Instructions to use HPLT/hplt_bert_base_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPLT/hplt_bert_base_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HPLT/hplt_bert_base_es", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_es", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from HPLT/hplt_bert_base_es: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/HPLT/hplt_bert_base_es/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://HPLT/hplt_bert_base_es/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/HPLT/hplt_bert_base_es/resolve/main/tokenizer_config.json
237 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "bos_token": "[BOS]", | |
| "eos_token": "[EOS]", | |
| "unk_token": "[UNK]", | |
| "sep_token": "[SEP]", | |
| "pad_token": "[PAD]", | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]" | |
| } | |