Instructions to use YakovElm/Cross_Project_5_Classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Cross_Project_5_Classic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Cross_Project_5_Classic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Cross_Project_5_Classic") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Cross_Project_5_Classic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Cross_Project_5_Classic: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Cross_Project_5_Classic/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Cross_Project_5_Classic/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Cross_Project_5_Classic/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 8b1e8ef128b84d3091adca8c5d96672300c5542a7e94530db7e71555df09a5ef
- Size of remote file:
- 438 MB
- SHA256:
- 612bd5d0ca7c2b077e1a33cc8e4f81351cc572e041bab9163e3f1eb51adbb392
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