Instructions to use hf-internal-testing/tiny-random-RoFormerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoFormerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-RoFormerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoFormerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-RoFormerForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 883f3cfc79dbfcaecb71f9cf0a4773d685c424dcfdb6bbc3bbc10f63aa5880eb
- Size of remote file:
- 6.69 MB
- SHA256:
- 547bc18b6db47ab374315067ed2cca62dc3abbae7316c39bb4530aae20b3bc99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.