Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/glue-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/glue-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/glue-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/glue-mnli") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/glue-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f6bfde01210e4c4655df6b1aade7042ef92f1e26702afde8623ffbe27ec5562
- Size of remote file:
- 3.44 kB
- SHA256:
- ac2314ec1e18067aa47607021ee49fdad702b69b7b9d8e44749fbde699ca05e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.