Instructions to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- daba8a42523c29c270ca64d6bca733922bbee8bce3c1de029982e41da6bd8cb1
- Size of remote file:
- 321 kB
- SHA256:
- d7990f41434a9854036b4bea370b3db3455641b2f1af58ad342372192016eeae
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