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
File size: 134 Bytes
ca19a0d | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:2962e83e42b409afe565abc4279e7d851ceb721a882220f8d9f37d9d82c09edd
size 541817711
|