Instructions to use hf-tiny-model-private/tiny-random-MBartForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MBartForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-MBartForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MBartForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MBartForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c0756be41308aa68505b2d7dc6dffc5807ab6fff2e8c5a0dc1fd87b5dec8176
- Size of remote file:
- 16.1 MB
- SHA256:
- 4b1b826fdc77ebd862a676f017a259e54a8f211e70e642cb74e8a3896fab9aac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.