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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. -->
# BertjeWDialDataALL03
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-bas... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALL03", "results": []}]} | Jeska/BertjeWDialDataALL03 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALL03
====================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9459
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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataALL04
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-bas... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALL04", "results": []}]} | Jeska/BertjeWDialDataALL04 | null | [
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALL04
====================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9717
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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataALLQonly
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly", "results": []}]} | Jeska/BertjeWDialDataALLQonly | null | [
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"generated_from_trainer",
"autotrain_compatible",
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#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly
=======================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9438
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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataALLQonly02
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly02", "results": []}]} | Jeska/BertjeWDialDataALLQonly02 | null | [
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"generated_from_trainer",
"autotrain_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly02
=========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9043
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataALLQonly03
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly03", "results": []}]} | Jeska/BertjeWDialDataALLQonly03 | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly03
=========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9995
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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. -->
# BertjeWDialDataALLQonly05
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly05", "results": []}]} | Jeska/BertjeWDialDataALLQonly05 | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly05
=========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3921
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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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. -->
# BertjeWDialDataALLQonly07
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly07", "results": []}]} | Jeska/BertjeWDialDataALLQonly07 | null | [
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"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly07
=========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1135
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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. -->
# BertjeWDialDataALLQonly09
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly09", "results": []}]} | Jeska/BertjeWDialDataALLQonly09 | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly09
=========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9043
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataALLQonly128
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/be... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALLQonly128", "results": []}]} | Jeska/BertjeWDialDataALLQonly128 | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALLQonly128
==========================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0364
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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. -->
# BertjeWDialDataQA20k
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-bas... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataQA20k", "results": []}]} | Jeska/BertjeWDialDataQA20k | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataQA20k
====================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9208
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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggin... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTje", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTje | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTje
=============================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6223
* Accuracy: 0.9068
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 15.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggi... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTje2", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2 | null | [
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTje2
==============================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5112
* Accuracy: 0.9004
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialog
This model is a fine-tuned version of [outputDA/checkpoint-7710](https://... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialog", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialog | null | [
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"text-classification",
"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTje2\_DAdialog
========================================================
This model is a fine-tuned version of outputDA/checkpoint-7710 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5025
* Accuracy: 0.9077
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly
This model is a fine-tuned version of [outputDAQonly/checkpoint-8710... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly | null | [
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"generated_from_trainer",
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTje2\_DAdialogQonly
=============================================================
This model is a fine-tuned version of outputDAQonly/checkpoint-8710 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5008
* Accuracy: 0.9068
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09
This model is a fine-tuned version of [outputDAQonly09/](https://h... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09 | null | [
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTje2\_DAdialogQonly09
===============================================================
This model is a fine-tuned version of outputDAQonly09/ on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4978
* Accuracy: 0.9031
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTjeDIAL
This model is a fine-tuned version of [Jeska/BertjeWDialDataQA20k](https://hugg... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTjeDIAL", "results": []}]} | Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL | null | [
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| VaccinChatSentenceClassifierDutch\_fromBERTjeDIAL
=================================================
This model is a fine-tuned version of Jeska/BertjeWDialDataQA20k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8355
* Accuracy: 0.6322
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22144706
- CO2 Emissions (in grams): 27.135492487925884
## Validation Metrics
- Loss: 1.81697416305542
- Accuracy: 0.6377269139700079
- Macro F1: 0.5181293370145044
- Micro F1: 0.6377269139700079
- Weighted F1: 0.631117826235572
- ... | {"language": "unk", "tags": "autonlp", "datasets": ["Jeska/autonlp-data-vaccinfaq"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 27.135492487925884} | Jeska/autonlp-vaccinfaq-22144706 | null | [
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# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22144706
- CO2 Emissions (in grams): 27.135492487925884
## Validation Metrics
- Loss: 1.81697416305542
- Accuracy: 0.6377269139700079
- Macro F1: 0.5181293370145044
- Micro F1: 0.6377269139700079
- Weighted F1: 0.631117826235572
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null | null | `LOREN` is an interpretable fact verification model trained on [FEVER](https://fever.ai), which aims to predict the veracity of a textual claim against a trustworthy knowledge source such as Wikipedia.
`LOREN` also decomposes the verification and makes accurate and faithful phrase-level veracity predictions without an... | {} | jiangjiechen/loren | null | [
"arxiv:2012.13577",
"region:us"
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#arxiv-2012.13577 #region-us
| 'LOREN' is an interpretable fact verification model trained on FEVER, which aims to predict the veracity of a textual claim against a trustworthy knowledge source such as Wikipedia.
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-nli
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["accuracy"], "model_index": [{"name": "bert-base-finetuned-nli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "nli"}, "metric": {"name": "Accuracy", "type": ... | Jihyun22/bert-base-finetuned-nli | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-nli
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1357
* Accuracy: 0.756
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: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testing
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the G... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "testing", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}... | LysandreJik/testing | null | [
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|
# testing
This model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6644
- Accuracy: 0.6814
- F1: 0.8105
- Combined Score: 0.7459
## Model description
More information needed
## Intended uses & limitations
More info... | [
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text-generation | transformers |
# Jimmy's character DialoGPT model | {"tags": ["conversational"]} | JimmyHodl/DialoGPT-medium | null | [
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null | transformers |
# KrELECTRA-base-mecab
Korean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)
## Usage
### Load model and tokenizer
```python
>>> from transformers import AutoTokenizer, AutoModelForPreTraining
>>> model = AutoModelForPreTraining.from_pretrained("Jinhwan/krelectra-base-mecab")
>>> toke... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | Jinhwan/krelectra-base-mecab | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# KrELECTRA-base-mecab
Korean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)
## Usage
### Load model and tokenizer
### Tokenizer example
'''python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab")
>>> tokenizer.... | [
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null | null | for test | {"license": "afl-3.0"} | Jira/first_test | null | [
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zero-shot-classification | transformers |
# XLM-roBERTa-large-it-mnli
## Version 0.1
| | matched-it acc | mismatched-it acc |
| -------------------------------------------------------------------------------- |----------------|-------------------|
| XLM-roBERTa-large-it-mnli ... | {"language": "it", "license": "mit", "tags": ["text-classification", "pytorch", "tensorflow"], "datasets": ["multi_nli", "glue"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "La seconda guerra mondiale vide contrapporsi, tra il 1939 e il 1945, le cosiddette potenze dell'Asse e gli Alleati che, come ... | Jiva/xlm-roberta-large-it-mnli | null | [
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| XLM-roBERTa-large-it-mnli
=========================
Version 0.1
-----------
matched-it acc: XLM-roBERTa-large-it-mnli, mismatched-it acc: 84.75
Model Description
-----------------
This model takes xlm-roberta-large and fine-tunes it on a subset of NLI data taken from a automatically translated version of the MN... | [
"#### With the zero-shot classification pipeline\n\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\nYou can then classify in any of the above languages. You can even pass the labels in one language and the sequence to\nclassify in another:\n\n\nThe default hypothesis template is... | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | Jllama/dialoGPT-small-Joshua-test | null | [
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text-classification | transformers |
# roberta-base-bne-finetuned-catalonia-independence-detector
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the catalonia_independence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9415
- Accuracy: 0.7881
<details>
... | {"language": "es", "license": "apache-2.0", "tags": ["spanish"], "datasets": ["catalonia_independence"], "metrics": ["accuracy"], "widget": [{"text": "Junqueras, sobre la decisi\u00f3n judicial sobre Puigdemont: La justicia que falta en el Estado llega y llegar\u00e1 de Europa"}, {"text": "Desconvocada la manifestaci\u... | JonatanGk/roberta-base-bne-finetuned-catalonia-independence-detector | null | [
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| roberta-base-bne-finetuned-catalonia-independence-detector
==========================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the catalonia\_independence dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9415
* Accuracy: 0.7881
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-classification | transformers |
# roberta-base-bne-finetuned-ciberbullying-spanish
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect ciberbullying on Spanish.
It achieves the following results on... | {"language": "es", "tags": ["spanish"], "metrics": ["accuracy"], "widget": [{"text": "Eres mas peque\u00f1o que un pitufo!"}, {"text": "Eres muy feo!"}, {"text": "Odio tu forma de hablar!"}, {"text": "Eres tan fea que cuando eras peque\u00f1a te echaban de comer por debajo de la puerta."}]} | JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish | null | [
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| roberta-base-bne-finetuned-ciberbullying-spanish
================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect ciberbullying on Spanish.
It achieves the following results on the... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-mnli
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeM... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-mnli", "results": []}]} | JonatanGk/roberta-base-bne-finetuned-hate-speech-offensive-spanish | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| roberta-base-bne-finetuned-mnli
===============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2869
* Accuracy: 0.9012
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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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. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/Pl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sqac"], "model-index": [{"name": "roberta-base-bne-finetuned-sqac", "results": []}]} | JonatanGk/roberta-base-bne-finetuned-sqac | null | [
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| roberta-base-bne-finetuned-sqac
===============================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the sqac dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2066
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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text-classification | transformers |
# roberta-base-ca-finetuned-catalonia-independence-detector
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the catalonia_independence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6065
- Accuracy: 0.7612
<details>
##... | {"language": "ca", "license": "apache-2.0", "tags": ["catalan"], "datasets": ["catalonia_independence"], "metrics": ["accuracy"], "widget": [{"text": "Puigdemont, a l'estat espanyol: Quatre anys despr\u00e9s, ens hem guanyat el dret a dir prou"}, {"text": "Llarena demana la detenci\u00f3 de Com\u00edn i Ponsat\u00ed ap... | JonatanGk/roberta-base-ca-finetuned-catalonia-independence-detector | null | [
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"tensorboard",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ca"
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| roberta-base-ca-finetuned-catalonia-independence-detector
=========================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the catalonia\_independence dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6065
* Accuracy: 0.7612
Tra... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-classification | transformers | # roberta-base-ca-finetuned-cyberbullying-catalan
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.
It achieves the following results on the... | {"language": "ca", "tags": ["catalan"], "metrics": ["accuracy"], "widget": [{"text": "Ets m\u00e9s petita que un barrufet!!"}, {"text": "Ets tan lletja que et donaven de menjar per sota la porta."}]} | JonatanGk/roberta-base-ca-finetuned-cyberbullying-catalan | null | [
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"ca",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #ca #autotrain_compatible #endpoints_compatible #has_space #region-us
| # roberta-base-ca-finetuned-cyberbullying-catalan
This model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.
It achieves the following results on the evaluation set:
- Loss: 0.1508
- Accuracy: 0.9665
... | [
"# roberta-base-ca-finetuned-cyberbullying-catalan\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.1508\n- Accura... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ca-finetuned-mnli
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-ca-finetuned-mnli", "results": []}]} | JonatanGk/roberta-base-ca-finetuned-hate-speech-offensive-catalan | null | [
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| roberta-base-ca-finetuned-mnli
==============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4137
* Accuracy: 0.8778
Model description
-----------------
More information needed
Intended uses... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ca-finetuned-mnli
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tecla"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-ca-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tecla", "type": "tecla", "args": "tecla"}, "m... | JonatanGk/roberta-base-ca-finetuned-tecla | null | [
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| roberta-base-ca-finetuned-mnli
==============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the tecla dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9354
* Accuracy: 0.7362
Model description
-----------------
More information needed
Intended use... | [
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text-generation | transformers | # Barney Calhoun DialoGPT Model | {"tags": ["conversational"]} | Jonesy/DialoGPT-medium_Barney | null | [
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text-generation | transformers | # Family Guy DialoGPT Model | {"tags": ["conversational"]} | Jonesy/FG_OLD | null | [
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text-generation | transformers | # Johnny Test DialoGPT Model | {"tags": ["conversational"]} | Jonesy/DialoGPT-small_JT | null | [
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text2text-generation | transformers | This is a smaller version of the google/mt5-base model with only Spanish and some English embeddings trained on 60k Spanish MLSum for summarization.
You can use it with the command "summarize:"
| {"language": "es"} | JorgeSarry/est5-summarize | null | [
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| This is a smaller version of the google/mt5-base model with only Spanish and some English embeddings trained on 60k Spanish MLSum for summarization.
You can use it with the command "summarize:"
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text2text-generation | transformers | This is a smaller version of the google/mt5-base model with only Spanish and some English embeddings trained on 60k Spanish WikiEdits for sentence simplification.
You can use it with the command "simplify:"
| {"language": "es"} | JorgeSarry/est5base-simplify | null | [
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You can use it with the command "simplify:"
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text2text-generation | transformers | This is a smaller version of the google/mt5-base model with only Spanish and some English embeddings left following the procedure outlined here https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90
The original model has 582M parameters, with 384M of them being input a... | {"language": "es"} | JorgeSarry/est5base | null | [
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The original model has 582M parameters, with 384M of them being input and output embeddings.
After shrinking the sentencepiece vocabulary from 250K to 30K (top 10K Englis... | [] | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-ner
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on th... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "My name is Scott and I live in Columbus."}, {"text": "Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne."}], "base_m... | Jorgeutd/albert-base-v2-finetuned-ner | null | [
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| albert-base-v2-finetuned-ner
============================
This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0626
* Precision: 0.9252
* Recall: 0.9330
* F1: 0.9291
* Accuracy: 0.9848
Model description
-----------------
... | [
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text-classification | transformers |
## bert-base-uncased
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Text Classification(adverse drug effects detection).
## Hyperparameters
```json
{
"do_eval": true,
"do_train": true,
"fp16": true,
"load_best_model_at_end": true,
"... | {"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bert-base-uncased", "text classification"], "datasets": ["adecorpusv2"], "widget": [{"text": "I got a rash from taking acetaminophen"}], "model-index": [{"name": "BERT-ade_corpus", "results": [{"task": {"type": "text-classification", "name": "Text Class... | Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2 | null | [
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| bert-base-uncased
-----------------
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
* Problem type: Text Classification(adverse drug effects detection).
Hyperparameters
---------------
Validation Metrics
------------------
Usage
-----
You can use cURL to acce... | [] | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-surveyclassification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.c... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "widget": [{"text": "The agent on the phone was very helpful and nice to me."}], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-finetuned-surveyclassification", "results": []}]} | Jorgeutd/bert-base-uncased-finetuned-surveyclassification | null | [
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| bert-base-uncased-finetuned-surveyclassification
================================================
This model is a fine-tuned version of bert-base-uncased on a custom survey dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2818
* Accuracy: 0.9097
* F1: 0.9097
Model description
----------... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-finetuned-ner
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-un... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "My name is Scott and I live in Columbus."}, {"text": "My name is Scott and I am calling from Buffalo, NY. I would like to file a complain ... | Jorgeutd/bert-large-uncased-finetuned-ner | null | [
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| bert-large-uncased-finetuned-ner
================================
This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0778
* Precision: 0.9505
* Recall: 0.9575
* F1: 0.9540
* Accuracy: 0.9886
Model description
------... | [
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text-classification | transformers | ## roberta-base
This model is a fine-tuned model that was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Multi Class Text Classification (emotion detection).
It achieves the following results on the evaluation set:
- Loss: 0.1613253802061081
- f1: 0.9413321705151999
... | {"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "roberta-base", "text classification"], "datasets": ["emotion"], "widget": [{"text": "I am really upset that I have to call up to three times to the number on the back of my insurance card for my call to be answer"}], "model-index": [{"name": "sagemaker-... | Jorgeutd/sagemaker-roberta-base-emotion | null | [
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| roberta-base
------------
This model is a fine-tuned model that was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
* Problem type: Multi Class Text Classification (emotion detection).
It achieves the following results on the evaluation set:
* Loss: 0.1613253802061081
* f1: 0.94... | [] | [
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audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
sa... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | JorisCos/ConvTasNet_Libri1Mix_enhsingle_16k | null | [
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#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
This work ... | [
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audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri2Mix_sepclean_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the Libri2Mix dataset.
Training config:
```yaml
data:
n_src: 2
... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri2Mix", "sep_clean"]} | JorisCos/ConvTasNet_Libri2Mix_sepclean_16k | null | [
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|
## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_clean' task of the Libri2Mix dataset.
Training config:
Results :
On Libri2Mix min test set :
License notice:
This work "Conv... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense noti... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \n... | [
57,
181
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri2Mix_sepclean_8k`
Imported from [Zenodo](https://zenodo.org/record/3873572#.X9M69cLjJH4)
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of th... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri2Mix", "sep_clean"]} | JorisCos/ConvTasNet_Libri2Mix_sepclean_8k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri2Mix",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'
Imported from Zenodo
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_clean' task of the Libri2Mix dataset.
Training config:
Results :
On Libri2Mix min test set :
License noti... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test se... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in As... | [
53,
186
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_noisy` task of the Libri2Mix dataset.
Training config:
```yml
data:
n_src: 2
s... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri2Mix", "sep_noisy"]} | JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri2Mix",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri2Mix dataset.
Training config:
Results:
On Libri2Mix min test set :
License notice:
This work "C... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\n\nOn Libri2Mix min test set :\n\n\n\nLicens... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nI... | [
57,
214
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was ... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k`
Imported from [Zenodo](https://zenodo.org/record/3874420#.X9I6NcLjJH4)
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_noisy` task of the... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri2Mix", "sep_noisy"]} | JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri2Mix",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'
Imported from Zenodo
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri2Mix dataset.
Training config:
Results:
On Libri2Mix min test set :
License n... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri2Mix min test... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in As... | [
53,
220
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepclean_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the Libri3Mix dataset.
Training config:
```yaml
data:
n_src: 3
... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri3Mix", "sep_clean"]} | JorisCos/ConvTasNet_Libri3Mix_sepclean_16k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri3Mix",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_clean' task of the Libri3Mix dataset.
Training config:
Results :
On Libri3Mix min test set :
License notice:
This work "Conv... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense noti... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trai... | [
53,
181
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepclean_8k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the Libri3Mix dataset.
Training config:
```yml
data:
n_src: 3
sa... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri3Mix", "sep_clean"]} | JorisCos/ConvTasNet_Libri3Mix_sepclean_8k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri3Mix",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_clean' task of the Libri3Mix dataset.
Training config:
Results :
On Libri3Mix min test set :
License notice:
This work "ConvTa... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was train... | [
53,
181
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on ... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_noisy` task of the Libri3Mix dataset.
Training config:
```yml
data:
n_src: 3
samp... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri3Mix", "sep_noisy"]} | JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri3Mix",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri3Mix dataset.
Training config:
Results:
On Libri3Mix min test set :
License notice:
This work "C... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was train... | [
53,
210
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on ... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_noisy` task of the Libri3Mix dataset.
Training config:
```yml
data:
n_src: 3
sampl... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri3Mix", "sep_noisy"]} | JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri3Mix",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri3Mix dataset.
Training config:
Results:
On Libri3Mix min test set :
License notice:
This work "Co... | [
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense ... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was traine... | [
53,
214
] | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on t... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/DCCRNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
sampl... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DCCRNet", "audio-to-audio", "speech-enhancement"], "datasets": ["Libri1Mix", "enh_single"]} | JorisCos/DCCRNet_Libri1Mix_enhsingle_16k | null | [
"asteroid",
"pytorch",
"audio",
"DCCRNet",
"audio-to-audio",
"speech-enhancement",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #DCCRNet #audio-to-audio #speech-enhancement #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
This work "DC... | [
"## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense ... | [
"TAGS\n#asteroid #pytorch #audio #DCCRNet #audio-to-audio #speech-enhancement #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe... | [
61,
212
] | [
"TAGS\n#asteroid #pytorch #audio #DCCRNet #audio-to-audio #speech-enhancement #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in As... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/DCUNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
sample... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DCUNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | JorisCos/DCUNet_Libri1Mix_enhsingle_16k | null | [
"asteroid",
"pytorch",
"audio",
"DCUNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #DCUNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
This work "DCU... | [
"## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense n... | [
"TAGS\n#asteroid #pytorch #audio #DCUNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was ... | [
57,
212
] | [
"TAGS\n#asteroid #pytorch #audio #DCUNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was traine... |
audio-to-audio | asteroid |
## Asteroid model `JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
s... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DPRNNTasNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k | null | [
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"DPRNNTasNet",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #DPRNNTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
This work... | [
"## Asteroid model 'JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLice... | [
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audio-to-audio | asteroid |
## Asteroid model `JorisCos/DPTNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
sample... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DPTNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | JorisCos/DPTNet_Libri1Mix_enhsingle_16k | null | [
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"dataset:Libri1Mix",
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#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'JorisCos/DPTNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
This work "DPT... | [
"## Asteroid model 'JorisCos/DPTNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense n... | [
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null | asteroid |
## Asteroid model `JorisCos/VAD_Net`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
segment: 3
train_dir: /home/jcosentino... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "VADNet", "VAD", "Voice Activity Detection"], "datasets": ["LibriVAD"]} | JorisCos/VAD_Net | null | [
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"audio",
"VADNet",
"VAD",
"Voice Activity Detection",
"dataset:LibriVAD",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #VADNet #VAD #Voice Activity Detection #dataset-LibriVAD #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'JorisCos/VAD_Net'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On LibriVAD min test set :
License notice:
This work "VAD_Net" is a derivative of... | [
"## Asteroid model 'JorisCos/VAD_Net'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn LibriVAD min test set :\n\n\n\nLicense notice:\n\nThis work \"VA... | [
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text2text-generation | transformers | # BART_Finetuned_CNN_dailymail
The following repo contains a [bart-base](https://huggingface.co/facebook/bart-base) model that was finetuned using the dataset [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) | {} | Josmar/BART_Finetuned_CNN_dailymail | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # BART_Finetuned_CNN_dailymail
The following repo contains a bart-base model that was finetuned using the dataset cnn_dailymail | [
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] |
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. -->
# m2m100_418M-fr
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the ... | {"license": "mit", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "m2m100_418M-fr", "results": []}]} | Jour/m2m100_418M-fr | null | [
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"m2m_100",
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"translation",
"generated_from_trainer",
"dataset:kde4",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# m2m100_418M-fr
This model is a fine-tuned version of facebook/m2m100_418M on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
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text-generation | transformers |
# Morty DialoGPT Model | {"tags": ["conversational"]} | Julianqll/DialoGPT-small-finalmorty | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | Julianqll/DialoGPT-small-ricksanchez | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-classification | transformers | ## Model description
This model was trained on the XED dataset and achieved
validation loss: 0.5995
validation acc: 84.28% (ROC-AUC)
Labels are based on Plutchik's model of emotions and may be combined:
![image](https://user-images.githubusercontent.com/12978899/122398897-f60d2500-cf97-11eb-8991-61e68f4ea1fc.png... | {} | JuliusAlphonso/dear-jarvis-monolith-xed-en | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## Model description
This model was trained on the XED dataset and achieved
validation loss: 0.5995
validation acc: 84.28% (ROC-AUC)
Labels are based on Plutchik's model of emotions and may be combined:
!image
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.8.0
- Tokenizers 0.10.3... | [
"## Model description\nThis model was trained on the XED dataset and achieved \nvalidation loss: 0.5995 \nvalidation acc: 84.28% (ROC-AUC) \n\nLabels are based on Plutchik's model of emotions and may be combined:\n!image",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dear-jarvis-v5
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on th... | {"license": "apache-2.0", "datasets": [], "model_index": [{"name": "dear-jarvis-v5", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]} | JuliusAlphonso/dear-jarvis-v5 | null | [
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"pytorch",
"distilbert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| dear-jarvis-v5
==============
This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3148
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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text-classification | transformers | Labels are based on Plutchik's model of emotions and may be combined:
 | {} | JuliusAlphonso/distilbert-plutchik | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Labels are based on Plutchik's model of emotions and may be combined:
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | Jungwoo/distilbert-base-uncased-finetuned-cola | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
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"model-index",
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7470
* Matthews Correlation: 0.5414
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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null | asteroid | ## Asteroid model
## Description:
- Code: The code corresponding to this pretrained model can be found [here](https://github.com/asteroid-team/asteroid/tree/master/egs/wsj0-mix-var/Multi-Decoder-DPRNN).
- Notebook: Colab Notebook with examples can be found [here](https://colab.research.google.com/drive/11MGx3_sgOrQrB... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "MultiDecoderDPRNN"], "datasets": ["Wsj0MixVar", "sep_clean"]} | JunzheJosephZhu/MultiDecoderDPRNN | null | [
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"audio",
"MultiDecoderDPRNN",
"dataset:Wsj0MixVar",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #MultiDecoderDPRNN #dataset-Wsj0MixVar #dataset-sep_clean #license-cc-by-sa-4.0 #region-us
| ## Asteroid model
## Description:
- Code: The code corresponding to this pretrained model can be found here.
- Notebook: Colab Notebook with examples can be found here
- Paper: "Multi-Decoder DPRNN: High Accuracy Source Counting and Separation", Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson. ICASSP(2021).
- Summa... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29016523
- CO2 Emissions (in grams): 3.273303707756322
## Validation Metrics
- Loss: 0.6093757748603821
- Accuracy: 0.8333333333333334
- Macro F1: 0.7937936978656889
- Micro F1: 0.8333333333333334
- Weighted F1: 0.8239843785760546
... | {"language": "en", "tags": "autonlp", "datasets": ["Jush/autonlp-data-bp"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.273303707756322} | JushBJJ/autonlp-bp-29016523 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29016523
- CO2 Emissions (in grams): 3.273303707756322
## Validation Metrics
- Loss: 0.6093757748603821
- Accuracy: 0.8333333333333334
- Macro F1: 0.7937936978656889
- Micro F1: 0.8333333333333334
- Weighted F1: 0.8239843785760546
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fill-mask | transformers | FidicBERT is a pre-trained language model to analyze legal text. It is built by further training the Roberta language model in the legal domain, using an extensive legal and contract corpus and thereby fine-tuning for classifying and clustering contractual documents.
| {} | Jzz/FidicBERT | null | [
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| FidicBERT is a pre-trained language model to analyze legal text. It is built by further training the Roberta language model in the legal domain, using an extensive legal and contract corpus and thereby fine-tuning for classifying and clustering contractual documents.
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translation | transformers |
This model is finetuned from [mt5-base](https://huggingface.co/google/mt5-base).
The model vocabulary is trimmed to ~1/3 by selecting top 85000 tokens in the training data. The code to trim the vocabulary can be found [here](https://gist.github.com/K024/4a100a0f4f4b07208958e0f3244da6ad).
Usage:
```python
from... | {"language": ["zh", "ja", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["translation"], "widget": [{"text": "ja2zh: \u543e\u8f29\u306f\u732b\u3067\u3042\u308b\u3002\u540d\u524d\u306f\u307e\u3060\u7121\u3044\u3002"}]} | K024/mt5-zh-ja-en-trimmed | null | [
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|
This model is finetuned from mt5-base.
The model vocabulary is trimmed to ~1/3 by selecting top 85000 tokens in the training data. The code to trim the vocabulary can be found here.
Usage:
Training data:
License: [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: URL
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null | null | yes | {} | K3LLiN/Kellin | null | [
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text-generation | transformers |
#Rick DialoGPT Model | {"tags": ["conversational"]} | KAIHATSU/DialoGPT-small-rick | null | [
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#Rick DialoGPT Model | [] | [
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null | transformers |
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aim... | {"language": "sv"} | KBLab/albert-base-swedish-cased-alpha | null | [
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| Swedish BERT Models
===================
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and in... | [
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token-classification | transformers |
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aim... | {"language": "sv"} | KBLab/bert-base-swedish-cased-ner | null | [
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| Swedish BERT Models
===================
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and in... | [
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fill-mask | transformers |
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aimi... | {"language": "sv", "arxiv": "https://arxiv.org/abs/2007.01658"} | KBLab/bert-base-swedish-cased | null | [
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| Swedish BERT Models
===================
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and int... | [
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automatic-speech-recognition | transformers |
Test | {"tags": ["automatic-speech-recognition", "generated_from_trainer", "asr_seq2seq"]} | KBLab/asr-voxrex-bart-base | null | [
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text2text-generation | transformers |
## KB-BART
A [BART](https://arxiv.org/abs/1910.13461) model trained on a Swedish corpus consisting of 15 billion tokens (about 80GB of text). The model was trained with [Fairseq](https://github.com/pytorch/fairseq), and converted to be compatible with Huggingface.
Training code can be found [here](https://github.co... | {"language": "sv", "widget": [{"text": "Jag har \u00e4tit en <mask>"}]} | KBLab/bart-base-swedish-cased | null | [
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|
## KB-BART
A BART model trained on a Swedish corpus consisting of 15 billion tokens (about 80GB of text). The model was trained with Fairseq, and converted to be compatible with Huggingface.
Training code can be found here.
## Usage
## Acknowledgements
We gratefully acknowledge the HPC RIVR consortium (URL) a... | [
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fill-mask | transformers |
# 🤗 BERT Swedish
This BERT model was trained using the 🤗 transformers library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
To avoid excessive padding d... | {"language": ["sv"]} | KBLab/bert-base-swedish-cased-new | null | [
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# BERT Swedish
This BERT model was trained using the transformers library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
To avoid excessive padding docum... | [
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token-classification | transformers |
# KB-BERT for NER
## Cased data
This model is based on [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased) and was fine-tuned on the [SUCX 3.0 - NER](https://huggingface.co/datasets/KBLab/sucx3_ner) corpus, using the _simple_ tags and cased data.
For this model we used a variation of the data that did **not... | {"language": "sv", "tags": ["token-classification", "sequence-tagger-model", "bert"], "datasets": ["KBLab/sucx3_ner"], "widget": [{"text": "Emil bor i L\u00f6nneberga"}]} | KBLab/bert-base-swedish-cased-reallysimple-ner | null | [
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# KB-BERT for NER
## Cased data
This model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and cased data.
For this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.
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token-classification | transformers |
# KB-BERT for NER
## Mixed cased and uncased data
This model is based on [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased) and was fine-tuned on the [SUCX 3.0 - NER](https://huggingface.co/datasets/KBLab/sucx3_ner) corpus, using the _simple_ tags and partially lowercased data.
For this model we used a var... | {"language": "sv", "tags": ["token-classification", "sequence-tagger-model", "bert"], "datasets": ["KBLab/sucx3_ner"], "model": ["KB/bert-base-swedish-cased"], "widget": [{"text": "Emil bor i L\u00f6nneberga"}]} | KBLab/bert-base-swedish-lowermix-reallysimple-ner | null | [
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# KB-BERT for NER
## Mixed cased and uncased data
This model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and partially lowercased data.
For this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and inside... | [
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fill-mask | transformers |
# Megatron-BERT-base Swedish 600k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was do... | {"language": ["sv"]} | KBLab/megatron-bert-base-swedish-cased-600k | null | [
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#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us
|
# Megatron-BERT-base Swedish 600k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was do... | [
"# Megatron-BERT-base Swedish 600k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTrain... | [
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fill-mask | transformers |
# Megatron-BERT-base Swedish 125k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was do... | {"language": ["sv"]} | KBLab/megatron-bert-base-swedish-cased-125k | null | [
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"safetensors",
"megatron-bert",
"fill-mask",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us
|
# Megatron-BERT-base Swedish 125k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-base with 110M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was do... | [
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fill-mask | transformers | # Roberta base TEST | {} | KBLab/roberta-base-swedish-cased | null | [
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#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Roberta base TEST | [
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sentence-similarity | sentence-transformers |
# KBLab/sentence-bert-swedish-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to instruct... | {"language": ["sv"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "lang": ["sv"], "widget": [{"source_sentence": "Mannen \u00e5t mat.", "sentences": ["Han f\u00f6rt\u00e4rde en n\u00e4rande och nyttig m\u0... | KBLab/sentence-bert-swedish-cased | null | [
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"arxiv:2004.09813",
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] | null | 2022-03-02T23:29:04+00:00 | [
"2004.09813"
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| KBLab/sentence-bert-swedish-cased
=================================
This is a sentence-transformers model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to i... | [
"### Loading an older model version (Sentence-Transformers)\n\n\nCurrently, the easiest way to load an older model version is to clone the model repository and load it from disk. For example, to clone the v1.0 model:\n\n\nThen you can load the model by pointing to the local folder where you cloned the model:\n\n\nU... | [
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automatic-speech-recognition | transformers | # Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks [VoxPopuli-sv base](https://huggingface.co/facebook/wav2vec2-base-sv-voxpopuli) model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **5.62%**... | {"language": "sv-SE", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "speech", "voxpopuli"], "datasets": ["common_voice", "NST Swedish ASR Database"], "metrics": ["wer"]} | KBLab/wav2vec2-base-voxpopuli-sv-swedish | null | [
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| # Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.
When using this model,... | [
"# Wav2vec 2.0 base-voxpopuli-sv-swedish\nFinetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.\n\nWhen using thi... | [
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automatic-speech-recognition | transformers | # Wav2vec 2.0 large-voxpopuli-sv-swedish
**PLEASE NOTE that [this](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) model performs better and has a less restrictive license.**
Additionally pretrained and finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-sv-... | {"language": "sv-SE", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "speech", "voxpopuli"], "datasets": ["common_voice", "NST Swedish ASR Database"], "metrics": ["wer", "cer"], "model-index": [{"name": "Wav2vec 2.0 large VoxPopuli-sv swedish", "results": [{"task": {"type": "automatic-spee... | KBLab/wav2vec2-large-voxpopuli-sv-swedish | null | [
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#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us
| # Wav2vec 2.0 large-voxpopuli-sv-swedish
PLEASE NOTE that this model performs better and has a less restrictive license.
Additionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the follow... | [
"# Wav2vec 2.0 large-voxpopuli-sv-swedish\n\nPLEASE NOTE that this model performs better and has a less restrictive license.\n\n\nAdditionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n",
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automatic-speech-recognition | transformers | # Wav2vec 2.0 large VoxRex Swedish (C)
Finetuned version of KBs [VoxRex large](https://huggingface.co/KBLab/wav2vec2-large-voxrex) model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **... | {"language": "sv", "license": "cc0-1.0", "tags": ["audio", "automatic-speech-recognition", "speech", "hf-asr-leaderboard"], "datasets": ["common_voice", "NST_Swedish_ASR_Database", "P4"], "metrics": ["wer"], "arxiv": "https://arxiv.org/abs/2205.03026", "model-index": [{"name": "Wav2vec 2.0 large VoxRex Swedish", "resul... | KBLab/wav2vec2-large-voxrex-swedish | null | [
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... | null | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #sv #dataset-common_voice #dataset-NST_Swedish_ASR_Database #dataset-P4 #arxiv-2205.03026 #license-cc0-1.0 #model-index #endpoints_compatible #region-us
| # Wav2vec 2.0 large VoxRex Swedish (C)
Finetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 2.5%. WER for Common Voice test set is 8.49% directly an... | [
"# Wav2vec 2.0 large VoxRex Swedish (C)\n\nFinetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 2.5%. WER for Common Voice test set is 8.49% dire... | [
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automatic-speech-recognition | transformers |
# Wav2vec 2.0 large VoxRex (C)
**Please note:** The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at [KBLab/wav2vec2-large-voxrex-swedish](https://hu... | {"language": "sv", "license": "cc0-1.0", "tags": ["audio", "automatic-speech-recognition", "voxrex"]} | KBLab/wav2vec2-large-voxrex | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
"2205.03026"
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#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxrex #sv #arxiv-2205.03026 #license-cc0-1.0 #endpoints_compatible #region-us
|
# Wav2vec 2.0 large VoxRex (C)
Please note: The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at KBLab/wav2vec2-large-voxrex-swedish. The weights fou... | [
"# Wav2vec 2.0 large VoxRex (C)\n\nPlease note: The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at KBLab/wav2vec2-large-voxrex-swedish. The weigh... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxrex #sv #arxiv-2205.03026 #license-cc0-1.0 #endpoints_compatible #region-us \n",
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/).
When using this model, make sure that your speech input is sampled ... | {"language": "sv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "KTH/nst"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 Swedish by KBLab", "results": [{"task": {"type": "automatic-speech-recognition", "... | KBLab/wav2vec2-large-xlsr-53-swedish | null | [
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|
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the NST Swedish Dictation.
When using this model, make sure that your speech input is sampled at 16kHz.
Note: We recommend using our newer model wav2vec2-large-voxrex-swedish for the best performance.
## Usage
The model c... | [
"# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the NST Swedish Dictation.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: We recommend using our newer model wav2vec2-large-voxrex-swedish for the best performance.",
"## Usage\... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | KENNETHFOO/DialoGPT-medium-harrypotter | null | [
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"gpt2",
"text-generation",
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"endpoints_compatible",
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|
# Harry Potter DialoGPT Model | [
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text2text-generation | transformers |
# Model
This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was fine-tuned for sentence correction on normal English translation... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["sentence correction", "text2text-generation"], "datasets": ["jfleg"]} | KES/T5-KES | null | [
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"region:us"
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"1702.04066"
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"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #sentence correction #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model
This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will... | [
"# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #sentence correction #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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text2text-generation | transformers | # Trinidad English Creole Parser
This model was trained as a parser to Trinidad English Creole.
---
# Model
This model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised [JFLEG](https://arxiv.org/abs/1702.04066) dataset. JFLEG dataset was creolised using the f... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["Trinidad and Tobago English Parser", "text2text-generation", "Caribe"], "datasets": ["Custom dataset", "Creolised JFLEG"]} | KES/T5-TTParser | null | [
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"t5",
"text2text-generation",
"Trinidad and Tobago English Parser",
"Caribe",
"en",
"arxiv:1702.04066",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
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"text-generation-inference",
"region:us"
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"1702.04066"
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"en"
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| # Trinidad English Creole Parser
This model was trained as a parser to Trinidad English Creole.
---
# Model
This model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised JFLEG dataset. JFLEG dataset was creolised using the file encoding feature of the Caribe l... | [
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text2text-generation | transformers |
# Model Card for ke-t5-base-ko
# Model Details
## Model Description
- **Developed by:** Korea Electronics Technology Institute Artificial Intelligence Research Center
- **Shared by [Optional]:** More information needed
- **Model type:** Text2Text Generation
- **Language(s) (NLP):** More information needed
-... | {"language": "ko", "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-base-ko | null | [
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|
# Model Card for ke-t5-base-ko
# Model Details
## Model Description
- Developed by: Korea Electronics Technology Institute Artificial Intelligence Research Center
- Shared by [Optional]: More information needed
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More i... | [
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text2text-generation | transformers | # ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel, Aut... | {"language": ["ko", "en"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-base-newslike | null | [
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| # ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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text2text-generation | transformers |
# Model Card for ke-t5-base
# Model Details
## Model Description
The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html):
> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input... | {"language": ["en", "ko"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-base | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
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"ko"
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|
# Model Card for ke-t5-base
# Model Details
## Model Description
The developers of the Text-To-Text Transfer Transformer (T5) write:
> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can... | [
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text2text-generation | transformers |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel,... | {"language": "ko", "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-large-ko | null | [
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"text2text-generation",
"ko",
"license:apache-2.0",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
| [
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text2text-generation | transformers | # ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel, Aut... | {"language": ["ko", "en"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-large-newslike | null | [
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"text2text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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"## BibTeX entry and citation info"
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text2text-generation | transformers |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel,... | {"language": ["en", "ko"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-large | null | [
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en",
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|
# ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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text2text-generation | transformers |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel,... | {"language": "ko", "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-small-ko | null | [
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"text2text-generation",
"ko",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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