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:
- 5cdf7c7f3d44c12194737d5716e44c8b06ee14d6644a4543b6b03159118c1ef0
- Size of remote file:
- 321 kB
- SHA256:
- 32af31522c852bd7594619bba4ebd858f9fd6226afa4deaf0fa1dacc638847ab
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