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:
- f821a36f7a92e6c6b924ca98f3774d50721b3d16266dac5fe5795a5878443303
- Size of remote file:
- 181 kB
- SHA256:
- a66ef936372dd184da8372b2b6e4d1f98263f3b95b735ae4943f76ecf8802bf5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.