| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - gem |
| | model_index: |
| | - name: BART-commongen |
| | results: |
| | - task: |
| | name: Sequence-to-sequence Language Modeling |
| | type: text2text-generation |
| | dataset: |
| | name: gem |
| | type: gem |
| | args: common_gen |
| | --- |
| | |
| | <!-- 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-commongen |
| |
|
| | This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the gem dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1263 |
| | - Spice: 0.4178 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 1000 |
| | - training_steps: 6317 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Spice | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 9.0971 | 0.05 | 100 | 4.1336 | 0.3218 | |
| | | 3.5348 | 0.09 | 200 | 1.5467 | 0.3678 | |
| | | 1.5099 | 0.14 | 300 | 1.1280 | 0.3821 | |
| | | 1.2395 | 0.19 | 400 | 1.1178 | 0.3917 | |
| | | 1.1827 | 0.24 | 500 | 1.0919 | 0.4086 | |
| | | 1.1545 | 0.28 | 600 | 1.1028 | 0.4035 | |
| | | 1.1363 | 0.33 | 700 | 1.1021 | 0.4187 | |
| | | 1.1156 | 0.38 | 800 | 1.1231 | 0.4103 | |
| | | 1.1077 | 0.43 | 900 | 1.1221 | 0.4117 | |
| | | 1.0964 | 0.47 | 1000 | 1.1169 | 0.4088 | |
| | | 1.0704 | 0.52 | 1100 | 1.1143 | 0.4133 | |
| | | 1.0483 | 0.57 | 1200 | 1.1085 | 0.4058 | |
| | | 1.0556 | 0.62 | 1300 | 1.1059 | 0.4249 | |
| | | 1.0343 | 0.66 | 1400 | 1.0992 | 0.4102 | |
| | | 1.0123 | 0.71 | 1500 | 1.1126 | 0.4104 | |
| | | 1.0108 | 0.76 | 1600 | 1.1140 | 0.4177 | |
| | | 1.005 | 0.81 | 1700 | 1.1264 | 0.4078 | |
| | | 0.9822 | 0.85 | 1800 | 1.1256 | 0.4158 | |
| | | 0.9918 | 0.9 | 1900 | 1.1345 | 0.4118 | |
| | | 0.9664 | 0.95 | 2000 | 1.1087 | 0.4073 | |
| | | 0.9532 | 1.0 | 2100 | 1.1217 | 0.4063 | |
| | | 0.8799 | 1.04 | 2200 | 1.1229 | 0.4115 | |
| | | 0.8665 | 1.09 | 2300 | 1.1263 | 0.4178 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.9.2 |
| | - Pytorch 1.9.0+cu102 |
| | - Datasets 1.11.1.dev0 |
| | - Tokenizers 0.10.3 |
| | |
