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translation | null | # OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for German to En... | {"language": ["de", "en"], "license": "mit", "tags": ["translation", "pytorch"], "datasets": ["IWSLT \u201814 DE-EN"], "metrics": ["bleu"]} | malloc/OpenNMT-py-German-English-2-layer-BiLSTM | null | [
"translation",
"pytorch",
"de",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"en"
] | TAGS
#translation #pytorch #de #en #license-mit #region-us
| # OpenNMT-py-English-German-Transformer
OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several pretrained models. This one is trained particularly for German to English translation.
- Configuration: 2-layer BiLSTM with hidden size 500 tr... | [
"# OpenNMT-py-English-German-Transformer\nOpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.\nOpenNMT has several pretrained models. This one is trained particularly for German to English translation.\n\n- Configuration: 2-layer BiLSTM with hidden si... | [
"TAGS\n#translation #pytorch #de #en #license-mit #region-us \n",
"# OpenNMT-py-English-German-Transformer\nOpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.\nOpenNMT has several pretrained models. This one is trained particularly for German to En... |
null | transformers |
# Aspect-based Document Similarity for Research Papers
A `scibert-scivocab-uncased` model fine-tuned on the ACL Anthology corpus as in [Aspect-based Document Similarity for Research Papers](https://arxiv.org/abs/2010.06395).
<img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/docrel... | {"language": ["sci", "en", "multilingual"], "license": "mit", "tags": ["classification", "similarity"], "datasets": ["acl-arc"]} | malteos/aspect-acl-scibert-scivocab-uncased | null | [
"transformers",
"pytorch",
"bert",
"classification",
"similarity",
"sci",
"en",
"multilingual",
"dataset:acl-arc",
"arxiv:2010.06395",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.06395"
] | [
"sci",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #bert #classification #similarity #sci #en #multilingual #dataset-acl-arc #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us
|
# Aspect-based Document Similarity for Research Papers
A 'scibert-scivocab-uncased' model fine-tuned on the ACL Anthology corpus as in Aspect-based Document Similarity for Research Papers.
<img src="URL
See GitHub for more details: URL
## Demo
<a href="URL src="URL alt="Google Colab"></a>
You can try our trained... | [
"# Aspect-based Document Similarity for Research Papers\n\nA 'scibert-scivocab-uncased' model fine-tuned on the ACL Anthology corpus as in Aspect-based Document Similarity for Research Papers.\n\n<img src=\"URL\n\nSee GitHub for more details: URL",
"## Demo\n\n<a href=\"URL src=\"URL alt=\"Google Colab\"></a>\n\n... | [
"TAGS\n#transformers #pytorch #bert #classification #similarity #sci #en #multilingual #dataset-acl-arc #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us \n",
"# Aspect-based Document Similarity for Research Papers\n\nA 'scibert-scivocab-uncased' model fine-tuned on the ACL Anthology corpus as in As... |
null | transformers |
# Aspect-based Document Similarity for Research Papers
A `scibert-scivocab-uncased` model fine-tuned on the CORD-19 corpus as in [Aspect-based Document Similarity for Research Papers](https://arxiv.org/abs/2010.06395).
<img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/docrel.png">... | {"language": ["sci", "en"], "license": "mit", "tags": ["classification", "similarity"], "datasets": ["cord19"]} | malteos/aspect-cord19-scibert-scivocab-uncased | null | [
"transformers",
"pytorch",
"bert",
"classification",
"similarity",
"sci",
"en",
"dataset:cord19",
"arxiv:2010.06395",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.06395"
] | [
"sci",
"en"
] | TAGS
#transformers #pytorch #bert #classification #similarity #sci #en #dataset-cord19 #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us
|
# Aspect-based Document Similarity for Research Papers
A 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.
<img src="URL
See GitHub for more details: URL
## Demo
<a href="URL src="URL alt="Google Colab"></a>
You can try our trained model... | [
"# Aspect-based Document Similarity for Research Papers\n\nA 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.\n\n<img src=\"URL\n\nSee GitHub for more details: URL",
"## Demo\n\n<a href=\"URL src=\"URL alt=\"Google Colab\"></a>\n\nYou ca... | [
"TAGS\n#transformers #pytorch #bert #classification #similarity #sci #en #dataset-cord19 #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us \n",
"# Aspect-based Document Similarity for Research Papers\n\nA 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document S... |
feature-extraction | transformers |
## SciNCL
SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers.
It uses the citation graph neighborhood to generate samples for contrastive learning.
Prior to the contrastive training, the model is initialized with weights from [scibert-scivocab-uncased](https://hugging... | {"language": "en", "license": "mit", "tags": ["feature-extraction"], "datasets": ["SciDocs", "s2orc"], "metrics": ["F1", "accuracy", "map", "ndcg"]} | malteos/scincl | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"en",
"dataset:SciDocs",
"dataset:s2orc",
"arxiv:2202.06671",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.06671"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #en #dataset-SciDocs #dataset-s2orc #arxiv-2202.06671 #license-mit #endpoints_compatible #has_space #region-us
| SciNCL
------
SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers.
It uses the citation graph neighborhood to generate samples for contrastive learning.
Prior to the contrastive training, the model is initialized with weights from scibert-scivocab-uncased.
The underlyi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #en #dataset-SciDocs #dataset-s2orc #arxiv-2202.06671 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
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| {} | mami/malingkundonagn | null | [
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#region-us
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| [] | [
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] |
null | null | https://zambiainc.com/advert/uptobox-sub-bg-nobody-2021-%d0%b1%d0%b3-%d0%b0%d1%83%d0%b4%d0%b8%d0%be-%d0%b8%d0%b7%d1%82%d0%b5%d0%b3%d0%bb%d1%8f%d0%bd%d0%b5-%d0%be%d0%bd%d0%bb%d0%b0%d0%b9%d0%bd-%d0%b1%d0%b3-%d0%b0%d1%83%d0%b4/
https://zambiainc.com/advert/uptobox-sub-bg-%d1%80%d0%b0%d1%8f-%d0%b8-%d0%bf%d0%be%d1%81%d0%bb%... | {} | mami/santuycuy | null | [
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| [] | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_base_race_cosmos_qa
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the race dataset.
It a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["race"], "metrics": ["accuracy"], "model-index": [{"name": "t5_base_race_cosmos_qa", "results": []}]} | mamlong34/t5_base_race_cosmos_qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:race",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-race #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_base\_race\_cosmos\_qa
==========================
This model is a fine-tuned version of t5-base on the race dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4414
* Accuracy: 0.7424
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-race #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_large_race_cosmos_qa
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the race dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["race"], "metrics": ["accuracy"], "model-index": [{"name": "t5_large_race_cosmos_qa", "results": []}]} | mamlong34/t5_large_race_cosmos_qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:race",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-race #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_large\_race\_cosmos\_qa
===========================
This model is a fine-tuned version of t5-large on the race dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4382
* Accuracy: 0.8023
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-race #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_small_cosmos_qa
This model is a fine-tuned version of [mamlong34/t5_small_race_mutlirc](https://huggingface.co/mamlong34/t5_s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cosmos_qa"], "metrics": ["accuracy"], "model-index": [{"name": "t5_small_cosmos_qa", "results": []}]} | mamlong34/t5_small_cosmos_qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cosmos_qa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cosmos_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_small\_cosmos\_qa
=====================
This model is a fine-tuned version of mamlong34/t5\_small\_race\_mutlirc on the cosmos\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5614
* Accuracy: 0.6067
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cosmos_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_small_race_mutlirc
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "t5_small_race_mutlirc", "results": []}]} | mamlong34/t5_small_race_mutlirc | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_small\_race\_mutlirc
========================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5760
* Accuracy: 0.5259
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* t... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Irish 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 can be used ... | {"language": "ga", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Irish by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech... | manandey/wav2vec2-large-xlsr-_irish | null | [
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"dataset:common_voice",
"doi:10.57967/hf/0190",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ga #dataset-common_voice #doi-10.57967/hf/0190 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Irish 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 as follows on th... | [
"# Wav2Vec2-Large-XLSR-53-Irish\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Irish using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluate... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ga #dataset-common_voice #doi-10.57967/hf/0190 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Irish\nFine-tuned facebook/wav2vec2-... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Assamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Assamese 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 can... | {"language": "as", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Assamese by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | manandey/wav2vec2-large-xlsr-assamese | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"as"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Assamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Assamese 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 as f... | [
"# Wav2Vec2-Large-XLSR-53-Assamese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Assamese 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 can... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Assamese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Assamese using the C... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton 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 can be... | {"language": "br", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Breton by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "data... | manandey/wav2vec2-large-xlsr-breton | null | [
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"xlsr-fine-tuning-week",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"br"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Breton
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Breton 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 as fol... | [
"# Wav2Vec2-Large-XLSR-53-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Breton 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 can be ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Breton using the Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian 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 ca... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Estonian by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "da... | manandey/wav2vec2-large-xlsr-estonian | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian 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 as... | [
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian 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 can... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian using the C... |
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 Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "d... | manandey/wav2vec2-large-xlsr-mongolian | null | [
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"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
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#transformers #pytorch #jax #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... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Punjabi 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 can be u... | {"language": "pa-IN", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Punjabi by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "S... | manandey/wav2vec2-large-xlsr-punjabi | null | [
"transformers",
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"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"dataset:common_voice",
"doi:10.57967/hf/0714",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #dataset-common_voice #doi-10.57967/hf/0714 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Punjabi 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 as follows o... | [
"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Punjabi using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be eval... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #dataset-common_voice #doi-10.57967/hf/0714 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned faceboo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil 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 can be u... | {"language": "ta", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Tamil by Manan Dey", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech... | manandey/wav2vec2-large-xlsr-tamil | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"ta",
"dataset:common_voice",
"doi:10.57967/hf/0191",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ta #dataset-common_voice #doi-10.57967/hf/0191 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil 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 as follo... | [
"# Wav2Vec2-Large-XLSR-53-Tamil\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil 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 can be ev... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ta #dataset-common_voice #doi-10.57967/hf/0191 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Tamil\n\nFine-tuned facebook/wav2vec... |
question-answering | transformers | This a BERT-based QA model finetuned to answer causal questions. The original model this is based on can be found [here](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2). Analysis of this model is associated with the work found at the following [repo](https://github.com/kstats/CausalQG). | {} | manav/causal_qa | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| This a BERT-based QA model finetuned to answer causal questions. The original model this is based on can be found here. Analysis of this model is associated with the work found at the following repo. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
## Model description
Finetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed. | {"tags": ["conversational"]} | manav/dialogpt-large-kanye-reddit | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model description
Finetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed. | [
"## Model description\n\nFinetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed."
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\nFinetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly v... |
text-generation | transformers |
## Model description
Finetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed. | {"tags": ["conversational"]} | manav/dialogpt-medium-berkeley-reddit | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## Model description
Finetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed. | [
"## Model description\n\nFinetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed."
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Model description\n\nFinetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn... |
text-generation | transformers |
# Michael Scott DialoGPT Bot. | {"tags": ["conversational"]} | maniacGhost24/MichaelScott-bot-push-small | 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
|
# Michael Scott DialoGPT Bot. | [
"# Michael Scott DialoGPT Bot."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Bot."
] |
automatic-speech-recognition | transformers |
## XLSR-300m Persian
Fine-tuned on commom voice FA
| {"language": "fa", "tags": ["hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "widget": [{"example_title": "Common Voice sample 2978", "src": "https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample2978.flac"}, {"example_title": "Common Voice sample 5168", "src": "https://huggingface.... | manifoldix/xlsr-fa-lm | null | [
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"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"fa",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #fa #dataset-common_voice #model-index #endpoints_compatible #region-us
|
## XLSR-300m Persian
Fine-tuned on commom voice FA
| [
"## XLSR-300m Persian\r\nFine-tuned on commom voice FA"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #fa #dataset-common_voice #model-index #endpoints_compatible #region-us \n",
"## XLSR-300m Persian\r\nFine-tuned on commom voice FA"
] |
automatic-speech-recognition | transformers |
## XLSR-1b Swiss German
Fine-tuned on the Swiss parliament dataset from FHNW v1 (70h).
Tested on the Swiss parliament test set with a WER of 34.6%
Tested on the "Swiss German Dialects" with a WER of 40%
Both test sets can be accessed here: [fhnw_datasets](https://www.cs.technik.fhnw.ch/i4ds-datasets)
Th... | {"language": "gsw", "tags": ["hf-asr-leaderboard", "robust-speech-event"], "widget": [{"example_title": "swiss parliament sample 1", "src": "https://huggingface.co/manifoldix/xlsr-sg-lm/resolve/main/07e73bcaa2ab192aea9524d72db45f34f274d1b3d5672434c462d32d44d792be.mp3"}, {"example_title": "swiss parliament sample 2", "s... | manifoldix/xlsr-sg-lm | null | [
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"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"gsw",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gsw"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #gsw #model-index #endpoints_compatible #region-us
|
## XLSR-1b Swiss German
Fine-tuned on the Swiss parliament dataset from FHNW v1 (70h).
Tested on the Swiss parliament test set with a WER of 34.6%
Tested on the "Swiss German Dialects" with a WER of 40%
Both test sets can be accessed here: fhnw_datasets
The Swiss German dialect private test set has been... | [
"## XLSR-1b Swiss German\r\nFine-tuned on the Swiss parliament dataset from FHNW v1 (70h).\r\n\r\nTested on the Swiss parliament test set with a WER of 34.6%\r\n\r\nTested on the \"Swiss German Dialects\" with a WER of 40%\r\n\r\nBoth test sets can be accessed here: fhnw_datasets\r\n\r\nThe Swiss German dialect pri... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #gsw #model-index #endpoints_compatible #region-us \n",
"## XLSR-1b Swiss German\r\nFine-tuned on the Swiss parliament dataset from FHNW v1 (70h).\r\n\r\nTested on the Swiss parliament test set with a WE... |
text-generation | transformers | # Harry Potter DialoGPT Model | {"tags": ["conversational"]} | manraf/DialoGPT-smmall-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"
] |
text2text-generation | transformers | # T5-Paraphrase pretrained using the CORD-19 dataset.
The base model is manueldeprada/t5-cord19, which has been pretrained with the text and abstracts from the CORD-19 dataset.
It has been finetuned in paraphrasing text like ceshine/t5-paraphrase-paws-msrp-opinosis, using the scripts from [ceshine/finetuning-t5 Githu... | {} | manueldeprada/t5-cord19-paraphrase-paws-msrp-opinosis | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5-Paraphrase pretrained using the CORD-19 dataset.
The base model is manueldeprada/t5-cord19, which has been pretrained with the text and abstracts from the CORD-19 dataset.
It has been finetuned in paraphrasing text like ceshine/t5-paraphrase-paws-msrp-opinosis, using the scripts from ceshine/finetuning-t5 Github... | [
"# T5-Paraphrase pretrained using the CORD-19 dataset.\n\nThe base model is manueldeprada/t5-cord19, which has been pretrained with the text and abstracts from the CORD-19 dataset.\n\nIt has been finetuned in paraphrasing text like ceshine/t5-paraphrase-paws-msrp-opinosis, using the scripts from ceshine/finetuning-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-Paraphrase pretrained using the CORD-19 dataset.\n\nThe base model is manueldeprada/t5-cord19, which has been pretrained with the text and abstracts from the CO... |
text2text-generation | transformers | # T5-base pretrained on CORD-19 dataset
The model has been pretrained on text and abstracts from the CORD-19 dataset, using a manually implemented denoising objetive similar to the original T5 denoising objective.
Model needs to be finetuned on downstream tasks.
Code avaliable in github: [https://github.com/manuelde... | {} | manueldeprada/t5-cord19 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5-base pretrained on CORD-19 dataset
The model has been pretrained on text and abstracts from the CORD-19 dataset, using a manually implemented denoising objetive similar to the original T5 denoising objective.
Model needs to be finetuned on downstream tasks.
Code avaliable in github: URL | [
"# T5-base pretrained on CORD-19 dataset\n\nThe model has been pretrained on text and abstracts from the CORD-19 dataset, using a manually implemented denoising objetive similar to the original T5 denoising objective.\n\nModel needs to be finetuned on downstream tasks.\n\nCode avaliable in github: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-base pretrained on CORD-19 dataset\n\nThe model has been pretrained on text and abstracts from the CORD-19 dataset, using a manually implemented denoising objetive s... |
null | transformers | All information can be found here: https://github.com/manueltonneau/covid-berts
If you find this model useful, please cite:
```
@misc {manuel_tonneau_2023,
author = { {Manuel Tonneau} },
title = { biocovid-bert-large-cased (Revision 1549866) },
year = 2023,
url = { https://huggingfac... | {"language": ["en"]} | manueltonneau/biocovid-bert-large-cased | null | [
"transformers",
"pytorch",
"en",
"doi:10.57967/hf/0869",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #doi-10.57967/hf/0869 #endpoints_compatible #region-us
| All information can be found here: URL
If you find this model useful, please cite:
| [] | [
"TAGS\n#transformers #pytorch #en #doi-10.57967/hf/0869 #endpoints_compatible #region-us \n"
] |
null | transformers | All information can be found here: https://github.com/manueltonneau/covid-berts
If you find this model useful, please cite:
```
@misc {manuel_tonneau_2023,
author = { {Manuel Tonneau} },
title = { clinicalcovid-bert-base-cased (Revision feaed90) },
year = 2023,
url = { https://huggin... | {"language": ["en"]} | manueltonneau/clinicalcovid-bert-base-cased | null | [
"transformers",
"pytorch",
"en",
"doi:10.57967/hf/0867",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #doi-10.57967/hf/0867 #endpoints_compatible #region-us
| All information can be found here: URL
If you find this model useful, please cite:
| [] | [
"TAGS\n#transformers #pytorch #en #doi-10.57967/hf/0867 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | All information can be found here: https://github.com/manueltonneau/covid-berts
If you find this model useful, please cite:
```
@misc {manuel_tonneau_2023,
author = { {Manuel Tonneau} },
title = { clinicalcovid-bert-nli (Revision 9a0bad1) },
year = 2023,
url = { https://huggingface.... | {"language": ["en"]} | manueltonneau/clinicalcovid-bert-nli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"doi:10.57967/hf/0868",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #en #doi-10.57967/hf/0868 #endpoints_compatible #region-us
| All information can be found here: URL
If you find this model useful, please cite:
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #en #doi-10.57967/hf/0868 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset
| {} | marbogusz/bert-multi-cased-squad_sv | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mo... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset"... | marcel/wav2vec2-large-xlsr-53-german | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"dataset:wer",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #dataset-wer #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned facebook/wav2vec2-large-xlsr-53 on German using the Common Voice dataset.
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 evalua... | [
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German using the Common Voice dataset. \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 mod... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #dataset-wer #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German us... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using 3% of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset"... | marcel/wav2vec2-large-xlsr-german-demo | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"dataset:wer",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #dataset-wer #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned facebook/wav2vec2-large-xlsr-53 on German using 3% of the Common Voice dataset.
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 ... | [
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German using 3% of the Common Voice dataset. \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\nT... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #dataset-wer #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German us... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "defau... | marcelcastrobr/sagemaker-distilbert-emotion-2 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion-2
==============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1442
* Accuracy: 0.9315
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | marcelcastrobr/sagemaker-distilbert-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion
============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1477
* Accuracy: 0.928
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-de-to-en-fp16
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-to-en-fp16", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | marciovbarbosa/t5-small-finetuned-de-to-en-fp16 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-to-en-fp16
================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9416
* Bleu: 9.2226
* Gen Len: 17.3311
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-de-to-en-lr1e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-to-en-lr1e-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", ... | marciovbarbosa/t5-small-finetuned-de-to-en-lr1e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-to-en-lr1e-4
==================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8228
* Bleu: 11.427
* Gen Len: 17.2674
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-de-to-en-lr3e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-to-en-lr3e-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", ... | marciovbarbosa/t5-small-finetuned-de-to-en-lr3e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-to-en-lr3e-4
==================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9059
* Bleu: 11.9094
* Gen Len: 17.2257
Model description
-----------------
More information needed
Inte... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-de-to-en-swd
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-to-en-swd", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "ar... | marciovbarbosa/t5-small-finetuned-de-to-en-swd | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-to-en-swd
===============================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9422
* Bleu: 9.2293
* Gen Len: 17.3454
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-de-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args":... | marciovbarbosa/t5-small-finetuned-de-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-to-en
===========================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9417
* Bleu: 9.2166
* Gen Len: 17.3404
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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. -->
# Hps_seed1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "Hps_seed1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics":... | marcolatella/Hps_seed1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"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-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Hps\_seed1
==========
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9681
* F1: 0.7177
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.6525359309081455e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
<!-- 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. -->
# emotion_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "emotion_trained", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emotion"}, "metri... | marcolatella/emotion_trained | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| emotion\_trained
================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9362
* F1: 0.7378
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.961635072722524e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4"... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# emotion_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "emotion_trained_1234567", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emotion"}... | marcolatella/emotion_trained_1234567 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| emotion\_trained\_1234567
=========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9045
* F1: 0.7328
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.961635072722524e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 1234567\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoc... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# emotion_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "emotion_trained_31415", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emotion"}, ... | marcolatella/emotion_trained_31415 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| emotion\_trained\_31415
=======================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9166
* F1: 0.7213
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.961635072722524e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 31415\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# emotion_trained_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "emotion_trained_42", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emotion"}, "me... | marcolatella/emotion_trained_42 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| emotion\_trained\_42
====================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8988
* F1: 0.7319
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.961635072722524e-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... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# hate_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "hate_trained", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "metrics": [... | marcolatella/hate_trained | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hate\_trained
=============
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8182
* F1: 0.7876
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.7272339744854407e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# hate_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "hate_trained_1234567", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "met... | marcolatella/hate_trained_1234567 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hate\_trained\_1234567
======================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7927
* F1: 0.7751
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.7272339744854407e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 1234567\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epo... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# hate_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "hate_trained_31415", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "metri... | marcolatella/hate_trained_31415 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hate\_trained\_31415
====================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8507
* F1: 0.7719
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.7272339744854407e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 31415\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoch... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# hate_trained_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "hate_trained_42", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "metrics"... | marcolatella/hate_trained_42 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hate\_trained\_42
=================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8996
* F1: 0.7665
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.7272339744854407e-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: ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# irony_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "irony_trained", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "irony"}, "metrics":... | marcolatella/irony_trained | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| irony\_trained
==============
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6720
* F1: 0.6946
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.6375567293432486e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# prova_Classi2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "prova_Classi2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metri... | marcolatella/prova_Classi2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"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-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| prova\_Classi2
==============
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0183
* F1: 0.2019
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002739353542073378\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 18\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1"... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
<!-- 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. -->
# prova_Classi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["accuracy"], "model-index": [{"name": "prova_Classi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "... | marcolatella/tweet_eval_bench | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"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-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| prova\_Classi
=============
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5530
* Accuracy: 0.716
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00013441028267541125\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 17\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
fill-mask | transformers | Hi!
This model has been trained on Italian biomedical data.
For further information, do not hesitate to send me a message! ;)
marco.postiglione@unina.it (Marco Postiglione) | {} | marcopost-it/biobert-it | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hi!
This model has been trained on Italian biomedical data.
For further information, do not hesitate to send me a message! ;)
marco.postiglione@URL (Marco Postiglione) | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT for Galician (Base)
This is a base pre-trained BERT model (12 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a [dataset to identify homonymy and synonymy in context](https://github.com/marcospln/homonymy_acl21), and presented at ACL 2021.
There is also a sma... | {"language": ["gl", "pt"], "license": "agpl-3.0", "widget": [{"text": "A mesa estaba feita de [MASK]."}]} | marcosgg/bert-base-gl-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gl",
"pt",
"arxiv:2106.13553",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.13553"
] | [
"gl",
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #gl #pt #arxiv-2106.13553 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT for Galician (Base)
This is a base pre-trained BERT model (12 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a dataset to identify homonymy and synonymy in context, and presented at ACL 2021.
There is also a small version (6 layers, cased): 'marcosgg/bert-sm... | [
"# BERT for Galician (Base)\n\nThis is a base pre-trained BERT model (12 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a dataset to identify homonymy and synonymy in context, and presented at ACL 2021.\n\nThere is also a small version (6 layers, cased): 'marcosgg... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gl #pt #arxiv-2106.13553 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT for Galician (Base)\n\nThis is a base pre-trained BERT model (12 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics task... |
fill-mask | transformers |
# BERT for Galician (Small)
This is a small pre-trained BERT model (6 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a [dataset to identify homonymy and synonymy in context](https://github.com/marcospln/homonymy_acl21), and presented at ACL 2021.
There is also a ba... | {"language": ["gl", "pt"], "license": "agpl-3.0", "widget": [{"text": "A mesa estaba feita de [MASK]."}]} | marcosgg/bert-small-gl-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gl",
"pt",
"arxiv:2106.13553",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.13553"
] | [
"gl",
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #gl #pt #arxiv-2106.13553 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT for Galician (Small)
This is a small pre-trained BERT model (6 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a dataset to identify homonymy and synonymy in context, and presented at ACL 2021.
There is also a base version (12 layers, cased): 'marcosgg/bert-b... | [
"# BERT for Galician (Small)\n\nThis is a small pre-trained BERT model (6 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tasks, using a dataset to identify homonymy and synonymy in context, and presented at ACL 2021.\n\nThere is also a base version (12 layers, cased): 'marcosg... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gl #pt #arxiv-2106.13553 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT for Galician (Small)\n\nThis is a small pre-trained BERT model (6 layers, cased) for Galician (ILG/RAG spelling). It was evaluated on lexical semantics tas... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args":... | marcosscarpim/t5-small-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro
===========================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4088
* Bleu: 7.3228
* Gen Len: 18.2581
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
translation | transformers |
# Marefa-Mt-En-Ar
# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية
## Model description
This is a model for translating English to Arabic. The special about this model that is take into considration the
using of additional Arabic characters like `پ` or `گ`.
## عن النموذج
هذا النموذج للترجمة الآلية م... | {"language": ["en", "ar"], "license": "apache-2.0", "tags": ["translation", "Arabic Abjad Characters", "Arabic"], "datasets": ["marefa-mt"]} | marefa-nlp/marefa-mt-en-ar | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"Arabic Abjad Characters",
"Arabic",
"en",
"ar",
"dataset:marefa-mt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ar"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #Arabic Abjad Characters #Arabic #en #ar #dataset-marefa-mt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Marefa-Mt-En-Ar
# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية
## Model description
This is a model for translating English to Arabic. The special about this model that is take into considration the
using of additional Arabic characters like 'پ' or 'گ'.
## عن النموذج
هذا النموذج للترجمة الآلية م... | [
"# Marefa-Mt-En-Ar",
"# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية",
"## Model description\r\n\r\nThis is a model for translating English to Arabic. The special about this model that is take into considration the\r\nusing of additional Arabic characters like 'پ' or 'گ'.",
"## عن النموذج\r\nهذا النموذج... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #Arabic Abjad Characters #Arabic #en #ar #dataset-marefa-mt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Marefa-Mt-En-Ar",
"# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية",
"## Model descriptio... |
token-classification | transformers |
# Tebyan تبيـان
## Marefa Arabic Named Entity Recognition Model
## نموذج المعرفة لتصنيف أجزاء النص
<p align="center">
<img src="https://huggingface.co/marefa-nlp/marefa-ner/resolve/main/assets/marefa-tebyan-banner.png" alt="Marfa Arabic NER Model" width="600"/>
</p?
---------
**Version**: 1.3
**Last Up... | {"language": "ar", "datasets": ["Marefa-NER"], "widget": [{"text": "\u0641\u064a \u0627\u0633\u062a\u0627\u062f \u0627\u0644\u0642\u0627\u0647\u0631\u0629\u060c \u0628\u062f\u0623 \u062d\u0641\u0644 \u0627\u0641\u062a\u062a\u0627\u062d \u0628\u0637\u0648\u0644\u0629 \u0643\u0623\u0633 \u0627\u0644\u0623\u0645\u0645 \u0... | marefa-nlp/marefa-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"ar",
"dataset:Marefa-NER",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #ar #dataset-Marefa-NER #autotrain_compatible #endpoints_compatible #has_space #region-us
| Tebyan تبيـان
=============
Marefa Arabic Named Entity Recognition Model
--------------------------------------------
نموذج المعرفة لتصنيف أجزاء النص
-------------------------------
Version: 1.3
Last Update: 3-12-2021
Model description
-----------------
Marefa-NER is a Large Arabic Named Ent... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #ar #dataset-Marefa-NER #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | ------------
## Arabic and English News Summarization NLP Model
### About
This model is for summarizing news stories in short highlights for both Arabic and English tasks.
نموذج معرفي متخصص في تلخيص الأخبار العربية و الإنجليزية الى مجموعة من أهم النقاط
### Fine-Tuning
The model was finetuned using the [Arabic T5 M... | {} | marefa-nlp/summarization-arabic-english-news | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ------------
## Arabic and English News Summarization NLP Model
### About
This model is for summarizing news stories in short highlights for both Arabic and English tasks.
نموذج معرفي متخصص في تلخيص الأخبار العربية و الإنجليزية الى مجموعة من أهم النقاط
### Fine-Tuning
The model was finetuned using the Arabic T5 Mo... | [
"## Arabic and English News Summarization NLP Model",
"### About\n\nThis model is for summarizing news stories in short highlights for both Arabic and English tasks.\n\nنموذج معرفي متخصص في تلخيص الأخبار العربية و الإنجليزية الى مجموعة من أهم النقاط",
"### Fine-Tuning\n\nThe model was finetuned using the Arabic... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Arabic and English News Summarization NLP Model",
"### About\n\nThis model is for summarizing news stories in short highlights for both Arabic and English... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-freeform
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-finetuned-freeform", "results": []}]} | maretamasaeva/roberta-finetuned-freeform | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"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 #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-finetuned-freeform
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6989
* Accuracy: 0.4668
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Trainin... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_si... |
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-sms-spam-detection
This model is a fine-tuned version of [distilbert-base-uncased](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sms_spam"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sms-spam-detection", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sms_spam", "type": "... | mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection | null | [
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"safetensors",
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"dataset:sms_spam",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-sms_spam #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-sms-spam-detection
====================================================
This model is a fine-tuned version of distilbert-base-uncased on the sms\_spam dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0426
* Accuracy: 0.9921
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sms-spam-detection
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["sms_spam"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-finetuned-sms-spam-detection", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sms_spam... | mariagrandury/roberta-base-finetuned-sms-spam-detection | null | [
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"tensorboard",
"safetensors",
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"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| roberta-base-finetuned-sms-spam-detection
=========================================
This model is a fine-tuned version of roberta-base on the sms\_spam dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0133
* Accuracy: 0.998
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters... |
automatic-speech-recognition | transformers | #
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - SV-SE dataset.
It achieves the following results on the evaluation set ("test" split, without LM):
- Loss: 0.1318
- Wer: 0.1121
## Model description
... | {"language": ["sv"], "license": "cc0-1.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_9_0", "generated_from_trainer", "sv"], "datasets": ["mozilla-foundation/common_voice_9_0"], "model-index": [{"name": "XLS-R-300M - Swedish", "results": [{"task": {"type": "automatic-speech-recognition", ... | marinone94/xls-r-300m-sv-robust | null | [
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"license:cc0-1.0",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
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|
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON\_VOICE\_9\_0 - SV-SE dataset.
It achieves the following results on the evaluation set ("test" split, without LM):
* Loss: 0.1318
* Wer: 0.1121
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters w... |
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": []}]} | marioarteaga/distilbert-base-uncased-finetuned-squad | null | [
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"license:apache-2.0",
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"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: 1.2052
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: 1",
"### Traini... | [
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fill-mask | transformers |
## XLM-R Longformer Model / XLM-Long
XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allena... | {"language": "multilingual", "license": "apache-2.0", "tags": ["longformer"], "datasets": ["wikitext"]} | markussagen/xlm-roberta-longformer-base-4096 | null | [
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"multilingual",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #longformer #multilingual #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## XLM-R Longformer Model / XLM-Long
XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103... | [
"## XLM-R Longformer Model / XLM-Long \nXLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiTex... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #longformer #multilingual #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## XLM-R Longformer Model / XLM-Long \nXLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to ... |
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-dutch-cased-finetuned-mark
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-dutch-cased-finetuned-mark", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | markverschuren/bert-base-dutch-cased-finetuned-mark | null | [
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"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-dutch-cased-finetuned-mark
====================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5468
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: 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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text-classification | transformers | Experimental sentiment analysis based on ~20k of App Store reviews in Swedish.
### Usage
```python
from transformers import pipeline
>>> sa = pipeline('sentiment-analysis', model='marma/bert-base-swedish-cased-sentiment')
>>> sa('Det här är ju fantastiskt!')
[{'label': 'POSITIVE', 'score': 0.9974609613418579}]
>>> s... | {} | marma/bert-base-swedish-cased-sentiment | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| Experimental sentiment analysis based on ~20k of App Store reviews in Swedish.
### Usage
| [
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automatic-speech-recognition | transformers | ## Test | {"language": "sv", "tags": ["speech", "audio", "automatic-speech-recognition"]} | marma/test | null | [
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"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"sv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #sv #endpoints_compatible #region-us
| ## Test | [
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] | [
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"## Test"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Swedish
This model has moved [here](https://huggingface.co/KBLab/wav2vec2-large-xlsr-53-swedish) | {"language": "sv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Swedish by Marma", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset... | marma/wav2vec2-large-xlsr-swedish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Swedish
This model has moved here | [
"# Wav2Vec2-Large-XLSR-53-Swedish\n\nThis model has moved here"
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"# Wav2Vec2-Large-XLSR-53-Swedish\n\nThis model has moved here"
] |
question-answering | transformers |
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://github.com/marshmellow77/cuad-demo
and htt... | {"language": "en", "datasets": ["cuad"]} | marshmellow77/roberta-base-cuad | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us
|
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
using CUAD dataset URL
Link for model checkpoint: URL
For the use of the model with CUAD: URL
and URL
Related blog posts:
- URL
- URL
| [
"# RoBERTa Base Model fine-tuned with CUAD dataset\nThis model is the fine-tuned version of \"RoBERTa Base\" \nusing CUAD dataset URL\n\nLink for model checkpoint: URL\n\nFor the use of the model with CUAD: URL\nand URL\n\nRelated blog posts:\n- URL\n- URL"
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text-classification | transformers |
## Model description
This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments.
## How to use
You can use the model with the following code.
```python
from transformers import AutoModelForSequenceClassification, AutoTokeni... | {"language": "en"} | martin-ha/toxic-comment-model | null | [
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"pytorch",
"distilbert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model description
-----------------
This model is a fine-tuned version of the DistilBERT model to classify toxic comments.
How to use
----------
You can use the model with the following code.
Limitations and Bias
--------------------
This model is intended to use for classify toxic online classifications. How... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | dasdasda | {} | martodaniel/fafafa | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| dasdasda | [] | [
"TAGS\n#region-us \n"
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text2text-generation | transformers | # m2m100_418M-fon-fr-mt
## Model description
**m2m100_418M-fon-fr-mt** is a **machine translation** model from Fon to French based on a fine-tuned facebook/m2m100_418M model. It establishes a **baseline** for automatically translating texts from Fon to French.
#### Limitations and bias
This model is limited by its... | {"language": ["fon", "fr"], "datasets": ["JW300 + [LAFAND](https://github.com/masakhane-io/lafand-mt)"]} | masakhane/m2m100_418M_fon_fr_rel_news | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fon",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fon",
"fr"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #fon #fr #autotrain_compatible #endpoints_compatible #region-us
| # m2m100_418M-fon-fr-mt
## Model description
m2m100_418M-fon-fr-mt is a machine translation model from Fon to French based on a fine-tuned facebook/m2m100_418M model. It establishes a baseline for automatically translating texts from Fon to French.
#### Limitations and bias
This model is limited by its training da... | [
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"## Model description\nm2m100_418M-fon-fr-mt is a machine translation model from Fon to French based on a fine-tuned facebook/m2m100_418M model. It establishes a baseline for automatically translating texts from Fon to French.",
"#### Limitations and bias\nThis model is limited by it... | [
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"## Model description\nm2m100_418M-fon-fr-mt is a machine translation model from Fon to French based on a fine-tuned facebook/m2m100_418M model. It establ... |
text2text-generation | transformers | # m2m100_418M-fr-fon-mt
## Model description
**m2m100_418M-fr-fon-mt** is a **machine translation** model from French to Fon based on a fine-tuned facebook/m2m100_418M model. It establishes a **baseline** for automatically translating texts from French to Fon.
#### Limitations and bias
This model is limited by its... | {"language": ["fr", "fon"], "datasets": ["JW300 + [LAFAND](https://github.com/masakhane-io/lafand-mt)"]} | masakhane/m2m100_418M_fr_fon_rel_news | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"fon",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr",
"fon"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #fr #fon #autotrain_compatible #endpoints_compatible #region-us
| # m2m100_418M-fr-fon-mt
## Model description
m2m100_418M-fr-fon-mt is a machine translation model from French to Fon based on a fine-tuned facebook/m2m100_418M model. It establishes a baseline for automatically translating texts from French to Fon.
#### Limitations and bias
This model is limited by its training da... | [
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"#### Limitations and bias\nThis model is limited by it... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #fr #fon #autotrain_compatible #endpoints_compatible #region-us \n",
"# m2m100_418M-fr-fon-mt",
"## Model description\nm2m100_418M-fr-fon-mt is a machine translation model from French to Fon based on a fine-tuned facebook/m2m100_418M model. It establ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | masapasa/sagemaker-distilbert-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion
============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2590
* Accuracy: 0.915
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | masapasa/wav2vec2-large-xls-r-300m-turkish-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-turkish-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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 n... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["it"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic... | masapasa/xls-r-300m-it-cv8-ds13 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #it #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3549
* Wer: 0.3827
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #it #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\n... |
automatic-speech-recognition | transformers | language:
- it
license: apache-2.0
tags:
- robust-speech-event
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-F... | {} | masapasa/xls-r-300m-it-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| language:
* it
license: apache-2.0
tags:
* robust-speech-event
* automatic-speech-recognition
* mozilla-foundation/common\_voice\_8\_0
* generated\_from\_trainer
datasets:
* common\_voice
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
... | [
"### 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 #endpoints_compatible #region-us \n",
"### 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\\_acc... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["robust-speech-event", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "automa... | masapasa/xls-r-300m-sv-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3347
* Wer: 1.0286
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### 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 #robust-speech-event #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe ... |
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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | masapasa/xls-r-ab-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 140.0674
- Wer: 1.1193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 140.0674\n- Wer: 1.1193",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AB datase... |
automatic-speech-recognition | transformers | # wav2vec 2.0 multilingual ( Finetued )
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out [this blog](https://huggingface.co... | {} | masoudmzb/wav2vec2-xlsr-multilingual-53-fa | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:2006.13979",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.13979"
] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #arxiv-2006.13979 #endpoints_compatible #has_space #region-us
| wav2vec 2.0 multilingual ( Finetued )
=====================================
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check ... | [
"### Use FineTuned Model\n\n\nThis model is finetuned on m3hrdadfi/wav2vec2-large-xlsr-persian-v3 , so the process for train or evaluation is same\n\n\n\nNormalizer\n\n\nIf you are not sure your transcriptions are clean or not (having weird characters or any other alphabete chars ) use this code provided by m3hrdad... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #arxiv-2006.13979 #endpoints_compatible #has_space #region-us \n",
"### Use FineTuned Model\n\n\nThis model is finetuned on m3hrdadfi/wav2vec2-large-xlsr-persian-v3 , so the process for train or evaluation is same\n\n\n\nNormalizer\n\n\nIf you ... |
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. -->
# xlm-roberta-base-finetuned-marc-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": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | mateocolina/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9276
* Mae: 0.5366
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-finetuned-funsd-1024
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-funsd-1024", "results": []}]} | mathew/layoutlmv2-finetuned-funsd-1024 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-funsd-1024
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# layoutlmv2-finetuned-funsd-1024\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-funsd-1024\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset... |
text-generation | transformers |
# GPT | {"tags": ["conversational"]} | matprado/DialoGPT-small-rick-sanchez | 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
|
# GPT | [
"# GPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT"
] |
null | null | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
be... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | matrix/test | null | [
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #region-us
| # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a ... | [
"# BERT base model (uncased)\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\nDisclaimer: The team releasing BERT did no... | [
"TAGS\n#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #region-us \n",
"# BERT base model (uncased)\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model i... |
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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"t... | mattchurgin/distilbert-mrpc | null | [
"transformers",
"pytorch",
"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 #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-mrpc
===============
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: 0.6783
* Accuracy: 0.8480
* F1: 0.8935
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #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\\_rate: 5e-0... |
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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "distilbert-sst2", "results": []}]} | mattchurgin/distilbert-sst2 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-sst2
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4182
- eval_accuracy: 0.8911
- eval_runtime: 1.8021
- eval_samples_per_second: 483.882
- eval_steps_per_second: 60.485
- epoch: 0.8
- step: 670... | [
"# distilbert-sst2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the glue dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4182\n- eval_accuracy: 0.8911\n- eval_runtime: 1.8021\n- eval_samples_per_second: 483.882\n- eval_steps_per_second: 60.485\n- epoch: 0.8\... | [
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"# distilbert-sst2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the glue dataset.\nIt achieves the following r... |
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. -->
#
This model is a fine-tuned version of [patrickvonplaten/wav2vec2_tiny_random_robust](https://huggingface.co/patrickvonplaten/wa... | {"language": ["ab"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | mattchurgin/xls-r-eng | null | [
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"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of patrickvonplaten/wav2vec2_tiny_random_robust on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More informat... | [
"# \n\nThis model is a fine-tuned version of patrickvonplaten/wav2vec2_tiny_random_robust on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: inf\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
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# Better Language Models and Their Implications (GPT2)
<b>Paper:</b> <a href="https://openai.com/blog/better-language-models/">https://openai.com/blog/better-language-mode... | {} | matthias-wright/gpt2 | null | [
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#region-us
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# Better Language Models and Their Implications (GPT2)
<b>Paper:</b> <a href="URL/URL
# About
These are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from ... | [
"# Better Language Models and Their Implications (GPT2)\n<b>Paper:</b> <a href=\"URL/URL",
"# About\nThese are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documentation that explains the preprocessing steps as well ... | [
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"# About\nThese are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documentation that explains the pre... |
null | null | # Deep Residual Learning for Image Recognition
<b>Paper:</b> <a href="https://arxiv.org/abs/1512.03385">https://arxiv.org/abs/1512.03385</a>
# About
These are the pretrained weights for [this](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/resnet) ResNet implementation in Jax/Flax. The weights a... | {} | matthias-wright/resnet | null | [
"arxiv:1512.03385",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385"
] | [] | TAGS
#arxiv-1512.03385 #region-us
| # Deep Residual Learning for Image Recognition
<b>Paper:</b> <a href="URL/URL
# About
These are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from this repository.
# Documentation
Here is a documentation that explains the preprocessing steps as well as the format of the ... | [
"# Deep Residual Learning for Image Recognition\n<b>Paper:</b> <a href=\"URL/URL",
"# About\nThese are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documentation that explains the preprocessing steps as well as the f... | [
"TAGS\n#arxiv-1512.03385 #region-us \n",
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"# About\nThese are the pretrained weights for this ResNet implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documentation that explai... |
null | null | # Analyzing and Improving the Image Quality of StyleGAN
<b>Paper:</b> <a href="https://arxiv.org/abs/1912.04958">https://arxiv.org/abs/1912.04958</a>
# About
These are the pretrained weights for [this](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/stylegan2) StyleGAN2 implementation in Jax/Flax... | {} | matthias-wright/stylegan2 | null | [
"arxiv:1912.04958",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.04958"
] | [] | TAGS
#arxiv-1912.04958 #region-us
| # Analyzing and Improving the Image Quality of StyleGAN
<b>Paper:</b> <a href="URL/URL
# About
These are the pretrained weights for this StyleGAN2 implementation in Jax/Flax. The weights are taken from this and this repository.
# Documentation
Here is a documentation that explains the preprocessing steps as well ... | [
"# Analyzing and Improving the Image Quality of StyleGAN\n<b>Paper:</b> <a href=\"URL/URL",
"# About\nThese are the pretrained weights for this StyleGAN2 implementation in Jax/Flax. The weights are taken from this and this repository.",
"# Documentation\nHere is a documentation that explains the preprocessing s... | [
"TAGS\n#arxiv-1912.04958 #region-us \n",
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"# About\nThese are the pretrained weights for this StyleGAN2 implementation in Jax/Flax. The weights are taken from this and this repository.",
"# Documentation\nHere is a docu... |
null | null | # Very Deep Convolutional Networks for Large-Scale Image Recognition
<b>Paper:</b> <a href="https://arxiv.org/abs/1409.1556">https://arxiv.org/abs/1409.1556</a>
# About
These are the pretrained weights for [this](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/vgg) VGG implementation in Jax/Flax.... | {} | matthias-wright/vgg | null | [
"arxiv:1409.1556",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.1556"
] | [] | TAGS
#arxiv-1409.1556 #region-us
| # Very Deep Convolutional Networks for Large-Scale Image Recognition
<b>Paper:</b> <a href="URL/URL
# About
These are the pretrained weights for this VGG implementation in Jax/Flax. The weights are taken from this repository.
# Documentation
Here is a documentation that explains the preprocessing steps as well as... | [
"# Very Deep Convolutional Networks for Large-Scale Image Recognition\n<b>Paper:</b> <a href=\"URL/URL",
"# About\nThese are the pretrained weights for this VGG implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documentation that explains the preprocessing ste... | [
"TAGS\n#arxiv-1409.1556 #region-us \n",
"# Very Deep Convolutional Networks for Large-Scale Image Recognition\n<b>Paper:</b> <a href=\"URL/URL",
"# About\nThese are the pretrained weights for this VGG implementation in Jax/Flax. The weights are taken from this repository.",
"# Documentation\nHere is a documen... |
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... | mattmcclean/distilbert-base-uncased-finetuned-emotion | null | [
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"generated_from_trainer",
"dataset:emotion",
"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-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2173
* Accuracy: 0.925
* F1: 0.9252
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
fill-mask | transformers | # PolitBERT
## Background
This model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.
## Training
The model was initialized using the pre-trained weights of BERT<sub>BASE</sub> and trained for 20 epochs on the standard MLM task with default parameters.
... | {} | maurice/PolitBERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # PolitBERT
## Background
This model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.
## Training
The model was initialized using the pre-trained weights of BERT<sub>BASE</sub> and trained for 20 epochs on the standard MLM task with default parameters.
... | [
"# PolitBERT",
"## Background\n\nThis model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.",
"## Training\nThe model was initialized using the pre-trained weights of BERT<sub>BASE</sub> and trained for 20 epochs on the standard MLM task with defa... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# PolitBERT",
"## Background\n\nThis model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.",
"## Training\nThe model was initialized... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mod... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 CV-de", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recog... | maxidl/wav2vec2-large-xlsr-german | null | [
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"pytorch",
"jax",
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"automatic-speech-recognition",
"audio",
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"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned facebook/wav2vec2-large-xlsr-53 on German using the Common Voice dataset.
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 evaluat... | [
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German using the Common Voice dataset.\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 mode... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on German using the Commo... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22134694
- CO2 Emissions (in grams): 14.525955245648218
## Validation Metrics
- Loss: 1.7039562463760376
- Accuracy: 0.6369376479873717
- Macro F1: 0.5363181342408181
- Micro F1: 0.6369376479873717
- Weighted F1: 0.6309793486221543... | {"language": "nl", "tags": "autonlp", "datasets": ["maximedb/autonlp-data-vaccinchat"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 14.525955245648218} | maximedb/autonlp-vaccinchat-22134694 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22134694
- CO2 Emissions (in grams): 14.525955245648218
## Validation Metrics
- Loss: 1.7039562463760376
- Accuracy: 0.6369376479873717
- Macro F1: 0.5363181342408181
- Micro F1: 0.6369376479873717
- Weighted F1: 0.6309793486221543... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 22134694\n- CO2 Emissions (... |
text-classification | transformers | hello
| {} | maximedb/mqa-cross-encoder | null | [
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"text-classification",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT https://huggingface.co/maxpe/bertin-roberta-base-spanish_sem_eval_2018_task_1
# BERTIN-roBERTa-base-Spanish_SemEval18_Emodetection
This is a BERTIN-roBERTa-base-Spanish model trained on ~3500 tweets in Spanish annotated for 11 emotion categories in [SemEval-2018 Task 1: A... | {} | maxpe/bertin-roberta-base-spanish_semeval18_emodetection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT URL
# BERTIN-roBERTa-base-Spanish_SemEval18_Emodetection
This is a BERTIN-roBERTa-base-Spanish model trained on ~3500 tweets in Spanish annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
Run the classifier on... | [
"# UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT URL",
"# BERTIN-roBERTa-base-Spanish_SemEval18_Emodetection\n\nThis is a BERTIN-roBERTa-base-Spanish model trained on ~3500 tweets in Spanish annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification. \n\nRun the ... | [
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"# BERTIN-roBERTa-base-Spanish_SemEval18_Emodetection\n\nThis is a BERTIN-roBERTa-base-Spanish model trained on ~3500 tweets in Spanish ann... |
text-classification | transformers | # UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT https://huggingface.co/maxpe/twitter-roberta-base-jun2022_sem_eval_2018_task_1
# Twitter-roBERTa-base_SemEval18_Emodetection
This is a Twitter-roBERTa-base model trained on ~7000 tweets in English annotated for 11 emotion categories in [SemEval-2018 Task 1: Affect in Twee... | {} | maxpe/twitter-roberta-base_semeval18_emodetection | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT URL
# Twitter-roBERTa-base_SemEval18_Emodetection
This is a Twitter-roBERTa-base model trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
Run the classifier on the test set ... | [
"# UPDATE: NEW AND IMPROVED MODEL AVAILABLE AT URL",
"# Twitter-roBERTa-base_SemEval18_Emodetection\n\nThis is a Twitter-roBERTa-base model trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification. \n\nRun the classifier on ... | [
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"# Twitter-roBERTa-base_SemEval18_Emodetection\n\nThis is a Twitter-roBERTa-base model trained on ~7000 tweets in English annotated for 11 ... |
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-italian-uncased-finetuned-ComunaliRoma
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https:/... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-italian-uncased-finetuned-ComunaliRoma", "results": []}]} | maxspaziani/bert-base-italian-uncased-finetuned-ComunaliRoma | null | [
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"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-italian-uncased-finetuned-ComunaliRoma
================================================
This model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0398
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\... |
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-italian-xxl-uncased-finetuned-ComunaliRoma
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-uncased]... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-italian-xxl-uncased-finetuned-ComunaliRoma", "results": []}]} | maxspaziani/bert-base-italian-xxl-uncased-finetuned-ComunaliRoma | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
====================================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5095
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\... |
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