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:
- c76a01113aa217d34ea9fb28407b0cd3d773eb1a27c4872e1f9d38093875254f
- Size of remote file:
- 4.86 kB
- SHA256:
- 5034a1abc208d7fad6584303d55db6ec0dd61dcf33f9dafee944d92d313cd2a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.