Instructions to use Pierre-Arthur/distilroberta_base_eurolex_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pierre-Arthur/distilroberta_base_eurolex_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Pierre-Arthur/distilroberta_base_eurolex_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Pierre-Arthur/distilroberta_base_eurolex_mlm_model") model = AutoModelForMaskedLM.from_pretrained("Pierre-Arthur/distilroberta_base_eurolex_mlm_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Pierre-Arthur/distilroberta_base_eurolex_mlm_model: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/Pierre-Arthur/distilroberta_base_eurolex_mlm_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Pierre-Arthur/distilroberta_base_eurolex_mlm_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Pierre-Arthur/distilroberta_base_eurolex_mlm_model/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 0145f22b231b9786d04e121ad4b0b620df80337f2130a46e4cb14d8226640cc1
- Size of remote file:
- 329 MB
- SHA256:
- 6820880d776972314a58b75a6f9e90f80ec69c1a48458b44243c0fe43983a7a4
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