Instructions to use a2ran/FingerFriend-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a2ran/FingerFriend-t5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("a2ran/FingerFriend-t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("a2ran/FingerFriend-t5-base") - Notebooks
- Google Colab
- Kaggle
| base_model: eenzeenee/t5-base-korean-summarization | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: FingerFriend-t5-base | |
| 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. --> | |
| # FingerFriend-t5-base | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 20 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.87 | 1.0 | 683 | 0.5576 | | |
| | 0.5197 | 2.0 | 1366 | 0.4856 | | |
| | 0.4303 | 3.0 | 2049 | 0.4572 | | |
| | 0.373 | 4.0 | 2732 | 0.4446 | | |
| | 0.332 | 5.0 | 3415 | 0.4330 | | |
| | 0.2961 | 6.0 | 4098 | 0.4322 | | |
| | 0.2673 | 7.0 | 4781 | 0.4406 | | |
| ### Framework versions | |
| - Transformers 4.33.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |