Instructions to use monideep2255/spell_correction_M04_verification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monideep2255/spell_correction_M04_verification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("monideep2255/spell_correction_M04_verification") model = AutoModelForSeq2SeqLM.from_pretrained("monideep2255/spell_correction_M04_verification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: spell_correction_M04_verification | |
| 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. --> | |
| # spell_correction_M04_verification | |
| This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0588 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 30 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 269 | 0.3070 | | |
| | 1.8826 | 2.0 | 538 | 0.0769 | | |
| | 1.8826 | 3.0 | 807 | 0.0592 | | |
| | 0.0711 | 4.0 | 1076 | 0.0577 | | |
| | 0.0711 | 5.0 | 1345 | 0.0563 | | |
| | 0.04 | 6.0 | 1614 | 0.0562 | | |
| | 0.04 | 7.0 | 1883 | 0.0560 | | |
| | 0.0265 | 8.0 | 2152 | 0.0544 | | |
| | 0.0265 | 9.0 | 2421 | 0.0540 | | |
| | 0.0196 | 10.0 | 2690 | 0.0534 | | |
| | 0.0196 | 11.0 | 2959 | 0.0548 | | |
| | 0.015 | 12.0 | 3228 | 0.0552 | | |
| | 0.015 | 13.0 | 3497 | 0.0578 | | |
| | 0.0123 | 14.0 | 3766 | 0.0591 | | |
| | 0.0116 | 15.0 | 4035 | 0.0578 | | |
| | 0.0116 | 16.0 | 4304 | 0.0580 | | |
| | 0.0091 | 17.0 | 4573 | 0.0592 | | |
| | 0.0091 | 18.0 | 4842 | 0.0596 | | |
| | 0.0088 | 19.0 | 5111 | 0.0605 | | |
| | 0.0088 | 20.0 | 5380 | 0.0569 | | |
| | 0.0074 | 21.0 | 5649 | 0.0598 | | |
| | 0.0074 | 22.0 | 5918 | 0.0587 | | |
| | 0.0078 | 23.0 | 6187 | 0.0589 | | |
| | 0.0078 | 24.0 | 6456 | 0.0586 | | |
| | 0.0068 | 25.0 | 6725 | 0.0588 | | |
| | 0.0068 | 26.0 | 6994 | 0.0591 | | |
| | 0.0076 | 27.0 | 7263 | 0.0590 | | |
| | 0.0072 | 28.0 | 7532 | 0.0587 | | |
| | 0.0072 | 29.0 | 7801 | 0.0587 | | |
| | 0.0059 | 30.0 | 8070 | 0.0588 | | |
| ### Framework versions | |
| - Transformers 4.28.0 | |
| - Pytorch 1.12.1+cu102 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |