Instructions to use dill-lab/oath-frames-flant5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dill-lab/oath-frames-flant5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dill-lab/oath-frames-flant5-base") model = AutoModelForSeq2SeqLM.from_pretrained("dill-lab/oath-frames-flant5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from dill-lab/oath-frames-flant5-base: direct link, hf CLI and curl.
- Browser
- Download file 787 Bytes
-
https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/all_results.json
- Command line
-
hf download hf://dill-lab/oath-frames-flant5-base/all_results.json
-
curl -L -o all_results.json https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/all_results.json
787 Bytes
| { | |
| "epoch": 6.0, | |
| "eval_f1": 0.5757671862207844, | |
| "eval_loss": 0.25109806656837463, | |
| "eval_precision": 0.6050661809680828, | |
| "eval_recall": 0.556382019200333, | |
| "eval_runtime": 55.8419, | |
| "eval_samples": 913, | |
| "eval_samples_per_second": 16.35, | |
| "eval_steps_per_second": 2.059, | |
| "predict_f1": 0.25451566111608764, | |
| "predict_loss": 0.318584680557251, | |
| "predict_precision": 0.27110114948598263, | |
| "predict_recall": 0.24454478631214052, | |
| "predict_runtime": 115.3998, | |
| "predict_samples": 1280, | |
| "predict_samples_per_second": 11.092, | |
| "predict_steps_per_second": 5.546, | |
| "train_loss": 0.23737415118809163, | |
| "train_runtime": 2537.1213, | |
| "train_samples": 8217, | |
| "train_samples_per_second": 80.968, | |
| "train_steps_per_second": 20.249 | |
| } |