Instructions to use Ching/negation_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ching/negation_detector with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Ching/negation_detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ching/negation_detector") model = AutoModelForQuestionAnswering.from_pretrained("Ching/negation_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from Ching/negation_detector: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/Ching/negation_detector/resolve/main/trainer_state.json
- Command line
-
hf download hf://Ching/negation_detector/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Ching/negation_detector/resolve/main/trainer_state.json
1.2 kB
| { | |
| "best_metric": 0.7185248136520386, | |
| "best_model_checkpoint": "test-squad\\checkpoint-144", | |
| "epoch": 3.0, | |
| "global_step": 144, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 0.00013333333333333334, | |
| "loss": 1.3306, | |
| "step": 48 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 0.8487878441810608, | |
| "eval_runtime": 72.0966, | |
| "eval_samples_per_second": 1.664, | |
| "step": 48 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "learning_rate": 6.666666666666667e-05, | |
| "loss": 0.9434, | |
| "step": 96 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_loss": 0.7361217141151428, | |
| "eval_runtime": 77.1446, | |
| "eval_samples_per_second": 1.556, | |
| "step": 96 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "learning_rate": 0.0, | |
| "loss": 0.3291, | |
| "step": 144 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_loss": 0.7185248136520386, | |
| "eval_runtime": 70.5701, | |
| "eval_samples_per_second": 1.7, | |
| "step": 144 | |
| } | |
| ], | |
| "max_steps": 144, | |
| "num_train_epochs": 3, | |
| "total_flos": 10718488339200.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |