Instructions to use ypl/bart_test_p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ypl/bart_test_p2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ypl/bart_test_p2") model = AutoModelForSeq2SeqLM.from_pretrained("ypl/bart_test_p2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bart_test_p2 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bart_test_p2 | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0076 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.018 | 0.18 | 500 | 0.0096 | | |
| | 0.0189 | 0.35 | 1000 | 0.0097 | | |
| | 0.0184 | 0.53 | 1500 | 0.0098 | | |
| | 0.0167 | 0.7 | 2000 | 0.0094 | | |
| | 0.0162 | 0.88 | 2500 | 0.0092 | | |
| | 0.0162 | 1.05 | 3000 | 0.0086 | | |
| | 0.0124 | 1.23 | 3500 | 0.0086 | | |
| | 0.0127 | 1.4 | 4000 | 0.0084 | | |
| | 0.0129 | 1.58 | 4500 | 0.0083 | | |
| | 0.0123 | 1.75 | 5000 | 0.0080 | | |
| | 0.0123 | 1.93 | 5500 | 0.0081 | | |
| | 0.0104 | 2.1 | 6000 | 0.0079 | | |
| | 0.0094 | 2.28 | 6500 | 0.0079 | | |
| | 0.0103 | 2.45 | 7000 | 0.0077 | | |
| | 0.01 | 2.63 | 7500 | 0.0077 | | |
| | 0.0098 | 2.8 | 8000 | 0.0077 | | |
| | 0.0095 | 2.98 | 8500 | 0.0076 | | |
| ### Framework versions | |
| - Transformers 4.37.0.dev0 | |
| - Pytorch 2.1.0.dev20230621+cu117 | |
| - Datasets 2.17.0 | |
| - Tokenizers 0.15.0 | |