| --- |
| license: mit |
| base_model: facebook/bart-large-xsum |
| tags: |
| - generated_from_trainer |
| datasets: |
| - samsum |
| metrics: |
| - rouge |
| model-index: |
| - name: conversation-summ |
| results: |
| - task: |
| name: Sequence-to-sequence Language Modeling |
| type: text2text-generation |
| dataset: |
| name: samsum |
| type: samsum |
| config: samsum |
| split: validation |
| args: samsum |
| metrics: |
| - name: Rouge1 |
| type: rouge |
| value: 52.5102 |
| --- |
| |
| <!-- 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. --> |
|
|
| # conversation-summ |
|
|
| This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5467 |
| - Rouge1: 52.5102 |
| - Rouge2: 26.7766 |
| - Rougel: 42.7536 |
| - Rougelsum: 48.004 |
| - Gen Len: 30.9487 |
|
|
| ## 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: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 4 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 0.0464 | 1.0 | 3683 | 0.5467 | 52.5102 | 26.7766 | 42.7536 | 48.004 | 30.9487 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.33.0 |
| - Pytorch 2.0.0 |
| - Datasets 2.1.0 |
| - Tokenizers 0.13.3 |
| |
