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:
- 221495a1824138fca627861a46e2289de74a178aab9c2f2fcb8a1e25b6a89691
- Size of remote file:
- 2.74 kB
- SHA256:
- 766d48b6509420d8a1f65f3d02bd7ddd2330d776d35a5158a13573ec7272241a
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