|
--- |
|
license: llama2 |
|
library_name: peft |
|
tags: |
|
- trl |
|
- sft |
|
- generated_from_trainer |
|
datasets: |
|
- dialogstudio |
|
base_model: TencentARC/LLaMA-Pro-8B |
|
model-index: |
|
- name: llama-pro-8b-tweet-summarization-gradnorm-0.3 |
|
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. --> |
|
|
|
# llama-pro-8b-tweet-summarization-gradnorm-0.3 |
|
|
|
This model is a fine-tuned version of [TencentARC/LLaMA-Pro-8B](https://huggingface.co/TencentARC/LLaMA-Pro-8B) on the dialogstudio dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.9796 |
|
- Rouge Scores: {'rouge1': 93.71888929189157, 'rouge2': 77.8377567936117, 'rougeL': 64.47906852741538, 'rougeLsum': 93.71298018429633} |
|
- Bleu Scores: [0.9470990193868204, 0.9341779145832757, 0.9064440397746264, 0.8744914403659334] |
|
- Gen Len: 463.0182 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- num_epochs: 7 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge Scores | Bleu Scores | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|:--------:| |
|
| 1.9065 | 1.0 | 220 | 1.8530 | {'rouge1': 92.83694737064799, 'rouge2': 78.72458121869542, 'rougeL': 67.88788283384865, 'rougeLsum': 92.83768512059282} | [0.8739198483584956, 0.8530170264142946, 0.8271978418182495, 0.7998377773703629] | 463.0182 | |
|
| 1.6363 | 2.0 | 440 | 1.8633 | {'rouge1': 93.54135671371444, 'rouge2': 78.96116387599493, 'rougeL': 67.77857901494997, 'rougeLsum': 93.54432289584433} | [0.8758125801988195, 0.8577741180618648, 0.8322886881519586, 0.80457236049974] | 463.0182 | |
|
| 1.2817 | 3.0 | 660 | 2.0098 | {'rouge1': 87.30764070509844, 'rouge2': 73.12328274037898, 'rougeL': 62.00625532521349, 'rougeLsum': 87.29149649901954} | [0.8757949025917542, 0.8593181834244542, 0.8334473061685955, 0.8048319452251607] | 463.0182 | |
|
| 0.9049 | 4.0 | 880 | 2.2481 | {'rouge1': 87.35996946418575, 'rouge2': 72.87802745947901, 'rougeL': 61.35206821444361, 'rougeLsum': 87.32662841081371} | [0.8755472589597261, 0.859572654041077, 0.8333237300074641, 0.804082483213136] | 463.0182 | |
|
| 0.5916 | 5.0 | 1100 | 2.5061 | {'rouge1': 78.38431994557745, 'rouge2': 64.89809559762811, 'rougeL': 53.805209482421525, 'rougeLsum': 78.30608179426231} | [0.747179346815877, 0.7352208249958618, 0.7126103103040894, 0.6869428956670465] | 463.0182 | |
|
| 0.3898 | 6.0 | 1320 | 2.8150 | {'rouge1': 93.77539618029996, 'rouge2': 78.03050568501187, 'rougeL': 64.82344374456906, 'rougeLsum': 93.76894400818286} | [0.9469183628254614, 0.9342162110956728, 0.9067374010427977, 0.8750430150656403] | 463.0182 | |
|
| 0.2961 | 7.0 | 1540 | 2.9796 | {'rouge1': 93.71888929189157, 'rouge2': 77.8377567936117, 'rougeL': 64.47906852741538, 'rougeLsum': 93.71298018429633} | [0.9470990193868204, 0.9341779145832757, 0.9064440397746264, 0.8744914403659334] | 463.0182 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.8.2.dev0 |
|
- Transformers 4.38.0.dev0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.2.dev0 |
|
- Tokenizers 0.15.1 |
