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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
feature-extraction | transformers | This model is converted from the original BPR [repo](https://github.com/studio-ousia/bpr) and fitted into Pyserini:
> Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882. | {} | castorini/bpr-nq-question-encoder | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us
| This model is converted from the original BPR repo and fitted into Pyserini:
> Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882. | [] | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This model is converted from the original DKRR [repo](https://github.com/facebookresearch/FiD) and ported into Pyserini:
```
@misc{izacard2020distilling,
title={Distilling Knowledge from Reader to Retriever for Question Answering},
author={Gautier Izacard and Edouard Grave},
year={2020},
eprin... | {} | castorini/dkrr-dpr-nq-retriever | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2012.04584",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.04584"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2012.04584 #endpoints_compatible #has_space #region-us
| This model is converted from the original DKRR repo and ported into Pyserini:
| [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2012.04584 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | This model is converted from the original DKRR [repo](https://github.com/facebookresearch/FiD) and ported into Pyserini:
```
@misc{izacard2020distilling,
title={Distilling Knowledge from Reader to Retriever for Question Answering},
author={Gautier Izacard and Edouard Grave},
year={2020},
eprin... | {} | castorini/dkrr-dpr-tqa-retriever | null | [
"transformers",
"pytorch",
"bert",
"arxiv:2012.04584",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.04584"
] | [] | TAGS
#transformers #pytorch #bert #arxiv-2012.04584 #endpoints_compatible #has_space #region-us
| This model is converted from the original DKRR repo and ported into Pyserini:
| [] | [
"TAGS\n#transformers #pytorch #bert #arxiv-2012.04584 #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | For more information, check [doc2query.ai](http://doc2query.ai) | {} | castorini/doc2query-t5-base-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| For more information, check URL | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | For more information, check [doc2query.ai](http://doc2query.ai) | {} | castorini/doc2query-t5-large-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| For more information, check URL | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This model is a T5-3B reranker pre-finetuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) on the pairwise task and then finetuned on MedMARCO (from [Sledge-Z paper](https://www.aclweb.org/anthology/2020.emnlp-main.341.pdf)) for 1K steps on the pairwise task.
For more details on how to use it, check [pyga... | {} | castorini/duot5-3b-med-msmarco | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"arxiv:2101.05667",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.05667"
] | [] | TAGS
#transformers #pytorch #t5 #feature-extraction #arxiv-2101.05667 #endpoints_compatible #text-generation-inference #region-us
| This model is a T5-3B reranker pre-finetuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) on the pairwise task and then finetuned on MedMARCO (from Sledge-Z paper) for 1K steps on the pairwise task.
For more details on how to use it, check URL!
Paper describing the model: The Expando-Mono-Duo Design Pat... | [] | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #arxiv-2101.05667 #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This model is a T5-3B reranker, initialized with our pointwise ranker, [castorini/monot5-3b-msmarco](https://huggingface.co/castorini/monot5-3b-msmarco), and finetuned on the MS MARCO passage dataset for 50K steps (or 5 epochs) on the pairwise reranking task.
For more details on how to use it, check [pygaggle.ai](pyga... | {} | castorini/duot5-3b-msmarco | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"arxiv:2101.05667",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.05667"
] | [] | TAGS
#transformers #pytorch #t5 #feature-extraction #arxiv-2101.05667 #endpoints_compatible #text-generation-inference #region-us
| This model is a T5-3B reranker, initialized with our pointwise ranker, castorini/monot5-3b-msmarco, and finetuned on the MS MARCO passage dataset for 50K steps (or 5 epochs) on the pairwise reranking task.
For more details on how to use it, check URL!
Paper describing the model: The Expando-Mono-Duo Design Pattern fo... | [] | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #arxiv-2101.05667 #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv... | {} | castorini/duot5-base-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:2101.05667",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.05667"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2101.05667 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).
For more details on how to use it, check URL
Paper describing the model: The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2101.05667 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers | # Model Description
This checkpoint is a direct conversion of [BERT_Large_trained_on_MSMARCO.zip](https://drive.google.com/open?id=1crlASTMlsihALlkabAQP6JTYIZwC1Wm8) from the original [repo](https://github.com/nyu-dl/dl4marco-bert/).
The corresponding model class is BertForSequenceClassification, and its purpose is for... | {} | castorini/monobert-large-msmarco-finetune-only | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Model Description
This checkpoint is a direct conversion of BERT_Large_trained_on_MSMARCO.zip from the original repo.
The corresponding model class is BertForSequenceClassification, and its purpose is for MS MARCO passage ranking.
Please find the original repo for more detail of its training settings regarding hyperp... | [
"# Model Description\nThis checkpoint is a direct conversion of BERT_Large_trained_on_MSMARCO.zip from the original repo.\nThe corresponding model class is BertForSequenceClassification, and its purpose is for MS MARCO passage ranking.\nPlease find the original repo for more detail of its training settings regardin... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Description\nThis checkpoint is a direct conversion of BERT_Large_trained_on_MSMARCO.zip from the original repo.\nThe corresponding model class is BertForSequenceClassification, and i... |
feature-extraction | transformers | This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) and then fine-tuned again on MedMARCO (from [Sledge-Z paper](https://www.aclweb.org/anthology/2020.emnlp-main.341.pdf)) for 1K steps.
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)!
Paper describi... | {} | castorini/monot5-3b-med-msmarco | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) and then fine-tuned again on MedMARCO (from Sledge-Z paper) for 1K steps.
For more details on how to use it, check URL!
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model | [] | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.... | {} | castorini/monot5-3b-msmarco | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For more details on how to use it, check URL
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model | [] | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch) and then fine-tuned again on MedMARCO (from [Sledge-Z paper](https://www.aclweb.org/anthology/2020.emnlp-main.341.pdf) for 1k steps.
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describi... | {} | castorini/monot5-base-med-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch) and then fine-tuned again on MedMARCO (from Sledge-Z paper for 1k steps.
For more details on how to use it, check URL
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than `monot5-base-msmarco`, i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- [A sim... | {} | castorini/monot5-base-msmarco-10k | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than 'monot5-base-msmarco', i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- A simp... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For better zero-shot performance (i.e., inference on other datasets), we recommend using `castorini/monot5-base-msmarco-10k`.
For more details on how to use it, check the following links:
- [A simple reranking e... | {} | castorini/monot5-base-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For better zero-shot performance (i.e., inference on other datasets), we recommend using 'castorini/monot5-base-msmarco-10k'.
For more details on how to use it, check the following links:
- A simple reranking ex... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than `monot5-large-msmarco`, i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- [A s... | {} | castorini/monot5-large-msmarco-10k | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than 'monot5-large-msmarco', i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- A si... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For more details on how to use it, check the following links:
- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
- [Rerank MS MARCO passages](https://github.com/cast... | {} | castorini/monot5-large-msmarco | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For more details on how to use it, check the following links:
- A simple reranking example
- Rerank MS MARCO passages
- Rerank Robust04 documents
Paper describing the model: Document Ranking with a Pretrained S... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model is trained for conversational question rewriting.
Usage:
Source text format: ${HISTORY} ||| ${CURRENT_QUESTION}
example from [CANARD](https://sites.google.com/view/qanta/projects/canard):
Frank Zappa ||| Disbandment ||| What group disbanded ||| Zappa and the Mothers of Invention ||| When did they disband?... | {} | castorini/t5-base-canard | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model is trained for conversational question rewriting.
Usage:
Source text format: ${HISTORY} ||| ${CURRENT_QUESTION}
example from CANARD:
Frank Zappa ||| Disbandment ||| What group disbanded ||| Zappa and the Mothers of Invention ||| When did they disband?
Target text:
When did Zappa and the Mothers of Invent... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | This model is to reproduce the TCT-ColBERT dense retrieval described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [Distilling Dense Representations for Ranking using Tightly-Coupled Teachers.](https://arxiv.org/abs/2010.11386) arXiv:2010.11386, October 2020.
For more details on how to u... | {} | castorini/tct_colbert-msmarco | null | [
"transformers",
"pytorch",
"arxiv:2010.11386",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11386"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11386 #endpoints_compatible #has_space #region-us
| This model is to reproduce the TCT-ColBERT dense retrieval described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. Distilling Dense Representations for Ranking using Tightly-Coupled Teachers. arXiv:2010.11386, October 2020.
For more details on how to use it, check our experiments in Pyse... | [] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11386 #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_20... | {} | castorini/tct_colbert-v2-hn-msmarco | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us
| This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval. _RepL4NLP 2021_.
You can find our reproduction report in Py... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_202... | {} | castorini/tct_colbert-v2-hnp-msmarco-r2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval. _RepL4NLP 2021_.
Specifically, this checkpoint is finetuned ... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_20... | {} | castorini/tct_colbert-v2-hnp-msmarco | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us
| This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval. _RepL4NLP 2021_.
You can find our reproduction report in Py... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | This model is to reproduce Contextualized Query Embeddings for Conversational Search described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [Contextualized Query Embeddings for Conversational Search.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_EMNLP2021.pdf) EMNLP, Nov 2021. ... | {} | castorini/tct_colbert-v2-msmarco-cqe | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| This model is to reproduce Contextualized Query Embeddings for Conversational Search described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. Contextualized Query Embeddings for Conversational Search. EMNLP, Nov 2021.
This model is finetuend only on query ecoder with frezzed passage encod... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_20... | {} | castorini/tct_colbert-v2-msmarco | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us
| This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval. _RepL4NLP 2021_.
You can find our reproduction report in Py... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
null | null | An NER model to detect company and person names from news articles. | {} | cb-insights-team/news_ner | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| An NER model to detect company and person names from news articles. | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["long context", "legal"], "pipeline_tag": "fill-mask"} | ccdv/lsg-legal-base-uncased-4096 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"long context",
"legal",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #long context #legal #fill-mask #custom_code #en #arxiv-2210.15497 #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is adapted from LEGAL-... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #bert #pretraining #long context #legal #fill-mask #custom_code #en #arxiv-2210.15497 #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["long context", "legal"], "pipeline_tag": "fill-mask"} | ccdv/lsg-legal-small-uncased-4096 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"long context",
"legal",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #long context #legal #fill-mask #custom_code #en #arxiv-2210.15497 #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is a small version of ... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #bert #pretraining #long context #legal #fill-mask #custom_code #en #arxiv-2210.15497 #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["en"], "tags": ["summarization", "bart", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-bart-base-4096 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"long context",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1910.13461",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497",
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from BART-base for encoder-decoder t... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\nThis model is adapted from BART-base ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["en"], "tags": ["summarization", "bart", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-bart-large-4096 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"long context",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1910.13461",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497",
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from BART-large for encoder-decoder ... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\nThis model is adapted from BART-large... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["fr"], "tags": ["summarization", "bart", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-barthez-4096 | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"bart",
"long context",
"fill-mask",
"custom_code",
"fr",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #bart #long context #fill-mask #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from BARThez for encoder-decoder tas... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\nThis model is adapted from BARThez fo... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #bart #long context #fill-mask #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#1... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "fr", "tags": ["camembert", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-camembert-base-4096 | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"long context",
"custom_code",
"fr",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fill-mask #long context #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is adapted from CamemB... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #long context #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/con... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["long context"]} | ccdv/lsg-base-4096 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"long context",
"custom_code",
"en",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model can handle long sequen... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conve... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["en"], "tags": ["summarization", "pegasus", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-pegasus-large-4096 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"long context",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1912.08777",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.15497",
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1912.08777 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from Pegasus-large for encoder-decod... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\nThis model is adapted from Pegasus-la... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1912.08777 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=Tr... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-100k-VoxPopuli-Català
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL:**
https://huggingface.co/softcatala/wav2vec2-large-100k-voxpopuli-catala
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Catalan language using t... | {"language": "ca", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "speech-to-text"], "datasets": ["common_voice", "parlament_parla"], "metrics": ["wer"]} | ccoreilly/wav2vec2-large-100k-voxpopuli-catala | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"speech-to-text",
"ca",
"dataset:common_voice",
"dataset:parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #speech-to-text #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-100k-VoxPopuli-Català
====================================
️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL:
URL
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli on Catalan language using the Common Voice and ParlamentParla datasets.
Attention: The split train/dev/test used does not fully ... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #speech-to-text #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Català
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
**Attention:** The split train/dev/t... | {"language": "ca", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "parlament_parla"], "metrics": ["wer"]} | ccoreilly/wav2vec2-large-xlsr-catala | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ca",
"dataset:common_voice",
"dataset:parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-XLSR-Català
==========================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Catalan language using the Common Voice and ParlamentParla datasets.
Attention: The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoic... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# GIMPLEARN knows modeltest2
# To generate conversation use input such as Human: What should I do?\nAI: | {"tags": ["Text Generation"]} | cd-dvd/testmodel2 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"Text Generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #Text Generation #autotrain_compatible #endpoints_compatible #region-us
|
# GIMPLEARN knows modeltest2
# To generate conversation use input such as Human: What should I do?\nAI: | [
"# GIMPLEARN knows modeltest2",
"# To generate conversation use input such as Human: What should I do?\\nAI:"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #Text Generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# GIMPLEARN knows modeltest2",
"# To generate conversation use input such as Human: What should I do?\\nAI:"
] |
text-generation | transformers |
## a dialoggpt model trained on french opensubtitles with custom tokenizer
trained with this notebook
https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#scrollTo=AyImj9qZYLRi&uniqifier=3
config from microsoft/DialoGPT-medium
dataset generated from 2018 opensubtitle downloaded from opus folowing... | {"language": "fr", "tags": ["conversational"], "widget": [{"text": "bonjour."}, {"text": "mais encore"}, {"text": "est ce que l'argent achete le bonheur?"}]} | cedpsam/chatbot_fr | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #conversational #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## a dialoggpt model trained on french opensubtitles with custom tokenizer
trained with this notebook
URL
config from microsoft/DialoGPT-medium
dataset generated from 2018 opensubtitle downloaded from opus folowing these guidelines
URL with this notebook
URL
### How to use
Now we are ready to try out how the model w... | [
"## a dialoggpt model trained on french opensubtitles with custom tokenizer\ntrained with this notebook\nURL\n\nconfig from microsoft/DialoGPT-medium\ndataset generated from 2018 opensubtitle downloaded from opus folowing these guidelines\nURL with this notebook\nURL",
"### How to use\n\nNow we are ready to try o... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #conversational #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## a dialoggpt model trained on french opensubtitles with custom tokenizer\ntrained with this notebook\nURL\n\nconfig from m... |
text-classification | transformers |
话题分类模型，使用某乎"环境"话题下所有子话题，过滤后得69类。
top1 acc 60.7,
top3 acc 81.6，
可以用于中文环境文本挖掘的预处理步骤。
标签：
"生态环境","水污染", "野生动物保护", "太阳能", "环保经济", "污水处理", "绿色建筑", "水处理", "噪音污染", "温室效应", "净水设备",
"净水器", "自来水", "生活", "环境评估", "空气污染", "环境评价", "工业污染", "雾霾", "植树", "环保行业", "水处理工程", "沙漠治理",
"巴黎协定", "核能", "噪音", "环评工程师", "二氧化碳", "低碳", "自然环... | {"language": "zh", "tags": ["pretrain", "pytorch", "environment", "classification", "topic classification"], "widget": [{"text": "\u7f8e\u56fd\u9000\u51fa\u300a\u5df4\u9ece\u534f\u5b9a\u300b"}, {"text": "\u6c61\u6c34\u5904\u7406\u5382\u4e2d\u7684\u529f\u8017\u9700\u8981\u51cf\u5c11"}]} | celtics1863/env-bert-topic | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pretrain",
"environment",
"classification",
"topic classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #pretrain #environment #classification #topic classification #zh #autotrain_compatible #endpoints_compatible #region-us
|
话题分类模型，使用某乎"环境"话题下所有子话题，过滤后得69类。
top1 acc 60.7,
top3 acc 81.6，
可以用于中文环境文本挖掘的预处理步骤。
标签：
"生态环境","水污染", "野生动物保护", "太阳能", "环保经济", "污水处理", "绿色建筑", "水处理", "噪音污染", "温室效应", "净水设备",
"净水器", "自来水", "生活", "环境评估", "空气污染", "环境评价", "工业污染", "雾霾", "植树", "环保行业", "水处理工程", "沙漠治理",
"巴黎协定", "核能", "噪音", "环评工程师", "二氧化碳", "低碳", "自然环... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #pretrain #environment #classification #topic classification #zh #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | tags:
- array
- of
- tags
license: "any valid license identifier" | {} | cemigo/cemigo-test-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| tags:
- array
- of
- tags
license: "any valid license identifier" | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | centon21/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Harry Potter Fanfiction Generator
This is a pre-trained GPT-2 generative text model that allows you to generate your own Harry Potter fanfiction, trained off of the top 100 rated fanficition stories. We intend for this to be used for individual fun and experimentation and not as a commercial product.
| {"language": ["en"], "license": "mit", "tags": ["harry-potter"]} | ceostroff/harry-potter-gpt2-fanfiction | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"harry-potter",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #harry-potter #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Harry Potter Fanfiction Generator
This is a pre-trained GPT-2 generative text model that allows you to generate your own Harry Potter fanfiction, trained off of the top 100 rated fanficition stories. We intend for this to be used for individual fun and experimentation and not as a commercial product.
| [
"# Harry Potter Fanfiction Generator\n\nThis is a pre-trained GPT-2 generative text model that allows you to generate your own Harry Potter fanfiction, trained off of the top 100 rated fanficition stories. We intend for this to be used for individual fun and experimentation and not as a commercial product."
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #harry-potter #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Harry Potter Fanfiction Generator\n\nThis is a pre-trained GPT-2 generative text model that allows you to generate your ... |
feature-extraction | transformers | # TinyBERT_L-4_H-312_v2 English Sentence Encoder
This is distilled from the `bert-base-nli-stsb-mean-tokens` pre-trained model from [Sentence-Transformers](https://sbert.net/).
The embedding vector is obtained by mean/average pooling of the last layer's hidden states.
Update 20210325: Added the attention matrices im... | {} | ceshine/TinyBERT_L-4_H-312_v2-distill-AllNLI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| TinyBERT\_L-4\_H-312\_v2 English Sentence Encoder
=================================================
This is distilled from the 'bert-base-nli-stsb-mean-tokens' pre-trained model from Sentence-Transformers.
The embedding vector is obtained by mean/average pooling of the last layer's hidden states.
Update 20210325:... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis
More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase) | {"language": "en", "license": "apache-2.0", "tags": ["t5", "paraphrasing", "paraphrase"]} | ceshine/t5-paraphrase-paws-msrp-opinosis | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"paraphrasing",
"paraphrase",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #paraphrasing #paraphrase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis
More details in ceshine/finetuning-t5 Github repo | [
"# T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis\n\nMore details in ceshine/finetuning-t5 Github repo"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #paraphrasing #paraphrase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis\n\nMore details in ces... |
text2text-generation | transformers |
# T5-base Parapharasing model fine-tuned on PAWS and Quora
More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase) | {"language": "en", "license": "apache-2.0", "tags": ["t5", "paraphrasing", "paraphrase"]} | ceshine/t5-paraphrase-quora-paws | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"paraphrasing",
"paraphrase",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #paraphrasing #paraphrase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5-base Parapharasing model fine-tuned on PAWS and Quora
More details in ceshine/finetuning-t5 Github repo | [
"# T5-base Parapharasing model fine-tuned on PAWS and Quora\n\nMore details in ceshine/finetuning-t5 Github repo"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #paraphrasing #paraphrase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-base Parapharasing model fine-tuned on PAWS and Quora\n\nMore details in ceshine/finet... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-760-Turkish
# TBA
Pretrained Turkish model [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760). Fine-tuned on Turkish 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... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2-Base Turkish by Ceyda Cinarel", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | ceyda/wav2vec2-base-760-turkish | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Base-760-Turkish
# TBA
Pretrained Turkish model ceyda/wav2vec2-base-760. Fine-tuned on Turkish 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 ca... | [
"# Wav2Vec2-Base-760-Turkish",
"# TBA\nPretrained Turkish model ceyda/wav2vec2-base-760. Fine-tuned on Turkish 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:",
"## Evaluat... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-760-Turkish",
"# TBA\nPretrained Turkish model ceyda/wav2vec2-base-760. Fine-... |
feature-extraction | transformers | Pretrained on 720h~ of Turkish speech data
TBA | {} | ceyda/wav2vec2-base-760 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us
| Pretrained on 720h~ of Turkish speech data
TBA | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Ceyda Cinarel", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | ceyda/wav2vec2-large-xlsr-53-turkish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
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"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Com... |
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. -->
# punct_restore_fr
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on a raw, French ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "punct_restore_fr", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.991500810518732}}... | cfinley/punct_restore_fr | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# punct_restore_fr
This model is a fine-tuned version of camembert-base on a raw, French opensubtitles dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0301
- Precision: 0.9601
- Recall: 0.9527
- F1: 0.9564
- Accuracy: 0.9915
## Model description
Classifies tokens based on beginning of ... | [
"# punct_restore_fr\n\nThis model is a fine-tuned version of camembert-base on a raw, French opensubtitles dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0301\n- Precision: 0.9601\n- Recall: 0.9527\n- F1: 0.9564\n- Accuracy: 0.9915",
"## Model description\n\nClassifies tokens based ... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# punct_restore_fr\n\nThis model is a fine-tuned version of camembert-base on a raw, French opensubtitles dataset.\nIt achieves the following results on ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | cfisicaro/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"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 #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0629
* Precision: 0.9282
* Recall: 0.9356
* F1: 0.9319
* Accuracy: 0.9838
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
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. -->
# custom_german
This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-ge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "custom_german", "results": []}]} | chaitanya97/custom_german | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| custom\_german
==============
This model is a fine-tuned version of flozi00/wav2vec-xlsr-german on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6832
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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. -->
# german_pretrained
This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xls... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "german_pretrained", "results": []}]} | chaitanya97/german_pretrained | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| german\_pretrained
==================
This model is a fine-tuned version of flozi00/wav2vec-xlsr-german on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9812
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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. -->
# german_trained
This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "german_trained", "results": []}]} | chaitanya97/german_trained | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| german\_trained
===============
This model is a fine-tuned version of flozi00/wav2vec-xlsr-german on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9367
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-3", "results": []}]} | chaitanya97/wav2vec2-large-xls-r-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-3
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# wav2vec2-large-xls-r-3\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-3\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | chaitanya97/wav2vec2-large-xls-r-300m-hindi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-hindi-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 7.2810
* Wer: 1.0
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | chaitanya97/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 33.1265
* Wer: 1.0
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-generation | transformers |
# Rick DialoGPT model | {"tags": ["conversational"]} | chaitrabhat/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT model | [
"# Rick DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT model"
] |
text-generation | transformers |
# Sokka DialoGPT Model | {"tags": ["conversational"]} | chamodkarunasena/DialoGPT-medium-sokka | 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
|
# Sokka DialoGPT Model | [
"# Sokka DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sokka DialoGPT Model"
] |
text-generation | transformers |
# DialoGPT Medium JAB
| {"tags": ["conversational"]} | chan030609/DialoGPT-medium-JAB | 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 Medium JAB
| [
"# DialoGPT Medium JAB"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Medium JAB"
] |
text-generation | transformers |
# DialoGPT Small JAB | {"tags": ["conversational"]} | chan030609/DialoGPT-small-JAB | 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 Small JAB | [
"# DialoGPT Small JAB"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Small JAB"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | chanaa/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"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 #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0609
* Precision: 0.9244
* Recall: 0.9374
* F1: 0.9308
* Accuracy: 0.9836
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* 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. -->
# bart-base-finetuned-kaggglenews-baseline-final
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-baseline-final", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-baseline-final | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-baseline-final
==============================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6942
* Rouge1: 28.581
* Rouge2: 16.3417
* Rougel: 24.1277
* Rougelsum: 25.979... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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: 0.0002\n* train\\_batch... |
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. -->
# bart-base-finetuned-kaggglenews-batch8-LR1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8-LR1", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8-LR1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8-LR1
==========================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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: 1e-05\n* train\\_batch\... |
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. -->
# bart-base-finetuned-kaggglenews-batch8-LR2E6
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8-LR2E6", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8-LR2E6 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8-LR2E6
============================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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-06\n* train\\_batch\... |
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. -->
# bart-base-finetuned-kaggglenews-batch8-LR4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8-LR4", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8-LR4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8-LR4
==========================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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: 4e-05\n* train\\_batch\... |
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. -->
# bart-base-finetuned-kaggglenews-batch8-epochs10
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8-epochs10", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8-epochs10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8-epochs10
===============================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5763
* Rouge1: 28.693
* Rouge2: 16.666
* Rougel: 24.2361
* Rougelsum: 26.02... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
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. -->
# bart-base-finetuned-kaggglenews-batch8-epochs3
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8-epochs3", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8-epochs3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8-epochs3
==============================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5635
* Rouge1: 28.2335
* Rouge2: 16.0201
* Rougel: 24.0315
* Rougelsum: 25.64... | [
"### 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 #bart #text2text-generation #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\... |
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. -->
# bart-base-finetuned-kaggglenews-batch8
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-batch8", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-batch8 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-batch8
======================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
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. -->
# bart-base-finetuned-kaggglenews-fact-corrector-I
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-fact-corrector-I", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-fact-corrector-I | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-fact-corrector-I
================================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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: 0.0002\n* train\\_batch... |
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. -->
# bart-base-finetuned-kaggglenews-fact-corrector-II
This model is a fine-tuned version of [facebook/bart-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-finetuned-kaggglenews-fact-corrector-II", "results": []}]} | chandank/bart-base-finetuned-kaggglenews-fact-corrector-II | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews-fact-corrector-II
=================================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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: 0.0002\n* train\\_batch... |
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. -->
# bart-base-finetuned-kaggglenews
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-kaggglenews", "results": []}]} | chandank/bart-base-finetuned-kaggglenews | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kaggglenews
===============================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6240
* Rouge1: 28.3618
* Rouge2: 15.9828
* Rougel: 24.078
* Rougelsum: 25.565
* Gen Len: 20.0
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
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. -->
# bart-base-finetuned-kagglenews-entityfiltering
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-kagglenews-entityfiltering", "results": []}]} | chandank/bart-base-finetuned-kagglenews-entityfiltering | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-kagglenews-entityfiltering
==============================================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5703
* Rouge1: 28.2719
* Rouge2: 15.6883
* Rougel: 24.0674
* Rougelsum: 25.61... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
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. -->
# bart-base-finetuned-xsum
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) o... | {"tags": ["generated_from_trainer"], "datasets": [], "metrics": ["rouge"], "model_index": [{"name": "bart-base-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Rouge1", "type": "rouge", "value": 27.887}}]}]} | chandank/bart-base-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-xsum
========================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5925
* Rouge1: 27.887
* Rouge2: 16.1414
* Rougel: 24.0525
* Rougelsum: 25.4029
* Gen Len: 19.9841
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | charlecheng/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"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 #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0607
* Precision: 0.9276
* Recall: 0.9366
* F1: 0.9321
* Accuracy: 0.9841
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
null | 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. -->
# contest_train
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-... | {"language": ["ru", "en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "contest_train", "results": []}]} | elezhergina/MedMTEVAL_baseline | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
|
# contest_train
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4420
- Bleu: 67.6003
- Gen Len: 35.605
## Model description
More information needed
## Intended uses & limitations
More information needed
#... | [
"# contest_train\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4420\n- Bleu: 67.6003\n- Gen Len: 35.605",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# contest_train\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4420\n- Bleu: 67.6003\n- Gen Len: 35.605",
"## Model description\n\... |
token-classification | spacy |
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>
# DaCy large
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for a... | {"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"],... | chcaa/da_dacy_large_trf | null | [
"spacy",
"dacy",
"danish",
"token-classification",
"pos tagging",
"morphological analysis",
"lemmatization",
"dependency parsing",
"named entity recognition",
"coreference resolution",
"named entity linking",
"named entity disambiguation",
"da",
"dataset:universal_dependencies",
"dataset... | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.... | <a href="URL src="URL width="175" height="175" align="right" />
DaCy large
==========
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging a... | [
"### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)",
"### Accuracy",
"### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here."
] | [
"TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apa... |
token-classification | spacy |
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>
# DaCy medium
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for ... | {"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"],... | chcaa/da_dacy_medium_trf | null | [
"spacy",
"dacy",
"danish",
"token-classification",
"pos tagging",
"morphological analysis",
"lemmatization",
"dependency parsing",
"named entity recognition",
"coreference resolution",
"named entity linking",
"named entity disambiguation",
"da",
"dataset:universal_dependencies",
"dataset... | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.... | <a href="URL src="URL width="175" height="175" align="right" />
DaCy medium
===========
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging... | [
"### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)",
"### Accuracy",
"### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here."
] | [
"TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apa... |
token-classification | spacy |
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>
# DaCy small
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for a... | {"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"],... | chcaa/da_dacy_small_trf | null | [
"spacy",
"dacy",
"danish",
"token-classification",
"pos tagging",
"morphological analysis",
"lemmatization",
"dependency parsing",
"named entity recognition",
"coreference resolution",
"named entity linking",
"named entity disambiguation",
"da",
"dataset:universal_dependencies",
"dataset... | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.... | <a href="URL src="URL width="175" height="175" align="right" />
DaCy small
==========
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging a... | [
"### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)",
"### Accuracy",
"### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here."
] | [
"TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apa... |
text-generation | transformers |
#Chizuru Ichinose~ DialoGPT Model | {"tags": ["conversational"]} | chellver24/DialoGPT-medium-chizuru_ichinose | 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
|
#Chizuru Ichinose~ DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# bart-large-chinese-cnhdwriter
This model is a fine-tuned version of [fnlp/bart-large-chinese](https://huggingface.co/fnlp/bart-l... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "fnlp/bart-large-chinese", "model-index": [{"name": "bart-large-chinese-cnhdwriter", "results": []}]} | chinhon/bart-large-chinese-cnhdwriter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:fnlp/bart-large-chinese",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-fnlp/bart-large-chinese #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-large-chinese-cnhdwriter
=============================
This model is a fine-tuned version of fnlp/bart-large-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3859
* Rouge1: 16.8496
* Rouge2: 2.5548
* Rougel: 16.8123
* Rougelsum: 16.8056
* Gen Len: 18.9357
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-fnlp/bart-large-chinese #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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. -->
# bart-large-cnn-summarizer_03
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bar... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-large-cnn-summarizer_03", "results": []}]} | chinhon/bart-large-cnn-summarizer_03 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-large-cnn-summarizer\_03
=============================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0999
* Rouge1: 51.6222
* Rouge2: 33.428
* Rougel: 40.2093
* Rougelsum: 47.7154
* Gen Len: 102.7962
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-commentaries_hdwriter
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-commentaries_hdwriter", "results": []}]} | chinhon/bart-large-commentaries_hdwriter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-large-commentaries\_hdwriter
=================================
This model is a fine-tuned version of facebook/bart-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1619
* Rouge1: 26.1101
* Rouge2: 9.928
* Rougel: 22.9007
* Rougelsum: 23.117
* Gen Len: 15.9536
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-sgnews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-sgnews", "results": []}]} | chinhon/distilgpt2-sgnews | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"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 #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-sgnews
=================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1516
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #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\... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965855
- CO2 Emissions (in grams): 114.71292762345828
## Validation Metrics
- Loss: 1.3862273693084717
- Rouge1: 52.4988
- Rouge2: 31.6973
- RougeL: 47.1727
- RougeLsum: 47.1576
- Gen Len: 17.6194
## Usage
You can use cURL to access this mo... | {"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 114.71292762345828} | chinhon/headline_writer | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:chinhon/autonlp-data-sg_headline_generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965855
- CO2 Emissions (in grams): 114.71292762345828
## Validation Metrics
- Loss: 1.3862273693084717
- Rouge1: 52.4988
- Rouge2: 31.6973
- RougeL: 47.1727
- RougeLsum: 47.1576
- Gen Len: 17.6194
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965855\n- CO2 Emissions (in grams): 114.71292762345828",
"## Validation Metrics\n\n- Loss: 1.3862273693084717\n- Rouge1: 52.4988\n- Rouge2: 31.6973\n- RougeL: 47.1727\n- RougeLsum: 47.1576\n- Gen Len: 17.6194",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965855\n- ... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965856
- CO2 Emissions (in grams): 396.629376395644
## Validation Metrics
- Loss: 1.4130597114562988
- Rouge1: 51.7922
- Rouge2: 30.8259
- RougeL: 46.4585
- RougeLsum: 46.4807
- Gen Len: 15.8411
## Usage
You can use cURL to access this mode... | {"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 396.629376395644} | chinhon/headline_writer2 | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:chinhon/autonlp-data-sg_headline_generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965856
- CO2 Emissions (in grams): 396.629376395644
## Validation Metrics
- Loss: 1.4130597114562988
- Rouge1: 51.7922
- Rouge2: 30.8259
- RougeL: 46.4585
- RougeLsum: 46.4807
- Gen Len: 15.8411
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965856\n- CO2 Emissions (in grams): 396.629376395644",
"## Validation Metrics\n\n- Loss: 1.4130597114562988\n- Rouge1: 51.7922\n- Rouge2: 30.8259\n- RougeL: 46.4585\n- RougeLsum: 46.4807\n- Gen Len: 15.8411",
"## Usage\n\nYou can use... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965856\n- CO2 Emissio... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-large-commentaries_hd
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-large", "model-index": [{"name": "pegasus-large-commentaries_hd", "results": []}]} | chinhon/pegasus-large-commentaries_hd | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-large",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-large #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-large-commentaries\_hd
==============================
This model is a fine-tuned version of google/pegasus-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5453
* Rouge1: 26.3475
* Rouge2: 9.5095
* Rougel: 22.6367
* Rougelsum: 22.8127
* Gen Len: 14.4789
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-large #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-commentaries_hdwriter
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.c... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-commentaries_hdwriter", "results": []}]} | chinhon/pegasus-multi_news-commentaries_hdwriter | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-multi\_news-commentaries\_hdwriter
==========================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7259
* Rouge1: 21.3899
* Rouge2: 6.2409
* Rougel: 16.6172
* Rougelsum: 17.8... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-headline
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pega... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-headline", "results": []}]} | chinhon/pegasus-multi_news-headline | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-multi\_news-headline
============================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4421
* Rouge1: 41.616
* Rouge2: 22.922
* Rougel: 35.2189
* Rougelsum: 35.3561
* Gen Len: 33.9532
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-malay_headlines_02
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/g... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-malay_headlines_02", "results": []}]} | chinhon/pegasus-multi_news-malay_headlines_02 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-multi\_news-malay\_headlines\_02
========================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9295
* Rouge1: 39.9859
* Rouge2: 20.1943
* Rougel: 36.1927
* Rougelsum: 36.2105... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-summarizer_01
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-summarizer_01", "results": []}]} | chinhon/pegasus-multi_news-summarizer_01 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #region-us
| pegasus-multi\_news-summarizer\_01
==================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2794
* Rouge1: 52.1693
* Rouge2: 34.8989
* Rougel: 41.2385
* Rougelsum: 48.4365
* Gen Len: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-commentaries_hdwriter
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/go... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-commentaries_hdwriter", "results": []}]} | chinhon/pegasus-newsroom-commentaries_hdwriter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-commentaries\_hdwriter
=======================================
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5316
* Rouge1: 21.4079
* Rouge2: 6.2399
* Rougel: 16.6644
* Rougelsum: 17.8501
* Gen... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #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\\_si... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-headline_writer
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/p... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-headline_writer", "results": []}]} | chinhon/pegasus-newsroom-headline_writer | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-newsroom-headline\_writer
=================================
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3988
* Rouge1: 41.8748
* Rouge2: 23.1947
* Rougel: 35.6263
* Rougelsum: 35.7355
* Gen Len: 34.12... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-malay_headlines
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/p... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-malay_headlines", "results": []}]} | chinhon/pegasus-newsroom-malay_headlines | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-newsroom-malay\_headlines
=================================
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6603
* Rouge1: 42.6667
* Rouge2: 22.8739
* Rougel: 38.6684
* Rougelsum: 38.6928
* Gen Len: 34.79... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-summarizer_02
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/peg... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-summarizer_02", "results": []}]} | chinhon/pegasus-newsroom-summarizer_02 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-newsroom-summarizer\_02
===============================
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2204
* Rouge1: 52.4459
* Rouge2: 35.2568
* Rougel: 41.6213
* Rougelsum: 48.7859
* Gen Len: 98.0627
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
text-generation | transformers |
Chizuru Ichinose DialoGPT Model. | {"tags": ["conversational"]} | chip/DialoGPT-small-chizuru | 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
|
Chizuru Ichinose DialoGPT Model. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | ### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad [NICE classifications](https://www.wipo.int/classifications/nice/en/)
| {} | chisadi/nice-distilbert-v2 | 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
| ### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications
| [
"### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications"
] |
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. -->
# finetune-paraphrase-model
This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingf... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetune-paraphrase-model", "results": []}]} | chitra/finetune-paraphrase-model | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finetune-paraphrase-model
=========================
This model is a fine-tuned version of coderpotter/adversarial-paraphrasing-detector on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\... |
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. -->
# finetuned-adversarial-paraphrase-model
This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](htt... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-adversarial-paraphrase-model", "results": []}]} | chitra/finetuned-adversarial-paraphrase-model | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finetuned-adversarial-paraphrase-model
======================================
This model is a fine-tuned version of coderpotter/adversarial-paraphrasing-detector on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.5680
Model description
-----------------
More information ne... | [
"### 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 #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\... |
text-classification | transformers |
### Welcome to RoBERTArg!
🤖 **Model description**
This model was trained on ~25k heterogeneous manually annotated sentences (📚 [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1).
🗃 **Dataset... | {"language": "en", "widget": [{"text": "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."}]} | chkla/roberta-argument | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Welcome to RoBERTArg!
Model description
This model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of controversial topics to classify text into one of two labels: NON-ARGUMENT (0) and ARGUMENT (1).
Dataset
The dataset ( Stab et al. 2018) consists of ARGUMENTS (~11k) that ... | [
"### Welcome to RoBERTArg!\n\n\nModel description\n\n\nThis model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of controversial topics to classify text into one of two labels: NON-ARGUMENT (0) and ARGUMENT (1).\n\n\nDataset\n\n\nThe dataset ( Stab et al. 2018) consists of ARGUM... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Welcome to RoBERTArg!\n\n\nModel description\n\n\nThis model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of contr... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OP... | {"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-re... | chmanoj/xls-r-1B-te | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compati... | null | 2022-03-02T23:29:05+00:00 | [] | [
"te"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the OPENSLR\_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3119
* Wer: 0.2613
### Evaluation metrics
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n----------... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Evaluation metrics\n\n\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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OP... | {"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-re... | chmanoj/xls-r-2B-te | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_spac... | null | 2022-03-02T23:29:05+00:00 | [] | [
"te"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the OPENSLR\_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4253
* Wer: 0.5109
### Evaluation metrics
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n----------... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Evaluation metrics\n\n\n\nMo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | chmanoj/xls-r-300m-sv | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8004
* Wer: 0.7139
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #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* lear... |
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