Instructions to use BioMedTok/SentencePieceBPE-CC100-FR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BioMedTok/SentencePieceBPE-CC100-FR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BioMedTok/SentencePieceBPE-CC100-FR")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BioMedTok/SentencePieceBPE-CC100-FR") model = AutoModelForMaskedLM.from_pretrained("BioMedTok/SentencePieceBPE-CC100-FR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BioMedTok/SentencePieceBPE-CC100-FR: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/BioMedTok/SentencePieceBPE-CC100-FR/resolve/main/training_args.bin
- Command line
-
hf download hf://BioMedTok/SentencePieceBPE-CC100-FR/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BioMedTok/SentencePieceBPE-CC100-FR/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- 08c76d8233e9e42622dd2b406669707012ec5ac50e1bd8511ab0d16f952bc4b9
- Size of remote file:
- 3.5 kB
- SHA256:
- 80727d8c7373194404f35e28265cd96a42fcd8af5d4e1615c221a35a968389b2
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