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 pytorch_model.bin from HPLT/hplt_bert_base_es: direct link, hf CLI and curl.
- Browser
- Download file 525 MB
-
https://huggingface.co/HPLT/hplt_bert_base_es/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HPLT/hplt_bert_base_es/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HPLT/hplt_bert_base_es/resolve/main/pytorch_model.bin
525 MB
- Xet hash:
- 028f041d802c52bdfc83d1a8fa27ec4976bf3b4c54b75d33361d5a74ca0ebd1c
- Size of remote file:
- 525 MB
- SHA256:
- e1777612ea9340a4821919c6964885efad6db70bd7e90854caa3460def7aaef6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.