Feature Extraction
Transformers
PyTorch
Safetensors
Spanish
xlm-roberta
beto
galen
text-embeddings-inference
Instructions to use IIC/XLM-R_Galen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/XLM-R_Galen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="IIC/XLM-R_Galen")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("IIC/XLM-R_Galen") model = AutoModel.from_pretrained("IIC/XLM-R_Galen") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4780e332daf599f15e3ec4797ebadfc6b9eb1ff548644cd3ae22f467f13a78d
- Size of remote file:
- 1.11 GB
- SHA256:
- 0730002d47838a233e8ef6f1397daadbf2a77edcd906175435b3df22f47596d7
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