Instructions to use mbruton/gal_en_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_en_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_en_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_en_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_en_XLM-R") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 159374de77ac19491660fe5a22a77f088f12ad76daff4a9ced439d80af442744
- Size of remote file:
- 3.5 kB
- SHA256:
- 0ecbb2c4be3a26e064e4b00435191727fcb4d36a1a1ba4a675a431239ff45f31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.