Instructions to use hf-internal-testing/tiny-random-FalconForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FalconForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-FalconForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FalconForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-FalconForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d84d02e4ae8660a0e1b8b7aedd43721ceb40f24f4d37979494c4b0e300c8da26
- Size of remote file:
- 233 kB
- SHA256:
- daa53dfbdaa0f1783efc65da99b3fda7d15cb07d1a22db1f1c08dcd44207a9f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.