Instructions to use lijingxin/xlm-roberta-base-finetuned-panx-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lijingxin/xlm-roberta-base-finetuned-panx-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lijingxin/xlm-roberta-base-finetuned-panx-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lijingxin/xlm-roberta-base-finetuned-panx-en") model = AutoModelForTokenClassification.from_pretrained("lijingxin/xlm-roberta-base-finetuned-panx-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57fe5eecb0b074c8a23e389ae9c10de207f88fc093bac3137d73d8d5168c9af5
- Size of remote file:
- 1.11 GB
- SHA256:
- 8faf6709bc58dce1bd1fb3802a8276dd4123d16a3afca98faba45641a7333973
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.