Instructions to use ixa-ehu/roberta-eus-euscrawl-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ixa-ehu/roberta-eus-euscrawl-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ixa-ehu/roberta-eus-euscrawl-large-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ixa-ehu/roberta-eus-euscrawl-large-cased") model = AutoModelForMaskedLM.from_pretrained("ixa-ehu/roberta-eus-euscrawl-large-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ixa-ehu/roberta-eus-euscrawl-large-cased: direct link, hf CLI and curl.
- Browser
- Download file 351 Bytes
-
https://huggingface.co/ixa-ehu/roberta-eus-euscrawl-large-cased/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ixa-ehu/roberta-eus-euscrawl-large-cased/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ixa-ehu/roberta-eus-euscrawl-large-cased/resolve/main/tokenizer_config.json
351 Bytes
| {"do_lower_case":false, "bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "tokenizer_class": "XLMRobertaTokenizer"} | |