---
license: apache-2.0
library_name: peft
tags:
- Summarization
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
base_model: google/flan-t5-base
model-index:
- name: flan-t5-base-finetuned-QLoRA-v2
  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. -->

# flan-t5-base-finetuned-QLoRA-v2

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0254
- Rouge1: 0.244
- Rouge2: 0.111
- Rougel: 0.2032
- Rougelsum: 0.2292

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.0551        | 1.0   | 500  | 2.2941          | 0.2336 | 0.1092 | 0.1969 | 0.217     |
| 1.6422        | 2.0   | 1000 | 1.1665          | 0.2459 | 0.1088 | 0.1991 | 0.227     |
| 1.4067        | 3.0   | 1500 | 1.0762          | 0.2462 | 0.1089 | 0.1982 | 0.2296    |
| 1.2856        | 4.0   | 2000 | 1.0518          | 0.2448 | 0.1112 | 0.2036 | 0.2298    |
| 1.3478        | 5.0   | 2500 | 1.0393          | 0.2458 | 0.1125 | 0.2056 | 0.2303    |
| 1.2114        | 6.0   | 3000 | 1.0340          | 0.2497 | 0.1145 | 0.2084 | 0.2333    |
| 1.3311        | 7.0   | 3500 | 1.0298          | 0.2479 | 0.1143 | 0.207  | 0.233     |
| 1.3081        | 8.0   | 4000 | 1.0270          | 0.2448 | 0.1112 | 0.2035 | 0.2301    |
| 1.1794        | 9.0   | 4500 | 1.0258          | 0.2449 | 0.1112 | 0.2036 | 0.2301    |
| 1.2407        | 10.0  | 5000 | 1.0254          | 0.244  | 0.111  | 0.2032 | 0.2292    |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1