Instructions to use icpro/trained-model-classification-evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use icpro/trained-model-classification-evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="icpro/trained-model-classification-evaluation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("icpro/trained-model-classification-evaluation") model = AutoModelForSequenceClassification.from_pretrained("icpro/trained-model-classification-evaluation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from icpro/trained-model-classification-evaluation: direct link, hf CLI and curl.
- Browser
- Download file 587 kB
-
https://huggingface.co/icpro/trained-model-classification-evaluation/resolve/main/tokenizer.model
- Command line
-
hf download hf://icpro/trained-model-classification-evaluation/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/icpro/trained-model-classification-evaluation/resolve/main/tokenizer.model
587 kB
- Xet hash:
- 70de076fa18896073beef6149fbfc8ac2a287bc510c1a6f422ca4b8538b7a952
- Size of remote file:
- 587 kB
- SHA256:
- 37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
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