|
--- |
|
license: apache-2.0 |
|
base_model: facebook/bart-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: all_6417_bart-base |
|
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. --> |
|
|
|
# all_6417_bart-base |
|
|
|
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: 1.0206 |
|
- Rouge1: 0.2426 |
|
- Rouge2: 0.1209 |
|
- Rougel: 0.2027 |
|
- Rougelsum: 0.2266 |
|
- Gen Len: 19.9945 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 20 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 16 |
|
- total_train_batch_size: 512 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| 2.7151 | 0.8 | 500 | 1.1257 | 0.2361 | 0.1122 | 0.1957 | 0.2196 | 19.9978 | |
|
| 1.0837 | 1.61 | 1000 | 1.0810 | 0.2401 | 0.1176 | 0.1998 | 0.2237 | 19.9953 | |
|
| 1.0348 | 2.41 | 1500 | 1.0651 | 0.2401 | 0.1179 | 0.2 | 0.2239 | 19.9957 | |
|
| 1.0059 | 3.21 | 2000 | 1.0522 | 0.2403 | 0.1183 | 0.2002 | 0.2242 | 19.996 | |
|
| 0.9855 | 4.02 | 2500 | 1.0439 | 0.2416 | 0.1198 | 0.2015 | 0.2257 | 19.9948 | |
|
| 0.9642 | 4.82 | 3000 | 1.0361 | 0.2421 | 0.1201 | 0.202 | 0.2263 | 19.9936 | |
|
| 0.9519 | 5.63 | 3500 | 1.0329 | 0.2415 | 0.12 | 0.2017 | 0.2259 | 19.9948 | |
|
| 0.9389 | 6.43 | 4000 | 1.0278 | 0.2424 | 0.1204 | 0.2023 | 0.2265 | 19.9942 | |
|
| 0.9302 | 7.23 | 4500 | 1.0273 | 0.2422 | 0.1204 | 0.2022 | 0.2264 | 19.9943 | |
|
| 0.9225 | 8.04 | 5000 | 1.0219 | 0.2421 | 0.1209 | 0.2023 | 0.2263 | 19.9946 | |
|
| 0.9152 | 8.84 | 5500 | 1.0219 | 0.2429 | 0.1209 | 0.2028 | 0.227 | 19.9948 | |
|
| 0.911 | 9.64 | 6000 | 1.0206 | 0.2426 | 0.1209 | 0.2027 | 0.2266 | 19.9945 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
