Instructions to use dill-lab/oath-frames-flant5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dill-lab/oath-frames-flant5-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dill-lab/oath-frames-flant5-large") model = AutoModelForSeq2SeqLM.from_pretrained("dill-lab/oath-frames-flant5-large") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 15.0, | |
| "eval_f1": 0.6081487065194826, | |
| "eval_loss": 0.22290533781051636, | |
| "eval_precision": 0.6323140438141116, | |
| "eval_recall": 0.588223635460403, | |
| "eval_runtime": 40.7747, | |
| "eval_samples": 913, | |
| "eval_samples_per_second": 22.391, | |
| "eval_steps_per_second": 0.049, | |
| "predict_f1": 0.2645656484880222, | |
| "predict_loss": 0.3003044128417969, | |
| "predict_precision": 0.2839500743890114, | |
| "predict_recall": 0.2542304547210576, | |
| "predict_runtime": 239.0642, | |
| "predict_samples": 1280, | |
| "predict_samples_per_second": 5.354, | |
| "predict_steps_per_second": 2.677, | |
| "train_loss": 0.27247093051087623, | |
| "train_runtime": 6452.7815, | |
| "train_samples": 8217, | |
| "train_samples_per_second": 31.835, | |
| "train_steps_per_second": 0.066 | |
| } |