OpenMOSS-Team / elasticbert-large

huggingface.co
Total runs: 32
24-hour runs: 3
7-day runs: 2
30-day runs: 6
Model's Last Updated: December 12 2022
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Introduction of elasticbert-large

Model Details of elasticbert-large

ElasticBERT-LARGE

Model description

This is an implementation of the large version of ElasticBERT.

Towards Efficient NLP: A Standard Evaluation and A Strong Baseline

Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu

Code link

fastnlp/elasticbert

Usage
>>> from transformers import BertTokenizer as ElasticBertTokenizer
>>> from models.configuration_elasticbert import ElasticBertConfig
>>> from models.modeling_elasticbert import ElasticBertForSequenceClassification
>>> num_output_layers = 1
>>> config = ElasticBertConfig.from_pretrained('fnlp/elasticbert-large', num_output_layers=num_output_layers )
>>> tokenizer = ElasticBertTokenizer.from_pretrained('fnlp/elasticbert-large')
>>> model = ElasticBertForSequenceClassification.from_pretrained('fnlp/elasticbert-large', config=config)
>>> input_ids = tokenizer.encode('The actors are fantastic .', return_tensors='pt')
>>> outputs = model(input_ids)
Citation
@article{liu2021elasticbert,
  author    = {Xiangyang Liu and
               Tianxiang Sun and
               Junliang He and
               Lingling Wu and
               Xinyu Zhang and
               Hao Jiang and
               Zhao Cao and
               Xuanjing Huang and
               Xipeng Qiu},
  title     = {Towards Efficient {NLP:} {A} Standard Evaluation and {A} Strong Baseline},
  journal   = {CoRR},
  volume    = {abs/2110.07038},
  year      = {2021},
  url       = {https://arxiv.org/abs/2110.07038},
  eprinttype = {arXiv},
  eprint    = {2110.07038},
  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-07038.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Runs of OpenMOSS-Team elasticbert-large on huggingface.co

32
Total runs
3
24-hour runs
3
3-day runs
2
7-day runs
6
30-day runs

More Information About elasticbert-large huggingface.co Model

elasticbert-large huggingface.co

elasticbert-large huggingface.co is an AI model on huggingface.co that provides elasticbert-large's model effect (), which can be used instantly with this OpenMOSS-Team elasticbert-large model. huggingface.co supports a free trial of the elasticbert-large model, and also provides paid use of the elasticbert-large. Support call elasticbert-large model through api, including Node.js, Python, http.

OpenMOSS-Team elasticbert-large online free

elasticbert-large huggingface.co is an online trial and call api platform, which integrates elasticbert-large's modeling effects, including api services, and provides a free online trial of elasticbert-large, you can try elasticbert-large online for free by clicking the link below.

OpenMOSS-Team elasticbert-large online free url in huggingface.co:

https://huggingface.co/OpenMOSS-Team/elasticbert-large

elasticbert-large install

elasticbert-large is an open source model from GitHub that offers a free installation service, and any user can find elasticbert-large on GitHub to install. At the same time, huggingface.co provides the effect of elasticbert-large install, users can directly use elasticbert-large installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

elasticbert-large install url in huggingface.co:

https://huggingface.co/OpenMOSS-Team/elasticbert-large

Url of elasticbert-large

Provider of elasticbert-large huggingface.co

OpenMOSS-Team
ORGANIZATIONS

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huggingface.co

Total runs: 108
Run Growth: -201
Growth Rate: -184.40%
Updated:September 26 2023