Instructions to use apwic/nerugm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apwic/nerugm-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("apwic/nerugm-base") model = AutoModel.from_pretrained("apwic/nerugm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from apwic/nerugm-base: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/apwic/nerugm-base/resolve/main/trainer_state.json
- Command line
-
hf download hf://apwic/nerugm-base/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/apwic/nerugm-base/resolve/main/trainer_state.json
1.22 kB
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 528, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "grad_norm": 1.4932644367218018, | |
| "learning_rate": 0.0, | |
| "loss": 0.226, | |
| "step": 528 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.963231640663818, | |
| "eval_f1": 0.8287262872628726, | |
| "eval_loss": 0.15753257274627686, | |
| "eval_precision": 0.776536312849162, | |
| "eval_recall": 0.8884369552585706, | |
| "eval_runtime": 5.4112, | |
| "eval_samples_per_second": 172.791, | |
| "eval_steps_per_second": 2.772, | |
| "step": 528 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "step": 528, | |
| "total_flos": 230610745968444.0, | |
| "train_loss": 0.2260448426911325, | |
| "train_runtime": 127.3009, | |
| "train_samples_per_second": 66.276, | |
| "train_steps_per_second": 4.148 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 528, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 500, | |
| "total_flos": 230610745968444.0, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |