Instructions to use a2ran/FingerFriend-t5-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a2ran/FingerFriend-t5-base-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("a2ran/FingerFriend-t5-base-v1") model = AutoModelForSeq2SeqLM.from_pretrained("a2ran/FingerFriend-t5-base-v1") - Notebooks
- Google Colab
- Kaggle
| base_model: eenzeenee/t5-base-korean-summarization | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: FingerFriend-t5-base-v1 | |
| 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-v1 | |
| This model is a fine-tuned version of [eenzeenee/t5-base-korean-summarization](https://huggingface.co/eenzeenee/t5-base-korean-summarization) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6214 | |
| ## 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: 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 | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 1.2945 | 1.0 | 1735 | 0.7757 | | |
| | 0.8016 | 2.0 | 3470 | 0.6987 | | |
| | 0.7058 | 3.0 | 5205 | 0.6617 | | |
| | 0.6402 | 4.0 | 6940 | 0.6387 | | |
| | 0.5864 | 5.0 | 8675 | 0.6283 | | |
| | 0.5476 | 6.0 | 10410 | 0.6214 | | |
| ### Framework versions | |
| - Transformers 4.33.3 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |