Instructions to use quangtqv/cross_encoder_tool_learning_23_7_2024_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quangtqv/cross_encoder_tool_learning_23_7_2024_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quangtqv/cross_encoder_tool_learning_23_7_2024_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quangtqv/cross_encoder_tool_learning_23_7_2024_test") model = AutoModelForSequenceClassification.from_pretrained("quangtqv/cross_encoder_tool_learning_23_7_2024_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from quangtqv/cross_encoder_tool_learning_23_7_2024_test: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/quangtqv/cross_encoder_tool_learning_23_7_2024_test/resolve/main/tokenizer.json
- Command line
-
hf download hf://quangtqv/cross_encoder_tool_learning_23_7_2024_test/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/quangtqv/cross_encoder_tool_learning_23_7_2024_test/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- cd3819a723640fb8f36eaabfc8bf539b1b0cb387d49ef25622fee8a0275243e0
- Size of remote file:
- 17.1 MB
- SHA256:
- 6ba9aaf9ea4012b3012b5b285a0b28b1c5b5f1a9111b80d9ac2a5deba3b1a939
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