Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Cheng98/bert-base-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/bert-base-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-base-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-base-rte") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-base-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "global_step": 780, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 3.21, | |
| "learning_rate": 7.230769230769231e-06, | |
| "loss": 0.5104, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "step": 780, | |
| "total_flos": 818933159808000.0, | |
| "train_loss": 0.39309821984706783, | |
| "train_runtime": 41.8687, | |
| "train_samples_per_second": 297.358, | |
| "train_steps_per_second": 18.63 | |
| } | |
| ], | |
| "max_steps": 780, | |
| "num_train_epochs": 5, | |
| "total_flos": 818933159808000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |