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values | library_name stringclasses 198
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
summarization | transformers |
## Model description
[PEGASUS](https://github.com/google-research/pegasus) fine-tuned for summarization
## Install "sentencepiece" library required for tokenizer
```
pip install sentencepiece
```
## Model in Action 🚀
```
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_n... | {"language": "en", "license": "apache-2.0", "tags": ["pegasus", "seq2seq", "summarization"], "model-index": [{"name": "tuner007/pegasus_summarizer", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.0.0", "split": "train"}... | tuner007/pegasus_summarizer | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
PEGASUS fine-tuned for summarization
## Install "sentencepiece" library required for tokenizer
## Model in Action
#### Example:
context = """"
India wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing th... | [
"## Model description\nPEGASUS fine-tuned for summarization",
"## Install \"sentencepiece\" library required for tokenizer",
"## Model in Action",
"#### Example: \ncontext = \"\"\"\"\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fie... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\nPEGASUS fine-tuned for summarization",
"## Install \"sentencepiece\" library required for tokeni... |
text2text-generation | transformers | # T5 for abstractive question-answering
This is T5-base model fine-tuned for abstractive QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 2 epochs
## Model in Action 🚀
```
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from... | {} | tuner007/t5_abs_qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # T5 for abstractive question-answering
This is T5-base model fine-tuned for abstractive QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 2 epochs
## Model in Action
#### Example 1: Answer available
#### Example 2: Answer not available
> Created by Arpit Ra... | [
"# T5 for abstractive question-answering\nThis is T5-base model fine-tuned for abstractive QA using text-to-text approach",
"## Model training\nThis model was trained on colab TPU with 35GB RAM for 2 epochs",
"## Model in Action",
"#### Example 1: Answer available",
"#### Example 2: Answer not available \n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5 for abstractive question-answering\nThis is T5-base model fine-tuned for abstractive QA using text-to-text approach",
"## Model training\nThis model was... |
null | transformers | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... | {} | tunib/electra-ko-base | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us
| TUNiB-Electra
=============
We release several new versions of the ELECTRA model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of... | [
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us \n",
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------"
] |
null | transformers | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... | {} | tunib/electra-ko-en-base | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us
| TUNiB-Electra
=============
We release several new versions of the ELECTRA model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of... | [
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------\n\n\n\nResults on English downstream tasks\n-----------------------------------"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us \n",
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------\n\n\n\nResults on English downstream tasks\n-----------------------------------"
] |
null | transformers | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... | {} | tunib/electra-ko-en-small | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us
| TUNiB-Electra
=============
We release several new versions of the ELECTRA model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of... | [
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------\n\n\n\nResults on English downstream tasks\n-----------------------------------"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us \n",
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------\n\n\n\nResults on English downstream tasks\n-----------------------------------"
] |
null | transformers | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... | {} | tunib/electra-ko-small | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us
| TUNiB-Electra
=============
We release several new versions of the ELECTRA model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of... | [
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #arxiv-2003.10555 #endpoints_compatible #region-us \n",
"### Tokenizer example\n\n\nResults on Korean downstream tasks\n----------------------------------"
] |
text-generation | transformers |
# Generate Thai Lyrics (แต่งเพลงไทยด้วย GPT-2)
GPT-2 for Thai lyrics generation. We use [GPT-2 base Thai](https://huggingface.co/flax-community/gpt2-base-thai) as a pre-trained model
for [Siamzone lyrics](https://www.siamzone.com/music/thailyric/)
เราเทรนโมเดล GPT-2 สำหรับใช้แต่งเนื้อเพลงไทยด้วยเนื้อเพลงจากเว็บไซต์ ... | {"language": ["th"], "widget": [{"text": "\u0e04\u0e27\u0e32\u0e21\u0e23\u0e31\u0e01"}, {"text": "\u0e2d\u0e22\u0e32\u0e01\u0e23\u0e39\u0e49"}, {"text": "\u0e44\u0e2b\u0e19\u0e27\u0e48\u0e32"}]} | tupleblog/generate-thai-lyrics | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"th",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #th #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Generate Thai Lyrics (แต่งเพลงไทยด้วย GPT-2)
GPT-2 for Thai lyrics generation. We use GPT-2 base Thai as a pre-trained model
for Siamzone lyrics
เราเทรนโมเดล GPT-2 สำหรับใช้แต่งเนื้อเพลงไทยด้วยเนื้อเพลงจากเว็บไซต์ Siamzone
## Example use
| [
"# Generate Thai Lyrics (แต่งเพลงไทยด้วย GPT-2)\n\nGPT-2 for Thai lyrics generation. We use GPT-2 base Thai as a pre-trained model\nfor Siamzone lyrics\n\nเราเทรนโมเดล GPT-2 สำหรับใช้แต่งเนื้อเพลงไทยด้วยเนื้อเพลงจากเว็บไซต์ Siamzone",
"## Example use"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #th #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Generate Thai Lyrics (แต่งเพลงไทยด้วย GPT-2)\n\nGPT-2 for Thai lyrics generation. We use GPT-2 base Thai as a pre-trained model\nfor Siamzone lyrics\n\nเราเทรนโมเดล GP... |
text-classification | transformers |
# Salim-Classifier
**วัตถุประสงค์:** ทุกวันนี้หาเพื่อนที่รักชาติ ศาสนา พระมหากษัตริย์ รัฐบาลยากเหลือเกิน มีแต่พวกสามกีบ ควายแดงคอยจ้องจะทำร้าย
ทางทีมของเราจึงสร้างโมเดลมาเพื่อช่วยหาเพื่อนสลิ่มจากคอมเม้น ที่นับ... | {"widget": [{"text": "\u0e23\u0e31\u0e10\u0e23\u0e31\u0e1a\u0e1c\u0e34\u0e14\u0e0a\u0e2d\u0e1a\u0e17\u0e38\u0e01\u0e0a\u0e35\u0e27\u0e34\u0e15\u0e44\u0e21\u0e48\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e2d\u0e01\u0e04\u0e19\u0e43\u0e2b\u0e49\u0e1a\u0e23\u0e34\u0e01\u0e32\u0e23\u0e15\u0e49\u0e2d\u0e07\u0e08\u0e31\u0e14\u0e01\u0e... | tupleblog/salim-classifier | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| !Salim Word Cloud
Salim-Classifier
================
วัตถุประสงค์: ทุกวันนี้หาเพื่อนที่รักชาติ ศาสนา พระมหากษัตริย์ รัฐบาลยากเหลือเกิน มีแต่พวกสามกีบ ควายแดงคอยจ้องจะทำร้าย
ทางทีมของเราจึงสร้างโมเดลมาเพื่อช่วยหาเพื่อนสลิ่มจากคอมเม้น ที่นับวันจะหลงเหลืออยู่น้อยยิ่งนักในสังคมไทย ทั้งนี้เพื่อเป็นแนวทางในการสร้างสังคมสล... | [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# FinBertPTBR : Financial Bert PT BR (Depreciated model)
> **Info**
> Newer version available on https://huggingface.co/lucas-leme/FinBERT-PT-BR
FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the fin... | {"language": "pt", "license": "apache-2.0", "widget": [{"text": "O futuro de DI caiu 20 bps nesta manh\u00e3", "example_title": "Example 1"}, {"text": "O Nubank decidiu cortar a faixa de pre\u00e7o da oferta p\u00fablica inicial (IPO) ap\u00f3s rev\u00e9s no humor dos mercados internacionais com as fintechs.", "example... | turing-usp/FinBertPTBR | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# FinBertPTBR : Financial Bert PT BR (Depreciated model)
> Info
> Newer version available on URL
FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and ... | [
"# FinBertPTBR : Financial Bert PT BR (Depreciated model)\n\n> Info\n> Newer version available on URL\n\nFinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial co... | [
"TAGS\n#transformers #pytorch #bert #text-classification #pt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# FinBertPTBR : Financial Bert PT BR (Depreciated model)\n\n> Info\n> Newer version available on URL\n\nFinBertPTBR is a pre-trained NLP model to analyze sentime... |
text-generation | transformers | Fine-tuned on short news articles for summarization with GPT-neo 1.3B parameters | {"license": "apache-2.0"} | turing1729/gpt-neo-1.3B-news | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned on short news articles for summarization with GPT-neo 1.3B parameters | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | tushar-rishav/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"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 #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1196
* Precision: 0.7872
* Recall: 0.8292
* F1: 0.8077
* Accuracy: 0.9722
Model description
-----------------
More information nee... | [
"### 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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #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\... |
text-generation | transformers | <h1>BreitBot</h1><h2>Timothy W. Dooley</h2>___________________________________________________<h3>GitHub</h3>The GitHub for the project can be found [here](https://github.com/twdooley/election_news)<h3>Model</h3><br>This model was trained on about 16,000 headlines from Breitbart.com spannning March 2019- 11 November 20... | {} | twdooley/breitbot | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <h1>BreitBot</h1><h2>Timothy W. Dooley</h2>___________________________________________________<h3>GitHub</h3>The GitHub for the project can be found here<h3>Model</h3><br>This model was trained on about 16,000 headlines from URL spannning March 2019- 11 November 2020. The purpose of this project was to better understan... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers | This model is [ALBERT base v2](https://huggingface.co/albert-base-v2) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type albert
--model_name_or_path albert-base-v2
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_negativ... | {} | twmkn9/albert-base-v2-squad2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
| This model is ALBERT base v2 trained on SQuAD v2 as:
Performance on a dev subset is close to the original paper:
We are hopeful this might save you time, energy, and compute. Cheers! | [] | [
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | This model is [BERT base uncased](https://huggingface.co/bert-base-uncased) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type bert
--model_name_or_path bert-base-uncased
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_... | {} | twmkn9/bert-base-uncased-squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| This model is BERT base uncased trained on SQuAD v2 as:
Performance on a dev subset is close to the original paper:
We are hopeful this might save you time, energy, and compute. Cheers! | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | This model is [Distilbert base uncased](https://huggingface.co/distilbert-base-uncased) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type distilbert
--model_name_or_path distilbert-base-uncased
--do_train
--do_eval
--overwrite_cache
--do_lower_case... | {} | twmkn9/distilbert-base-uncased-squad2 | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
| This model is Distilbert base uncased trained on SQuAD v2 as:
Performance on a dev subset is close to the original paper:
We are hopeful this might save you time, energy, and compute. Cheers! | [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | This model is [Distilroberta base](https://huggingface.co/distilroberta-base) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type robberta
--model_name_or_path distilroberta-base
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_... | {} | twmkn9/distilroberta-base-squad2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us
| This model is Distilroberta base trained on SQuAD v2 as:
Performance on a dev subset is close to the original paper:
We are hopeful this might save you time, energy, and compute. Cheers! | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us \n"
] |
null | null | Hugging Face's logo
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#region-us
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Hugging Face
Search models, datasets, users...
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] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-ncj/nah
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Nahuatl specifically of the Nort of Puebla (ncj) using a derivate of [SLR92](https://www.openslr.org/92/), and some samples of `es` and `de` datasets from [Common Voice](https://hug... | {"language": "nah specifically ncj", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["created a new dataset based on https://www.openslr.org/92/"], "metrics": ["wer"]} | tyoc213/wav2vec2-large-xlsr-nahuatl | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nah specifically ncj"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-ncj/nah
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Nahuatl specifically of the Nort of Puebla (ncj) using a derivate of SLR92, and some samples of 'es' and 'de' datasets from Common Voice.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
T... | [
"# Wav2Vec2-Large-XLSR-53-ncj/nah\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nahuatl specifically of the Nort of Puebla (ncj) using a derivate of SLR92, and some samples of 'es' and 'de' datasets from Common Voice.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-ncj/nah\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nahuatl specifically of the Nort of Puebla (ncj) usi... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-11body-0context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-11body-0context", "results": []}]} | tyoyo/t5-base-TEDxJP-11body-0context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-TEDxJP-11body-0context
==============================
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8068
* Wer: 0.1976
* Mer: 0.1904
* Wil: 0.2816
* Wip: 0.7184
* Hits: 602335
* Substitutions: 7505... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-1body-10context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-1body-10context", "results": []}]} | tyoyo/t5-base-TEDxJP-1body-10context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-TEDxJP-1body-10context
==============================
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3833
* Wer: 0.1983
* Mer: 0.1900
* Wil: 0.2778
* Wip: 0.7222
* Hits: 56229
* Substitutions: 6686
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-1body-1context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-1body-1context", "results": []}]} | tyoyo/t5-base-TEDxJP-1body-1context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-TEDxJP-1body-1context
=============================
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5061
* Wer: 0.1990
* Mer: 0.1913
* Wil: 0.2823
* Wip: 0.7177
* Hits: 55830
* Substitutions: 6943
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-1body-2context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-1body-2context", "results": []}]} | tyoyo/t5-base-TEDxJP-1body-2context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-TEDxJP-1body-2context
=============================
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4968
* Wer: 0.1969
* Mer: 0.1895
* Wil: 0.2801
* Wip: 0.7199
* Hits: 55902
* Substitutions: 6899
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-1body-3context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-1body-3context", "results": []}]} | tyoyo/t5-base-TEDxJP-1body-3context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-TEDxJP-1body-3context
=============================
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4926
* Wer: 0.1968
* Mer: 0.1894
* Wil: 0.2793
* Wip: 0.7207
* Hits: 55899
* Substitutions: 6836
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #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 | Epoch Training Loss Validation Loss Wer Mer Wil Wip Hits Substitutions Deletions Insertions Cer
1 0.572400 0.447836 0.262284 0.241764 0.333088 0.666912 54709 7126 4673 5645 0.242417
2 0.492700 0.400297 0.203600 0.196446 0.285798 0.714202 55389 6777 4342 2422 0.183740
3 0.429200 0.385705 0.201179 0.193641 0.282458 0.717... | {} | tyoyo/t5-base-TEDxJP-1body-5context | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Epoch Training Loss Validation Loss Wer Mer Wil Wip Hits Substitutions Deletions Insertions Cer
1 0.572400 0.447836 0.262284 0.241764 0.333088 0.666912 54709 7126 4673 5645 0.242417
2 0.492700 0.400297 0.203600 0.196446 0.285798 0.714202 55389 6777 4342 2422 0.183740
3 0.429200 0.385705 0.201179 0.193641 0.282458 0.717... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
zero-shot-classification | transformers |
# DistilBERT base model (uncased)
## 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 Impa... | {"language": "en", "tags": ["distilbert"], "datasets": ["multi_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | typeform/distilbert-base-uncased-mnli | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"distilbert",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"arxiv:1910.09700",
"arxiv:2105.09680",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700",
"2105.09680"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #distilbert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBERT base model (uncased)
===============================
Table of Contents
-----------------
* Model Details
* How to Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
Model Details
-------------
Model Description: This is the uncased DistilB... | [
"#### Training Data\n\n\nThis model of DistilBERT-uncased is pretrained on the Multi-Genre Natural Language Inference (MultiNLI) corpus. It is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus covers a range of genres of spoken and written text, and supports... | [
"TAGS\n#transformers #pytorch #tf #safetensors #distilbert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Training Data\n\n\nThis model of DistilBERT-uncased is pretrained on the... |
fill-mask | transformers |
# DistilRoBERTa base model
Forked from https://huggingface.co/distilroberta-base
| {"language": "en", "license": "apache-2.0", "datasets": ["openwebtext"]} | typeform/distilroberta-base-v2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #en #dataset-openwebtext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilRoBERTa base model
Forked from URL
| [
"# DistilRoBERTa base model\n\nForked from URL"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #en #dataset-openwebtext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilRoBERTa base model\n\nForked from URL"
] |
fill-mask | transformers |
# DistilRoBERTa base model
Forked from https://huggingface.co/distilroberta-base
| {"language": "en", "license": "apache-2.0", "datasets": ["openwebtext"]} | typeform/distilroberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #dataset-openwebtext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilRoBERTa base model
Forked from URL
| [
"# DistilRoBERTa base model\n\nForked from URL"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #dataset-openwebtext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilRoBERTa base model\n\nForked from URL"
] |
zero-shot-classification | transformers |
# Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
# Model Details
## Model Description
This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https://huggingface.co/google/mobilebert-uncased).
- **Developed b... | {"language": "en", "tags": ["mobilebert"], "datasets": ["multi_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | typeform/mobilebert-uncased-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mobilebert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
# Model Details
## Model Description
This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the uncased MobileBERT model.
- Developed by: Typeform
- Shared by [Optional]: Typeform
- Model t... | [
"# Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices",
"# Model Details",
"## Model Description\n \nThis model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the uncased MobileBERT model.\n \n- Developed by: Typeform\n- Shared by [Optional]: Type... | [
"TAGS\n#transformers #pytorch #safetensors #mobilebert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices",
"# Model... |
zero-shot-classification | transformers |
# RoBERTa Large Multilanguage | {"language": "multilingual", "pipeline_tag": "zero-shot-classification"} | typeform/roberta-large-mnli | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"zero-shot-classification",
"multilingual",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #tf #roberta #text-classification #zero-shot-classification #multilingual #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Large Multilanguage | [
"# RoBERTa Large Multilanguage"
] | [
"TAGS\n#transformers #tf #roberta #text-classification #zero-shot-classification #multilingual #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Large Multilanguage"
] |
zero-shot-classification | transformers |
# SqueezeBERT | {"language": "en", "tags": ["squeezebert"], "datasets": ["mulit_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | typeform/squeezebert-mnli | null | [
"transformers",
"pytorch",
"squeezebert",
"zero-shot-classification",
"en",
"dataset:mulit_nli",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #squeezebert #zero-shot-classification #en #dataset-mulit_nli #endpoints_compatible #region-us
|
# SqueezeBERT | [
"# SqueezeBERT"
] | [
"TAGS\n#transformers #pytorch #squeezebert #zero-shot-classification #en #dataset-mulit_nli #endpoints_compatible #region-us \n",
"# SqueezeBERT"
] |
text-classification | transformers |
# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset
Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper [IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding](http... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": true} | tyqiangz/indobert-lite-large-p2-smsa | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"text-classification",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #safetensors #albert #text-classification #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset
Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding.
##... | [
"# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset\r\n\r\nFinetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian\r\nNatural Language Understandi... | [
"TAGS\n#transformers #pytorch #safetensors #albert #text-classification #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset\r\n\r\nFinetune... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-chaii
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "xlm-roberta-base-finetuned-chaii", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]} | tyqiangz/xlm-roberta-base-finetuned-chaii | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-chaii
================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4651
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #license-mit #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: 16\... |
null | null | https://teacher.desmos.com/activitybuilder/teacherguide/604249659240440d25a27d0c
https://teacher.desmos.com/activitybuilder/teacherguide/604249a365ecd40d30b4ad18
https://teacher.desmos.com/activitybuilder/teacherguide/604249e2cfb0a20d51e13768
https://teacher.desmos.com/activitybuilder/teacherguide/60424a1c9240440d25a27... | {} | uasoyasser/eefdfgdg | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
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URL
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URL | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"language": ["ro"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-ro", "results": [{"task":... | ubamba98/wav2vec2-xls-r-1b-ro | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ro #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-ro
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - RO dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1113
* Wer: 0.4770
* Cer: 0.0306
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ro #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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-xls-r-300m-CV8-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": ["ro"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-CV8-ro", "results": []}... | ubamba98/wav2vec2-xls-r-300m-CV8-ro | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ro #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-CV8-ro
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - RO dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1578
* Wer: 0.6040
* Cer: 0.0475
Model description
---------------... | [
"### 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: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ro #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\n... |
text-classification | keras |
# Measuring hate speech: RoBERTa-Large
This model predicts a continuous hate speech score as described in Kennedy et al. (2020).
## Citation
```
@article{kennedy2020constructing,
title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
author=... | {"language": ["en"], "tags": ["text-classification", "hate-speech", "counterspeech", "irt", "arxiv:2009.10277"], "datasets": ["ucberkeley-dlab/measuring-hate-speech"]} | ucberkeley-dlab/hate-measure-roberta-large | null | [
"keras",
"text-classification",
"hate-speech",
"counterspeech",
"irt",
"arxiv:2009.10277",
"en",
"dataset:ucberkeley-dlab/measuring-hate-speech",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.10277"
] | [
"en"
] | TAGS
#keras #text-classification #hate-speech #counterspeech #irt #arxiv-2009.10277 #en #dataset-ucberkeley-dlab/measuring-hate-speech #has_space #region-us
|
# Measuring hate speech: RoBERTa-Large
This model predicts a continuous hate speech score as described in Kennedy et al. (2020).
## References
Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech applic... | [
"# Measuring hate speech: RoBERTa-Large\n\nThis model predicts a continuous hate speech score as described in Kennedy et al. (2020).",
"## References\n\nKennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate s... | [
"TAGS\n#keras #text-classification #hate-speech #counterspeech #irt #arxiv-2009.10277 #en #dataset-ucberkeley-dlab/measuring-hate-speech #has_space #region-us \n",
"# Measuring hate speech: RoBERTa-Large\n\nThis model predicts a continuous hate speech score as described in Kennedy et al. (2020).",
"## Reference... |
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. -->
# IceBERT-finetuned-grouped
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on a... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "IceBERT-finetuned-grouped", "results": []}]} | ueb1/IceBERT-finetuned-grouped | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-grouped
=========================
This model is a fine-tuned version of vesteinn/IceBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5660
* Accuracy: 0.2259
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #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\\... |
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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "typ... | ueb1/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0799
* Precision: 0.8927
* Recall: 0.8649
* F1: 0.8786
* Accuracy: 0.9853
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# IceBERT-finetuned
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on an unknow... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "IceBERT-finetuned", "results": []}]} | ueb1/IceBERT-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned
=================
This model is a fine-tuned version of vesteinn/IceBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7361
* Accuracy: 0.352
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #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\\... |
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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | ueb1/XLMR-ENIS-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0940
* Precision: 0.8685
* Recall: 0.8413
* F1: 0.8547
* Accuracy: 0.9825
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | ueb1/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: 0.0608
* Precision: 0.9290
* Recall: 0.9371
* F1: 0.9331
* Accuracy: 0.9840
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... |
fill-mask | transformers |
# Chinese ALBERT
## Model description
This is the set of Chinese ALBERT models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretra... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4e2d\u56fd\u7684\u9996\u90fd\u662f[MASK]\u4eac"}]} | uer/albert-base-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese ALBERT
==============
Model description
-----------------
This is the set of Chinese ALBERT models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters abov... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese ALBERT
## Model description
This is the set of Chinese ALBERT models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretra... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4e2d\u56fd\u7684\u9996\u90fd\u662f[MASK]\u4eac"}]} | uer/albert-large-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese ALBERT
==============
Model description
-----------------
This is the set of Chinese ALBERT models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters abov... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
# Chinese BART
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper]... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50[MASK]\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3a[MASK]\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7[MASK]\u7684\u8001\u677f\u3002"}]} | uer/bart-base-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese BART
============
Model description
-----------------
This model is pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it t... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-10_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-10_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-10_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-10_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-12_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-12_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-12_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-12_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-2_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-2_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-2_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-2_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-4_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-4_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-4_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-4_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-6_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-6_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-6_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-6_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-8_H-128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-8_H-256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-8_H-512 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/chinese_roberta_L-8_H-768 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa Miniatures
==========================
Model description
-----------------
This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
# Chinese Ancient GPT2 Model
## Model description
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in... | {"language": "zh", "widget": [{"text": "[CLS]\u5f53\u662f\u65f6"}]} | uer/gpt2-chinese-ancient | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Chinese Ancient GPT2 Model
## Model description
The model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal... | [
"# Chinese Ancient GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a m... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Chinese Ancient GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced... |
text-generation | transformers |
# Chinese GPT2 Models
## Model description
The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). The GPT2-xlarge model is pre-trained by [TencentPretrain](https://github.com/Tencent/... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u7c73\u996d\u662f\u4e00\u79cd\u7528\u7a3b\u7c73\u4e0e\u6c34\u716e\u6210\u7684\u98df\u7269"}]} | uer/gpt2-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Chinese GPT2 Models
===================
Model description
-----------------
The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by UER-py, which is introduced in this paper. The GPT2-xlarge model is pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models wi... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
# Chinese Couplet GPT2 Model
## Model description
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced i... | {"language": "zh", "widget": [{"text": "[CLS]\u56fd \u8272 \u5929 \u9999 \uff0c \u59f9 \u7d2b \u5ae3 \u7ea2 \uff0c \u78a7 \u6c34 \u9752 \u4e91 \u6b23 \u5171 \u8d4f -"}]} | uer/gpt2-chinese-couplet | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Chinese Couplet GPT2 Model
## Model description
The model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimoda... | [
"# Chinese Couplet GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a m... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Chinese Couplet GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced... |
text-generation | transformers |
# Chinese GPT2 Lyric Model
## Model description
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [... | {"language": "zh", "widget": [{"text": "\u6700\u7f8e\u7684\u4e0d\u662f\u4e0b\u96e8\u5929\uff0c\u662f\u66fe\u4e0e\u4f60\u8eb2\u8fc7\u96e8\u7684\u5c4b\u6a90"}]} | uer/gpt2-chinese-lyric | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Chinese GPT2 Lyric Model
## Model description
The model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal p... | [
"# Chinese GPT2 Lyric Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a mul... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Chinese GPT2 Lyric Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced i... |
text-generation | transformers |
# Chinese Poem GPT2 Model
## Model description
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [t... | {"language": "zh", "widget": [{"text": "[CLS] \u4e07 \u53e0 \u6625 \u5c71 \u79ef \u96e8 \u6674 \uff0c"}, {"text": "[CLS] \u5927 \u6f20"}]} | uer/gpt2-chinese-poem | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Chinese Poem GPT2 Model
## Model description
The model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pr... | [
"# Chinese Poem GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a mult... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Chinese Poem GPT2 Model",
"## Model description\n\nThe model is pre-trained by UER-py, which is introduced in... |
text-generation | transformers |
# Chinese GPT2 Models
## Model description
The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). The GPT2-xlarge model is pre-trained by [TencentPretrain](https://github.com/Tencent/... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u7c73\u996d\u662f\u4e00\u79cd\u7528\u7a3b\u7c73\u4e0e\u6c34\u716e\u6210\u7684\u98df\u7269"}]} | uer/gpt2-distil-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Chinese GPT2 Models
===================
Model description
-----------------
The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by UER-py, which is introduced in this paper. The GPT2-xlarge model is pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models wi... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
# Chinese Pegasus
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this pap... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5185\u5bb9\u4e30\u5bcc\u3001\u7248\u5f0f\u8bbe\u8ba1\u8003\u7a76\u3001\u56fe\u7247\u534e\u4e3d\u3001\u5370\u5236\u7cbe\u7f8e\u3002[MASK]\u7eb8\u7bb1\u5185\u8fd8\u653e\u4e86\u5145\u6c14\u888b\u7528\u4e8e\u4fdd\u62a4\u3002"}]} | uer/pegasus-base-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"pegasus",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #pegasus #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Pegasus
===============
Model description
-----------------
This model is pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extend... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #pegasus #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
question-answering | transformers |
# Chinese RoBERTa-Base Model for QA
## Model description
The model is used for extractive question answering. It is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be fine-tuned by [TencentPretrain](https:/... | {"language": "zh", "widget": [{"text": "\u8457\u540d\u8bd7\u6b4c\u300a\u5047\u5982\u751f\u6d3b\u6b3a\u9a97\u4e86\u4f60\u300b\u7684\u4f5c\u8005\u662f", "context": "\u666e\u5e0c\u91d1\u4ece\u90a3\u91cc\u5b66\u4e60\u4eba\u6c11\u7684\u8bed\u8a00\uff0c\u5438\u53d6\u4e86\u8bb8\u591a\u6709\u76ca\u7684\u517b\u6599\uff0c\u8fd9\... | uer/roberta-base-chinese-extractive-qa | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #zh #arxiv-1909.05658 #arxiv-2212.06385 #endpoints_compatible #has_space #region-us
|
# Chinese RoBERTa-Base Model for QA
## Model description
The model is used for extractive question answering. It is fine-tuned by UER-py, which is introduced in this paper. Besides, the model could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters... | [
"# Chinese RoBERTa-Base Model for QA",
"## Model description\n\nThe model is used for extractive question answering. It is fine-tuned by UER-py, which is introduced in this paper. Besides, the model could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #zh #arxiv-1909.05658 #arxiv-2212.06385 #endpoints_compatible #has_space #region-us \n",
"# Chinese RoBERTa-Base Model for QA",
"## Model description\n\nThe model is used for extractive question answering. It is fine-tuned by UER-py, which is intr... |
text-classification | transformers |
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [... | {"language": "zh", "widget": [{"text": "\u8fd9\u672c\u4e66\u771f\u7684\u5f88\u4e0d\u9519"}]} | uer/roberta-base-finetuned-chinanews-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1708.02657",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1708.02657"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa-Base Models for Text Classification
===================================================
Model description
-----------------
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by Tencent... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
token-classification | transformers |
# Chinese RoBERTa-Base Model for NER
## Model description
The model is used for named entity recognition. It is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be fine-tuned by [TencentPretrain](https://git... | {"language": "zh", "widget": [{"text": "\u6c5f\u82cf\u8b66\u65b9\u901a\u62a5\u7279\u65af\u62c9\u51b2\u8fdb\u5e97\u94fa"}]} | uer/roberta-base-finetuned-cluener2020-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Chinese RoBERTa-Base Model for NER
## Model description
The model is used for named entity recognition. It is fine-tuned by UER-py, which is introduced in this paper. Besides, the model could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters abo... | [
"# Chinese RoBERTa-Base Model for NER",
"## Model description\n\nThe model is used for named entity recognition. It is fine-tuned by UER-py, which is introduced in this paper. Besides, the model could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with para... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Chinese RoBERTa-Base Model for NER",
"## Model description\n\nThe model is used for named entity recognition. It is fine-tuned by U... |
text-classification | transformers |
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [... | {"language": "zh", "widget": [{"text": "\u8fd9\u672c\u4e66\u771f\u7684\u5f88\u4e0d\u9519"}]} | uer/roberta-base-finetuned-dianping-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1708.02657",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1708.02657"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese RoBERTa-Base Models for Text Classification
===================================================
Model description
-----------------
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by Tencent... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
text-classification | transformers |
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [... | {"language": "zh", "widget": [{"text": "\u8fd9\u672c\u4e66\u771f\u7684\u5f88\u4e0d\u9519"}]} | uer/roberta-base-finetuned-ifeng-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1708.02657",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1708.02657"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa-Base Models for Text Classification
===================================================
Model description
-----------------
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by Tencent... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text-classification | transformers |
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [... | {"language": "zh", "widget": [{"text": "\u8fd9\u672c\u4e66\u771f\u7684\u5f88\u4e0d\u9519"}]} | uer/roberta-base-finetuned-jd-binary-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1708.02657",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1708.02657"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese RoBERTa-Base Models for Text Classification
===================================================
Model description
-----------------
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by Tencent... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
text-classification | transformers |
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [... | {"language": "zh", "widget": [{"text": "\u8fd9\u672c\u4e66\u771f\u7684\u5f88\u4e0d\u9519"}]} | uer/roberta-base-finetuned-jd-full-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1708.02657",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385",
"1708.02657"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us
| Chinese RoBERTa-Base Models for Text Classification
===================================================
Model description
-----------------
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by Tencent... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #zh #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1708.02657 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](htt... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u6700\u8fd1\u4e00\u8d9f\u53bb\u5317\u4eac\u7684[MASK]\u51e0\u70b9\u53d1\u8f66"}]} | uer/roberta-base-word-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese word-based RoBERTa Miniatures
=====================================
Model description
-----------------
This is the set of 5 Chinese word-based RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](htt... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u6700\u8fd1\u4e00\u8d9f\u53bb\u5317\u4eac\u7684[MASK]\u51e0\u70b9\u53d1\u8f66"}]} | uer/roberta-medium-word-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese word-based RoBERTa Miniatures
=====================================
Model description
-----------------
This is the set of 5 Chinese word-based RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](htt... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u6700\u8fd1\u4e00\u8d9f\u53bb\u5317\u4eac\u7684[MASK]\u51e0\u70b9\u53d1\u8f66"}]} | uer/roberta-mini-word-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese word-based RoBERTa Miniatures
=====================================
Model description
-----------------
This is the set of 5 Chinese word-based RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](htt... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u6700\u8fd1\u4e00\u8d9f\u53bb\u5317\u4eac\u7684[MASK]\u51e0\u70b9\u53d1\u8f66"}]} | uer/roberta-small-word-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese word-based RoBERTa Miniatures
=====================================
Model description
-----------------
This is the set of 5 Chinese word-based RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](htt... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u6700\u8fd1\u4e00\u8d9f\u53bb\u5317\u4eac\u7684[MASK]\u51e0\u70b9\u53d1\u8f66"}]} | uer/roberta-tiny-word-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese word-based RoBERTa Miniatures
=====================================
Model description
-----------------
This is the set of 5 Chinese word-based RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
sentence-similarity | sentence-transformers |
# Chinese Sentence BERT
## Model description
This is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretr... | {"language": "zh", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "\u90a3\u4e2a\u4eba\u5f88\u5f00\u5fc3", "sentences": ["\u90a3\u4e2a\u4eba\u975e\u5e38\u5f00\u5fc3", "\u90a3\u5... | uer/sbert-base-chinese-nli | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"zh",
"arxiv:1909.05658",
"arxiv:2212.06385",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #zh #arxiv-1909.05658 #arxiv-2212.06385 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Chinese Sentence BERT
## Model description
This is the sentence embedding model pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends ... | [
"# Chinese Sentence BERT",
"## Model description\n\nThis is the sentence embedding model pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, an... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #zh #arxiv-1909.05658 #arxiv-2212.06385 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Chinese Sentence BERT",
"## Model description\n\nThis is the sentence embedding model pre-trained by... |
text2text-generation | transformers |
# Chinese T5
## Model description
This is the set of Chinese T5 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) intr... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50extra0\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3aextra1\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7extra2\u7684\u8001\u677f\u3002"}]} | uer/t5-base-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Chinese T5
==========
Model description
-----------------
This is the set of Chinese T5 models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billio... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
# Chinese T5
## Model description
This is the set of Chinese T5 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) intr... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50extra0\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3aextra1\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7extra2\u7684\u8001\u677f\u3002"}]} | uer/t5-small-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Chinese T5
==========
Model description
-----------------
This is the set of Chinese T5 models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billio... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
# Chinese T5 Version 1.1
## Model description
This is the set of Chinese T5 Version 1.1 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50extra0\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3aextra1\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7extra2\u7684\u8001\u677f\u3002"}]} | uer/t5-v1_1-base-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"mt5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #mt5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Chinese T5 Version 1.1
======================
Model description
-----------------
This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support mod... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #mt5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
# Chinese T5 Version 1.1
## Model description
This is the set of Chinese T5 Version 1.1 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tence... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50extra0\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3aextra1\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7extra2\u7684\u8001\u677f\u3002"}]} | uer/t5-v1_1-small-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"jax",
"mt5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #mt5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Chinese T5 Version 1.1
======================
Model description
-----------------
This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support mod... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #mt5 #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
# SafeMathBot for NLP tasks in math learning environments
This model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.math... | {"language": ["en"], "license": "mit", "tags": ["generation", "math learning", "education"], "metrics": ["PerspectiveAPI"], "widget": [{"text": "<bos><speaker1>Hello! My name is CL. Nice meeting y'all!<speaker2>[SAFE]", "example_title": "Safe Response"}, {"text": "<bos><speaker1>Hello! My name is CL. Nice meeting y'all... | uf-aice-lab/SafeMathBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generation",
"math learning",
"education",
"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 #gpt2 #text-generation #generation #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# SafeMathBot for NLP tasks in math learning environments
This model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (URL SafeMathBot ... | [
"# SafeMathBot for NLP tasks in math learning environments\n\nThis model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (URL SafeMa... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generation #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# SafeMathBot for NLP tasks in math learning environments\n\nThis model is fine-tuned with GPT2-xl with 8 N... |
text-generation | transformers | # Math-RoBerta for NLP tasks in math learning environments
This model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). MathRoBERTa has 24 layers, and 355 million parameters and its publi... | {"language": ["en"], "license": "mit", "tags": ["nlp", "math learning", "education"]} | uf-aice-lab/math-roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-generation",
"nlp",
"math learning",
"education",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-generation #nlp #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Math-RoBerta for NLP tasks in math learning environments
This model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (URL MathRoBERTa has 24 layers, and 355 million parameters and its published model weights take up... | [
"# Math-RoBerta for NLP tasks in math learning environments\n\nThis model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (URL MathRoBERTa has 24 layers, and 355 million parameters and its published model weights ... | [
"TAGS\n#transformers #pytorch #roberta #text-generation #nlp #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Math-RoBerta for NLP tasks in math learning environments\n\nThis model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Danish version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://no... | {"language": "da", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-da | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"da",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #da #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Danish version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langua... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Danish version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 1... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #da #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (German version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://no... | {"language": "de", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-de | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lexical normalization",
"de",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #lexical normalization #de #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (German version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langua... | [
"# Fine-tuned ByT5-small for MultiLexNorm (German version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 1... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #lexical normalization #de #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-small for Multi... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (English version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | {"language": "en", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-en | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"en",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #en #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (English version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langu... | [
"# Fine-tuned ByT5-small for MultiLexNorm (English version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #en #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Spanish version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | {"language": "es", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-es | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"es",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #es #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Spanish version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langu... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Spanish version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #es #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Croatian version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://... | {"language": "hr", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-hr | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"hr",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"hr"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #hr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Croatian version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 lang... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Croatian version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #hr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task... | {"language": ["id", "en", "multilingual"], "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-iden | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"id",
"en",
"multilingual",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_co... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"id",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #id #en #multilingual #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets ... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media d... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #id #en #multilingual #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ... |
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