Instructions to use hf-internal-testing/tiny-random-MistralForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MistralForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-MistralForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-MistralForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c604895cebeda8dd9bfac35ca052f52b1734475ba632ffe90306c2b48ce77cba
- Size of remote file:
- 4.15 MB
- SHA256:
- 1f66b2aed61fd5e421c39ca03d2dd7be7a58270369a7984b1a13024a9437a88e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.