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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-greek
This model was trained from scratch on the common_voice dataset.
It achieves the following resul... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-greek", "results": []}]} | jerrychatz/wav2vec2-large-xls-r-300m-greek | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-greek
===============================
This model was trained from scratch on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4823
* Wer: 0.3338
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
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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. -->
# test-german-t5-prompted-germanquad
eval_loss = 0.5907255411148071
eval_rouge1 = 62.0922
eval_rouge2 = 47.2761
eval_rougeL ... | {"tags": ["generated_from_trainer"], "widget": [{"text": "Philipp ist 26 Jahre alt und lebt in N\u00fcrnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um k\u00fcnstliche Intelligenz durch Open Source und Open Science zu demokratisieren.\n\nWelches Ziel hat Hugging F... | GermanT5/german-t5-oscar-ep1-prompted-germanquad | null | [
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# test-german-t5-prompted-germanquad
eval_loss = 0.5907255411148071
eval_rouge1 = 62.0922
eval_rouge2 = 47.2761
eval_rougeL = 61.7706
eval_rougeLsum = 61.8036
eval_runtime = 4501.8065
eval_samples_per_second = 5.487
eval_steps_per_second = 2.743
## Model description
More information needed
## I... | [
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text-classification | transformers |
## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.
### Lijdende en Bedrijvende vorm classificatie voor zinnen
#### Examples
Try the following examples in the Hosted inference API:
1. Jan werd opgehaald door zijn moeder.
2. Wie niet weg is, is gezien
3. Ik ben van plan om morgen te gaan werken
4. De ma... | {"language": ["nl"], "license": "apache-2.0", "tags": ["bert", "passive", "active"]} | Gerwin/bert-for-pac | null | [
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|
## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.
### Lijdende en Bedrijvende vorm classificatie voor zinnen
#### Examples
Try the following examples in the Hosted inference API:
1. Jan werd opgehaald door zijn moeder.
2. Wie niet weg is, is gezien
3. Ik ben van plan om morgen te gaan werken
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | Giannipinelli/xlm-roberta-base-finetuned-marc-en | null | [
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| 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: 0.9161
* Mae: 0.4634
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",
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automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned: facebook/wav2vec2-large-xlsr-53 | {} | Gigworks/ASR_id | null | [
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| # Wav2Vec2-Large-XLSR-Indonesian
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] |
null | null | <b>Speech-To-Text Chinese Model</b>
<br/><br/>
Reference: <br/>
Model - https://huggingface.co/espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char <br/>
Code - https://huggingface.co/spaces/akhaliq/espnet2_asr/blob/main/app.py
| {} | Gigworks/ASR_zh_espnet2 | null | [
"region:us"
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#region-us
| <b>Speech-To-Text Chinese Model</b>
<br/><br/>
Reference: <br/>
Model - URL <br/>
Code - URL
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feature-extraction | transformers | # FongBERT
FongBERT is a BERT model trained on 68.363 sentences in [Fon](https://en.wikipedia.org/wiki/Fon_language). The data are compiled from [JW300](https://opus.nlpl.eu/JW300.php) and other additional data I scraped from the [JW](https://www.jw.org/en/) website.
It is the first pretrained model to leverage transf... | {} | Gilles/FongBERT | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| # FongBERT
FongBERT is a BERT model trained on 68.363 sentences in Fon. The data are compiled from JW300 and other additional data I scraped from the JW website.
It is the first pretrained model to leverage transfer learning for downtream tasks for Fon.
Below are some examples of missing word prediction.
from transf... | [
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image-classification | transformers |
# places
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Giuliano/places | null | [
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"tensorboard",
"vit",
"image-classification",
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"model-index",
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#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# places
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Beach
!Beach
#### City
!City
#### Forest
!Forest | [
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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. -->
# Mandarin
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "Mandarin", "results": []}]} | GleamEyeBeast/Mandarin | null | [
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|
# Mandarin
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
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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. -->
# Mandarin_naive
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "Mandarin_naive", "results": []}]} | GleamEyeBeast/Mandarin_naive | null | [
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| Mandarin\_naive
===============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4584
* Wer: 0.3999
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
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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. -->
# test
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "test", "results": []}]} | GleamEyeBeast/test | null | [
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| test
====
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1761
* Wer: 0.2161
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Gonalb/xlm-roberta-base-finetuned-panx-de | null | [
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"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1373
* F1: 0.8630
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... | [
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text-generation | transformers |
# Jackie DialoGPT Model | {"tags": ["conversational"]} | Gowtham25/DialoGPT-small-jackie | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jackie DialoGPT Model | [
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null | null |
# Graphcore/bart-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’... | {"license": "apache-2.0"} | Graphcore/bart-base-ipu | null | [
"optimum_graphcore",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#optimum_graphcore #license-apache-2.0 #region-us
|
# Graphcore/bart-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’... | [
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null | null | # Graphcore/bert-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s... | {} | Graphcore/bert-base-ipu | null | [
"optimum_graphcore",
"region:us"
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#optimum_graphcore #region-us
| # Graphcore/bert-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s... | [
"# Graphcore/bert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Grap... | [
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null | null | # Graphcore/bert-large-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’... | {} | Graphcore/bert-large-ipu | null | [
"optimum_graphcore",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#optimum_graphcore #region-us
| # Graphcore/bert-large-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’... | [
"# Graphcore/bert-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gra... | [
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question-answering | transformers |
# Graphcore/bert-large-uncased-squad
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "Graphcore/bert-large-uncased-squad", "results": []}]} | Graphcore/bert-large-uncased-squad | null | [
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#transformers #pytorch #safetensors #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# Graphcore/bert-large-uncased-squad
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"# Graphcore/bert-large-uncased-squad\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run model... | [
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null | transformers |
# Graphcore/bert-large-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graph... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Graphcore/wikipedia-bert-128", "Graphcore/wikipedia-bert-512"], "model-index": [{"name": "Graphcore/bert-large-uncased", "results": []}]} | Graphcore/bert-large-uncased | null | [
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"dataset:Graphcore/wikipedia-bert-128",
"dataset:Graphcore/wikipedia-bert-512",
"arxiv:1904.00962",
"license:apache-2.0",
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|
# Graphcore/bert-large-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graph... | [
"# Graphcore/bert-large-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on... | [
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null | null | # Graphcore/deberta-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcor... | {} | Graphcore/deberta-base-ipu | null | [
"optimum_graphcore",
"arxiv:2006.03654",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [] | TAGS
#optimum_graphcore #arxiv-2006.03654 #region-us
| # Graphcore/deberta-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcor... | [
"# Graphcore/deberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on G... | [
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null | null |
# Graphcore/gpt2-medium-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcor... | {"license": "apache-2.0"} | Graphcore/gpt2-medium-ipu | null | [
"optimum_graphcore",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#optimum_graphcore #license-apache-2.0 #region-us
|
# Graphcore/gpt2-medium-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcor... | [
"# Graphcore/gpt2-medium-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gr... | [
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null | null |
# Graphcore/gpt2-small-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore... | {"license": "apache-2.0"} | Graphcore/gpt2-small-ipu | null | [
"optimum_graphcore",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#optimum_graphcore #license-apache-2.0 #region-us
|
# Graphcore/gpt2-small-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore... | [
"# Graphcore/gpt2-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gra... | [
"TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n",
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null | null |
# Graphcore/roberta-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphco... | {"license": "apache-2.0"} | Graphcore/roberta-base-ipu | null | [
"optimum_graphcore",
"arxiv:1907.11692",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1907.11692"
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#optimum_graphcore #arxiv-1907.11692 #license-apache-2.0 #region-us
|
# Graphcore/roberta-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphco... | [
"# Graphcore/roberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on G... | [
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null | null | # Graphcore/roberta-large-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphco... | {} | Graphcore/roberta-large-ipu | null | [
"optimum_graphcore",
"arxiv:1907.11692",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1907.11692"
] | [] | TAGS
#optimum_graphcore #arxiv-1907.11692 #region-us
| # Graphcore/roberta-large-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphco... | [
"# Graphcore/roberta-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
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null | null |
# Graphcore/t5-small-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s... | {"license": "apache-2.0"} | Graphcore/t5-small-ipu | null | [
"optimum_graphcore",
"arxiv:1910.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1910.10683"
] | [] | TAGS
#optimum_graphcore #arxiv-1910.10683 #license-apache-2.0 #region-us
|
# Graphcore/t5-small-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s... | [
"# Graphcore/t5-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graph... | [
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null | null | # Graphcore/vit-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s ... | {} | Graphcore/vit-base-ipu | null | [
"optimum_graphcore",
"arxiv:2010.11929",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2010.11929"
] | [] | TAGS
#optimum_graphcore #arxiv-2010.11929 #region-us
| # Graphcore/vit-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s ... | [
"# Graphcore/vit-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graph... | [
"TAGS\n#optimum_graphcore #arxiv-2010.11929 #region-us \n",
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null | adapter-transformers |
# Adapter `Gregor/bert-base-multilingual-cased-wmt21-qe` for bert-base-multilingual-cased
An [adapter](https://adapterhub.ml) for the bert-base-multilingual-cased model that was trained on the [quality_estimation/wmt21](https://adapterhub.ml/explore/quality_estimation/wmt21/) dataset and includes a prediction head fo... | {"tags": ["adapter-transformers", "adapterhub:quality_estimation/wmt21", "bert"]} | Gregor/bert-base-multilingual-cased-wmt21-qe | null | [
"adapter-transformers",
"bert",
"adapterhub:quality_estimation/wmt21",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#adapter-transformers #bert #adapterhub-quality_estimation/wmt21 #region-us
|
# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased
An adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers ... | [
"# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased\n\nAn adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-trans... | [
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"TAGS\n#adapter-transformers #bert #adapterhub-quality_estimation/wmt21 #region-us \n# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased\n\nAn adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction h... |
null | adapter-transformers |
# Adapter `Gregor/xlm-roberta-base-wmt21-qe` for xlm-roberta-base
An [adapter](https://adapterhub.ml) for the xlm-roberta-base model that was trained on the [quality_estimation/wmt21](https://adapterhub.ml/explore/quality_estimation/wmt21/) dataset and includes a prediction head for classification.
This adapter was ... | {"tags": ["adapter-transformers", "adapterhub:quality_estimation/wmt21", "xlm-roberta"]} | Gregor/xlm-roberta-base-wmt21-qe | null | [
"adapter-transformers",
"xlm-roberta",
"adapterhub:quality_estimation/wmt21",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us
|
# Adapter 'Gregor/xlm-roberta-base-wmt21-qe' for xlm-roberta-base
An adapter for the xlm-roberta-base model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install '... | [
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null | adapter-transformers |
# Adapter `Gregor/xlm-roberta-large-wmt21-qe` for xlm-roberta-large
An [adapter](https://adapterhub.ml) for the xlm-roberta-large model that was trained on the [quality_estimation/wmt21](https://adapterhub.ml/explore/quality_estimation/wmt21/) dataset and includes a prediction head for classification.
This adapter w... | {"tags": ["adapter-transformers", "xlm-roberta", "adapterhub:quality_estimation/wmt21"]} | Gregor/xlm-roberta-large-wmt21-qe | null | [
"adapter-transformers",
"xlm-roberta",
"adapterhub:quality_estimation/wmt21",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us
|
# Adapter 'Gregor/xlm-roberta-large-wmt21-qe' for xlm-roberta-large
An adapter for the xlm-roberta-large model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, instal... | [
"# Adapter 'Gregor/xlm-roberta-large-wmt21-qe' for xlm-roberta-large\n\nAn adapter for the xlm-roberta-large model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\... | [
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text-generation | transformers |
# rick and morty | {"tags": ["conversational", "PyTorch", "Transformers", "gpt2", "lm-head", "causal-lm", "text-generation"]} | Gregor-Davies/DialoGPT-small-rick | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #PyTorch #Transformers #lm-head #causal-lm #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# rick and morty | [
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] |
text-generation | transformers |
# The Owl House DialoGPT Model | {"tags": ["conversational"]} | Greysan/DialoGPT-medium-TOH | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# The Owl House DialoGPT Model | [
"# The Owl House DialoGPT Model"
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] |
fill-mask | transformers |
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- 📝 [Paper](https://arxiv.org/abs/2105.02855)
- 💻 [Code](https://github.com/wietsedv/low-resource-adapt)
## Models
The... | {"language": "fy", "tags": ["BERTje"]} | GroNLP/bert-base-dutch-cased-frisian | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"BERTje",
"fy",
"arxiv:2105.02855",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2105.02855"
] | [
"fy"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us
|
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- Paper
- Code
## Models
The best fine-tuned models for Gronings and West Frisian are available on the HuggingFace mod... | [
"# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code",
"## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:",
"### Lexical layers\nThese models are identi... | [
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"# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code",
"## Models\n\nT... | [
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fill-mask | transformers |
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- 📝 [Paper](https://arxiv.org/abs/2105.02855)
- 💻 [Code](https://github.com/wietsedv/low-resource-adapt)
## Models
The... | {"language": "gos", "tags": ["BERTje"]} | GroNLP/bert-base-dutch-cased-gronings | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"BERTje",
"gos",
"arxiv:2105.02855",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2105.02855"
] | [
"gos"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us
|
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- Paper
- Code
## Models
The best fine-tuned models for Gronings and West Frisian are available on the HuggingFace mod... | [
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"### Lexical layers\nThese models are identi... | [
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"# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code",
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"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fin... |
token-classification | transformers |
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- 📝 [Paper](https://arxiv.org/abs/2105.02855)
- 💻 [Code](https://github.com/wietsedv/low-resource-adapt)
## Models
The... | {"language": "fy", "tags": ["BERTje", "pos"]} | GroNLP/bert-base-dutch-cased-upos-alpino-frisian | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"BERTje",
"pos",
"fy",
"arxiv:2105.02855",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2105.02855"
] | [
"fy"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us
|
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- Paper
- Code
## Models
The best fine-tuned models for Gronings and West Frisian are available on the HuggingFace mod... | [
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"### Lexical layers\nThese models are identi... | [
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"# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code",
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token-classification | transformers |
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- 📝 [Paper](https://arxiv.org/abs/2105.02855)
- 💻 [Code](https://github.com/wietsedv/low-resource-adapt)
## Models
The... | {"language": "gos", "tags": ["BERTje", "pos"]} | GroNLP/bert-base-dutch-cased-upos-alpino-gronings | null | [
"transformers",
"pytorch",
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"token-classification",
"BERTje",
"pos",
"gos",
"arxiv:2105.02855",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2105.02855"
] | [
"gos"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us
|
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- Paper
- Code
## Models
The best fine-tuned models for Gronings and West Frisian are available on the HuggingFace mod... | [
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"### Lexical layers\nThese models are identi... | [
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"# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code",
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token-classification | transformers |
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- 📝 [Paper](https://arxiv.org/abs/2105.02855)
- 💻 [Code](https://github.com/wietsedv/low-resource-adapt)
## Models
The... | {"language": "nl", "tags": ["BERTje", "pos"]} | GroNLP/bert-base-dutch-cased-upos-alpino | null | [
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"token-classification",
"BERTje",
"pos",
"nl",
"arxiv:2105.02855",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2105.02855"
] | [
"nl"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #BERTje #pos #nl #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us
|
Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling
# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
This model is part of this paper + code:
- Paper
- Code
## Models
The best fine-tuned models for Gronings and West Frisian are available on the HuggingFace mod... | [
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"### Lexical layers\nThese models are identi... | [
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fill-mask | transformers |
# BERTje: A Dutch BERT model
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Andreas van Cranenburgh](https://www.semanticscholar.org/author/Andreas-van-Cranenburgh/2791585) •
[Arianna Bisazza](https://www.semanticscholar.org/author/Arianna-Bisazza/3242253) •
[Tommaso Caselli](ht... | {"language": "nl", "tags": ["BERTje"], "thumbnail": "https://raw.githubusercontent.com/wietsedv/bertje/master/bertje.png"} | GroNLP/bert-base-dutch-cased | null | [
"transformers",
"pytorch",
"tf",
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"nl",
"arxiv:1912.09582",
"doi:10.57967/hf/0149",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1912.09582"
] | [
"nl"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #BERTje #nl #arxiv-1912.09582 #doi-10.57967/hf/0149 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERTje: A Dutch BERT model
==========================
Wietse de Vries •
Andreas van Cranenburgh •
Arianna Bisazza •
Tommaso Caselli •
Gertjan van Noord •
Malvina Nissim
Model description
-----------------
BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.
<img src="URL height="25... | [
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"### BibTeX entry and citation info"
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] |
text-generation | transformers |
# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the medium OpenAI GPT-2 ([`gpt2-medium`](https:/... | {"language": "nl", "tags": ["adaption", "recycled", "gpt2-medium"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-medium-dutch-embeddings | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"adaption",
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"autotrain_compatible",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2012.05628"
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|
# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.
The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retraine... | [
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text-generation | transformers |
# GPT-2 recycled for Italian (medium, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the medium OpenAI GPT-2 ([`gpt2-medium`](https... | {"language": "it", "tags": ["adaption", "recycled", "gpt2-medium"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-medium-italian-embeddings | null | [
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|
# GPT-2 recycled for Italian (medium, adapted lexical embeddings)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.
The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrai... | [
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text-generation | transformers |
# GPT-2 recycled for Dutch (small, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingf... | {"language": "nl", "tags": ["adaption", "recycled", "gpt2-small"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-small-dutch-embeddings | null | [
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|
# GPT-2 recycled for Dutch (small, adapted lexical embeddings)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the small OpenAI GPT-2 ('gpt2') model.
The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a D... | [
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text-generation | transformers |
# GPT-2 recycled for Dutch (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model.
For de... | {"language": "nl", "tags": ["adaption", "recycled", "gpt2-small"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-small-dutch | null | [
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|
# GPT-2 recycled for Dutch (small)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the small OpenAI GPT-2 ('gpt2') model.
For details, check out our paper on arXiv and the code on Github.
## Related models
### Dutch
- 'gpt2-small-dutch-embeddings': Small model size with only retrain... | [
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"### Dutch\n - 'gpt2-small-dutch-embeddings': Small model ... | [
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text-generation | transformers |
# GPT-2 recycled for Italian (small, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggin... | {"language": "it", "tags": ["adaption", "recycled", "gpt2-small"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-small-italian-embeddings | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2012.05628"
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|
# GPT-2 recycled for Italian (small, adapted lexical embeddings)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the small OpenAI GPT-2 ('gpt2') model.
The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a... | [
"# GPT-2 recycled for Italian (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim",
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text-generation | transformers |
# GPT-2 recycled for Italian (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model.
For ... | {"language": "it", "tags": ["adaption", "recycled", "gpt2-small"], "pipeline_tag": "text-generation"} | GroNLP/gpt2-small-italian | null | [
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"2012.05628"
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|
# GPT-2 recycled for Italian (small)
Wietse de Vries •
Malvina Nissim
## Model description
This model is based on the small OpenAI GPT-2 ('gpt2') model.
For details, check out our paper on arXiv and the code on Github.
## Related models
### Dutch
- 'gpt2-small-dutch-embeddings': Small model size with only retra... | [
"# GPT-2 recycled for Italian (small)\nWietse de Vries •\nMalvina Nissim",
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"## Related models",
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fill-mask | transformers |
#
[Tommaso Caselli](https://www.semanticscholar.org/author/Tommaso-Caselli/1864635) •
[Valerio Basile](https://www.semanticscholar.org/author/Valerio-Basile/3101511) •
[Jelena Mitrovic](https://www.semanticscholar.org/author/Jelena-Mitrovic/145157863) •
[Michael Granizter](https://www.semanticscholar.org/author/M.-Gr... | {"language": "en", "tags": ["HateBERT", "text classification", "abusive language", "hate speech", "offensive language"]} | GroNLP/hateBERT | null | [
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"safetensors",
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"text classification",
"abusive language",
"hate speech",
"offensive language",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
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|
#
Tommaso Caselli •
Valerio Basile •
Jelena Mitrovic •
Michael Granizter
## Model description
HateBERT is an English pre-trained BERT model obtained by further training the English BERT base uncased model with more than 1 million posts from banned communites from Reddit. The model has been developed as a collabora... | [
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null | null | ### The MelGAN vocoder for StyleSpeech
#### About StyleSpeech
* StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation
* The StyleSpeech model can be trained by official implementation (https://github.com/KevinMIN95/StyleSpeech).
#### About MelGAN vocoder
* This MelGAN vocoder i... | {} | Guan-Ting/StyleSpeech-MelGAN-vocoder-16kHz | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| ### The MelGAN vocoder for StyleSpeech
#### About StyleSpeech
* StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation
* The StyleSpeech model can be trained by official implementation (URL
#### About MelGAN vocoder
* This MelGAN vocoder is used to transform the mel-spectrogram ... | [
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text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | Guard-SK/DialoGPT-medium-ricksanchez | null | [
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text-generation | transformers |
#Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | Guard-SK/DialoGPT-small-ricksanchez | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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text-generation | transformers |
# Game of Thrones DialoGPT Model | {"tags": ["conversational"]} | GunjanPantha/DialoGPT-small-gameofthrones | null | [
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text-to-speech | espnet |
## ESPnet2 TTS model
### `GunnarThor/talromur_f_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 81522029063e42ce807d9d145b64d3f9aca45987
pip install -e .
cd egs2/talromur/tts1
./ru... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | GunnarThor/talromur_f_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
|
## ESPnet2 TTS model
### 'GunnarThor/talromur_f_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
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null | null | Modified from: https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
1. remove unused parts by ctc greedy search for tutorial only.
| {} | GuoLiyong/cn_conformer_encoder_aishell | null | [
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null | null | The original link of these models is:
https://zenodo.org/record/4604066#.YKtNrqgzZPY
which is accessible by espnet utils
The are ported to this repo for users who don't have espnet dependencies.
| {} | GuoLiyong/snowfall_model_zoo | null | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-finetuned
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-cased-finetuned", "results": []}]} | GusNicho/distilbert-base-cased-finetuned | null | [
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| distilbert-base-cased-finetuned
===============================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9161
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned", "results": []}]} | GusNicho/roberta-base-finetuned | null | [
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# roberta-base-finetuned
This model is a fine-tuned version of roberta-base on an unknown dataset.
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- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053
## Model description
M... | [
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text-classification | transformers |
# DKbert-hatespeech-classification
Use this model to detect hatespeech in Danish. For details, guide and command line tool see [DK hate github](https://github.com/Guscode/DKbert-hatespeech-detection)
## Training data
Training data is from OffensEval2020 which can be found [here]( https://figshare.com/articles/data... | {"language": ["da"], "license": "mit", "tags": ["Hatespeech", "Danish", "BERT"], "datasets": ["DKHate - OffensEval2020"], "Classes": ["Hateful", "Not Hateful"]} | Guscode/DKbert-hatespeech-detection | null | [
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# DKbert-hatespeech-classification
Use this model to detect hatespeech in Danish. For details, guide and command line tool see DK hate github
## Training data
Training data is from OffensEval2020 which can be found here
## Performance
The model achieves a macro F1-score of 0.78
Precision hateful: 0.77
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text-generation | transformers |
#Batman Botty gpt model | {"tags": ["conversational"]} | Guy0/DialoGPT-small-Batmanbotty | null | [
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text-generation | transformers |
# Zero Two DialoGPT Model | {"tags": ["conversational"]} | HAttORi/DialoGPT-Medium-zerotwo | null | [
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text2text-generation | transformers |
## DistilLED Large CNN 16384
*distil-led-large-cnn-16384* was initialized from [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6), in a fashion similar to [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384).
To be able to process 16K tokens, *sshleifer/distilb... | {"language": "en", "license": "apache-2.0", "datasets": ["cnn_dailymail"]} | HHousen/distil-led-large-cnn-16384 | null | [
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## DistilLED Large CNN 16384
*distil-led-large-cnn-16384* was initialized from sshleifer/distilbart-cnn-12-6, in a fashion similar to allenai/led-large-16384.
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image-classification | transformers |
# household-rooms
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugg... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | HHousen/household-rooms | null | [
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# household-rooms
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### bathroom
!bathroom
#### bedroom
!bedroom
#### dining room
!dining room
#### kitchen
!kitchen
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text-generation | transformers | basically, it makes pickup lines
https://huggingface.co/gpt2
| {} | HJK/PickupLineGenerator | null | [
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| basically, it makes pickup lines
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text-generation | transformers | The model that generates the My little pony script
Fine tuning data: [Kaggle](https://www.kaggle.com/liury123/my-little-pony-transcript?select=clean_dialog.csv)
API page: [Ainize](https://ainize.ai/fpem123/GPT2-MyLittlePony)
Demo page: [End point](https://master-gpt2-my-little-pony-fpem123.endpoint.ainize.ai/)
### ... | {} | HScomcom/gpt2-MyLittlePony | null | [
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| The model that generates the My little pony script
Fine tuning data: Kaggle
API page: Ainize
Demo page: End point
### Model information
Base model: gpt-2 large
Epoch: 30
Train runtime: 4943.9641 secs
Loss: 0.0291
###===Teachable NLP===
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text-generation | transformers | ### Model information
Fine tuning data: https://www.kaggle.com/cuddlefish/fairy-tales
License: CC0: Public Domain
Base model: gpt-2 large
Epoch: 30
Train runtime: 17861.6048 secs
Loss: 0.0412
API page: [Ainize](https://ainize.ai/fpem123/GPT2-FairyTales?branch=master)
Demo page: [End-... | {} | HScomcom/gpt2-fairytales | null | [
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Fine tuning data: URL
License: CC0: Public Domain
Base model: gpt-2 large
Epoch: 30
Train runtime: 17861.6048 secs
Loss: 0.0412
API page: Ainize
Demo page: End-point
### ===Teachable NLP=== ###
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text-generation | transformers | ### Model information
Fine tuning data: https://www.kaggle.com/bennijesus/lovecraft-fiction
License: CC0: Public Domain
Base model: gpt-2 large
Epoch: 30
Train runtime: 10307.3488 secs
Loss: 0.0292
API page: [Ainize](https://ainize.ai/fpem123/GPT2-LoveCraft?branch=master)
Demo page: [End-poi... | {} | HScomcom/gpt2-lovecraft | null | [
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| ### Model information
Fine tuning data: URL
License: CC0: Public Domain
Base model: gpt-2 large
Epoch: 30
Train runtime: 10307.3488 secs
Loss: 0.0292
API page: Ainize
Demo page: End-point
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null | null | This is a RainGAN model | {} | HVH/RainGAN | null | [
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text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | HackyHackyMan/DialoGPT-small-harrypotter | null | [
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|
#Harry Potter DialoGPT Model | [] | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | Hadron/DialoGPT-medium-nino | null | [
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text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | Hallzy/Peterbot | null | [
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text-generation | transformers |
# Jake DialoGPT-large-jake
| {"tags": ["conversational"]} | Hamas/DialoGPT-large-jake | null | [
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# Jake DialoGPT-large-jake
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text-generation | transformers |
# Jake DialoGPT-large-jake2
| {"tags": ["conversational"]} | Hamas/DialoGPT-large-jake2 | null | [
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"autotrain_compatible",
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|
# Jake DialoGPT-large-jake2
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text-generation | transformers |
# Jake DialoGPT-large-jake
| {"tags": ["conversational"]} | Hamas/DialoGPT-large-jake3 | null | [
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# Jake DialoGPT-large-jake
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text-generation | transformers |
# Jake DialoGPT-large-jake
| {"tags": ["conversational"]} | Hamas/DialoGPT-large-jake4 | null | [
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text-generation | transformers |
#Rick DialoGPT Model | {"tags": ["conversational"]} | Hamhams/DialoGPT-small-rick | null | [
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"endpoints_compatible",
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text-generation | transformers |
## GPT2-Home
This model is fine-tuned using GPT-2 on amazon home products metadata.
It can generate descriptions for your **home** products by getting a text prompt.
### Model description
[GPT-2](https://openai.com/blog/better-language-models/) is a large [transformer](https://arxiv.org/abs/1706.03762)-based lang... | {"language": "en", "license": "apache-2.0", "tags": ["text-generation"], "widget": [{"text": "Maximize your bedroom space without sacrificing style with the storage bed."}, {"text": "Handcrafted of solid acacia in weathered gray, our round Jozy drop-leaf dining table is a space-saving."}, {"text": "Our plush and luxuri... | HamidRezaAttar/gpt2-product-description-generator | null | [
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"text-generation",
"en",
"arxiv:1706.03762",
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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|
## GPT2-Home
This model is fine-tuned using GPT-2 on amazon home products metadata.
It can generate descriptions for your home products by getting a text prompt.
### Model description
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 ... | [
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null | null | Model Description | {} | Hanchen/testRepo | null | [
"region:us"
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | Hank/distilbert-base-uncased-finetuned-ner | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0612
* Precision: 0.9259
* Recall: 0.9369
* F1: 0.9314
* Accuracy: 0.9839
Model des... | [
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text-generation | transformers |
# Rick from Rick & Morty DialoGPT Model | {"tags": ["conversational"]} | HansAnonymous/DialoGPT-medium-rick | null | [
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text-generation | transformers |
# Shrek from Shrek DialoGPT Model | {"tags": ["conversational"]} | HansAnonymous/DialoGPT-small-shrek | null | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | Haotian/distilgpt2-finetuned-wikitext2 | null | [
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"tensorboard",
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"generated_from_trainer",
"license:apache-2.0",
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"text-generation-inference",
"region:us"
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#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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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-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "aut... | HarrisDePerceptron/xls-r-1b-ur | null | [
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... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9613
* Wer: 0.5376
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: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | HarrisDePerceptron/xls-r-300m-ur-cv7 | null | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2924
* Wer: 0.7201
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
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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 [DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3](https://huggingface.co/DrishtiSharma/wav2... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | HarrisDePerceptron/xls-r-300m-ur-cv8-hi | null | [
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|
This model is a fine-tuned version of DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5443
* Wer: 0.7030
Model description
-----------------
More information needed
Intended uses & limit... | [
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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 [HarrisDePerceptron/xls-r-300m-ur](https://huggingface.co/HarrisDePerceptron/xls-r-300m-u... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "aut... | HarrisDePerceptron/xls-r-300m-ur | null | [
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... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
This model is a fine-tuned version of HarrisDePerceptron/xls-r-300m-ur on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0517
* WER: 0.5151291512915129
* CER: 0.23689640940982254
Model description
-----------------
More information need... | [
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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-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "aut... | HarrisDePerceptron/xlsr-large-53-ur | null | [
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... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8888
* Wer: 0.6642
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
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text-generation | transformers |
# Harry Potter DailogGPT Model | {"tags": ["conversational"]} | HarryPuttar/HarryPotterDC | null | [
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text-generation | transformers |
# Jack Sparrow GPT | {"tags": ["conversational"]} | Harshal6927/Jack_Sparrow_GPT | null | [
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text-generation | transformers |
# Tony Stark GPT
My first AI model still learning, used small dataset so don't expect much | {"tags": ["conversational"]} | Harshal6927/Tony_Stark_GPT | null | [
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# Tony Stark GPT
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
## Validation Metrics
- Loss: 0.5410276651382446
- MSE: 0.5410276651382446
- MAE: 0.5694561004638672
- R2: 0.6830431129198475
- RMSE: 0.735545814037323
- Explained Variance: 0.68... | {"language": "en", "tags": "autonlp", "datasets": ["Harshveer/autonlp-data-formality_scoring_2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 8.655894631203154} | Harshveer/autonlp-formality_scoring_2-32597818 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
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automatic-speech-recognition | transformers |
# hindi_base_wav2vec2 | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "hi", "model_for_talk", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["Harveenchadha/indic-voice"], "model-index": [{"name": "Hindi Large", "results": [{"task": {"type": "automatic-... | Harveenchadha/hindi_base_wav2vec2 | null | [
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text2text-generation | transformers | **Work in progress** | {} | Harveenchadha/indictrans | null | [
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null | keras |
## Multimodal entailment
Author: Sayak Paul
Date created: 2021/08/08
Last modified: 2021/08/15
Description: Training a multimodal model for predicting entailment.
### What is multimodal entailment?
On social media platforms, to audit and moderate content we may want to find answers to the following questions in near ... | {"library_name": "keras", "tags": ["nlp"]} | Harveenchadha/model-entailment | null | [
"keras",
"nlp",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#keras #nlp #region-us
|
## Multimodal entailment
Author: Sayak Paul
Date created: 2021/08/08
Last modified: 2021/08/15
Description: Training a multimodal model for predicting entailment.
### What is multimodal entailment?
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automatic-speech-recognition | transformers |
## Spaces Demo
Check the spaces demo [here](https://huggingface.co/spaces/Harveenchadha/wav2vec2-vakyansh-hindi/tree/main)
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-Ek... | {"language": "hi", "license": "mit", "tags": ["audio", "automatic-speech-recognition", "speech"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Vakyansh Hindi Model by Harveen Chadha", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice... | Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 | null | [
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|
## Spaces Demo
Check the spaces demo here
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.
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automatic-speech-recognition | transformers |
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
**Note: The result from this model is without... | {"language": "pa", "license": "mit", "tags": ["audio", "automatic-speech-recognition", "speech"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Vakyansh Punjabi Model by Harveen Chadha", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voi... | Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 | null | [
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Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.
Note: The result from this model is without a language model so you may witness a higher WER in some cases.
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automatic-speech-recognition | transformers |
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
**Note: The result from... | {"language": "ta", "license": "mit", "tags": ["audio", "automatic-speech-recognition", "speech"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Vakyansh Tamil Model by Harveen Chadha", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice... | Harveenchadha/vakyansh-wav2vec2-tamil-tam-250 | null | [
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|
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.
Note: The result from this model is without a language model so you may witness a higher WER in some cases.
## Data... | [
"## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases."... | [
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null | transformers |
Hindi Pretrained model on 4200 hours. [Link](https://arxiv.org/abs/2107.07402) | {"language": "hi", "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "hi", "model_for_talk", "pretrained", "robust-speech-event", "speech"]} | Harveenchadha/vakyansh_hindi_base_pretrained | null | [
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Hindi Pretrained model on 4200 hours. Link | [] | [
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] |
feature-extraction | transformers | ## Overview
We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
lingual speech representations from raw audio across **23 Indic languages**. It is built on top of wav2vec
2.0 which is solved by training a contrast... | {} | Harveenchadha/wav2vec2-pretrained-clsril-23-10k | null | [
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"wav2vec2",
"feature-extraction",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.07402"
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#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us
| Overview
--------
We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
lingual speech representations from raw audio across 23 Indic languages. It is built on top of wav2vec
2.0 which is solved by training a contr... | [] | [
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] |
text-classification | transformers |
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Technical Specifications](#technical-specifications)
... | {"language": "en", "license": "apache-2.0", "datasets": ["hatexplain"]} | Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two | null | [
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"2012.10289"
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"en"
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#transformers #pytorch #bert #text-classification #en #dataset-hatexplain #arxiv-2012.10289 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Table of Contents
- Model Details
- How to Get Started With the Model
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Technical Specifications
- Citation Information
## Model Details
Model Description:
The model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The ... | [
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text-classification | transformers | The model is used for classifying a text as **Hatespeech**, **Offensive**, or **Normal**. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and models are available here: https://github.com/punyajoy/HateXplain
... | {"language": "en", "license": "apache-2.0", "datasets": ["hatexplain"]} | Hate-speech-CNERG/bert-base-uncased-hatexplain | null | [
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| The model is used for classifying a text as Hatespeech, Offensive, or Normal. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and models are available here: URL
For more details about our paper
Binny Mathew,... | [] | [
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text-classification | transformers |
This model is used detecting **hatespeech** in **Arabic language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved i... | {"language": "ar", "license": "apache-2.0"} | Hate-speech-CNERG/dehatebert-mono-arabic | null | [
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|
This model is used detecting hatespeech in Arabic language. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.8776... | [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep... | [
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text-classification | transformers | This model is used detecting **hatespeech** in **English language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved ... | {"language": "en", "license": "apache-2.0"} | Hate-speech-CNERG/dehatebert-mono-english | null | [
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#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is used detecting hatespeech in English language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.726... | [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep... | [
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text-classification | transformers |
This model is used detecting **hatespeech** in **French language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved ... | {"language": "fr", "license": "apache-2.0"} | Hate-speech-CNERG/dehatebert-mono-french | null | [
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"fr"
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|
This model is used detecting hatespeech in French language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.692... | [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep... | [
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text-classification | transformers |
This model is used detecting **hatespeech** in **German language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved ... | {"language": "de", "license": "apache-2.0"} | Hate-speech-CNERG/dehatebert-mono-german | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"de"
] | TAGS
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|
This model is used detecting hatespeech in German language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.649... | [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep... | [
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text-classification | transformers | This model is used detecting **hatespeech** in **Indonesian language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieve... | {} | Hate-speech-CNERG/dehatebert-mono-indonesian | null | [
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"pytorch",
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"text-classification",
"arxiv:2004.06465",
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"2004.06465"
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#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us
| This model is used detecting hatespeech in Indonesian language. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.8... | [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep... | [
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