Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/MiniLM-L12-H384-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/MiniLM-L12-H384-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/MiniLM-L12-H384-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d5a09a606b7e2fff7874408145a71196b1f49f5ab397de1a03ab3e4b0116e4d
- Size of remote file:
- 3.12 kB
- SHA256:
- 116fe6c5264391eac7d9a8935fac4c151d34392600f814bb492145fd7d6b1324
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