Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
spanish
xnli
text-embeddings-inference
Instructions to use bertin-project/bertin-base-xnli-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bertin-project/bertin-base-xnli-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bertin-project/bertin-base-xnli-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bertin-project/bertin-base-xnli-es") model = AutoModelForSequenceClassification.from_pretrained("bertin-project/bertin-base-xnli-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fa16037aaf018e041ab0e0b83f82b65546c1a981fa43e20897a09668b0a83d7
- Size of remote file:
- 499 MB
- SHA256:
- 45180fd0e967ae53e7e105be5e14cc2aa53a5ad1b2a7aaa8c2e9eece6ef2309f
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