Instructions to use FINDA-FIT/inter-milan-false with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINDA-FIT/inter-milan-false with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINDA-FIT/inter-milan-false")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FINDA-FIT/inter-milan-false") model = AutoModelForSequenceClassification.from_pretrained("FINDA-FIT/inter-milan-false", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from FINDA-FIT/inter-milan-false: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/FINDA-FIT/inter-milan-false/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://FINDA-FIT/inter-milan-false@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/FINDA-FIT/inter-milan-false/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- fb783db9d1b9e2663d8320803a80ecc3a9347e3123793f985f33aaf76f4e8b07
- Size of remote file:
- 17.1 MB
- SHA256:
- b83050ca499108b9cd6b0fca42f13c95713dd3b5db7238c8703136a5ed0a2b3e
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