Instructions to use h1alexbel/github-samples-tclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h1alexbel/github-samples-tclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="h1alexbel/github-samples-tclassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("h1alexbel/github-samples-tclassifier") model = AutoModelForSequenceClassification.from_pretrained("h1alexbel/github-samples-tclassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from h1alexbel/github-samples-tclassifier: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/h1alexbel/github-samples-tclassifier/resolve/main/model.safetensors
- Command line
-
hf download hf://h1alexbel/github-samples-tclassifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/h1alexbel/github-samples-tclassifier/resolve/main/model.safetensors
268 MB
- Xet hash:
- 523a10517f382c63c5b2b1dc0d020c12eb01b1b8ebb159536de3b29fc6bbb24f
- Size of remote file:
- 268 MB
- SHA256:
- 3a4370c67cd134fba15436bca8a08b19f4fa5ce0ff2c9128139002dddcdebce3
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