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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 592 Bytes
6e19492 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"bos_token": "<s>",
"cls_token": "<s>",
"do_lower_case": false,
"eos_token": "</s>",
"keep_accents": true,
"mask_token": {
"__type": "AddedToken",
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"model_max_len": 512,
"model_max_length": 512,
"name_or_path": "models/XLM_R_Galen",
"pad_token": "<pad>",
"sep_token": "</s>",
"sp_model_kwargs": {},
"special_tokens_map_file": "models/XLM_R_Galen/special_tokens_map.json",
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
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