Instructions to use tengelisconsulting/email_classifier_gemma_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tengelisconsulting/email_classifier_gemma_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tengelisconsulting/email_classifier_gemma_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tengelisconsulting/email_classifier_gemma_2") model = AutoModelForSequenceClassification.from_pretrained("tengelisconsulting/email_classifier_gemma_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tengelisconsulting/email_classifier_gemma_2: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/tengelisconsulting/email_classifier_gemma_2/resolve/main/tokenizer.json
- Command line
-
hf download hf://tengelisconsulting/email_classifier_gemma_2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tengelisconsulting/email_classifier_gemma_2/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- 468f89786a084f976ebe64a0fb2ecf73029025701e08d5a125aa4239fff2908e
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
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