Instructions to use quangtqv/cross_encoder_tool_learning_best_model_9_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quangtqv/cross_encoder_tool_learning_best_model_9_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quangtqv/cross_encoder_tool_learning_best_model_9_6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quangtqv/cross_encoder_tool_learning_best_model_9_6") model = AutoModelForSequenceClassification.from_pretrained("quangtqv/cross_encoder_tool_learning_best_model_9_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from quangtqv/cross_encoder_tool_learning_best_model_9_6: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/quangtqv/cross_encoder_tool_learning_best_model_9_6/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://quangtqv/cross_encoder_tool_learning_best_model_9_6@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/quangtqv/cross_encoder_tool_learning_best_model_9_6/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 605cac8c9e1be844df4f8745178df6cfc571cfaa033b25da797db601a9d33667
- Size of remote file:
- 17.1 MB
- SHA256:
- b5f29bf7eb6767270cec638410e68f922c20a5a990f053650749dd43247aa562
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