Instructions to use AlanLiJHU/MLMA_Lab5_Task5_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlanLiJHU/MLMA_Lab5_Task5_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AlanLiJHU/MLMA_Lab5_Task5_3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AlanLiJHU/MLMA_Lab5_Task5_3") model = AutoModelForTokenClassification.from_pretrained("AlanLiJHU/MLMA_Lab5_Task5_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b98a3800a79189e4e1855c9fa708b015fa1687f8012dfd57e599195dfa3e45ad
- Size of remote file:
- 1.39 GB
- SHA256:
- d5ffaf7e0dcbfa1e3fa9d782ad71c4876b3601fe93365bca3ada4a298ea081b3
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