Instructions to use JAWCF/spladeX-TT-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JAWCF/spladeX-TT-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JAWCF/spladeX-TT-es")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JAWCF/spladeX-TT-es") model = AutoModelForMaskedLM.from_pretrained("JAWCF/spladeX-TT-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a07ea4131365eae0b70f0d4081df5712e00fec69678ac0e4a1a12802eec1c30a
- Size of remote file:
- 542 MB
- SHA256:
- 2962e83e42b409afe565abc4279e7d851ceb721a882220f8d9f37d9d82c09edd
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