Instructions to use anyuanay/my_finetuned_wnut_model_1012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anyuanay/my_finetuned_wnut_model_1012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anyuanay/my_finetuned_wnut_model_1012")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("anyuanay/my_finetuned_wnut_model_1012") model = AutoModelForTokenClassification.from_pretrained("anyuanay/my_finetuned_wnut_model_1012") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 832d9e1b9b5e5936be803cb20e02fb61a2bd302cccd29911dbd14f4c87e4d459
- Size of remote file:
- 431 MB
- SHA256:
- 941248f91f7b5648c193ee625b265f2f8d8162ce42f9237bbf2b5d06a0b77991
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.