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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. -->
# Facebook_Ohne_HPS
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Facebook_Ohne_HPS", "results": []}]} | toasthans/Facebook_Ohne_HPS | null | [
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"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Facebook\_Ohne\_HPS
===================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4648
* Accuracy: 0.9255
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Facebook_and_Twitter_Ohne_HPS
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-ge... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Facebook_and_Twitter_Ohne_HPS", "results": []}]} | toasthans/Facebook_and_Twitter_Ohne_HPS | null | [
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"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Facebook\_and\_Twitter\_Ohne\_HPS
=================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9218
* Accuracy: 0.8512
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Twitter_Mit_HPSearch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-case... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Twitter_Mit_HPSearch", "results": []}]} | toasthans/Twitter_Mit_HPSearch | null | [
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"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Twitter\_Mit\_HPSearch
======================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8389
* Accuracy: 0.8442
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.9771872814096894e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 23\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.9771872814096894e-05\n* train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Twitter_Ohne_HPSearch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Twitter_Ohne_HPSearch", "results": []}]} | toasthans/Twitter_Ohne_HPSearch | null | [
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"tensorboard",
"bert",
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"generated_from_trainer",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Twitter\_Ohne\_HPSearch
=======================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0262
* Accuracy: 0.8300
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
null | transformers |
# ELECTRA Hongkongese Base
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks catering... | {"language": "yue", "license": "apache-2.0", "metrics": ["DRCD", "openrice-senti", "lihkg-cat", "wordshk-sem"]} | toastynews/electra-hongkongese-base-discriminator | null | [
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"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us
| ELECTRA Hongkongese Base
========================
Model description
-----------------
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
Intended uses & limitations
---------------------------
This model is an alternative to ... | [
"#### How to use\n\n\nThis is the base model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limitations and bias\n\n\nThe training data consists of mostly news articles and blogs. There is probably a bias towards formal ... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThis is the base model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limitat... |
null | transformers |
# ELECTRA Hongkongese Large
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks caterin... | {"language": "yue", "license": "apache-2.0", "metrics": ["DRCD", "openrice-senti", "lihkg-cat", "wordshk-sem"]} | toastynews/electra-hongkongese-large-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us
| ELECTRA Hongkongese Large
=========================
Model description
-----------------
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
Intended uses & limitations
---------------------------
This model is an alternative t... | [
"#### How to use\n\n\nThis is the large model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limitations and bias\n\n\nThe training data consists of mostly news articles and blogs. There is probably a bias towards formal... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThis is the large model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limita... |
null | transformers |
# ELECTRA Hongkongese Small
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks caterin... | {"language": "yue", "license": "apache-2.0", "metrics": ["DRCD", "openrice-senti", "lihkg-cat", "wordshk-sem"]} | toastynews/electra-hongkongese-small-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us
| ELECTRA Hongkongese Small
=========================
Model description
-----------------
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
Intended uses & limitations
---------------------------
This model is an alternative t... | [
"#### How to use\n\n\nThis is the small model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limitations and bias\n\n\nThe training data consists of mostly news articles and blogs. There is probably a bias towards formal... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #yue #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThis is the small model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.",
"#### Limita... |
text-generation | transformers |
# XLNet Hongkongese Base
## Model description
XLNet trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks catering to ... | {"language": "yue", "license": "apache-2.0", "metrics": ["DRCD", "openrice-senti", "lihkg-cat", "wordshk-sem"]} | toastynews/xlnet-hongkongese-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlnet",
"text-generation",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #tf #safetensors #xlnet #text-generation #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| XLNet Hongkongese Base
======================
Model description
-----------------
XLNet trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
Intended uses & limitations
---------------------------
This model is an alternative to Chines... | [
"#### How to use\n\n\nThis is the base model trained from the official repo. Further finetuning will be needed for use on downstream tasks. It can also be used to generate text.",
"#### Limitations and bias\n\n\nThe training data consists of mostly news articles and blogs. There is probably a bias towards formal ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlnet #text-generation #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThis is the base model trained from the official repo. Further finetuning will be needed for use on downstream tasks. It can also be use... |
null | transformers | # BERT-uncased-2L-768H
This is a converted pytorch checkpoint for bert with 2L trained from scratch.
See [Google BERT](https://github.com/google-research/bert) for details.
| {} | tobiaslee/bert-2l-768h-uncased | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| # BERT-uncased-2L-768H
This is a converted pytorch checkpoint for bert with 2L trained from scratch.
See Google BERT for details.
| [
"# BERT-uncased-2L-768H\n\nThis is a converted pytorch checkpoint for bert with 2L trained from scratch.\n\nSee Google BERT for details."
] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# BERT-uncased-2L-768H\n\nThis is a converted pytorch checkpoint for bert with 2L trained from scratch.\n\nSee Google BERT for details."
] |
text-generation | transformers |
# Tony Stark DialoGPT Model | {"tags": ["conversational"]} | toiletwater/DialoGPT-medium-ironman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
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"# Tony Stark DialoGPT Model"
] |
summarization | transformers |
# T5 Large for Text Aggregation
## Model description
This is a T5 Large fine-tuned for crowdsourced text aggregation tasks. The model takes multiple performers' responses and yields a single aggregated response. This approach was introduced for the first time during [VLDB 2021 Crowd Science Challenge](https://crowds... | {"language": ["en"], "license": "apache-2.0", "tags": ["text aggregation", "summarization"], "datasets": ["toloka/CrowdSpeech"], "metrics": ["wer"]} | toloka/t5-large-for-text-aggregation | null | [
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"text2text-generation",
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"arxiv:2107.01091",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"reg... | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2107.01091"
] | [
"en"
] | TAGS
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| T5 Large for Text Aggregation
=============================
Model description
-----------------
This is a T5 Large fine-tuned for crowdsourced text aggregation tasks. The model takes multiple performers' responses and yields a single aggregated response. This approach was introduced for the first time during VLDB 2... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text aggregation #summarization #en #dataset-toloka/CrowdSpeech #arxiv-1910.10683 #arxiv-2107.01091 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | tom1804/HP | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | tom1804/HP_last | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
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"# My Awesome Model"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | tom1804/hp_new | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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# My Awesome Model | [
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"# My Awesome Model"
] |
text-generation | transformers |
# Rick DialogPT Model | {"tags": ["conversational"]} | tomascerejo12/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialogPT Model | [
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"# Rick DialogPT Model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-custom
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-custom", "results": []}]} | tomascufaro/wav2vec2-large-xls-r-300m-spanish-custom | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-custom
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4426
* Wer: 0.2117
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-small-v3
This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-... | {"tags": ["es", "robust-speech-event", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-small-v3", "results": []}]} | tomascufaro/wav2vec2-large-xls-r-300m-spanish-small-v3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"robust-speech-event",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #es #robust-speech-event #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-small-v3
==========================================
This model is a fine-tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3986
* Wer: 0.1980
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0004\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #es #robust-speech-event #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0004\n* train\\_b... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-small
This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-cus... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-small", "results": []}]} | tomascufaro/wav2vec2-large-xls-r-300m-spanish-small | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-small
=======================================
This model is a fine-tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3763
* Wer: 0.1791
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-es-test
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-l... | {"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "es", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-es-test", "results": [{"task": ... | tomascufaro/xls-r-es-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"es",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| xls-r-es-test
=============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ES dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1304
* WER: 0.1261
* CER: 0.035
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-960h
This repository is a reimplementation of [official Facebook’s wav2vec](https://huggingface.co/facebook/wav2vec2-base-960h).
There is no description of converting the wav2vec [pretrain model](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20) to a pytorch.bin file.
We are ... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media... | tommy19970714/wav2vec2-base-960h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Base-960h
==================
This repository is a reimplementation of official Facebook’s wav2vec.
There is no description of converting the wav2vec pretrain model to a URL file.
We are rebuilding URL from the pretrain model.
Here is the conversion method.
Usage
=====
To transcribe audio files the model ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300M-teste2
This model was trained from scratch on the common_voice dataset.
## Model description
More information ne... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-300M-teste2", "results": []}]} | tonyalves/wav2vec2-300M-teste2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
|
# wav2vec2-300M-teste2
This model was trained from scratch on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followin... | [
"# wav2vec2-300M-teste2\n\nThis model was trained from scratch on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"# wav2vec2-300M-teste2\n\nThis model was trained from scratch on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intende... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-teste4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-300m-teste4", "results": []}]} | tonyalves/wav2vec2-300m-teste4 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-teste4
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3276
* Wer: 0.3489
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pt-colab", "results": []}]} | tonyalves/wav2vec2-large-xls-r-300m-pt-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-pt-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3637
* Wer: 0.2982
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
text-generation | transformers | ----
tags:
- conversational
---
# Harry Potter DialoGPT Model | {} | torque29/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ----
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
## DialoGPT_MWOZ
This is a fine-tuned model of DialoGPT (medium) on the MultiWOZ v2.2 dataset. It is intended to be used as a conversational system.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, train, hospital and police.
The perplexity achieved... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["multi_woz_v22"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "I would like to have breakfast."}]} | tosin/dialogpt_mwoz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"dataset:multi_woz_v22",
"arxiv:2110.06273",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.06273"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-multi_woz_v22 #arxiv-2110.06273 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| DialoGPT\_MWOZ
--------------
This is a fine-tuned model of DialoGPT (medium) on the MultiWOZ v2.2 dataset. It is intended to be used as a conversational system.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, train, hospital and police.
The perpl... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\ntokenizer = AutoTokenizer.from\\_pretrained(\"tosin/dialogpt\\_mwoz\")\nmodel = AutoModelForCausalLM.from\\_pretrained(\"tosin/dialo... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-multi_woz_v22 #arxiv-2110.06273 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting ... |
text-generation | transformers |
## DialoGPT_SV
This is a fine-tuned model of the DialoGPT (medium) on the Swedish Gothenburg Dialogue Corpus (GDC). It is intended to be used as a Swedish conversational system. The GDC dataset it's trained on is limited in scope, as it's from the transcription of dialogues of about 25 different social activities, in... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["GDC"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "Jag ska fika."}]} | tosin/dialogpt_sv | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"dataset:GDC",
"arxiv:2110.06273",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.06273"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-GDC #arxiv-2110.06273 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DialoGPT\_SV
------------
This is a fine-tuned model of the DialoGPT (medium) on the Swedish Gothenburg Dialogue Corpus (GDC). It is intended to be used as a Swedish conversational system. The GDC dataset it's trained on is limited in scope, as it's from the transcription of dialogues of about 25 different social act... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\ntokenizer = AutoTokenizer.from\\_pretrained(\"tosin/dialogpt\\_sv\")\nmodel = AutoModelForCausalLM.from\\_pretrained(\"tosin/dialogp... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #dataset-GDC #arxiv-2110.06273 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''pyth... |
text2text-generation | transformers |
## T5Base-PCL
This is a fine-tuned model of T5 (base) on the patronizing and condenscending language (PCL) dataset by Pérez-Almendros et al (2020) used for Task 4 competition of SemEval-2022.
It is intended to be used as a classification model for identifying PCL (0 - neg; 1 - pos). The task prefix we used for the T5 ... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["text classification", "transformers"], "datasets": ["PCL"], "metrics": ["F1"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "inference": false} | tosin/pcl_22 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text classification",
"en",
"dataset:PCL",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text classification #en #dataset-PCL #license-cc-by-4.0 #autotrain_compatible #text-generation-inference #region-us
|
## T5Base-PCL
This is a fine-tuned model of T5 (base) on the patronizing and condenscending language (PCL) dataset by Pérez-Almendros et al (2020) used for Task 4 competition of SemEval-2022.
It is intended to be used as a classification model for identifying PCL (0 - neg; 1 - pos). The task prefix we used for the T5 ... | [
"## T5Base-PCL\nThis is a fine-tuned model of T5 (base) on the patronizing and condenscending language (PCL) dataset by Pérez-Almendros et al (2020) used for Task 4 competition of SemEval-2022.\nIt is intended to be used as a classification model for identifying PCL (0 - neg; 1 - pos). The task prefix we used for t... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text classification #en #dataset-PCL #license-cc-by-4.0 #autotrain_compatible #text-generation-inference #region-us \n",
"## T5Base-PCL\nThis is a fine-tuned model of T5 (base) on the patronizing and condenscending language (PCL) dataset by Pérez-Almendros ... |
text-generation | transformers |
# Addy DialoGPT Model | {"tags": ["conversational"]} | toyfreak/DialoGPT-small-addy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Addy DialoGPT Model | [
"# Addy DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Addy DialoGPT Model"
] |
text-generation | transformers |
# Shy DialoGPT Model | {"tags": ["conversational"]} | toyfreak/DialoGPT-small-shy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Shy DialoGPT Model | [
"# Shy DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Shy DialoGPT Model"
] |
text-generation | transformers |
#Parry Bot DialoGPT Model | {"tags": ["conversational"]} | tpri/DialoGPT-small-pa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Parry Bot DialoGPT Model | [] | [
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text-generation | transformers | # AAng Dialog-GPT Model | {"tags": ["conversational"]} | tprincessazula/Dialog-GPT-small-AANG | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-generation | transformers |
#KATARA DialoGPT Model | {"tags": ["conversational"]} | tprincessazula/Dialog-GPT-small-KATARA-AVATAR | null | [
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#KATARA DialoGPT Model | [] | [
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text-generation | transformers |
#SOKKA DialoGPT Model | {"tags": ["conversational"]} | tprincessazula/Dialog-GPT-small-SOKKA-AVATAR | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers | # Harry Potter Dialog-GPT Model | {"tags": ["conversational"]} | tprincessazula/Dialog-GPT-small-harrypotter | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-to-image | null |
This model is trained collaboratively — it is a part of the NeurIPS 2021 demonstration ["Training Transformers Together"](https://training-transformers-together.github.io/).
The latest model checkpoint will be uploaded to this repository every 6 hours until the training stops.
# Model Description
We train a model ... | {"tags": ["text-to-image", "torch"], "datasets": ["laion/laion_100m_vqgan_f8"], "inference": false} | training-transformers-together/dalle-demo-v1 | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#text-to-image #torch #dataset-laion/laion_100m_vqgan_f8 #has_space #region-us
|
This model is trained collaboratively — it is a part of the NeurIPS 2021 demonstration "Training Transformers Together".
The latest model checkpoint will be uploaded to this repository every 6 hours until the training stops.
# Model Description
We train a model similar to OpenAI DALL-E — a Transformer model that g... | [
"# Model Description \n\nWe train a model similar to OpenAI DALL-E — a Transformer model that generates images from text descriptions. Training happens collaboratively — volunteers from all over the Internet contribute to the training using hardware available to them. We use LAION-400M, the world's largest openly a... | [
"TAGS\n#text-to-image #torch #dataset-laion/laion_100m_vqgan_f8 #has_space #region-us \n",
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text-generation | null |
# Discord | {"tags": ["conversational"]} | transfaeries/DialoGPT-Discord | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Discord | [
"# Discord"
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text-generation | transformers |
# Discord Model Medium 7 epochs | {"tags": ["conversational"]} | transfaeries/DialoGPT-medium-Discord-1.0 | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers |
# Discord Model | {"tags": ["conversational"]} | transfaeries/DialoGPT-small-Discord-1.0 | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Discord Model | [
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text-generation | transformers |
# Twilight Model Medium 13 epochs | {"tags": ["conversational"]} | transfaeries/Twilight-Sparkle-GPT | null | [
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] |
text-classification | transformers | # Intent Detection with BERT
This model was trained on the [CLINC150](https://arxiv.org/abs/1909.02027) dataset for customer intent detection. The dataset can be found on the [Hub](https://huggingface.co/datasets/clinc_oos). The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with T... | {} | transformersbook/bert-base-uncased-finetuned-clinc | null | [
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"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1909.02027",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.02027"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1909.02027 #autotrain_compatible #endpoints_compatible #region-us
| # Intent Detection with BERT
This model was trained on the CLINC150 dataset for customer intent detection. The dataset can be found on the Hub. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository... | [
"# Intent Detection with BERT\n\nThis model was trained on the CLINC150 dataset for customer intent detection. The dataset can be found on the Hub. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github rep... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1909.02027 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Intent Detection with BERT\n\nThis model was trained on the CLINC150 dataset for customer intent detection. The dataset can be found on the Hub. The model is used in Chapt... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | transformersbook/bert-base-uncased-issues-128 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the GitHub issues dataset. The model is used in Chapter 9: Dealing with Few to No Labels in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achiev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Traini... | [
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null | null | # CodeParrot
This is a small version of the CodeParrot tokenizer trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The tokenizer is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/libr... | {} | transformersbook/codeparrot-small-vocabulary | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # CodeParrot
This is a small version of the CodeParrot tokenizer trained on the CodeParrot Python code dataset. The tokenizer is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository. | [
"# CodeParrot\n\nThis is a small version of the CodeParrot tokenizer trained on the CodeParrot Python code dataset. The tokenizer is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository."
] | [
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text-generation | transformers | # CodeParrot
CodeParrot (small) is a 110M parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/v... | {} | transformersbook/codeparrot-small | null | [
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"tensorboard",
"gpt2",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # CodeParrot
CodeParrot (small) is a 110M parameter GPT-2 model trained on the CodeParrot Python code dataset. The model is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository. | [
"# CodeParrot\n\nCodeParrot (small) is a 110M parameter GPT-2 model trained on the CodeParrot Python code dataset. The model is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository."
] | [
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text-generation | transformers | # CodeParrot
CodeParrot (large) is a 1.5B parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/v... | {} | transformersbook/codeparrot | null | [
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"gpt2",
"text-generation",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # CodeParrot
CodeParrot (large) is a 1.5B parameter GPT-2 model trained on the CodeParrot Python code dataset. The model is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository. | [
"# CodeParrot\n\nCodeParrot (large) is a 1.5B parameter GPT-2 model trained on the CodeParrot Python code dataset. The model is trained in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository."
] | [
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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-distilled-clinc
This model is a fine-tuned with knowledge distillation version of [distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | transformersbook/distilbert-base-uncased-distilled-clinc | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned with knowledge distillation version of distilbert-base-uncased on the clinc\_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can fi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | transformersbook/distilbert-base-uncased-finetuned-clinc | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the acco... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | transformersbook/distilbert-base-uncased-finetuned-emotion | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. The model is trained in Chapter 2: Text Classification in the NLP with Transformers book. You can find the full code in the accompanying Github repo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum-test
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-c... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum-test", "results": []}]} | transformersbook/pegasus-samsum | null | [
"transformers",
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"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum-test
===================
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset. The model is trained in Chapter 6: Summarization in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achieves the following results o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "en", "spl... | transformersbook/xlm-roberta-base-finetuned-panx-all | null | [
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"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-wikiann #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github reposit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | transformersbook/xlm-roberta-base-finetuned-panx-de-fr | null | [
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"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github rep... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | transformersbook/xlm-roberta-base-finetuned-panx-de | null | [
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"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repositor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | transformersbook/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repositor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | transformersbook/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repositor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | transformersbook/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repositor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | trig/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# multiverse but with swapped characters and more learning | {"tags": ["conversational"]} | trig/multiverse-second | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# multiverse but with swapped characters and more learning | [
"# multiverse but with swapped characters and more learning"
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"# multiverse but with swapped characters and more learning"
] |
text-generation | transformers |
# chatbot using multiple shows | {"tags": ["conversational"]} | trig/multiverse | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# chatbot using multiple shows | [
"# chatbot using multiple shows"
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"# chatbot using multiple shows"
] |
text-generation | transformers |
# chatbot test with sokka from atla | {"tags": ["conversational"]} | trig/sokka-chatbot-test | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# chatbot test with sokka from atla | [
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] |
text-generation | transformers |
# some test idk | {"tags": ["conversational"]} | trig/tlok-test | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# some test idk | [
"# some test idk"
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"# some test idk"
] |
null | transformers | ## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
model = BertForSequenceClassification.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-uncased")
tokenizer = BertTokenizer.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-unc... | {} | trituenhantaoio/bert-base-vietnamese-diacritics-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us
| ## Usage
### References
URL | [
"## Usage",
"### References\n\n\n\nURL"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us \n",
"## Usage",
"### References\n\n\n\nURL"
] |
null | transformers | ## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
model = BertForSequenceClassification.from_pretrained("trituenhantaoio/bert-base-vietnamese-uncased")
tokenizer = BertTokenizer.from_pretrained("trituenhantaoio/bert-base-vietnamese-uncased")
```
### Refere... | {} | trituenhantaoio/bert-base-vietnamese-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us
| ## Usage
### References
URL | [
"## Usage",
"### References\n\n\n\nURL"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us \n",
"## Usage",
"### References\n\n\n\nURL"
] |
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. -->
# twitter_emotions
This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "twitter_emotions", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics... | trnt/twitter_emotions | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| twitter\_emotions
=================
This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1647
* Accuracy: 0.9375
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-generation | transformers |
# Harry Potter DialogGPT | {"tags": ["conversational"]} | troythewar/DialogGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialogGPT | [
"# Harry Potter DialogGPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialogGPT"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 117396
## Validation Metrics
- Loss: 0.4094310998916626
- Accuracy: 0.8201678240740741
- Precision: 0.6750303520841765
- Recall: 0.7912713472485768
- AUC: 0.8927167943538512
- F1: 0.728543350076436
## Usage
You can use cURL to access ... | {"language": "ja", "tags": "autonlp", "datasets": ["trtd56/autonlp-data-wrime_joy_only"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | trtd56/autonlp-wrime_joy_only-117396 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:trtd56/autonlp-data-wrime_joy_only",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-trtd56/autonlp-data-wrime_joy_only #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 117396
## Validation Metrics
- Loss: 0.4094310998916626
- Accuracy: 0.8201678240740741
- Precision: 0.6750303520841765
- Recall: 0.7912713472485768
- AUC: 0.8927167943538512
- F1: 0.728543350076436
## Usage
You can use cURL to access ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 117396",
"## Validation Metrics\n\n- Loss: 0.4094310998916626\n- Accuracy: 0.8201678240740741\n- Precision: 0.6750303520841765\n- Recall: 0.7912713472485768\n- AUC: 0.8927167943538512\n- F1: 0.728543350076436",
"## Usage\n\nYou... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-trtd56/autonlp-data-wrime_joy_only #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 117396",
"## Validation Metrics\n\n- Loss: 0.409... |
null | transformers | # [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CE... | {} | trueto/medalbert-base-chinese | null | [
"transformers",
"pytorch",
"albert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #endpoints_compatible #region-us
| medbert
=======
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
评估基准
----
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
开源模型
----
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
性能表现
----
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
引用格式
----
| [] | [
"TAGS\n#transformers #pytorch #albert #endpoints_compatible #region-us \n"
] |
null | transformers | # [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CE... | {} | trueto/medalbert-base-wwm-chinese | null | [
"transformers",
"pytorch",
"albert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #endpoints_compatible #region-us
| medbert
=======
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
评估基准
----
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
开源模型
----
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
性能表现
----
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
引用格式
----
| [] | [
"TAGS\n#transformers #pytorch #albert #endpoints_compatible #region-us \n"
] |
null | transformers | # [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CE... | {} | trueto/medbert-base-chinese | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us
| medbert
=======
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
评估基准
----
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
开源模型
----
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
性能表现
----
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
引用格式
----
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | # [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CE... | {} | trueto/medbert-base-wwm-chinese | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us
| medbert
=======
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
评估基准
----
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
开源模型
----
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
性能表现
----
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
引用格式
----
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | # [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CE... | {} | trueto/medbert-kd-chinese | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us
| medbert
=======
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
评估基准
----
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
开源模型
----
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
性能表现
----
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
引用格式
----
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n"
] |
token-classification | transformers | # Vietnam Tourism Named Entity Recognition (English version)
We fine-tuned BERT to train Vietnam tourism dataset for a question answering system. The model was called NER2QUES because it detected tourism NER in a sentence. From that, the system generated questions corresponding to NER types.
# How to use
## You can use... | {} | truongphan/vntourismNER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # Vietnam Tourism Named Entity Recognition (English version)
We fine-tuned BERT to train Vietnam tourism dataset for a question answering system. The model was called NER2QUES because it detected tourism NER in a sentence. From that, the system generated questions corresponding to NER types.
# How to use
## You can use... | [
"# Vietnam Tourism Named Entity Recognition (English version)\nWe fine-tuned BERT to train Vietnam tourism dataset for a question answering system. The model was called NER2QUES because it detected tourism NER in a sentence. From that, the system generated questions corresponding to NER types.",
"# How to use",
... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Vietnam Tourism Named Entity Recognition (English version)\nWe fine-tuned BERT to train Vietnam tourism dataset for a question answering system. The model was called NER2QUES because it detecte... |
text-generation | transformers |
# DialoGPT Model: Eleventh Doctor from Doctor Who
so many bugs and I can not fix them | {"tags": ["conversational"]} | truthisneverlinear/EleventhDoctor | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Model: Eleventh Doctor from Doctor Who
so many bugs and I can not fix them | [
"# DialoGPT Model: Eleventh Doctor from Doctor Who\nso many bugs and I can not fix them"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Model: Eleventh Doctor from Doctor Who\nso many bugs and I can not fix them"
] |
text2text-generation | transformers |
## tscholak/1wnr382e
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-Large](https://huggingface.co/t5-large).
### Training Data
The model has been fine-tuned on the 7000 training examples in the [Sp... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["spider"], "metrics": ["spider"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["How many singers do we have? | concert_singer | stadium : stadium_id, location, na... | tscholak/1wnr382e | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## tscholak/1wnr382e
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-Large.
### Training Data
The model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot text-to-SQ... | [
"## tscholak/1wnr382e\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-Large.",
"### Training Data\n\nThe model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## tscholak/1wnr382e\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Reg... |
text2text-generation | transformers |
## tscholak/1zha5ono
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.base](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["spider"], "metrics": ["spider"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["How many singers do we have? | concert_singer | stadium : stadium_id, location, na... | tscholak/1zha5ono | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## tscholak/1zha5ono
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.
### Training Data
The model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot text-to-... | [
"## tscholak/1zha5ono\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.",
"### Training Data\n\nThe model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-sh... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## tscholak/1zha5ono\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrain... |
text2text-generation | transformers |
## tscholak/2e826ioa
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-3B](https://huggingface.co/t5-3b).
### Training Data
The model has been fine-tuned on the 2,164 training dialogues in the [CoSQL ... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["cosql", "spider"], "metrics": ["cosql"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["And the concert named Auditions? | concert_singer | stadium : stadium_id, ... | tscholak/2e826ioa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:cosql",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-cosql #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## tscholak/2e826ioa
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-3B.
### Training Data
The model has been fine-tuned on the 2,164 training dialogues in the CoSQL SQL-grounded dialogue state tracking dataset and the 7,000 training e... | [
"## tscholak/2e826ioa\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-3B.",
"### Training Data\n\nThe model has been fine-tuned on the 2,164 training dialogues in the CoSQL SQL-grounded dialogue state tracking dataset and the 7,000... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-cosql #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## tscholak/2e826ioa\n\nFine-tuned weights for PICARD - Parsing Incrementall... |
text2text-generation | transformers |
## tscholak/2jrayxos
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.large](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["cosql", "spider"], "metrics": ["cosql"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["And the concert named Auditions? | concert_singer | stadium : stadium_id, ... | tscholak/2jrayxos | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:cosql",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-cosql #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## tscholak/2jrayxos
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.
### Training Data
The model has been fine-tuned on the 2,164 training dialogues in the CoSQL SQL-grounded dialogue state tracking dataset and the 7,000 train... | [
"## tscholak/2jrayxos\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.",
"### Training Data\n\nThe model has been fine-tuned on the 2,164 training dialogues in the CoSQL SQL-grounded dialogue state tracking dataset and the ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-cosql #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## tscholak/2jrayxos\n\nFine-tuned weights for PICARD - Parsing Incrementally for Const... |
text2text-generation | transformers |
## tscholak/3vnuv1vf
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.large](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["spider"], "metrics": ["spider"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["How many singers do we have? | concert_singer | stadium : stadium_id, location, na... | tscholak/3vnuv1vf | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## tscholak/3vnuv1vf
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.
### Training Data
The model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot text-to-... | [
"## tscholak/3vnuv1vf\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on t5.1.1.URL.",
"### Training Data\n\nThe model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-sh... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## tscholak/3vnuv1vf\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Reg... |
text2text-generation | transformers |
## tscholak/cxmefzzi
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-3B](https://huggingface.co/t5-3b).
### Training Data
The model has been fine-tuned on the 7000 training examples in the [Spider t... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2sql"], "datasets": ["spider"], "metrics": ["spider"], "thumbnail": "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab", "widget": ["How many singers do we have? | concert_singer | stadium : stadium_id, location, na... | tscholak/cxmefzzi | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05093"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## tscholak/cxmefzzi
Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-3B.
### Training Data
The model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot text-to-SQL t... | [
"## tscholak/cxmefzzi\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-3B.",
"### Training Data\n\nThe model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot te... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2sql #en #dataset-spider #arxiv-2109.05093 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## tscholak/cxmefzzi\n\nFine-tuned weights for PICARD - Parsing Incrementally for Constrain... |
text-generation | transformers | Simple text to SQL | {} | tsdocode/text-to-sql | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Simple text to SQL | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Human GPT Model | {"tags": ["conversational"]} | ttntran/DialoGPT-small-human | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Human GPT Model | [
"# Human GPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Human GPT Model"
] |
text-generation | transformers | korean translated japan web novel finetuned from skt/kogpt2-base-v2 | {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]} | ttop324/kogpt2jnovel | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| korean translated japan web novel finetuned from skt/kogpt2-base-v2 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
novel finetuned from skt/kogpt2-base-v2 | {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]} | ttop324/kogpt2novel | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
novel finetuned from skt/kogpt2-base-v2 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | # wav2vec2-live-japanese
https://github.com/ttop32/wav2vec2-live-japanese-translator
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese hiragana using the
- [common_voice](https://huggingface.co/datasets/common_voice)
- [JSUT](https://sites.google.... | {"language": "ja", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-live-japanese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition... | ttop324/wav2vec2-live-japanese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ja",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # wav2vec2-live-japanese
URL
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese hiragana using the
- common_voice
- JSUT
- CSS10
- TEDxJP-10K
- JVS
- JSSS
## Inference
## Evaluation
| [
"# wav2vec2-live-japanese\nURL \nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese hiragana using the \n- common_voice \n- JSUT \n- CSS10 \n- TEDxJP-10K \n- JVS\n- JSSS",
"## Inference",
"## Evaluation"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-live-japanese\nURL \nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese hiragana using the... |
text-generation | transformers | # GPT-2 Fine-tuning With Vietnamese Six Eight Poems
## Model description
This is a Vietnamese GPT-2 Six Eight Poet Model which is trained on the 10mb of Six Eight poems dataset, based on the Vietnamese Wiki GPT2 pretrained model (https://huggingface.co/danghuy1999/gpt2-viwiki)
## Purpose
This model was made only for f... | {} | tuanle/GPT2_Poet | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-2 Fine-tuning With Vietnamese Six Eight Poems
## Model description
This is a Vietnamese GPT-2 Six Eight Poet Model which is trained on the 10mb of Six Eight poems dataset, based on the Vietnamese Wiki GPT2 pretrained model (URL
## Purpose
This model was made only for fun and experimental study
## Dataset
The da... | [
"# GPT-2 Fine-tuning With Vietnamese Six Eight Poems",
"## Model description\nThis is a Vietnamese GPT-2 Six Eight Poet Model which is trained on the 10mb of Six Eight poems dataset, based on the Vietnamese Wiki GPT2 pretrained model (URL",
"## Purpose\nThis model was made only for fun and experimental study",
... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 Fine-tuning With Vietnamese Six Eight Poems",
"## Model description\nThis is a Vietnamese GPT-2 Six Eight Poet Model which is trained on the 10mb of Six Eight poem... |
text-generation | transformers |
# GPT-2 Fine-tuning With Vietnamese News
## Model description
A Fine-tuned Vietnamese GPT2 model which can generate Vietnamese news based on context (category + headline), based on the Vietnamese Wiki GPT2 pretrained model (https://huggingface.co/danghuy1999/gpt2-viwiki)
## Github
- https://github.com/Tuan-Lee-23/Vi... | {"language": ["vi"], "tags": ["News", "Language model", "GPT2"], "datasets": ["Private Vietnamese News dataset"], "metrics": ["rouge", "wer"], "thumbnail": "url to a thumbnail used in social sharing"} | tuanle/VN-News-GPT2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"News",
"Language model",
"GPT2",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #News #Language model #GPT2 #vi #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT-2 Fine-tuning With Vietnamese News
## Model description
A Fine-tuned Vietnamese GPT2 model which can generate Vietnamese news based on context (category + headline), based on the Vietnamese Wiki GPT2 pretrained model (URL
## Github
- URL
## Purpose
This model has been made only for fun and experimental study.... | [
"# GPT-2 Fine-tuning With Vietnamese News",
"## Model description\nA Fine-tuned Vietnamese GPT2 model which can generate Vietnamese news based on context (category + headline), based on the Vietnamese Wiki GPT2 pretrained model (URL",
"## Github\n- URL",
"## Purpose\nThis model has been made only for fun and ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #News #Language model #GPT2 #vi #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT-2 Fine-tuning With Vietnamese News",
"## Model description\nA Fine-tuned Vietnamese GPT2 model which can generate Vietname... |
text-generation | transformers |
## A bot to chat with | {"tags": ["conversational"]} | tuantt/GroundNet | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## A bot to chat with | [
"## A bot to chat with"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## A bot to chat with"
] |
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... | tucan9389/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7501
* Matthews Correlation: 0.5309
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | tucan9389/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1560
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
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. -->
# kcbert-base-finetuned-squad
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) ... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "model-index": [{"name": "kcbert-base-finetuned-squad", "results": []}]} | tucan9389/kcbert-base-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:klue",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-klue #endpoints_compatible #region-us
| kcbert-base-finetuned-squad
===========================
This model is a fine-tuned version of beomi/kcbert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6736
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-klue #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_s... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kcbert-base-finetuned
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["accuracy"], "model-index": [{"name": "kcbert-base-finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metrics": [{"type": "accuracy", "value"... | tucan9389/kcbert-base-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| kcbert-base-finetuned
=====================
This model is a fine-tuned version of beomi/kcbert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7393
* Accuracy: 0.8330
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
fill-mask | transformers |
# BERT-BASE-MONGOLIAN-CASED
[Link to Official Mongolian-BERT repo](https://github.com/tugstugi/mongolian-bert)
## Model description
This repository contains pre-trained Mongolian [BERT](https://arxiv.org/abs/1810.04805) models trained by [tugstugi](https://github.com/tugstugi), [enod](https://github.com/enod) and [sh... | {"language": "mn", "tags": ["bert", "mongolian", "cased"]} | tugstugi/bert-base-mongolian-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"mongolian",
"cased",
"mn",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"mn"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #cased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT-BASE-MONGOLIAN-CASED
Link to Official Mongolian-BERT repo
## Model description
This repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.
Special thanks to nabar who provided 5x TPUs.
This repository is based on the following open source projects: google-research/be... | [
"# BERT-BASE-MONGOLIAN-CASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.\nSpecial thanks to nabar who provided 5x TPUs.\n\nThis repository is based on the following open source projects: googl... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #cased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-BASE-MONGOLIAN-CASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models train... |
fill-mask | transformers |
# BERT-BASE-MONGOLIAN-UNCASED
[Link to Official Mongolian-BERT repo](https://github.com/tugstugi/mongolian-bert)
## Model description
This repository contains pre-trained Mongolian [BERT](https://arxiv.org/abs/1810.04805) models trained by [tugstugi](https://github.com/tugstugi), [enod](https://github.com/enod) and [... | {"language": "mn", "tags": ["bert", "mongolian", "uncased"]} | tugstugi/bert-base-mongolian-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"mongolian",
"uncased",
"mn",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"mn"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #uncased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT-BASE-MONGOLIAN-UNCASED
Link to Official Mongolian-BERT repo
## Model description
This repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.
Special thanks to nabar who provided 5x TPUs.
This repository is based on the following open source projects: google-research/... | [
"# BERT-BASE-MONGOLIAN-UNCASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.\nSpecial thanks to nabar who provided 5x TPUs.\n\nThis repository is based on the following open source projects: goo... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #uncased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-BASE-MONGOLIAN-UNCASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models t... |
fill-mask | transformers |
# BERT-LARGE-MONGOLIAN-CASED
[Link to Official Mongolian-BERT repo](https://github.com/tugstugi/mongolian-bert)
## Model description
This repository contains pre-trained Mongolian [BERT](https://arxiv.org/abs/1810.04805) models trained by [tugstugi](https://github.com/tugstugi), [enod](https://github.com/enod) and [s... | {"language": "mn", "tags": ["bert", "mongolian", "cased"]} | tugstugi/bert-large-mongolian-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"mongolian",
"cased",
"mn",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"mn"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #cased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-LARGE-MONGOLIAN-CASED
Link to Official Mongolian-BERT repo
## Model description
This repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.
Special thanks to nabar who provided 5x TPUs.
This repository is based on the following open source projects: google-research/b... | [
"# BERT-LARGE-MONGOLIAN-CASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.\nSpecial thanks to nabar who provided 5x TPUs.\n\nThis repository is based on the following open source projects: goog... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #cased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-LARGE-MONGOLIAN-CASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT ... |
fill-mask | transformers |
# BERT-LARGE-MONGOLIAN-UNCASED
[Link to Official Mongolian-BERT repo](https://github.com/tugstugi/mongolian-bert)
## Model description
This repository contains pre-trained Mongolian [BERT](https://arxiv.org/abs/1810.04805) models trained by [tugstugi](https://github.com/tugstugi), [enod](https://github.com/enod) and ... | {"language": "mn", "tags": ["bert", "mongolian", "uncased"]} | tugstugi/bert-large-mongolian-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"mongolian",
"uncased",
"mn",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"mn"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #uncased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-LARGE-MONGOLIAN-UNCASED
Link to Official Mongolian-BERT repo
## Model description
This repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.
Special thanks to nabar who provided 5x TPUs.
This repository is based on the following open source projects: google-research... | [
"# BERT-LARGE-MONGOLIAN-UNCASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu.\nSpecial thanks to nabar who provided 5x TPUs.\n\nThis repository is based on the following open source projects: go... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #mongolian #uncased #mn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-LARGE-MONGOLIAN-UNCASED\nLink to Official Mongolian-BERT repo",
"## Model description\nThis repository contains pre-trained Mongolian B... |
automatic-speech-recognition | transformers |
## Info
This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp#datasets) created by a voice conversion model.
* 50% WER o... | {"language": "xal", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"]} | tugstugi/wav2vec2-large-xlsr-53-kalmyk | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"xal",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"xal"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xal #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## Info
This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordning... | [
"## Info\n\nThis Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.\n* 50% WER on a private test set created from Kalmyk TV rec... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xal #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## Info\n\nThis Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ... | {"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Mongolian by Tugstugi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "da... | tugstugi/wav2vec2-large-xlsr-53-mongolian | null | [
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"wav2vec2",
"automatic-speech-recognition",
"audio",
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"xlsr-fine-tuning-week",
"mn",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated ... | [
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model c... | [
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"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the Comm... |
null | transformers |
# Data
unsupervise train data is E-commerce dialogue.
## Model
model is [simcse](https://arxiv.org/abs/2104.08821).
### Usage
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> model = AutoModel.from_pretrained("tuhailong/SimCSE-bert-base")
>>> tokenizer = AutoTokenizer.from_pretrained("tuhailong/S... | {"language": "zh", "tags": ["simcse"], "datasets": ["dialogue"]} | tuhailong/SimCSE-bert-base | null | [
"transformers",
"pytorch",
"simcse",
"zh",
"dataset:dialogue",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [
"zh"
] | TAGS
#transformers #pytorch #simcse #zh #dataset-dialogue #arxiv-2104.08821 #endpoints_compatible #region-us
|
# Data
unsupervise train data is E-commerce dialogue.
## Model
model is simcse.
### Usage
| [
"# Data\nunsupervise train data is E-commerce dialogue.",
"## Model\nmodel is simcse.",
"### Usage"
] | [
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"# Data\nunsupervise train data is E-commerce dialogue.",
"## Model\nmodel is simcse.",
"### Usage"
] |
fill-mask | transformers | # Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs.
## Model
model is chinese-roberta-wwm-ext
### Usage
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> model = AutoModel.from_pretrained("tuhailong/chinese-roberta-wwm-ext")
>>> tokenizer = AutoTokenizer.from_pretrained("t... | {"language": "zh", "tags": ["chinese-roberta-wwm-ext"], "datasets": ["dialogue"]} | tuhailong/chinese-roberta-wwm-ext | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"chinese-roberta-wwm-ext",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #chinese-roberta-wwm-ext #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
| # Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs.
## Model
model is chinese-roberta-wwm-ext
### Usage
| [
"# Data\nunsupervise train data is E-commerce dialogue, about 20w sentence pairs.",
"## Model\nmodel is chinese-roberta-wwm-ext",
"### Usage"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #chinese-roberta-wwm-ext #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\nunsupervise train data is E-commerce dialogue, about 20w sentence pairs.",
"## Model\nmodel is chinese-roberta-wwm-ext",
"### Usage"
] |
text-classification | transformers |
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder
### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEnco... | {"language": "zh", "tags": ["sbert"], "datasets": ["dialogue"]} | tuhailong/cross-encoder-bert-base | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sbert",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sbert #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us
|
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
## Model
model created by sentence-tansformers，model struct is cross-encoder
### Usage
| [
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，model struct is cross-encoder",
"### Usage"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sbert #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\ntrain data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.",
"## Model\nmodel created by sentence-tansformers，mo... |
text2text-generation | transformers |
## Model description
[PEGASUS](https://github.com/google-research/pegasus) fine-tuned for paraphrasing
## Model in Action 🚀
```
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner007/pegasus_paraphrase'
torch_device = 'cuda' if torch.cuda.is_available() else 'c... | {"language": "en", "license": "apache-2.0", "tags": ["pegasus", "paraphrasing", "seq2seq"]} | tuner007/pegasus_paraphrase | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"paraphrasing",
"seq2seq",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #paraphrasing #seq2seq #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
PEGASUS fine-tuned for paraphrasing
## Model in Action
#### Example:
> Created by Arpit Rajauria
![Twitter icon](URL
| [
"## Model description\nPEGASUS fine-tuned for paraphrasing",
"## Model in Action",
"#### Example: \n\n\n> Created by Arpit Rajauria\n![Twitter icon](URL"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #paraphrasing #seq2seq #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\nPEGASUS fine-tuned for paraphrasing",
"## Model in Action",
"#### Example: \n\n\n> Created by Arpit Rajauria... |
text2text-generation | transformers | # Pegasus for question-answering
Pegasus model fine-tuned for QA using text-to-text approach
## Model in Action 🚀
```
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner007/pegasus_qa'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = Peg... | {} | tuner007/pegasus_qa | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # Pegasus for question-answering
Pegasus model fine-tuned for QA using text-to-text approach
## Model in Action
#### Example:
> Created by Arpit Rajauria
![Twitter icon](URL
| [
"# Pegasus for question-answering\nPegasus model fine-tuned for QA using text-to-text approach",
"## Model in Action",
"#### Example:\n\n\n\n> Created by Arpit Rajauria\n![Twitter icon](URL"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pegasus for question-answering\nPegasus model fine-tuned for QA using text-to-text approach",
"## Model in Action",
"#### Example:\n\n\n\n> Created by Arpit Rajauria\n![Twitter icon](URL... |
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