Instructions to use YakovElm/Cross_Project_5_Classic_without_validation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Cross_Project_5_Classic_without_validation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Cross_Project_5_Classic_without_validation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Cross_Project_5_Classic_without_validation") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Cross_Project_5_Classic_without_validation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Cross_Project_5_Classic_without_validation: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Cross_Project_5_Classic_without_validation/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Cross_Project_5_Classic_without_validation/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Cross_Project_5_Classic_without_validation/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 1f82a1376d853459e6115a6c348c189728f174cc6aa33e7544004ce6a3a5669c
- Size of remote file:
- 438 MB
- SHA256:
- fc7adecda598813c355a1128a3af0d6309f7929894bc6a678b83175e13b7dbbd
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