Instructions to use gaetangate/bart-large_genrl_qald9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gaetangate/bart-large_genrl_qald9 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gaetangate/bart-large_genrl_qald9") model = AutoModelForSeq2SeqLM.from_pretrained("gaetangate/bart-large_genrl_qald9") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 10.0, | |
| "global_step": 1000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 5.0, | |
| "learning_rate": 2.5e-05, | |
| "loss": 0.5463, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "learning_rate": 0.0, | |
| "loss": 0.155, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "step": 1000, | |
| "total_flos": 4079572509696000.0, | |
| "train_runtime": 595.2302, | |
| "train_samples_per_second": 1.68 | |
| } | |
| ], | |
| "max_steps": 1000, | |
| "num_train_epochs": 10, | |
| "total_flos": 4079572509696000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |