Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/rte") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from henryscheible/rte: direct link, hf CLI and curl.
- Browser
- Download file 591 Bytes
-
https://huggingface.co/henryscheible/rte/resolve/main/trainer_state.json
- Command line
-
hf download hf://henryscheible/rte/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/henryscheible/rte/resolve/main/trainer_state.json
591 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "global_step": 390, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 5.0, | |
| "step": 390, | |
| "total_flos": 818933159808000.0, | |
| "train_loss": 0.48275831173627803, | |
| "train_runtime": 237.1279, | |
| "train_samples_per_second": 52.503, | |
| "train_steps_per_second": 1.645 | |
| } | |
| ], | |
| "max_steps": 390, | |
| "num_train_epochs": 5, | |
| "total_flos": 818933159808000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |