Instructions to use quangtqv/cross_encoder_tool_learning_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quangtqv/cross_encoder_tool_learning_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quangtqv/cross_encoder_tool_learning_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quangtqv/cross_encoder_tool_learning_v2") model = AutoModelForSequenceClassification.from_pretrained("quangtqv/cross_encoder_tool_learning_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03f29614cc85d3bf5e499b79ebe9144e3479f010059ac79c578db6e74f3a1d8c
- Size of remote file:
- 1.11 GB
- SHA256:
- a5ee0bf474bf316ebb950456b2456131270a708038cf1d454807d8d4292b3e71
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