Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_v3_mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_v3_mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_v3_mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_v3_mrpc") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_v3_mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from henryscheible/eval_v3_mrpc: direct link, hf CLI and curl.
- Browser
- Download file 291 Bytes
-
https://huggingface.co/henryscheible/eval_v3_mrpc/resolve/main/eval_results.json
- Command line
-
hf download hf://henryscheible/eval_v3_mrpc/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/henryscheible/eval_v3_mrpc/resolve/main/eval_results.json
291 Bytes
| { | |
| "eval_accuracy": 0.664927536231884, | |
| "eval_combined_score": 0.7318370271688668, | |
| "eval_f1": 0.7987465181058496, | |
| "eval_loss": 0.6564045548439026, | |
| "eval_runtime": 5.045, | |
| "eval_samples": 1725, | |
| "eval_samples_per_second": 341.921, | |
| "eval_steps_per_second": 42.815 | |
| } |