Instructions to use AlanLiJHU/MLMA_Lab5_Task2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlanLiJHU/MLMA_Lab5_Task2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AlanLiJHU/MLMA_Lab5_Task2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AlanLiJHU/MLMA_Lab5_Task2") model = AutoModelForTokenClassification.from_pretrained("AlanLiJHU/MLMA_Lab5_Task2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d60f0f9921729df816c3a0ddc9c72a904fed833e8813afc3cda7b9bb9c9a657
- Size of remote file:
- 1.39 GB
- SHA256:
- 846049c913aa145af5b3749515d50618e44dd2309b4b9c36130e5c8e12439467
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.