Instructions to use terhdavid/test-train-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use terhdavid/test-train-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="terhdavid/test-train-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("terhdavid/test-train-model") model = AutoModelForTokenClassification.from_pretrained("terhdavid/test-train-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73183493ea8fe6588961a53793e6f99dcc36309434efb6061263e6cdec52be19
- Size of remote file:
- 266 MB
- SHA256:
- c276822d890a902f6d178b65278ab2c1a5d2300e72ceb0c60d6e27a32e097f27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.