Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
spanish
roberta-large-bne
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/roberta-large-bne-caresA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/roberta-large-bne-caresA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/roberta-large-bne-caresA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/roberta-large-bne-caresA") model = AutoModelForSequenceClassification.from_pretrained("IIC/roberta-large-bne-caresA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IIC/roberta-large-bne-caresA: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/IIC/roberta-large-bne-caresA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IIC/roberta-large-bne-caresA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IIC/roberta-large-bne-caresA/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- cfe955dd67bcd40fbba3911de04fda9eed8e6a27dab882016ee3dc4cf110b9e6
- Size of remote file:
- 1.42 GB
- SHA256:
- bfa8c3073e885eb5f25060ff1bf08c0bf46c138d22ece592968842646937f041
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