Instructions to use hf-internal-testing/tiny-random-Starcoder2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Starcoder2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-Starcoder2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Starcoder2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-Starcoder2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea54f4b18721cbc4648fe7856e5350157e12e1b3782a7a86e5bbd643125239b0
- Size of remote file:
- 181 kB
- SHA256:
- 0c49259c3d1060e511c9f167b18c0784f460370447cbb26622a2e343ae472f80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.