---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
metrics:
- rouge
- f1
- recall
- precision
model-index:
- name: KGAQ-2
  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. -->

# KGAQ-2

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0725
- Rouge1: 46.2376
- Rouge2: 21.4997
- Rougel: 39.6036
- Rougelsum: 46.3269
- Gen Len: 4.2121
- F1: 0.3205
- Recall: 0.6757
- Precision: 0.2101

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:------:|:------:|:---------:|
| No log        | 1.0   | 50   | 3.1330          | 38.9228 | 19.8713 | 34.8665 | 39.0223   | 3.6162  | 0.3373 | 0.5957 | 0.2353    |
| No log        | 2.0   | 100  | 3.2460          | 42.6051 | 20.0714 | 38.2234 | 42.823    | 3.9697  | 0.3275 | 0.5385 | 0.2353    |
| No log        | 3.0   | 150  | 3.4413          | 42.2575 | 19.3868 | 36.9508 | 42.1996   | 4.1313  | 0.3415 | 0.6222 | 0.2353    |
| 1.9251        | 4.0   | 200  | 3.6553          | 41.9902 | 19.8751 | 36.961  | 42.1914   | 3.9899  | 0.3522 | 0.7    | 0.2353    |
| 1.9251        | 5.0   | 250  | 3.9188          | 41.6177 | 19.8385 | 36.9836 | 41.7831   | 4.0404  | 0.3648 | 0.725  | 0.2437    |
| 1.9251        | 6.0   | 300  | 4.0309          | 40.2818 | 15.9608 | 35.0963 | 40.3224   | 4.3838  | 0.3522 | 0.7    | 0.2353    |
| 1.9251        | 7.0   | 350  | 4.4151          | 40.1585 | 14.4247 | 34.3216 | 40.2886   | 4.3131  | 0.1185 | 0.5    | 0.0672    |
| 0.6344        | 8.0   | 400  | 4.9239          | 42.9643 | 19.2829 | 36.6803 | 43.0145   | 4.4646  | 0.3097 | 0.6667 | 0.2017    |
| 0.6344        | 9.0   | 450  | 5.9057          | 45.7386 | 21.5407 | 39.3743 | 45.7904   | 4.5253  | 0.3205 | 0.6757 | 0.2101    |
| 0.6344        | 10.0  | 500  | 6.0725          | 46.2376 | 21.4997 | 39.6036 | 46.3269   | 4.2121  | 0.3205 | 0.6757 | 0.2101    |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1