Text Classification
Transformers
PyTorch
Safetensors
Spanish
xlm-roberta
biomedical
clinical
spanish
xlm-roberta-large
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/xlm-roberta-large-caresC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/xlm-roberta-large-caresC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/xlm-roberta-large-caresC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/xlm-roberta-large-caresC") model = AutoModelForSequenceClassification.from_pretrained("IIC/xlm-roberta-large-caresC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 618b5a3b941f3b1d10e2ca70863102d8642a877e3d09145a9e5b08cc875e6c33
- Size of remote file:
- 2.24 GB
- SHA256:
- 59bdae42ab6216b203148b27232d96e597288ca018defe366c4ebe2b2ea58b53
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