bert",
"sbert",
"zh",
"dataset:dialogue",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:09:36+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #sbert #zh #dataset-dialogue #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is bi-encoder
model's train code by PairSupCon
### Usage
URL
#### Code
train code from URL | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is bi-encoder\nmodel's train code by PairSupCon",
"### Usage\nURL",
"#### Code\ntrain code from URL"
] | [
"TAGS\n#transformers #pytorch #bert #sbert #zh #dataset-dialogue #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is bi-encoder\nmodel's train code by PairSupC... |
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... | jamie613/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 | 2022-04-19T12:11:45+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: 0.1339
* F1: 0.8653
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\\_... |
sentence-similarity | sentence-transformers |
# Conference Helper
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources.
## Usage (Sentence-Transformers)
The usage o... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | PBusienei/Nashville_Analytics_Summit_conference_helper | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:18:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
| Conference Helper
=================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources.
Usage (Sentence-Transformers)
---------------------------... | [
"### Pre-training\n\n\nThe pretrained 'nreimers/MiniLM-L6-H384-uncased' model.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sampled each dataset given a weighted probability which configuration is detailed i... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nThe pretrained 'nreimers/MiniLM-L6-H384-uncased' model.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we hav... |
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. -->
# segformer-finetuned-sidewalk-trainer
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-finetuned-sidewalk-trainer", "results": []}]} | nielsr/segformer-finetuned-sidewalk-trainer | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:23:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us
|
# segformer-finetuned-sidewalk-trainer
This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# segformer-finetuned-sidewalk-trainer\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# segformer-finetuned-sidewalk-trainer\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the None dataset.",
"## Model description\n\nMore in... |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext-large.
### Code
train code from https://github.com/TTurn/c... | {"language": "zh", "tags": ["cross-encoder"], "datasets": ["dialogue"]} | tuhailong/cross_encoder_roberta-wwm-ext-large | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:24:27+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext-large.
### Code
train code from URL
#### Usage
| [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext-large.",
"### Code\ntrain code from URL",
"#### Usage"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model s... |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder，pretrained model is hfl/chinese-electra-180g-large-discriminator.
### Usage
```python
>>> from sentence_tr... | {"language": "zh", "tags": ["cross-encoder"], "datasets": ["dialogue"]} | tuhailong/cross_encoder_electra-180g-large-discriminator | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:25:37+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #electra #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-electra-180g-large-discriminator.
### Usage
#### Code
train code from URL | [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-electra-180g-large-discriminator.",
"### Usage",
"#### Code\ntrain code from URL"
] | [
"TAGS\n#transformers #pytorch #electra #text-classification #cross-encoder #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，mode... |
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. -->
# xls-r-1b-bemba-10hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-1b-bemba-10hrs", "results": []}]} | csikasote/xls-r-1b-bemba-10hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:35:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-1b-bemba-10hrs
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2350
* Wer: 0.3524
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"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: 3e-05\n* train\\_batch\\_size: 4\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 758223271
- CO2 Emissions (in grams): 0.3313142450338848
## Validation Metrics
- Loss: 1.2496932744979858
- Accuracy: 0.6438828259620908
- Macro F1: 0.5757131072506373
- Micro F1: 0.6438828259620908
- Weighted F1: 0.6401462906378... | {"language": "en", "tags": "autotrain", "datasets": ["intellisr/autotrain-data-twitterMbti"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.3313142450338848} | intellisr/autotrain-twitterMbti-758223271 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:intellisr/autotrain-data-twitterMbti",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:43:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-intellisr/autotrain-data-twitterMbti #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 758223271
- CO2 Emissions (in grams): 0.3313142450338848
## Validation Metrics
- Loss: 1.2496932744979858
- Accuracy: 0.6438828259620908
- Macro F1: 0.5757131072506373
- Micro F1: 0.6438828259620908
- Weighted F1: 0.6401462906378... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 758223271\n- CO2 Emissions (in grams): 0.3313142450338848",
"## Validation Metrics\n\n- Loss: 1.2496932744979858\n- Accuracy: 0.6438828259620908\n- Macro F1: 0.5757131072506373\n- Micro F1: 0.6438828259620908\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-intellisr/autotrain-data-twitterMbti #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 758223271\n- CO2 Emissions... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=960.00 +/- 483.4252786108728
## Usage (wi... | {"tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | osanseviero/TEST_COLAB_ppo-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-04-19T12:43:58+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.
## Evaluation Results
mean_reward=960.00 +/- 483.4252786108728
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Results\n \n mean_reward=960.00 +/- 483.4252786108728\n \n ## Usage (with Stable-baselines3)\n\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Results... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Assyrian
Fine-tuned [facebom3hrdadfiok/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) in Assyrian (the Urmi dialect of North-Eastern Neo-Aramaic) using [Urmi Assyrian Voice](https://huggingface.co/datasets/mnazari/urmi-assyrian-voice).
**Please reach out t... | {"language": "aii", "license": "cc0-1.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["mnazari/urmi-assyrian-voice"], "metrics": ["cer"], "model-index": [{"name": "Wav2Vec2-Assyrian by Matthew Nazari", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognitio... | mnazari/wav2vec2-assyrian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"aii",
"dataset:mnazari/urmi-assyrian-voice",
"license:cc0-1.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T12:55:39+00:00 | [] | [
"aii"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #aii #dataset-mnazari/urmi-assyrian-voice #license-cc0-1.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Assyrian
Fine-tuned facebom3hrdadfiok/wav2vec2-large-xlsr-persian-v3 in Assyrian (the Urmi dialect of North-Eastern Neo-Aramaic) using Urmi Assyrian Voice.
Please reach out to me at matthewnazari@URL if you are Assyrian or a researcher. | [
"# Wav2Vec2-Assyrian\n\nFine-tuned facebom3hrdadfiok/wav2vec2-large-xlsr-persian-v3 in Assyrian (the Urmi dialect of North-Eastern Neo-Aramaic) using Urmi Assyrian Voice.\n\nPlease reach out to me at matthewnazari@URL if you are Assyrian or a researcher."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #aii #dataset-mnazari/urmi-assyrian-voice #license-cc0-1.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Assyrian\n\nFine-tuned facebom3hrdadfiok/wav2vec2-large-xlsr-persian-v3 in Assyrian (the Urmi dialect of No... |
token-classification | transformers |
# est-roberta-hist-ner
## Model description
est-roberta-hist-ner is an [Est-RoBERTa](https://huggingface.co/EMBEDDIA/est-roberta) based model fine-tuned for named entity recognition in Estonian 19th century parish court records (for details, see [this repository](https://github.com/soras/vk_ner_lrec_2022)).
The fo... | {"language": "et", "license": "cc-by-sa-4.0", "inference": false} | tartuNLP/est-roberta-hist-ner | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"et",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"region:us"
] | null | 2022-04-19T13:08:36+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #camembert #token-classification #et #license-cc-by-sa-4.0 #autotrain_compatible #region-us
|
# est-roberta-hist-ner
## Model description
est-roberta-hist-ner is an Est-RoBERTa based model fine-tuned for named entity recognition in Estonian 19th century parish court records (for details, see this repository).
The following types of entities are recognized: person names (PER), ambiguous locations-organizati... | [
"# est-roberta-hist-ner",
"## Model description \n\nest-roberta-hist-ner is an Est-RoBERTa based model fine-tuned for named entity recognition in Estonian 19th century parish court records (for details, see this repository). \nThe following types of entities are recognized: person names (PER), ambiguous locations... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #et #license-cc-by-sa-4.0 #autotrain_compatible #region-us \n",
"# est-roberta-hist-ner",
"## Model description \n\nest-roberta-hist-ner is an Est-RoBERTa based model fine-tuned for named entity recognition in Estonian 19th century parish court reco... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/arguana-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:04:13+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/climate-fever-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:04:32+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
text2text-generation | transformers |
# ROBERTA-TO-ROBERTA EncoderDecoder with Shared Weights
This model was introduced in [this paper](https://arxiv.org/pdf/1907.12461.pdf).
## Model description
The training data is private English-Bulgarian parallel data.
## Intended uses & limitations
You can use the raw model for translation from English to Bul... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/roberta2roberta-shared-nmt-bg | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1907.12461",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-19T14:11:13+00:00 | [
"1907.12461"
] | [
"bg"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1907.12461 #license-mit #autotrain_compatible #region-us
|
# ROBERTA-TO-ROBERTA EncoderDecoder with Shared Weights
This model was introduced in this paper.
## Model description
The training data is private English-Bulgarian parallel data.
## Intended uses & limitations
You can use the raw model for translation from English to Bulgarian.
### How to use
Here is how to ... | [
"# ROBERTA-TO-ROBERTA EncoderDecoder with Shared Weights\n\nThis model was introduced in this paper.",
"## Model description\n\nThe training data is private English-Bulgarian parallel data.",
"## Intended uses & limitations\n\nYou can use the raw model for translation from English to Bulgarian.",
"### How to ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1907.12461 #license-mit #autotrain_compatible #region-us \n",
"# ROBERTA-TO-ROBERTA EncoderDecoder with Shared Weights\n\nThis model was introduced in this paper.",
"## Mode... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/dbpedia-entity-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:13:26+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/fever-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:13:44+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/hotpotqa-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:14:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/newsqa-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:14:21+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/nfcorpus-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:14:39+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
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