Instructions to use sumit7488/MeetBrief with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumit7488/MeetBrief with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumit7488/MeetBrief") model = AutoModelForSeq2SeqLM.from_pretrained("sumit7488/MeetBrief") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: niteshsah-760/BART-LARGE-DIALOGSUM | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: BART-LARGE-fine_tuned | |
| 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-LARGE-fine_tuned | |
| This model is a fine-tuned version of [niteshsah-760/BART-LARGE-DIALOGSUM](https://huggingface.co/niteshsah-760/BART-LARGE-DIALOGSUM) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5467 | |
| - Rouge1: 58.168 | |
| - Rouge2: 45.9825 | |
| - Rougel: 54.3562 | |
| - Rougelsum: 54.4552 | |
| ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | |
| | 0.6587 | 1.0 | 563 | 0.6037 | 56.3206 | 44.2624 | 52.832 | 52.8704 | | |
| | 0.6162 | 2.0 | 1126 | 0.5719 | 56.8789 | 44.8139 | 53.3803 | 53.4437 | | |
| | 0.5815 | 3.0 | 1689 | 0.5560 | 57.6576 | 45.5559 | 53.943 | 54.0187 | | |
| | 0.5663 | 4.0 | 2252 | 0.5491 | 57.9815 | 45.9701 | 54.2183 | 54.3077 | | |
| | 0.546 | 5.0 | 2815 | 0.5467 | 58.168 | 45.9825 | 54.3562 | 54.4552 | | |
| ### Framework versions | |
| - Transformers 4.47.0 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |