Instructions to use facebook/mbart-large-cc25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mbart-large-cc25 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="facebook/mbart-large-cc25")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-cc25", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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Download README.md from facebook/mbart-large-cc25: direct link, hf CLI and curl.
- Browser
- Download file 699 Bytes
-
https://huggingface.co/facebook/mbart-large-cc25/resolve/main/README.md
- Command line
-
hf download hf://facebook/mbart-large-cc25/README.md
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curl -L -o README.md https://huggingface.co/facebook/mbart-large-cc25/resolve/main/README.md
699 Bytes
metadata
tags:
- translation
language:
- en
- ar
- cs
- de
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- multilingual
mbart-large-cc25
Pretrained (not finetuned) multilingual mbart model. Original Languages
export langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN
Original Code: https://github.com/pytorch/fairseq/tree/master/examples/mbart Docs: https://huggingface.co/transformers/master/model_doc/mbart.html Finetuning Code: examples/seq2seq/finetune.py (as of Aug 20, 2020)
Can also be finetuned for summarization.