Instructions to use LexFerrinson/FirstModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LexFerrinson/FirstModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LexFerrinson/FirstModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LexFerrinson/FirstModel") model = AutoModelForTokenClassification.from_pretrained("LexFerrinson/FirstModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75f1cd06f8a818b2fa383eafd1f18792fc3d1284f53282111bbc3475fbd02cc0
- Size of remote file:
- 266 MB
- SHA256:
- ca4c2f09cfeab2e5ecec7f599236cbc0feb651e72c6ffa290b49a3e92ccd42c1
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