infoxlm-large

Name: infoxlm-large
Author: microsoft

113.8K

514

Small context

558M

—

microsoft

Language Model

OTHER

Good

114K downloads

Production-ready

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

**InfoXLM** (NAACL 2021, [paper](https://arxiv.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

05b95b7d977450b364f8ea3269391953  config.json
c19438359fed6d36b0c1bbb107929579  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

text

@inproceedings{chi-etal-2021-infoxlm,
  title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
  author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
  booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.naacl-main.280",
  doi = "10.18653/v1/2021.naacl-main.280",
  pages = "3576--3588",}

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.