|
--- |
|
license: apache-2.0 |
|
base_model: facebook/bart-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: BARTModel_for_Ecommerce |
|
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. --> |
|
|
|
# BARTModel_for_Ecommerce |
|
|
|
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: 0.6537 |
|
- Rouge1: 0.3618 |
|
- Rouge2: 0.2634 |
|
- Rougel: 0.3348 |
|
- Rougelsum: 0.336 |
|
- Gen Len: 20.0 |
|
|
|
## 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: 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: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| No log | 1.0 | 27 | 3.3018 | 0.2994 | 0.1537 | 0.2528 | 0.2525 | 20.0 | |
|
| No log | 2.0 | 54 | 2.2697 | 0.3287 | 0.1959 | 0.286 | 0.2866 | 20.0 | |
|
| No log | 3.0 | 81 | 1.7739 | 0.3265 | 0.2103 | 0.2947 | 0.2954 | 20.0 | |
|
| No log | 4.0 | 108 | 1.4085 | 0.3257 | 0.2128 | 0.2931 | 0.2937 | 20.0 | |
|
| No log | 5.0 | 135 | 1.1230 | 0.3458 | 0.2307 | 0.3116 | 0.3124 | 20.0 | |
|
| No log | 6.0 | 162 | 0.9408 | 0.3448 | 0.2371 | 0.3129 | 0.3139 | 20.0 | |
|
| No log | 7.0 | 189 | 0.8269 | 0.3502 | 0.2479 | 0.3201 | 0.3212 | 20.0 | |
|
| No log | 8.0 | 216 | 0.7584 | 0.3442 | 0.2434 | 0.3145 | 0.3157 | 20.0 | |
|
| No log | 9.0 | 243 | 0.7075 | 0.3606 | 0.2619 | 0.3329 | 0.3339 | 20.0 | |
|
| No log | 10.0 | 270 | 0.6890 | 0.3508 | 0.2517 | 0.3208 | 0.3219 | 20.0 | |
|
| No log | 11.0 | 297 | 0.6761 | 0.3501 | 0.2483 | 0.3215 | 0.3228 | 20.0 | |
|
| No log | 12.0 | 324 | 0.6631 | 0.3532 | 0.2522 | 0.3237 | 0.3245 | 20.0 | |
|
| No log | 13.0 | 351 | 0.6573 | 0.3625 | 0.2661 | 0.3358 | 0.3372 | 20.0 | |
|
| No log | 14.0 | 378 | 0.6528 | 0.3594 | 0.2608 | 0.3322 | 0.3333 | 20.0 | |
|
| No log | 15.0 | 405 | 0.6537 | 0.3618 | 0.2634 | 0.3348 | 0.336 | 20.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
