Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/mrpc") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from henryscheible/mrpc: direct link, hf CLI and curl.
- Browser
- Download file 310 Bytes
-
https://huggingface.co/henryscheible/mrpc/resolve/main/eval_results.json
- Command line
-
hf download hf://henryscheible/mrpc/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/henryscheible/mrpc/resolve/main/eval_results.json
310 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.8553921568627451, | |
| "eval_combined_score": 0.8767461647871584, | |
| "eval_f1": 0.8981001727115717, | |
| "eval_loss": 0.49130943417549133, | |
| "eval_runtime": 4.5516, | |
| "eval_samples": 408, | |
| "eval_samples_per_second": 89.639, | |
| "eval_steps_per_second": 11.205 | |
| } |