Instructions to use datasetsANDmodels/message-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasetsANDmodels/message-extraction with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("datasetsANDmodels/message-extraction") model = AutoModelForSeq2SeqLM.from_pretrained("datasetsANDmodels/message-extraction") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: t5-large | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: message_extraction | |
| results: [] | |
| This model extracts the message from the text | |
| # message_extraction | |
| This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0185 | |
| ## 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: 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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.3131 | 1.0 | 191 | 0.3448 | | |
| | 0.2332 | 2.0 | 382 | 0.1029 | | |
| | 0.127 | 3.0 | 573 | 0.0606 | | |
| | 0.0685 | 4.0 | 764 | 0.0423 | | |
| | 0.052 | 5.0 | 955 | 0.0338 | | |
| | 0.0002 | 6.0 | 1146 | 0.0275 | | |
| | 0.0468 | 7.0 | 1337 | 0.0238 | | |
| | 0.02 | 8.0 | 1528 | 0.0207 | | |
| | 0.0567 | 9.0 | 1719 | 0.0189 | | |
| | 0.0044 | 10.0 | 1910 | 0.0185 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.1 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |