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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.875, | |
| "eval_combined_score": 0.8923672566371681, | |
| "eval_f1": 0.9097345132743363, | |
| "eval_loss": 0.43185245990753174, | |
| "eval_runtime": 3.0165, | |
| "eval_samples": 408, | |
| "eval_samples_per_second": 135.255, | |
| "eval_steps_per_second": 16.907 | |
| } |