Instructions to use splitner/single-qa-ontonotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/single-qa-ontonotes with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/single-qa-ontonotes") model = AutoModel.from_pretrained("splitner/single-qa-ontonotes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from splitner/single-qa-ontonotes: direct link, hf CLI and curl.
- Browser
- Download file 744 Bytes
-
https://huggingface.co/splitner/single-qa-ontonotes/resolve/refs%2Fpr%2F1/trainer_state.json
- Command line
-
hf download hf://splitner/single-qa-ontonotes@refs/pr/1/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/splitner/single-qa-ontonotes/resolve/refs%2Fpr%2F1/trainer_state.json
744 Bytes
| { | |
| "best_metric": 0.0, | |
| "best_model_checkpoint": "../out/onto_final/ner-roberta-qa4-querytype4-char-pattern3-subtext-dim50-new/checkpoints/checkpoint-30000", | |
| "epoch": 0.8659508139937652, | |
| "global_step": 30000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.8659508139937652, | |
| "learning_rate": 9.971134972866875e-05, | |
| "loss": 6.094925006230672e-05, | |
| "step": 30000 | |
| }, | |
| { | |
| "epoch": 0.8659508139937652, | |
| "eval_loss": 0.0, | |
| "eval_micro_f1": 0.0, | |
| "step": 30000 | |
| } | |
| ], | |
| "max_steps": 10393200, | |
| "num_train_epochs": 300, | |
| "total_flos": 156978982343677440, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |