Instructions to use Galvin/my_ebm_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Galvin/my_ebm_model_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Galvin/my_ebm_model_test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Galvin/my_ebm_model_test") model = AutoModelForTokenClassification.from_pretrained("Galvin/my_ebm_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c359ba2110424d3d681b4e9a5e2ad83868c87470e884be5e335eb623343ebb53
- Size of remote file:
- 431 MB
- SHA256:
- 7b13a33580cd8d67aaf324bb73039514c8fc9054ce420a6372902a2051e64469
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