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null | null | Test Summarization Model | {} | windwalkerby/test-summarization-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Test Summarization Model | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
# Turkish Named Entity Recognition (NER) Model
## This repository is cloned from https://huggingface.co/akdeniz27/bert-base-turkish-cased-ner. This is the tensorflow version.
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased"
using a reviewed version of well known Turkish NER dataset
(https://gi... | {"language": "tr", "widget": [{"text": "Mustafa Kemal Atat\u00fcrk 19 May\u0131s 1919'da Samsun'a \u00e7\u0131kt\u0131."}]} | winvoker/bert-base-turkish-cased-ner-tf | null | [
"transformers",
"tf",
"bert",
"token-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #tf #bert #token-classification #tr #autotrain_compatible #endpoints_compatible #region-us
|
# Turkish Named Entity Recognition (NER) Model
## This repository is cloned from URL This is the tensorflow version.
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased"
using a reviewed version of well known Turkish NER dataset
(URL
# Fine-tuning parameters:
# How to use:
Pls refer "URL for en... | [
"# Turkish Named Entity Recognition (NER) Model",
"## This repository is cloned from URL This is the tensorflow version.\n\n\nThis model is the fine-tuned model of \"dbmdz/bert-base-turkish-cased\" \nusing a reviewed version of well known Turkish NER dataset \n(URL",
"# Fine-tuning parameters:",
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fill-mask | transformers | test | {} | wisdomify/wisdomify | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| test | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<img src="https://raw.githubusercontent.com/WissamAntoun/arabic-wikipedia-qa-streamlit/main/is2alni_logo.png" width="150" align="center"/>
# Arabic QA
AraELECTRA powered Arabic Wikipedia QA system with Streamlit [](https://share... | {"language": "ar", "datasets": ["tydiqa"], "widget": [{"text": "\u0645\u0627 \u0647\u0648 \u0646\u0638\u0627\u0645 \u0627\u0644\u062d\u0643\u0645 \u0641\u064a \u0644\u0628\u0646\u0627\u0646\u061f", "context": "\u0644\u0628\u0646\u0627\u0646 \u0623\u0648 (\u0631\u0633\u0645\u064a\u0627: \u0627\u0644\u062c\u0645\u0647\u0... | wissamantoun/araelectra-base-artydiqa | null | [
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"safetensors",
"electra",
"question-answering",
"ar",
"dataset:tydiqa",
"arxiv:2012.15516",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15516"
] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #electra #question-answering #ar #dataset-tydiqa #arxiv-2012.15516 #endpoints_compatible #has_space #region-us
|
<img src="URL width="150" align="center"/>
# Arabic QA
AraELECTRA powered Arabic Wikipedia QA system with Streamlit ![Open in Streamlit](URL
This model is trained on the Arabic section of ArTyDiQA using the colab here ![Open In Colab](URL
# How to use:
# If you used this model please cite us as :
| [
"# Arabic QA\n\nAraELECTRA powered Arabic Wikipedia QA system with Streamlit ![Open in Streamlit](URL\n\nThis model is trained on the Arabic section of ArTyDiQA using the colab here ![Open In Colab](URL",
"# How to use:\n\n\n \n # If you used this model please cite us as :"
] | [
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"# Arabic QA\n\nAraELECTRA powered Arabic Wikipedia QA system with Streamlit ![Open in Streamlit](URL\n\nThis model is trained on the Arabic section of ArT... |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | wjching/DialoGPT-small-ricksanchez | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model | [
"# Rick Sanchez DialoGPT Model"
] | [
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"# Rick Sanchez DialoGPT Model"
] |
text2text-generation | transformers | ### Finetuned on annual report sentence pair
This marianMT has been further finetuned on annual report sentence pairs
## Test out at huggingface spaces!
https://huggingface.co/spaces/wolfrage89/finance_domain_translation_marianMT
## Sample colab notebook
https://colab.research.google.com/drive/1H57vwiah7n1JXvXYMqJ8dk... | {} | wolfrage89/annual_report_translation_id_en | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Finetuned on annual report sentence pair
This marianMT has been further finetuned on annual report sentence pairs
## Test out at huggingface spaces!
URL
## Sample colab notebook
URL
## How to use
### opus-mt-id-en (original model)
* source languages: id
* target languages: en
* OPUS readme: id-en
| [
"### Finetuned on annual report sentence pair\nThis marianMT has been further finetuned on annual report sentence pairs",
"## Test out at huggingface spaces!\nURL",
"## Sample colab notebook\nURL",
"## How to use",
"### opus-mt-id-en (original model)\n\n* source languages: id\n* target languages: en\n* OPU... | [
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"### Finetuned on annual report sentence pair\nThis marianMT has been further finetuned on annual report sentence pairs",
"## Test out at huggingface spaces!\nURL",
"... |
token-classification | transformers | ## Roberta based NER
This model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles
## Try out on huggingface Spaces
https://huggingface.co/spaces/wolfrage89/company_segments_ner
## colab sample notebook
https://colab.research.google.com/drive/165utMQzYVAX7-aQ... | {} | wolfrage89/company_segment_ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Roberta based NER
This model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles
## Try out on huggingface Spaces
URL
## colab sample notebook
URL
## How to use
| [
"## Roberta based NER\nThis model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles",
"## Try out on huggingface Spaces\nURL",
"## colab sample notebook\nURL",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Roberta based NER\nThis model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles",
"## Try out on huggingface Spaces\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | won/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Eleonora from worms3401 DialoGPT Model | {"tags": ["conversational"]} | worms3401/DialoGPT-small-Eleonora | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Eleonora from worms3401 DialoGPT Model | [
"# Eleonora from worms3401 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Eleonora from worms3401 DialoGPT Model"
] |
text-generation | transformers |
# DialoGPT Trained on the Speech of Fox Mulder from The X-Files | {"tags": ["conversational"]} | worsterman/DialoGPT-small-mulder | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of Fox Mulder from The X-Files | [
"# DialoGPT Trained on the Speech of Fox Mulder from The X-Files"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of Fox Mulder from The X-Files"
] |
token-classification | transformers |
# XLM-RoBERTa base ConLL-2002 Dutch
XLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named Entity Recognition dataset containing the following classes: PER, LOC, ORG and MISC.
Label mapping:
{
0: O,
1: B-PER,
2: I-PER,
3: B-ORG,
4: I-ORG,
5: B-LOC,
6: I-LOC,
7: B-MISC,
8... | {"language": ["nl"], "tags": ["Named Entity Recognition", "xlm-roberta"], "datasets": ["conll2002"], "metrics": [{"f1": 90.57}]} | wpnbos/xlm-roberta-base-conll2002-dutch | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"Named Entity Recognition",
"nl",
"dataset:conll2002",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116"
] | [
"nl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #Named Entity Recognition #nl #dataset-conll2002 #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa base ConLL-2002 Dutch
XLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named Entity Recognition dataset containing the following classes: PER, LOC, ORG and MISC.
Label mapping:
{
0: O,
1: B-PER,
2: I-PER,
3: B-ORG,
4: I-ORG,
5: B-LOC,
6: I-LOC,
7: B-MISC,
8... | [
"# XLM-RoBERTa base ConLL-2002 Dutch\n\nXLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named Entity Recognition dataset containing the following classes: PER, LOC, ORG and MISC. \n\nLabel mapping:\n{\n 0: O,\n 1: B-PER,\n 2: I-PER,\n 3: B-ORG,\n 4: I-ORG,\n 5: B-LOC,\n 6: I-LOC,\n... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Named Entity Recognition #nl #dataset-conll2002 #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa base ConLL-2002 Dutch\n\nXLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named E... |
question-answering | transformers |
# albert-chinese-large-qa
Albert large QA model pretrained from baidu webqa and baidu dureader datasets.
## Data source
+ baidu webqa 1.0
+ baidu dureader
## Traing Method
We combined the two datasets together and created a new dataset in squad format, including 705139 samples for training and 69638 samples for vali... | {"language": ["zh"], "license": "apache-2.0", "tags": ["Question Answering"], "datasets": ["webqa", "dureader"]} | wptoux/albert-chinese-large-qa | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"Question Answering",
"zh",
"dataset:webqa",
"dataset:dureader",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #question-answering #Question Answering #zh #dataset-webqa #dataset-dureader #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# albert-chinese-large-qa
Albert large QA model pretrained from baidu webqa and baidu dureader datasets.
## Data source
+ baidu webqa 1.0
+ baidu dureader
## Traing Method
We combined the two datasets together and created a new dataset in squad format, including 705139 samples for training and 69638 samples for vali... | [
"# albert-chinese-large-qa\nAlbert large QA model pretrained from baidu webqa and baidu dureader datasets.",
"## Data source\n+ baidu webqa 1.0\n+ baidu dureader",
"## Traing Method\nWe combined the two datasets together and created a new dataset in squad format, including 705139 samples for training and 69638 ... | [
"TAGS\n#transformers #pytorch #albert #question-answering #Question Answering #zh #dataset-webqa #dataset-dureader #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# albert-chinese-large-qa\nAlbert large QA model pretrained from baidu webqa and baidu dureader datasets.",
"## Data source\n+ ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-imdb
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imd... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-finetuned-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics":... | wrmurray/roberta-base-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-imdb #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-imdb
===========================
This model is a fine-tuned version of roberta-base on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1783
* Accuracy: 0.9552
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
# MLP DialoGPT Model based on Fluttershy | {"tags": ["conversational"]} | wtrClover/DialoGPT-small-Flutterbot | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MLP DialoGPT Model based on Fluttershy | [
"# MLP DialoGPT Model based on Fluttershy"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MLP DialoGPT Model based on Fluttershy"
] |
text-generation | transformers |
# MLP DialoGPT Model based on Twilight Sparkle | {"tags": ["conversational"]} | wtrClover/DialoGPT-small-TwilightBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MLP DialoGPT Model based on Twilight Sparkle | [
"# MLP DialoGPT Model based on Twilight Sparkle"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MLP DialoGPT Model based on Twilight Sparkle"
] |
fill-mask | transformers | Pretrained on:
* Masked amino acid modeling
Please see our [main model](https://huggingface.co/wukevin/tcr-bert) for additional details. | {} | wukevin/tcr-bert-mlm-only | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Pretrained on:
* Masked amino acid modeling
Please see our main model for additional details. | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # TCR transformer model
See our full [codebase](https://github.com/wukevin/tcr-bert) and our [preprint](https://www.biorxiv.org/content/10.1101/2021.11.18.469186v1) for more information.
This model is on:
- Masked language modeling (masked amino acid or MAA modeling)
- Classification across antigen labels from PIRD
... | {} | wukevin/tcr-bert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # TCR transformer model
See our full codebase and our preprint for more information.
This model is on:
- Masked language modeling (masked amino acid or MAA modeling)
- Classification across antigen labels from PIRD
If you are looking for a model trained only on MAA, please see our other model.
Example inputs:
* '... | [
"# TCR transformer model\n\nSee our full codebase and our preprint for more information.\n\nThis model is on:\n\n- Masked language modeling (masked amino acid or MAA modeling)\n- Classification across antigen labels from PIRD\n\nIf you are looking for a model trained only on MAA, please see our other model.\n\nExam... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# TCR transformer model\n\nSee our full codebase and our preprint for more information.\n\nThis model is on:\n\n- Masked language modeling (masked amino acid or MAA modeling)\n- Classification acr... |
token-classification | transformers | This is the model that can extract epidemiological information from rare disease abstracts. | {} | wzkariampuzha/EpiExtract4GARD | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This is the model that can extract epidemiological information from rare disease abstracts. | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on dataset [x-tech/canton... | {"language": ["yue", "zh", "multilingual"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["botisan-ai/cantonese-mandarin-translations"], "base_model": "google/mt5-base", "model-index": [{"name": "output", "results": []}]} | botisan-ai/mt5-translate-yue-zh | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"yue",
"zh",
"multilingual",
"dataset:botisan-ai/cantonese-mandarin-translations",
"base_model:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-... | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue",
"zh",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #yue #zh #multilingual #dataset-botisan-ai/cantonese-mandarin-translations #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# output
This model is a fine-tuned version of google/mt5-base on dataset x-tech/cantonese-mandarin-translations.
## Model description
The model translates Cantonese sentences to Mandarin.
## Intended uses & limitations
When you use the model, please make sure to add 'translate cantonese to mandarin: <sentence>... | [
"# output\n\nThis model is a fine-tuned version of google/mt5-base on dataset x-tech/cantonese-mandarin-translations.",
"## Model description\n\nThe model translates Cantonese sentences to Mandarin.",
"## Intended uses & limitations\n\nWhen you use the model, please make sure to add 'translate cantonese to mand... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #yue #zh #multilingual #dataset-botisan-ai/cantonese-mandarin-translations #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# output\... |
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. -->
# output
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on dataset [x-tech/canton... | {"language": ["zh", "yue", "multilingual"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["x-tech/cantonese-mandarin-translations"], "base_model": "google/mt5-base", "model-index": [{"name": "output", "results": []}]} | botisan-ai/mt5-translate-zh-yue | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"zh",
"yue",
"multilingual",
"dataset:x-tech/cantonese-mandarin-translations",
"base_model:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-gene... | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"yue",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #zh #yue #multilingual #dataset-x-tech/cantonese-mandarin-translations #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# output
This model is a fine-tuned version of google/mt5-base on dataset x-tech/cantonese-mandarin-translations.
## Model description
The model translates Mandarin sentences to Cantonese.
## Intended uses & limitations
When you use the model, please make sure to add 'translate mandarin to cantonese: <sentence>... | [
"# output\n\nThis model is a fine-tuned version of google/mt5-base on dataset x-tech/cantonese-mandarin-translations.",
"## Model description\n\nThe model translates Mandarin sentences to Cantonese.",
"## Intended uses & limitations\n\nWhen you use the model, please make sure to add 'translate mandarin to canto... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #zh #yue #multilingual #dataset-x-tech/cantonese-mandarin-translations #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# output\n\nT... |
fill-mask | transformers | ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org... | {} | xcjthu/Lawformer | null | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #longformer #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the huggingface model hub or from here.
### Easy Start
We have uploaded our model to the huggingface mod... | [
"## Lawformer",
"### Introduction\nThis repository provides the source code and checkpoints of the paper \"Lawformer: A Pre-trained Language Model forChinese Legal Long Documents\". You can download the checkpoint from the huggingface model hub or from here.",
"### Easy Start\nWe have uploaded our model to the ... | [
"TAGS\n#transformers #pytorch #longformer #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Lawformer",
"### Introduction\nThis repository provides the source code and checkpoints of the paper \"Lawformer: A Pre-trained Language Model forChinese Legal Long Documents\". You ca... |
null | null | This is a dummy model. | {} | xdcui/dummy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is a dummy model. | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# pretrained Cas Model | {"tags": ["conversational"]} | xdmason/pretrainedCas | null | [
"transformers",
"pytorch",
"gpt2",
"conversational",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #conversational #endpoints_compatible #text-generation-inference #region-us
|
# pretrained Cas Model | [
"# pretrained Cas Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #conversational #endpoints_compatible #text-generation-inference #region-us \n",
"# pretrained Cas Model"
] |
text-generation | transformers |
# Delish v6 (GPT-Neo 1.3B)
This model is from the DelishBot project.
| {} | xhyi/PT_GPTNEO1300_Delish_v6 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
|
# Delish v6 (GPT-Neo 1.3B)
This model is from the DelishBot project.
| [
"# Delish v6 (GPT-Neo 1.3B)\n\nThis model is from the DelishBot project."
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Delish v6 (GPT-Neo 1.3B)\n\nThis model is from the DelishBot project."
] |
text-generation | transformers |
# GPT NEO 350M
This hosts the pulled 350M that Eleuther removed. I am keeping it 😎 | {} | xhyi/PT_GPTNEO350_ATG | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT NEO 350M
This hosts the pulled 350M that Eleuther removed. I am keeping it | [
"# GPT NEO 350M\n\nThis hosts the pulled 350M that Eleuther removed. I am keeping it"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT NEO 350M\n\nThis hosts the pulled 350M that Eleuther removed. I am keeping it"
] |
text2text-generation | transformers | Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
240 2.513600 3.049892 0.082800 0.102600 0.085700
240 steps | {} | xhyi/distilLED1_08_31_2021_v3 | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
240 2.513600 3.049892 0.082800 0.102600 0.085700
240 steps | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | \nTraining Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
2.880900 2.715085 0.121400 0.142300 0.117100
+200 steps
total = 440 steps
tokenization:
max article: 8192
max abstract: 512 | {} | xhyi/distilLED3_08_31_2021_v5 | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| \nTraining Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
2.880900 2.715085 0.121400 0.142300 0.117100
+200 steps
total = 440 steps
tokenization:
max article: 8192
max abstract: 512 | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
100 3.049500 2.605496 0.172300 0.186900 0.151200
200 3.019400 2.567277 0.165100 0.189400 0.145000
300 3.014400 2.538830 0.157000 0.179200 0.134200
400 2.867200 2.490068 0.163600 0.177100 0.136200
500 2.723700 2.... | {} | xhyi/distilLED4_09_01_2021_v6_2 | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
100 3.049500 2.605496 0.172300 0.186900 0.151200
200 3.019400 2.567277 0.165100 0.189400 0.145000
300 3.014400 2.538830 0.157000 0.179200 0.134200
400 2.867200 2.490068 0.163600 0.177100 0.136200
500 2.723700 2.... | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | testing | {} | xiaodai/testing | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| testing | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Joseph Joestar DialoGPT Model | {"tags": ["conversational"]} | xiaoheiqaq/DialoGPT-mediumJojo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joseph Joestar DialoGPT Model | [
"# Joseph Joestar DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Joseph Joestar DialoGPT Model"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | xiaoheiqaq/DialoGPT-smallharrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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"# Harry Potter DialoGPT Model"
] |
null | null | This is super resolution model for anime like illustration that can upscale image 4x.
This model can upscale 256x256 image to 1024x1024 within around 30[ms] on GPU and around 300[ms] on CPU.
Example is [here](https://github.com/xiong-jie-y/ml-examples/tree/master/lightweight_real_esrgan_anime).
License: MIT License | {} | xiongjie/lightweight-real-ESRGAN-anime | null | [
"onnx",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #has_space #region-us
| This is super resolution model for anime like illustration that can upscale image 4x.
This model can upscale 256x256 image to 1024x1024 within around 30[ms] on GPU and around 300[ms] on CPU.
Example is here.
License: MIT License | [] | [
"TAGS\n#onnx #has_space #region-us \n"
] |
null | null | This is super resolution model to upscale anime like illustration image by 4x.
This model can upscale 256x256 image to 1024x1024 within around 20[ms] on GPU and around 250[ms] on CPU.
Example is [here](https://github.com/xiong-jie-y/ml-examples/tree/master/realtime_srgan_anime).
All the models in this repository is ... | {} | xiongjie/realtime-SRGAN-for-anime | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| This is super resolution model to upscale anime like illustration image by 4x.
This model can upscale 256x256 image to 1024x1024 within around 20[ms] on GPU and around 250[ms] on CPU.
Example is here.
All the models in this repository is under MIT License. | [] | [
"TAGS\n#has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | xkang/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0634
* Precision: 0.9392
* Recall: 0.9520
* F1: 0.9456
* Accuracy: 0.9864
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-uncased-finetuned-imdb-whole-word-masking
This model is a fine-tuned version of [distilbert-base-uncased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb-whole-word-masking", "results": []}]} | xkang/distilbert-base-uncased-finetuned-imdb-whole-word-masking | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb-whole-word-masking
=========================================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3043
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | xkang/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4717
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Georgian using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model c... | {"language": "ka", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec finetuned for Georgian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | xsway/wav2vec2-large-xlsr-georgian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ka",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ka"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Georgian using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated a... | [
"# Wav2Vec2-Large-XLSR-53-Georgian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Georgian using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ca... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Georgian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Georgian using the Common... |
null | null | import json
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://api-inference.huggingface.co/models/bert-base-uncased"
def query(payload):
data = json.dumps(payload)
response = requests.request("POST", API_URL, headers=headers, data=data)
return json.loads(response.conte... | {} | xujiacheng127/anchi-bert | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| import json
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "URL
def query(payload):
data = URL(payload)
response = requests.request("POST", API_URL, headers=headers, data=data)
return URL(URL("utf-8"))
data = query({"inputs": "The answer to the universe is [MASK]."}) | [] | [
"TAGS\n#pytorch #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-uncased-issues-128", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | xxr/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2109
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
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. -->
# sequence_classification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "sequence_classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type... | xysmalobia/sequence_classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sequence\_classification
========================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7738
* Accuracy: 0.8529
* F1: 0.8944
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during t... |
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. -->
# test-trainer
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "test-trainer", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", ... | xysmalobia/test-trainer | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test-trainer
============
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5802
* Accuracy: 0.8505
* F1: 0.8935
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
text-classification | transformers | hello
| {} | yacov/yacov-athena-DistilBertSC | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Eren dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-AOT-Eren | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Eren dialog | [
"# Eren dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Eren dialog"
] |
text-generation | transformers |
# L dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-DN-L | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# L dialog | [
"# L dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# L dialog"
] |
text-generation | transformers |
# Light dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-DN-Light | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Light dialog | [
"# Light dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Light dialog"
] |
text-generation | transformers |
# Ryuk dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-DN-Ryuk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ryuk dialog | [
"# Ryuk dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ryuk dialog"
] |
text-generation | transformers |
# Gintoki dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-Gintama-Gintoki | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Gintoki dialog | [
"# Gintoki dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Gintoki dialog"
] |
text-generation | transformers |
# Migi dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-Parasyte-Migi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Migi dialog | [
"# Migi dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Migi dialog"
] |
text-generation | transformers |
# Rem dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-ReZero-Rem | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Rem dialog | [
"# Rem dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Rem dialog"
] |
text-generation | transformers |
# Subaru dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-ReZero-Subaru | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Subaru dialog | [
"# Subaru dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Subaru dialog"
] |
text-generation | transformers |
# Ryuk dialog | {"tags": ["conversational"]} | yahya1994/DialoGPT-small-Ryuk | null | [
"transformers",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #conversational #endpoints_compatible #region-us
|
# Ryuk dialog | [
"# Ryuk dialog"
] | [
"TAGS\n#transformers #conversational #endpoints_compatible #region-us \n",
"# Ryuk dialog"
] |
text2text-generation | transformers |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
| {} | yair/HeadlineGeneration-sagemaker | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
- Training 3000 examples
| {} | yair/HeadlineGeneration-sagemaker2 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
- Training 3000 examples
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | hello
| {} | yair/HeadlineGeneration | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
- Training 3000 examples
| {} | yair/SummaryGeneration-sagemaker3 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
- Training 3000 examples
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | dfdsd | {} | yanchaocc/dsaf | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| dfdsd | [] | [
"TAGS\n#region-us \n"
] |
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-uncased-finetuned-existence
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-existence", "results": []}]} | yancong/distilbert-base-uncased-finetuned-existence | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-existence
===========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7925
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
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-uncased-finetuned-mi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mi", "results": []}]} | yancong/distilbert-base-uncased-finetuned-mi | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mi
====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8606
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
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-uncased-finetuned-quantifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-quantifier", "results": []}]} | yancong/distilbert-base-uncased-finetuned-quantifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-quantifier
============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7478
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
null | null | # T5 for Semantic Parsing
## Model description
T5 (small and large) finetuned on CoNaLa for semantic parsing (Natural Language descriptions to Python code)
Paper: https://arxiv.org/pdf/2101.07138.pdf
Code, data and how to use: https://github.com/ypapanik/t5-for-code-generation
### Cite
```
@misc{papanikolaou2021... | {} | yannis-papanikolaou/t5-code-generation | null | [
"arxiv:2101.07138",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07138"
] | [] | TAGS
#arxiv-2101.07138 #region-us
| # T5 for Semantic Parsing
## Model description
T5 (small and large) finetuned on CoNaLa for semantic parsing (Natural Language descriptions to Python code)
Paper: URL
Code, data and how to use: URL
### Cite
| [
"# T5 for Semantic Parsing",
"## Model description\n\nT5 (small and large) finetuned on CoNaLa for semantic parsing (Natural Language descriptions to Python code)\n\nPaper: URL\n\nCode, data and how to use: URL",
"### Cite"
] | [
"TAGS\n#arxiv-2101.07138 #region-us \n",
"# T5 for Semantic Parsing",
"## Model description\n\nT5 (small and large) finetuned on CoNaLa for semantic parsing (Natural Language descriptions to Python code)\n\nPaper: URL\n\nCode, data and how to use: URL",
"### Cite"
] |
null | null | >>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model.... | {} | yannobla/Sunshine2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| >>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model.... | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-yaswanth-hindi2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "xls-r-300m-yaswanth-hindi2", "results": []}... | yaswanth/xls-r-300m-yaswanth-hindi2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-yaswanth-hindi2
==========================
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: 1.7163
* Wer: 0.6951
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0007\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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. -->
# sparql-qald9-t5-base-2021-10-19_00-15
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "sparql-qald9-t5-base-2021-10-19_00-15", "results": []}]} | yazdipour/sparql-qald9-t5-base-2021-10-19_00-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparql-qald9-t5-base-2021-10-19\_00-15
======================================
This model is a fine-tuned version of yazdipour/text-to-sparql-t5-base-2021-10-18\_16-15 on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# sparql-qald9-t5-small-2021-10-19_00-01
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-2021-10-18_23-00... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "sparql-qald9-t5-small-2021-10-19_00-01", "results": []}]} | yazdipour/sparql-qald9-t5-small-2021-10-19_00-01 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparql-qald9-t5-small-2021-10-19\_00-01
=======================================
This model is a fine-tuned version of yazdipour/text-to-sparql-t5-small-2021-10-18\_23-00 on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# sparql-qald9-t5-small-2021-10-19_07-12_RAW
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "sparql-qald9-t5-small-2021-10-19_07-12_RAW", "results": []}]} | yazdipour/sparql-qald9-t5-small-2021-10-19_07-12_RAW | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparql-qald9-t5-small-2021-10-19\_07-12\_RAW
============================================
This model is a fine-tuned version of t5-small on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# text-to-sparql-t5-base-2021-10-17_23-40
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the N... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["f1"]} | yazdipour/text-to-sparql-t5-base-2021-10-17_23-40 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-base-2021-10-17\_23-40
========================================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2645
* Gen Len: 19.0
* P: 0.5125
* R: 0.0382
* F1: 0.2650
* Score: 5.1404
* Bleu-precisions: [88.492... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# text-to-sparql-t5-base-2021-10-18_16-15
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the N... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | yazdipour/text-to-sparql-t5-base-2021-10-18_16-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-base-2021-10-18\_16-15
========================================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1294
* Gen Len: 19.0
* Bertscorer-p: 0.5827
* Bertscorer-r: 0.0812
* Bertscorer-f1: 0.3202
* Sacrebl... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# sparql-qald9-t5-base-2021-10-19_23-02
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_l... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "sparql-qald9-t5-base-2021-10-19_23-02", "results": []}]} | yazdipour/text-to-sparql-t5-base-qald9 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparql-qald9-t5-base-2021-10-19\_23-02
======================================
This model is a fine-tuned version of yazdipour/text-to-sparql-t5-base-2021-10-19\_15-35\_lastDS on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_... |
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. -->
# text-to-sparql-t5-base-2021-10-19_15-35_lastDS
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["f1"]} | yazdipour/text-to-sparql-t5-base | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-base-2021-10-19\_15-35\_lastDS
================================================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1310
* Gen Len: 19.0
* P: 0.5807
* R: 0.0962
* F1: 0.3276
* Score: 6.4533
* Bleu-pre... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# text-to-sparql-t5-small-2021-10-15_01-00
This model was trained from scratch on the None dataset.
## Model description
More in... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "text-to-sparql-t5-small-2021-10-15_01-00", "results": []}]} | yazdipour/text-to-sparql-t5-small-2021-10-15_01-00 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-15\_01-00
=========================================
This model was trained from scratch on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation dat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# text-to-sparql-t5-small-2021-10-17_18-47
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["f1"]} | yazdipour/text-to-sparql-t5-small-2021-10-17_18-47 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-17\_18-47
=========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5258
* Gen Len: 19.0
* P: 0.4582
* R: 0.0278
* F1: 0.2346
* Score: 3.5848
* Bleu-precisions: [82.... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# text-to-sparql-t5-small-2021-10-18_09-32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["f1"]} | yazdipour/text-to-sparql-t5-small-2021-10-18_09-32 | null | [
"transformers",
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"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-18\_09-32
=========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5119
* Gen Len: 19.0
* P: 0.4884
* R: 0.0583
* F1: 0.2646
* Score: 3.5425
* Bleu-precisions: [82.... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# text-to-sparql-t5-small-2021-10-18_12-12
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | yazdipour/text-to-sparql-t5-small-2021-10-18_12-12 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-18\_12-12
=========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3284
* Gen Len: 19.0
* Bertscorer-p: 0.5420
* Bertscorer-r: 0.0732
* Bertscorer-f1: 0.2972
* Sacr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# text-to-sparql-t5-small-2021-10-18_23-00
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | yazdipour/text-to-sparql-t5-small-2021-10-18_23-00 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-18\_23-00
=========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2284
* Gen Len: 19.0
* Bertscorer-p: 0.5644
* Bertscorer-r: 0.0815
* Bertscorer-f1: 0.3120
* Sacr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# sparql-qald9-t5-small-2021-10-19_22-32
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-2021-10-19_10-17... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "sparql-qald9-t5-small-2021-10-19_22-32", "results": []}]} | yazdipour/text-to-sparql-t5-small-qald9 | null | [
"transformers",
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"safetensors",
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"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparql-qald9-t5-small-2021-10-19\_22-32
=======================================
This model is a fine-tuned version of yazdipour/text-to-sparql-t5-small-2021-10-19\_10-17\_lastDS on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# text-to-sparql-t5-small-2021-10-19_10-17_lastDS
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["f1"]} | yazdipour/text-to-sparql-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| text-to-sparql-t5-small-2021-10-19\_10-17\_lastDS
=================================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2335
* Gen Len: 19.0
* P: 0.5580
* R: 0.0884
* F1: 0.3129
* Score: 5.9585
* Bleu-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 6711455
## Validation Metrics
- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro P... | {"language": "ko", "tags": "autonlp", "datasets": ["ybybybybybybyb/autonlp-data-revanalysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | ybybybybybybyb/autonlp-revanalysis-6711455 | null | [
"transformers",
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"autonlp",
"ko",
"dataset:ybybybybybybyb/autonlp-data-revanalysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #funnel #text-classification #autonlp #ko #dataset-ybybybybybybyb/autonlp-data-revanalysis #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 6711455
## Validation Metrics
- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro P... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 6711455",
"## Validation Metrics\n\n- Loss: 0.8241586089134216\n- Accuracy: 0.7835820895522388\n- Macro F1: 0.5297383029341792\n- Micro F1: 0.783582089552239\n- Weighted F1: 0.7130091019920225\n- Macro Precision: 0.487870619... | [
"TAGS\n#transformers #pytorch #funnel #text-classification #autonlp #ko #dataset-ybybybybybybyb/autonlp-data-revanalysis #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 6711455",
"## Validation Metrics\n\n- Los... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | ychu4/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7512
* Matthews Correlation: 0.5097
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
null | null | ## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
```python
import requests
from PIL import Image
from transfo... | {} | ydshieh/_flax-vision-encoder-decoder-vit-gpt2-coco-en | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
| [
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless \nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:"
] | [
"TAGS\n#region-us \n",
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless \nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:"
] |
text2text-generation | transformers | # Bert2Bert Summarization with 🤗 EncoderDecoder Framework
[This is a TensorFlow version converted from the original PyTorch [Bert2Bert](https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16)]
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a `EncoderDecoderModel`, meaning th... | {} | ydshieh/bert2bert-cnn_dailymail-fp16 | null | [
"transformers",
"tf",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert2Bert Summarization with EncoderDecoder Framework
=====================================================
This is a TensorFlow version converted from the original PyTorch [Bert2Bert]
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a 'EncoderDecoderModel', meaning that both the encoder ... | [] | [
"TAGS\n#transformers #tf #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers |
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["scientific_papers"], "model-index": [{"name": "google/bigbird-pegasus-large-pubmed", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "scientific_papers", "type": "scientific_papers", "config... | ydshieh/clip-vit-base-patch32 | null | [
"transformers",
"tf",
"clip",
"zero-shot-image-classification",
"summarization",
"en",
"dataset:scientific_papers",
"arxiv:2007.14062",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #tf #clip #zero-shot-image-classification #summarization #en #dataset-scientific_papers #arxiv-2007.14062 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | [
"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. \n\nBigBir... | [
"TAGS\n#transformers #tf #clip #zero-shot-image-classification #summarization #en #dataset-scientific_papers #arxiv-2007.14062 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends Transformer based ... |
null | null | ## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
```python
import requests
from PIL import Image
from transfo... | {} | ydshieh/flax-vision-encoder-decoder-vit-gpt2-coco-en | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
| [
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless \nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:"
] | [
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"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless \nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:"
] |
image-classification | generic |
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
```python
import requests
from PIL import Image
from transfor... | {"library_name": "generic", "tags": ["image-classification"]} | ydshieh/vit-gpt2-coco-en-ckpts | null | [
"generic",
"pytorch",
"jax",
"tensorboard",
"vision-encoder-decoder",
"image-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #jax #tensorboard #vision-encoder-decoder #image-classification #has_space #region-us
|
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
| [
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless\nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:"
] | [
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"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless\nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVision... |
image-to-text | transformers |
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
**In PyTorch**
```python
import torch
import requests
from PI... | {"tags": ["image-to-text"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": "Football Match"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg", "example_title": "Dog & Cat"}]} | ydshieh/vit-gpt2-coco-en | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"vision-encoder-decoder",
"image-to-text",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #tensorboard #vision-encoder-decoder #image-to-text #endpoints_compatible #has_space #region-us
|
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
In PyTorch
In Flax
| [
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless\nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-concept for the FlaxVisionEncoderDecoder Framework.\n\nThe model can be used as follows:\n\nIn PyTorch\n\n\nIn Flax"
] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #vision-encoder-decoder #image-to-text #endpoints_compatible #has_space #region-us \n",
"## Example\n\nThe model is by no means a state-of-the-art model, but nevertheless\nproduces reasonable image captioning results. It was mainly fine-tuned \nas a proof-of-con... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese (zh-CN) using the [Common Voice](https://huggingface.co/datasets/common_voice), included [Common Voice](https://huggingface.co/datasets/common_voice) Chinese (zh-T... | {"language": "zh", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["cer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 - Chinese (zh-CN), by Yih-Dar SHIEH", "results": [{"task": {"type": "automatic-speech-recog... | ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"zh",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese (zh-CN) using the Common Voice, included Common Voice Chinese (zh-TW) dataset (converting the label text to simplified Chinese).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The... | [
"# Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese (zh-CN) using the Common Voice, included Common Voice Chinese (zh-TW) dataset (converting the label text to simplified Chinese). \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"##... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chine... |
text-generation | transformers |
# GPT-Neo 1.3B pre-trained model for Japanese
## Model Description
GPT2/GPT3 like model trained on Japanese.corpus.
## Training data
- cc100 ja
- oscar ja
- wikipedia ja
## How to use
```
from transformers import pipeline
>>> generator = pipeline('text-generation', model='yellowback/gpt-neo-japanese-1.3B')
>>> ... | {"language": ["ja"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm", "japanese"], "datasets": ["oscar", "cc100", "wikipedia"]} | yellowback/gpt-neo-japanese-1.3B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"causal-lm",
"japanese",
"ja",
"dataset:oscar",
"dataset:cc100",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #japanese #ja #dataset-oscar #dataset-cc100 #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT-Neo 1.3B pre-trained model for Japanese
## Model Description
GPT2/GPT3 like model trained on URL.
## Training data
- cc100 ja
- oscar ja
- wikipedia ja
## How to use
| [
"# GPT-Neo 1.3B pre-trained model for Japanese",
"## Model Description\n\nGPT2/GPT3 like model trained on URL.",
"## Training data\n\n- cc100 ja\n- oscar ja\n- wikipedia ja",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #japanese #ja #dataset-oscar #dataset-cc100 #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-Neo 1.3B pre-trained model for Japanese",
"## Model Description\n\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-2b-armenian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-2b-armenian-colab", "results": []}]} | yerevann/x-r-hy | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-2b-armenian-colab
======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5166
* Wer: 0.7397
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\... |
text-generation | transformers | # GPT Neo 1.3B pre-trained on cleaned Dutch mC4 🇳🇱
A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.
## How To Use
You can use this GPT-Neo model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt-neo-1.3B-dutch'
from transformers import pipeli... | {"language": "nl", "tags": ["gpt-neo-1.3B", "gpt-neo"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl ... | yhavinga/gpt-neo-1.3B-dutch | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"gpt-neo-1.3B",
"gpt-neo",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #gpt-neo-1.3B #gpt-neo #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #region-us
| GPT Neo 1.3B pre-trained on cleaned Dutch mC4 🇳🇱
================================================
A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.
How To Use
----------
You can use this GPT-Neo model directly with a pipeline for text generation.
*"1 - geel. 2 - groen. ... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #gpt-neo-1.3B #gpt-neo #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # GPT-Neo 125M pre-trained on cleaned Dutch mC4 🇳🇱
A GPT-Neo small model (125M paramters) trained from scratch on Dutch, with perplexity 19.9 on cleaned Dutch mC4.
## How To Use
You can use this GPT-Neo model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt-neo-125M-dutch'
from tran... | {"language": "nl", "tags": ["gpt2-medium", "gpt2"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl was ... | yhavinga/gpt-neo-125M-dutch-nedd | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"gpt2-medium",
"gpt2",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #gpt2-medium #gpt2 #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #region-us
| GPT-Neo 125M pre-trained on cleaned Dutch mC4 🇳🇱
================================================
A GPT-Neo small model (125M paramters) trained from scratch on Dutch, with perplexity 19.9 on cleaned Dutch mC4.
How To Use
----------
You can use this GPT-Neo model directly with a pipeline for text generation.
... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #gpt2-medium #gpt2 #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers | # GPT-Neo 125M pre-trained on cleaned Dutch mC4 🇳🇱
A GPT-Neo small model (125M paramters) trained from scratch on Dutch, with perplexity 20.9 on cleaned Dutch mC4.
## How To Use
You can use this GPT-Neo model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt-neo-125M-dutch'
from tran... | {"language": "nl", "tags": ["gpt2-medium", "gpt2"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl was ... | yhavinga/gpt-neo-125M-dutch | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"gpt2-medium",
"gpt2",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #gpt_neo #text-generation #gpt2-medium #gpt2 #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #region-us
| GPT-Neo 125M pre-trained on cleaned Dutch mC4 🇳🇱
================================================
A GPT-Neo small model (125M paramters) trained from scratch on Dutch, with perplexity 20.9 on cleaned Dutch mC4.
How To Use
----------
You can use this GPT-Neo model directly with a pipeline for text generation.
... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #gpt_neo #text-generation #gpt2-medium #gpt2 #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # GPT2-Large pre-trained on cleaned Dutch mC4 🇳🇱
A GPT2 large model (762M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
## How To Use
You can use this GPT2-model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt2-large-dutch'
from transformers ... | {"language": "nl", "tags": ["gpt2-large", "gpt2"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl was e... | yhavinga/gpt2-large-dutch | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"gpt2-large",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #gpt2-large #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT2-Large pre-trained on cleaned Dutch mC4 🇳🇱
==============================================
A GPT2 large model (762M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
How To Use
----------
You can use this GPT2-model directly with a pipeline for text generation.
*"Het eil... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #gpt2-large #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | # GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱
Datasets:
* [mC4 NL Cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), dataset config: full (33B tokens)
* A recreation of the TBC but for the Dutch language (see e.g.
https://github.com/sgraaf/Replicate-Toronto-BookCorpus)
Tokenizer:
* Tokenizer... | {"language": "nl", "tags": ["gpt2-medium", "gpt2"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl was ... | yhavinga/gpt2-medium-dutch-nedd | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"gpt2-medium",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #gpt2-medium #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱
Datasets:
* mC4 NL Cleaned, dataset config: full (33B tokens)
* A recreation of the TBC but for the Dutch language (see e.g.
URL
Tokenizer:
* Tokenizer trained on mC4 with scripts from the Huggingface
Transformers Flax examples
Training details:
* Trained fo... | [
"# GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱\n\nDatasets:\n\n* mC4 NL Cleaned, dataset config: full (33B tokens)\n* A recreation of the TBC but for the Dutch language (see e.g.\n URL\n\nTokenizer:\n\n* Tokenizer trained on mC4 with scripts from the Huggingface\n Transformers Flax examples\n\nTraining deta... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #gpt2-medium #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱\n\nDatasets:\n\n* mC4 NL Cleaned, dataset ... |
text-generation | transformers | # GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱
A GPT2 medium-sized model (345M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
## How To Use
You can use this GPT2-model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt2-medium-dutch'
from tran... | {"language": "nl", "tags": ["gpt2-medium", "gpt2"], "datasets": ["yhavinga/mc4_nl_cleaned"], "widget": [{"text": "In het jaar 2030 zullen we"}, {"text": "Toen ik gisteren volledig in de ban was van"}, {"text": "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"}, {"text": "In Isra\u00ebl was ... | yhavinga/gpt2-medium-dutch | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"gpt2-medium",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #gpt2-medium #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱
===============================================
A GPT2 medium-sized model (345M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
How To Use
----------
You can use this GPT2-model directly with a pipeline for text generation.
... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #gpt2-medium #nl #dataset-yhavinga/mc4_nl_cleaned #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
summarization | transformers |
# mt5-base-cnn-nl
mt5-base finetuned on CNN DM translated to nl (Dutch).
* Learning rate 1e-3
* Trained for 1 epoch
* Max source length 1024
* Max target length 142
* rouge1 31.1766
* rouge2 8.4538
* rougeL 17.8674
| {"language": ["dutch"], "tags": ["summarization"], "datasets": ["cnn_dm_nl"], "widget": [{"text": "(CNN) Skywatchers in West-Noord-Amerika zijn in voor een traktatie: een bijna vijf minuten totale maansverduistering vanmorgen. Hier is hoe het zich ontvouwt:. Het begon om 3:16 a.m. Pacific Daylight Tijd, toen de maan be... | yhavinga/mt5-base-cnn-nl | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"dataset:cnn_dm_nl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dutch"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #dataset-cnn_dm_nl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base-cnn-nl
mt5-base finetuned on CNN DM translated to nl (Dutch).
* Learning rate 1e-3
* Trained for 1 epoch
* Max source length 1024
* Max target length 142
* rouge1 31.1766
* rouge2 8.4538
* rougeL 17.8674
| [
"# mt5-base-cnn-nl\n\nmt5-base finetuned on CNN DM translated to nl (Dutch).\n\n* Learning rate 1e-3\n* Trained for 1 epoch\n* Max source length 1024\n* Max target length 142\n\n* rouge1 31.1766\n* rouge2 8.4538\n* rougeL 17.8674"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #dataset-cnn_dm_nl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-cnn-nl\n\nmt5-base finetuned on CNN DM translated to nl (Dutch).\n\n* Learning rate 1e-3\n* Trained for 1 epoch\n* Max sourc... |
summarization | transformers |
# mt5-base-mixednews-nl
mt5-base finetuned on three mixed news sources:
1. CNN DM translated to Dutch with MarianMT.
2. XSUM translated to Dutch with MarianMt.
3. News article summaries distilled from the nu.nl website.
Config:
* Learning rate 1e-3
* Trained for one epoch
* Max source length 1024
* Max targ... | {"language": ["dutch"], "tags": ["summarization"], "datasets": ["xsum_nl"], "widget": [{"text": "Onderzoekers ontdekten dat vier van de vijf kinderen in Engeland die op school lunches hadden gegeten, op school voedsel hadden geprobeerd dat ze thuis niet hadden geprobeerd.De helft van de ondervraagde ouders zei dat hun ... | yhavinga/mt5-base-mixednews-nl | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"dataset:xsum_nl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dutch"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #dataset-xsum_nl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base-mixednews-nl
mt5-base finetuned on three mixed news sources:
1. CNN DM translated to Dutch with MarianMT.
2. XSUM translated to Dutch with MarianMt.
3. News article summaries distilled from the URL website.
Config:
* Learning rate 1e-3
* Trained for one epoch
* Max source length 1024
* Max target... | [
"# mt5-base-mixednews-nl\n\nmt5-base finetuned on three mixed news sources:\n\n 1. CNN DM translated to Dutch with MarianMT.\n 2. XSUM translated to Dutch with MarianMt.\n 3. News article summaries distilled from the URL website.\n\nConfig:\n\n * Learning rate 1e-3\n * Trained for one epoch\n * Max source length 10... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #dataset-xsum_nl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-mixednews-nl\n\nmt5-base finetuned on three mixed news sources:\n\n 1. CNN DM translated to Dutch with MarianMT.\n 2. XSUM tra... |
text2text-generation | transformers |
# t5-base-dutch
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
& [Dat Nguyen](https://www.linkedin.com/in/dat-nguyen-49a641138/) during the [Hugging Face community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [H... | {"language": ["nl"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-base-dutch | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-base-dutch
=============
Created by Yeb Havinga
& Dat Nguyen during the Hugging Face community week, organized by HuggingFace and TPU usage sponsored by Google, for the project Pre-train T5 from scratch in Dutch.
See also the fine-tuned t5-base-dutch-demo model,
and the demo application Netherformer ,
that are bas... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# t5-v1.1-base-dutch-cased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5-v1.1** model has **247M** parameters.
It was pre-tra... | {"language": ["nl"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-v1.1-base-dutch-cased | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-v1.1-base-dutch-cased
========================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4.
This t5-v1.1 model has 247M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset
'mc4\_nl\_cleaned' config 'full'... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
summarization | transformers |
# T5 v1.1 Base finetuned for CNN news summarization in Dutch 🇳🇱
This model is [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)
For a demo of the Dutch CNN summarization models, head over to... | {"language": ["nl"], "license": "apache-2.0", "tags": ["summarization", "t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "ml6team/cnn_dailymail_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van Mary Cassatt, de Amerikaanse impr... | yhavinga/t5-v1.1-base-dutch-cnn-test | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:ml6team/cnn_dailymail_nl",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #t5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5 v1.1 Base finetuned for CNN news summarization in Dutch 🇳🇱
=============================================================
This model is t5-v1.1-base-dutch-cased finetuned on CNN Dailymail NL
For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the Netherformer example appli... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #t5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# t5-v1.1-base-dutch-uncased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5-v1.1** model has **247M** parameters.
It was pre-t... | {"language": ["nl"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-v1.1-base-dutch-uncased | null | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-v1.1-base-dutch-uncased
==========================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4.
This t5-v1.1 model has 247M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset
'mc4\_nl\_cleaned' config 'f... | [] | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
summarization | transformers |
# T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱
This model is [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)
For a demo of the Dutch CNN summarization models, head over... | {"language": ["nl"], "license": "apache-2.0", "tags": ["summarization", "t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "ml6team/cnn_dailymail_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van Mary Cassatt, de Amerikaanse impr... | yhavinga/t5-v1.1-large-dutch-cnn-test | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:ml6team/cnn_dailymail_nl",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"t... | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱
==============================================================
This model is t5-v1.1-large-dutch-cased finetuned on CNN Dailymail NL
For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the Netherformer example ap... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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