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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/mrpc") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/mrpc") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "global_step": 575, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 4.35, | |
| "learning_rate": 2.6086956521739132e-06, | |
| "loss": 0.3272, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "step": 575, | |
| "total_flos": 1206364188825600.0, | |
| "train_loss": 0.2986407089233398, | |
| "train_runtime": 429.5878, | |
| "train_samples_per_second": 42.692, | |
| "train_steps_per_second": 1.338 | |
| } | |
| ], | |
| "max_steps": 575, | |
| "num_train_epochs": 5, | |
| "total_flos": 1206364188825600.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |