Instructions to use DPhO05/my-unixcoder-RQ3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DPhO05/my-unixcoder-RQ3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DPhO05/my-unixcoder-RQ3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DPhO05/my-unixcoder-RQ3") model = AutoModelForSequenceClassification.from_pretrained("DPhO05/my-unixcoder-RQ3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b68683cfbdb0685b9eda62127ae7c6fecdad3911467b157282b05840d846d94
- Size of remote file:
- 504 MB
- SHA256:
- 947424c1b4c0182e046ad5a07c0ef07b1dc588fd6e34b7853f2b0eeaf13b6377
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