Instructions to use kevinjesse/graphcodebert-MT4TS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevinjesse/graphcodebert-MT4TS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kevinjesse/graphcodebert-MT4TS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kevinjesse/graphcodebert-MT4TS") model = AutoModelForTokenClassification.from_pretrained("kevinjesse/graphcodebert-MT4TS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kevinjesse/graphcodebert-MT4TS: direct link, hf CLI and curl.
- Browser
- Download file 650 MB
-
https://huggingface.co/kevinjesse/graphcodebert-MT4TS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kevinjesse/graphcodebert-MT4TS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kevinjesse/graphcodebert-MT4TS/resolve/main/pytorch_model.bin
650 MB
- Xet hash:
- b915475677738dde8679c1f44d9d028ee4747e48922ab8435ded2bbc89a06991
- Size of remote file:
- 650 MB
- SHA256:
- a9ae1aa1432dbcc55077f13dffc97b142d4120808676ad21c5816950679b09bb
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