---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: results_pegasus6_hiba_wiki
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# results_pegasus6_hiba_wiki

This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0699
- Rouge1 Fmeasure: 0.3654
- Rouge2 Fmeasure: 0.2931
- Rougel Fmeasure: 0.3466
- Meteor: 0.2623
- Bleu: 0.0000
- Bertscore P: 0.9267
- Bertscore R: 0.9204
- Bertscore F1: 0.9235

## 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: 4e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 Fmeasure | Rouge2 Fmeasure | Rougel Fmeasure | Meteor | Bleu   | Bertscore P | Bertscore R | Bertscore F1 |
|:-------------:|:------:|:----:|:---------------:|:---------------:|:---------------:|:---------------:|:------:|:------:|:-----------:|:-----------:|:------------:|
| 0.2433        | 0.5222 | 500  | 0.2175          | 0.0259          | 0.0060          | 0.0259          | 0.1659 | 0.0000 | 0.9282      | 0.8784      | 0.9026       |
| 0.2165        | 1.0444 | 1000 | 0.1818          | 0.0532          | 0.0247          | 0.0501          | 0.2343 | 0.0000 | 0.9405      | 0.9278      | 0.9341       |
| 0.1815        | 1.5666 | 1500 | 0.1535          | 0.1065          | 0.0529          | 0.0833          | 0.2365 | 0.0000 | 0.9369      | 0.9215      | 0.9291       |
| 0.1558        | 2.0888 | 2000 | 0.1352          | 0.1544          | 0.0836          | 0.1246          | 0.2442 | 0.0000 | 0.9342      | 0.9247      | 0.9294       |
| 0.1449        | 2.6110 | 2500 | 0.1250          | 0.1934          | 0.0940          | 0.1573          | 0.2439 | 0.0000 | 0.9338      | 0.9242      | 0.9289       |
| 0.1356        | 3.1332 | 3000 | 0.1107          | 0.2681          | 0.1641          | 0.2233          | 0.2477 | 0.0000 | 0.9259      | 0.9199      | 0.9229       |
| 0.1256        | 3.6554 | 3500 | 0.1063          | 0.2722          | 0.1793          | 0.2380          | 0.2497 | 0.0000 | 0.9263      | 0.9204      | 0.9233       |
| 0.1187        | 4.1775 | 4000 | 0.0975          | 0.2946          | 0.2058          | 0.2663          | 0.2534 | 0.0000 | 0.9272      | 0.9207      | 0.9239       |
| 0.1157        | 4.6997 | 4500 | 0.0936          | 0.2898          | 0.2032          | 0.2546          | 0.2456 | 0.0000 | 0.9289      | 0.9205      | 0.9247       |
| 0.1078        | 5.2219 | 5000 | 0.0876          | 0.3094          | 0.2179          | 0.2811          | 0.2521 | 0.0000 | 0.9272      | 0.9207      | 0.9239       |
| 0.1029        | 5.7441 | 5500 | 0.0849          | 0.3324          | 0.2481          | 0.3016          | 0.2571 | 0.0000 | 0.9267      | 0.9204      | 0.9235       |
| 0.1029        | 6.2663 | 6000 | 0.0827          | 0.3357          | 0.2546          | 0.3146          | 0.2575 | 0.0000 | 0.9284      | 0.9199      | 0.9241       |
| 0.0992        | 6.7885 | 6500 | 0.0783          | 0.3510          | 0.2765          | 0.3238          | 0.2606 | 0.0000 | 0.9268      | 0.9202      | 0.9234       |
| 0.0954        | 7.3107 | 7000 | 0.0754          | 0.3327          | 0.2611          | 0.3139          | 0.2588 | 0.0000 | 0.9267      | 0.9204      | 0.9235       |
| 0.0938        | 7.8329 | 7500 | 0.0746          | 0.3592          | 0.2863          | 0.3404          | 0.2614 | 0.0000 | 0.9300      | 0.9216      | 0.9258       |
| 0.0938        | 8.3551 | 8000 | 0.0728          | 0.3718          | 0.3022          | 0.3492          | 0.2613 | 0.0000 | 0.9268      | 0.9204      | 0.9236       |
| 0.09          | 8.8773 | 8500 | 0.0707          | 0.3600          | 0.2882          | 0.3442          | 0.2633 | 0.0000 | 0.9267      | 0.9204      | 0.9235       |
| 0.0911        | 9.3995 | 9000 | 0.0705          | 0.3677          | 0.2933          | 0.3464          | 0.2646 | 0.0000 | 0.9267      | 0.9204      | 0.9235       |
| 0.0881        | 9.9217 | 9500 | 0.0699          | 0.3654          | 0.2931          | 0.3466          | 0.2623 | 0.0000 | 0.9267      | 0.9204      | 0.9235       |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1