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class | text_length int64 201 598k | readme stringlengths 0 598k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
albert-base-v1 | null | albert | 8 | 74,071 | transformers | 1 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert'] | false | true | true | 9,789 |
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... |
albert-base-v2 | null | albert | 10 | 3,819,536 | transformers | 38 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 1 | 1 | 0 | 0 | 1 | 0 | 1 | [] | false | true | true | 9,643 |
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... |
albert-large-v1 | null | albert | 9 | 645 | transformers | 0 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,681 |
# ALBERT Large v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... |
albert-large-v2 | null | albert | 8 | 36,311 | transformers | 8 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,682 |
# ALBERT Large v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... |
albert-xlarge-v1 | null | albert | 8 | 222,319 | transformers | 0 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,689 |
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... |
albert-xlarge-v2 | null | albert | 8 | 275,390 | transformers | 3 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,690 |
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... |
albert-xxlarge-v1 | null | albert | 8 | 4,163 | transformers | 2 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | [] | false | true | true | 9,698 |
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... |
albert-xxlarge-v2 | null | albert | 8 | 64,439 | transformers | 7 | fill-mask | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert'] | false | true | true | 9,849 |
# ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... |
bert-base-cased | null | bert | 10 | 6,492,277 | transformers | 73 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 2 | 0 | 1 | 1 | 0 | 0 | 0 | ['exbert'] | false | true | true | 8,891 |
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference bet... |
bert-base-chinese | null | bert | 10 | 1,938,936 | transformers | 211 | fill-mask | true | true | true | null | ['zh'] | null | null | 4 | 1 | 2 | 1 | 3 | 2 | 1 | [] | false | true | true | 1,753 |
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
- **Model Descript... |
bert-base-german-cased | null | bert | 9 | 452,070 | transformers | 28 | fill-mask | true | true | true | mit | ['de'] | null | null | 2 | 1 | 0 | 1 | 0 | 0 | 0 | ['exbert'] | false | true | true | 4,053 |
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
## Overview
**Language model:** bert-base-cased
**L... |
bert-base-german-dbmdz-cased | null | bert | 8 | 979 | transformers | 0 | fill-mask | true | false | true | mit | ['de'] | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 240 |
This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. |
bert-base-german-dbmdz-uncased | null | bert | 8 | 10,564 | transformers | 2 | fill-mask | true | false | true | mit | ['de'] | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 247 |
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
|
bert-base-multilingual-cased | null | bert | 10 | 2,628,611 | transformers | 87 | fill-mask | true | true | true | apache-2.0 | ['multilingual', 'af', 'sq', 'ar', 'an', 'hy', 'ast', 'az', 'ba', 'eu', 'bar', 'be', 'bn', 'inc', 'bs', 'br', 'bg', 'my', 'ca', 'ceb', 'ce', 'zh', 'cv', 'hr', 'cs', 'da', 'nl', 'en', 'et', 'fi', 'fr', 'gl', 'ka', 'de', 'el', 'gu', 'ht', 'he', 'hi', 'hu', 'is', 'io', 'id', 'ga', 'it', 'ja', 'jv', 'kn', 'kk', 'ky', 'ko',... | ['wikipedia'] | null | 2 | 0 | 2 | 0 | 1 | 1 | 0 | [] | false | true | true | 6,498 |
# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model... |
bert-base-multilingual-uncased | null | bert | 9 | 577,315 | transformers | 30 | fill-mask | true | true | true | apache-2.0 | ['multilingual', 'af', 'sq', 'ar', 'an', 'hy', 'ast', 'az', 'ba', 'eu', 'bar', 'be', 'bn', 'inc', 'bs', 'br', 'bg', 'my', 'ca', 'ceb', 'ce', 'zh', 'cv', 'hr', 'cs', 'da', 'nl', 'en', 'et', 'fi', 'fr', 'gl', 'ka', 'de', 'el', 'gu', 'ht', 'he', 'hi', 'hu', 'is', 'io', 'id', 'ga', 'it', 'ja', 'jv', 'kn', 'kk', 'ky', 'ko',... | ['wikipedia'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 8,334 |
# BERT multilingual base model (uncased)
Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This mod... |
bert-base-uncased | null | bert | 12 | 33,665,287 | transformers | 499 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 10 | 0 | 5 | 5 | 11 | 11 | 0 | ['exbert'] | false | true | true | 10,426 |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... |
bert-large-cased-whole-word-masking-finetuned-squad | null | bert | 11 | 60,887 | transformers | 0 | question-answering | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 6,043 |
# BERT large model (cased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is ca... |
bert-large-cased-whole-word-masking | null | bert | 9 | 1,884 | transformers | 2 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,603 |
# BERT large model (cased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it makes a dif... |
bert-large-cased | null | bert | 10 | 160,722 | transformers | 4 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,138 |
# BERT large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it makes a difference
between eng... |
bert-large-uncased-whole-word-masking-finetuned-squad | null | bert | 10 | 1,097,869 | transformers | 59 | question-answering | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 6,164 |
# BERT large model (uncased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is ... |
bert-large-uncased-whole-word-masking | null | bert | 9 | 65,422 | transformers | 4 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,687 |
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does no... |
bert-large-uncased | null | bert | 11 | 1,151,242 | transformers | 17 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 8,885 |
# BERT large model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference... |
camembert-base | null | camembert | 7 | 908,145 | transformers | 26 | fill-mask | true | true | false | mit | ['fr'] | ['oscar'] | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 6,917 |
# CamemBERT: a Tasty French Language Model
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Started With the Model](#... |
ctrl | null | ctrl | 7 | 12,344 | transformers | 0 | null | true | true | false | bsd-3-clause | ['en'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 12,382 |
# ctrl
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8. [Citation](... |
distilbert-base-cased-distilled-squad | null | distilbert | 11 | 2,119,375 | transformers | 47 | question-answering | true | true | false | apache-2.0 | ['en'] | ['squad'] | null | 5 | 1 | 3 | 1 | 1 | 1 | 0 | [] | true | true | true | 8,504 |
# DistilBERT base cased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental... |
distilbert-base-cased | null | distilbert | 8 | 301,461 | transformers | 12 | null | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 2 | 0 | 2 | 0 | 1 | 1 | 0 | [] | false | true | true | 8,735 |
# Model Card for DistilBERT base model (cased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased).
It was introduced in [this paper](https://arxiv.org/abs/1910.01108).
The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/... |
distilbert-base-multilingual-cased | null | distilbert | 8 | 283,936 | transformers | 30 | fill-mask | true | true | false | apache-2.0 | ['multilingual', 'af', 'sq', 'ar', 'an', 'hy', 'ast', 'az', 'ba', 'eu', 'bar', 'be', 'bn', 'inc', 'bs', 'br', 'bg', 'my', 'ca', 'ceb', 'ce', 'zh', 'cv', 'hr', 'cs', 'da', 'nl', 'en', 'et', 'fi', 'fr', 'gl', 'ka', 'de', 'el', 'gu', 'ht', 'he', 'hi', 'hu', 'is', 'io', 'id', 'ga', 'it', 'ja', 'jv', 'kn', 'kk', 'ky', 'ko',... | ['wikipedia'] | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 | [] | false | true | true | 6,710 |
# Model Card for DistilBERT base multilingual (cased)
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citat... |
distilbert-base-uncased-distilled-squad | null | distilbert | 14 | 27,917 | transformers | 20 | question-answering | true | true | false | apache-2.0 | ['en'] | ['squad'] | null | 3 | 0 | 3 | 0 | 0 | 0 | 0 | [] | false | true | true | 8,586 |
# DistilBERT base uncased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environment... |
distilbert-base-uncased-finetuned-sst-2-english | null | distilbert | 10 | 3,865,408 | transformers | 146 | text-classification | true | true | false | apache-2.0 | ['en'] | ['sst2', 'glue'] | null | 8 | 0 | 7 | 1 | 8 | 8 | 0 | [] | true | true | true | 3,883 |
# DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
## Model Details
**Model Description:** T... |
distilbert-base-uncased | null | distilbert | 12 | 9,565,239 | transformers | 128 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 3 | 0 | 3 | 0 | 3 | 3 | 0 | ['exbert'] | false | true | true | 8,470 |
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/main/e... |
distilgpt2 | null | gpt2 | 15 | 729,611 | transformers | 123 | text-generation | true | true | true | apache-2.0 | ['en'] | ['openwebtext'] | 149200 | 6 | 0 | 5 | 1 | 1 | 1 | 0 | ['exbert'] | true | true | true | 10,594 |
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... |
distilroberta-base | null | roberta | 12 | 617,714 | transformers | 45 | fill-mask | true | true | true | apache-2.0 | ['en'] | ['openwebtext'] | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 | ['exbert'] | false | true | true | 7,417 |
# Model Card for DistilRoBERTa base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8.... |
gpt2-large | null | gpt2 | 12 | 465,180 | transformers | 40 | text-generation | true | true | true | mit | ['en'] | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 12,312 |
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-im... |
gpt2-medium | null | gpt2 | 12 | 933,383 | transformers | 22 | text-generation | true | true | true | mit | ['en'] | null | null | 3 | 1 | 2 | 0 | 1 | 0 | 1 | [] | false | true | true | 11,854 |
# GPT-2 Medium
## Model Details
**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
- **Developed by:** OpenAI, see [a... |
gpt2-xl | null | gpt2 | 12 | 543,672 | transformers | 55 | text-generation | true | true | true | mit | ['en'] | null | null | 4 | 1 | 1 | 2 | 0 | 0 | 0 | [] | false | true | true | 11,933 |
# GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impac... |
gpt2 | null | gpt2 | 15 | 19,945,996 | transformers | 546 | text-generation | true | true | true | mit | ['en'] | null | null | 18 | 7 | 4 | 7 | 8 | 5 | 3 | ['exbert'] | false | true | true | 8,040 |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... |
openai-gpt | null | openai-gpt | 11 | 48,638 | transformers | 27 | text-generation | true | true | false | mit | ['en'] | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 14,043 |
# OpenAI GPT
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-imp... |
roberta-base-openai-detector | null | roberta | 9 | 158,665 | transformers | 50 | text-classification | true | true | true | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 6 | 0 | 5 | 1 | 4 | 3 | 1 | ['exbert'] | false | true | true | 9,341 |
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specificati... |
roberta-base | null | roberta | 12 | 5,170,485 | transformers | 115 | fill-mask | true | true | true | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 2 | 0 | 1 | 1 | 2 | 1 | 1 | ['exbert'] | false | true | true | 8,979 |
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
mak... |
roberta-large-mnli | null | roberta | 9 | 68,661 | transformers | 51 | text-classification | true | true | true | mit | ['en'] | ['multi_nli', 'wikipedia', 'bookcorpus'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['autogenerated-modelcard'] | false | true | true | 10,597 |
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#e... |
roberta-large-openai-detector | null | roberta | 8 | 27,024 | transformers | 5 | text-classification | true | false | true | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | ['exbert'] | false | true | true | 9,097 |
# RoBERTa Large OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specificat... |
roberta-large | null | roberta | 10 | 1,844,084 | transformers | 81 | fill-mask | true | true | true | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 1 | 0 | 1 | 0 | 1 | 0 | 1 | ['exbert'] | false | true | true | 9,188 |
# RoBERTa large model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: ... |
t5-11b | null | t5 | 7 | 107,681 | transformers | 13 | translation | true | true | false | apache-2.0 | ['en', 'fr', 'ro', 'de', 'multilingual'] | ['c4'] | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 | ['summarization', 'translation'] | false | true | true | 8,462 |
# Model Card for T5 11B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [... |
t5-3b | null | t5 | 7 | 153,764 | transformers | 8 | translation | true | true | false | apache-2.0 | ['en', 'fr', 'ro', 'de', 'multilingual'] | ['c4'] | null | 3 | 1 | 2 | 0 | 0 | 0 | 0 | ['summarization', 'translation'] | false | true | true | 7,740 |
# Model Card for T5-3B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [B... |
t5-base | null | t5 | 10 | 5,629,273 | transformers | 111 | translation | true | true | true | apache-2.0 | ['en', 'fr', 'ro', 'de'] | ['c4'] | null | 8 | 5 | 2 | 1 | 2 | 2 | 0 | ['summarization', 'translation'] | false | true | true | 8,343 |
# Model Card for T5 Base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. ... |
t5-large | null | t5 | 9 | 721,827 | transformers | 36 | translation | true | true | true | apache-2.0 | ['en', 'fr', 'ro', 'de', 'multilingual'] | ['c4'] | null | 7 | 4 | 3 | 0 | 2 | 2 | 0 | ['summarization', 'translation'] | false | true | true | 8,348 |
# Model Card for T5 Large
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3.... |
t5-small | null | t5 | 10 | 6,342,510 | transformers | 65 | translation | true | true | true | apache-2.0 | ['en', 'fr', 'ro', 'de', 'multilingual'] | ['c4'] | null | 7 | 2 | 4 | 1 | 2 | 2 | 0 | ['summarization', 'translation'] | false | true | true | 8,348 |
# Model Card for T5 Small
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3.... |
transfo-xl-wt103 | null | transfo-xl | 11 | 51,976 | transformers | 4 | text-generation | true | true | false | null | ['en'] | ['wikitext-103'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['text-generation'] | true | true | true | 5,254 |
# Transfo-xl-wt103
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Started With the Model](#how-to-get-started-with... |
xlm-clm-ende-1024 | null | xlm | 9 | 13,844 | transformers | 0 | fill-mask | true | true | false | null | ['multilingual', 'en', 'de'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,934 |
# xlm-clm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... |
xlm-clm-enfr-1024 | null | xlm | 9 | 180 | transformers | 0 | fill-mask | true | true | false | null | ['multilingual', 'en', 'fr'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,958 |
# xlm-clm-enfr-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... |
xlm-mlm-100-1280 | null | xlm | 9 | 854 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'es', 'fr', 'de', 'zh', 'ru', 'pt', 'it', 'ar', 'ja', 'id', 'tr', 'nl', 'pl', 'fa', 'vi', 'sv', 'ko', 'he', 'ro', False, 'hi', 'uk', 'cs', 'fi', 'hu', 'th', 'da', 'ca', 'el', 'bg', 'sr', 'ms', 'bn', 'hr', 'sl', 'az', 'sk', 'eo', 'ta', 'sh', 'lt', 'et', 'ml', 'la', 'bs', 'sq', 'arz', 'af', 'ka', '... | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,524 |
# xlm-mlm-100-1280
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8.... |
xlm-mlm-17-1280 | null | xlm | 9 | 574 | transformers | 1 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'fr', 'es', 'de', 'it', 'pt', 'nl', 'sv', 'pl', 'ru', 'ar', 'tr', 'zh', 'ja', 'ko', 'hi', 'vi'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,468 |
# xlm-mlm-17-1280
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8. ... |
xlm-mlm-en-2048 | null | xlm | 9 | 5,367 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['en'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['exbert'] | false | true | true | 4,551 |
# xlm-mlm-en-2048
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8. [Model Card Authors](#model-card... |
xlm-mlm-ende-1024 | null | xlm | 9 | 189 | transformers | 1 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'de'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,709 |
# xlm-mlm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... |
xlm-mlm-enfr-1024 | null | xlm | 9 | 626 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'fr'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,709 |
# xlm-mlm-enfr-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... |
xlm-mlm-enro-1024 | null | xlm | 9 | 19 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'ro'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,715 |
# xlm-mlm-enro-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... |
xlm-mlm-tlm-xnli15-1024 | null | xlm | 9 | 37 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'fr', 'es', 'de', 'el', 'bg', 'ru', 'tr', 'ar', 'vi', 'th', 'zh', 'hi', 'sw', 'ur'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,794 |
# xlm-mlm-tlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#techn... |
xlm-mlm-xnli15-1024 | null | xlm | 9 | 125 | transformers | 0 | fill-mask | true | true | false | cc-by-nc-4.0 | ['multilingual', 'en', 'fr', 'es', 'de', 'el', 'bg', 'ru', 'tr', 'ar', 'vi', 'th', 'zh', 'hi', 'sw', 'ur'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 10,037 |
# xlm-mlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical... |
xlm-roberta-base | null | xlm-roberta | 9 | 8,993,144 | transformers | 175 | fill-mask | true | true | true | mit | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 8 | 2 | 3 | 3 | 4 | 1 | 3 | ['exbert'] | false | true | true | 4,711 |
# XLM-RoBERTa (base-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https... |
xlm-roberta-large-finetuned-conll02-dutch | null | xlm-roberta | 7 | 858 | transformers | 0 | fill-mask | true | false | false | null | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,855 |
# xlm-roberta-large-finetuned-conll02-dutch
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#tec... |
xlm-roberta-large-finetuned-conll02-spanish | null | xlm-roberta | 7 | 794 | transformers | 0 | fill-mask | true | false | false | null | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,980 |
# xlm-roberta-large-finetuned-conll02-spanish
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#t... |
xlm-roberta-large-finetuned-conll03-english | null | xlm-roberta | 7 | 422,801 | transformers | 46 | token-classification | true | false | false | null | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 3 | 2 | 1 | 0 | 2 | 1 | 1 | [] | false | true | true | 7,169 |
# xlm-roberta-large-finetuned-conll03-english
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#t... |
xlm-roberta-large-finetuned-conll03-german | null | xlm-roberta | 7 | 1,989 | transformers | 1 | token-classification | true | false | false | null | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,964 |
# xlm-roberta-large-finetuned-conll03-german
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#te... |
xlm-roberta-large | null | xlm-roberta | 8 | 11,565,644 | transformers | 84 | fill-mask | true | true | true | mit | ['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv... | null | null | 3 | 0 | 2 | 1 | 4 | 4 | 0 | ['exbert'] | false | true | true | 4,715 |
# XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](http... |
xlnet-base-cased | null | xlnet | 10 | 1,709,395 | transformers | 23 | text-generation | true | true | false | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,627 |
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaimer... |
xlnet-large-cased | null | xlnet | 9 | 13,580 | transformers | 7 | text-generation | true | true | false | mit | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,630 |
# XLNet (large-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaime... |
09panesara/distilbert-base-uncased-finetuned-cola | 09panesara | distilbert | 13 | 29 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,572 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... |
0x7194633/keyt5-base | 0x7194633 | t5 | 7 | 48 | transformers | 0 | text2text-generation | true | false | false | mit | ['ru'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,482 | ## keyT5. Base (small) version
[](https://github.com/0x7o/text2keywords "Go to GitHub repo")
[](https://github.com... |
0x7194633/keyt5-large | 0x7194633 | t5 | 7 | 42 | transformers | 0 | text2text-generation | true | false | false | mit | ['ru'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,475 | ## keyT5. Large version
[](https://github.com/0x7o/text2keywords "Go to GitHub repo")
[](https://github.com/0x7o/t... |
123abhiALFLKFO/distilbert-base-uncased-finetuned-cola | 123abhiALFLKFO | distilbert | 21 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | false | true | true | 1,570 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... |
202015004/wav2vec2-base-timit-demo-colab | 202015004 | wav2vec2 | 22 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 4,821 |
<!-- 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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... |
2umm3r/distilbert-base-uncased-finetuned-cola | 2umm3r | distilbert | 16 | 16 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,571 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... |
3koozy/gpt2-HxH | 3koozy | gpt2 | 8 | 0 | transformers | 0 | feature-extraction | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | false | true | 311 | this is a fine tuned GPT2 text generation model on a Hunter x Hunter TV anime series dataset.\
you can find a link to the used dataset here : https://www.kaggle.com/bkoozy/hunter-x-hunter-subtitles
you can find a colab notebook for fine-tuning the gpt2 model here : https://github.com/3koozy/fine-tune-gpt2-HxH/ |
9pinus/macbert-base-chinese-medical-collation | 9pinus | bert | 14 | 17 | transformers | 4 | token-classification | true | false | false | apache-2.0 | ['zh'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['Token Classification'] | false | true | true | 1,346 |
## Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding n... |
9pinus/macbert-base-chinese-medicine-recognition | 9pinus | bert | 9 | 5 | transformers | 2 | token-classification | true | false | false | apache-2.0 | ['zh'] | null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | ['Token Classification'] | false | true | true | 2,363 |
## Model description
This model is a fine-tuned version of bert-base-chinese for the purpose of medicine name recognition. We fine-tuned bert-base-chinese on a 500M dataset including 100K+ authorized medical articles on which we labeled all the medicine names. The model achieves 92% accuracy on our test dataset.
... |
A-bhimany-u08/bert-base-cased-qqp | A-bhimany-u08 | bert | 7 | 3 | transformers | 0 | text-classification | true | false | false | null | null | ['qqp'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 242 |
bert-base-cased model trained on quora question pair dataset. The task requires to predict whether the two given sentences (or questions) are `not_duplicate` (label 0) or `duplicate` (label 1). The model achieves 89% evaluation accuracy
|
AI-Growth-Lab/PatentSBERTa | AI-Growth-Lab | mpnet | 12 | 1,309 | sentence-transformers | 12 | sentence-similarity | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,735 |
# PatentSBERTa
## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
### Aalborg University Business School, AI: Growth-Lab
https://arxiv.org/abs/2103.11933
https://github.com/AI-Growth-Lab/PatentSBERTa
This is a [sentence-transformers](https://www.SBERT.ne... |
AI-Lab-Makerere/en_lg | AI-Lab-Makerere | marian | 10 | 2 | transformers | 0 | text2text-generation | true | false | false | null | ['unk'] | ['Eric Peter/autonlp-data-EN-LUG'] | 133.0219882109991 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | autonlp | false | true | true | 546 |
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 474612462
- CO2 Emissions (in grams): 133.0219882109991
## Validation Metrics
- Loss: 1.336498737335205
- Rouge1: 52.5404
- Rouge2: 31.6639
- RougeL: 50.1696
- RougeLsum: 50.3398
- Gen Len: 39.046
## Usage
You can use cURL to access thi... |
AI-Lab-Makerere/lg_en | AI-Lab-Makerere | marian | 10 | 3 | transformers | 1 | text2text-generation | true | false | false | null | ['unk'] | ['EricPeter/autonlp-data-MarianMT_lg_en'] | 126.34446293851818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | autonlp | false | true | true | 555 |
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 475112539
- CO2 Emissions (in grams): 126.34446293851818
## Validation Metrics
- Loss: 1.5376628637313843
- Rouge1: 62.4613
- Rouge2: 39.4759
- RougeL: 58.183
- RougeLsum: 58.226
- Gen Len: 26.5644
## Usage
You can use cURL to access th... |
AI-Nordics/bert-large-swedish-cased | AI-Nordics | megatron-bert | 8 | 44 | transformers | 6 | fill-mask | true | false | false | null | ['sv'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,530 |
# A Swedish Bert model
## Model description
This model follows the Bert Large model architecture as implemented in [Megatron-LM framework](https://github.com/NVIDIA/Megatron-LM). It was trained with a batch size of 512 in 600k steps. The model contains following parameters:
<figure>
| Hyperparameter | Value ... |
AIDA-UPM/MSTSb_paraphrase-multilingual-MiniLM-L12-v2 | AIDA-UPM | null | 14 | 3 | sentence-transformers | 0 | sentence-similarity | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,682 |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... |
AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1 | AIDA-UPM | xlm-roberta | 17 | 45 | sentence-transformers | 0 | sentence-similarity | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,722 |
# AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
... |
AIDA-UPM/MSTSb_stsb-xlm-r-multilingual | AIDA-UPM | xlm-roberta | 17 | 385 | sentence-transformers | 0 | sentence-similarity | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,688 |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... |
AIDA-UPM/bertweet-base-multi-mami | AIDA-UPM | roberta | 14 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classification', 'misogyny'] | false | true | true | 328 |
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.
# Multilabels
label2id={
"misogynous": 0,
"shaming": 1,
"stereotype": 2,
"objectification": 3,
"violence": 4,... |
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | AIDA-UPM | xlm-roberta | 12 | 208 | transformers | 3 | sentence-similarity | true | false | false | null | ['multilingual'] | null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | ['feature-extraction', 'sentence-similarity', 'transformers', 'multilingual'] | false | true | true | 8,755 |
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences &... |
AKulk/wav2vec2-base-timit-epochs10 | AKulk | wav2vec2 | 12 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,096 |
<!-- 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-base-timit-epochs10
This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs5](https://huggingface.co/AK... |
AKulk/wav2vec2-base-timit-epochs15 | AKulk | wav2vec2 | 12 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,098 |
<!-- 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-base-timit-epochs15
This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs10](https://huggingface.co/A... |
AKulk/wav2vec2-base-timit-epochs5 | AKulk | wav2vec2 | 12 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,101 |
<!-- 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-base-timit-epochs5
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/... |
ARTeLab/it5-summarization-fanpage | ARTeLab | t5 | 14 | 6 | transformers | 2 | summarization | true | false | false | null | ['it'] | ['ARTeLab/fanpage'] | null | 4 | 3 | 0 | 1 | 0 | 0 | 0 | ['summarization'] | true | true | true | 2,465 |
# summarization_fanpage128
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.5348
- Rouge1: 34.1882
- Rouge2: 15.7866
- Rougel: 25.141
- Rougelsum: 28.4882
- Gen Len: 69.3041
... |
ARTeLab/it5-summarization-ilpost | ARTeLab | t5 | 13 | 5 | transformers | 0 | summarization | true | false | false | null | ['it'] | ['ARTeLab/ilpost'] | null | 3 | 2 | 0 | 1 | 0 | 0 | 0 | ['summarization'] | true | true | true | 949 |
# summarization_ilpost
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.6020
- Rouge1: 33.7802
- Rouge2: 16.2953
- Rougel: 27.4797
- Rougelsum: 30.2273
- Gen Len: 45.3175
## U... |
ARTeLab/it5-summarization-mlsum | ARTeLab | t5 | 14 | 11 | transformers | 0 | summarization | true | false | false | null | ['it'] | ['ARTeLab/mlsum-it'] | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | true | true | true | 2,439 |
# summarization_mlsum
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on MLSum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 2.0190
- Rouge1: 19.3739
- Rouge2: 5.9753
- Rougel: 16.691
- Rougelsum: 16.7862
- Gen Len: 32.5268
## Usage
```... |
ARTeLab/mbart-summarization-fanpage | ARTeLab | mbart | 15 | 4 | transformers | 0 | summarization | true | false | false | null | ['it'] | ['ARTeLab/fanpage'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | true | true | true | 2,498 |
# mbart-summarization-fanpage
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.1833
- Rouge1: 36.5027
- Rouge2: 17.4428
- Rougel: 26.1734
- Rougelsum: 30.2... |
ARTeLab/mbart-summarization-ilpost | ARTeLab | mbart | 15 | 7 | transformers | 0 | summarization | true | false | false | null | ['it'] | ['ARTeLab/ilpost'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | true | true | true | 2,493 |
# mbart_summarization_ilpost
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.3640
- Rouge1: 38.9101
- Rouge2: 21.384
- Rougel: 32.0517
- Rougelsum: 35.0743... |
ARTeLab/mbart-summarization-mlsum | ARTeLab | mbart | 15 | 62 | transformers | 1 | summarization | true | false | false | null | ['it'] | ['ARTeLab/mlsum-it'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | true | true | true | 2,484 |
# mbart_summarization_mlsum
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 3.3336
- Rouge1: 19.3489
- Rouge2: 6.4028
- Rougel: 16.3497
- Rougelsum: 16.5387
- Gen ... |
ASCCCCCCCC/PENGMENGJIE-finetuned-emotion | ASCCCCCCCC | distilbert | 14 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | false | true | true | 915 |
<!-- 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. -->
# PENGMENGJIE-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... |
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 | ASCCCCCCCC | bert | 15 | 6 | transformers | 0 | text-classification | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,325 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/ber... |
ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 | ASCCCCCCCC | bert | 12 | 5 | transformers | 0 | text-classification | true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,212 |
<!-- 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-chinese-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-ba... |
ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000 | ASCCCCCCCC | distilbert | 12 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,257 |
<!-- 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-multilingual-cased-amazon_zh_20000
This model is a fine-tuned version of [distilbert-base-multilingual-cased](ht... |
ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000 | ASCCCCCCCC | distilbert | 12 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,233 |
<!-- 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-amazon_zh_20000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... |
ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | ASCCCCCCCC | distilbert | 22 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | false | true | true | 925 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... |
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