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 training_args.bin from Pierre-Arthur/distilroberta_base_eurolex_mlm_model: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Pierre-Arthur/distilroberta_base_eurolex_mlm_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Pierre-Arthur/distilroberta_base_eurolex_mlm_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Pierre-Arthur/distilroberta_base_eurolex_mlm_model/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 0086e6462038ba2286f087d7526623e5e2f89815b96762ec1e03dc6c1750303b
- Size of remote file:
- 3.96 kB
- SHA256:
- a7ac7da796bc140ce1ebc63f35bc5e7fdbc6eec78ee9c35a4fa58123b6ed878c
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