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:
- 02f3427a3b159443cd221b443789dacb4061a8327254f6089b603aa5d5430f34
- Size of remote file:
- 134 MB
- SHA256:
- 00d4a7d6b0409e0587fd4b06a4796cf41ad69438e3a3878b9074b924c0bc2291
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