---
license: other
tags:
- generated_from_trainer
base_model: boun-tabi-LMG/TURNA
metrics:
- rouge
- bleu
model-index:
- name: TURNA_spell_correction_product_search
  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. -->

# TURNA_spell_correction_product_search

This model is a fine-tuned version of [boun-tabi-LMG/TURNA](https://huggingface.co/boun-tabi-LMG/TURNA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1088
- Rouge1: 0.8437
- Rouge2: 0.7401
- Rougel: 0.8435
- Rougelsum: 0.8437
- Bleu: 0.8713
- Precisions: [0.8736109932988378, 0.8306083370157608, 0.8473118279569892, 0.9631336405529954]
- Brevity Penalty: 0.9932
- Length Ratio: 0.9933
- Translation Length: 11789
- Reference Length: 11869
- Meteor: 0.7484
- Score: 14.6658
- Num Edits: 1709
- Ref Length: 11653.0

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   | Precisions                                                                       | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Meteor | Score   | Num Edits | Ref Length |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:-------:|:---------:|:----------:|
| No log        | 0.3335 | 1253  | 0.2447          | 0.7099 | 0.5537 | 0.7097 | 0.7097    | 0.7033 | [0.7548184082863512, 0.6378386771213415, 0.6301176470588236, 0.906832298136646]  | 0.9711          | 0.9715       | 22881              | 23553            | 0.5852 | 27.8452 | 6474      | 23250.0    |
| No log        | 0.6670 | 2506  | 0.1886          | 0.7586 | 0.6231 | 0.7584 | 0.7585    | 0.7555 | [0.7995148154565933, 0.7114032405992051, 0.7142528735632184, 0.8698224852071006] | 0.9799          | 0.9801       | 23084              | 23553            | 0.6454 | 22.8860 | 5321      | 23250.0    |
| 0.3827        | 1.0005 | 3759  | 0.1571          | 0.7810 | 0.6561 | 0.7807 | 0.7809    | 0.7947 | [0.8183424557169332, 0.7418011058092858, 0.7430269775948788, 0.939297124600639]  | 0.9850          | 0.9851       | 23203              | 23553            | 0.6742 | 20.6710 | 4806      | 23250.0    |
| 0.3827        | 1.3340 | 5012  | 0.1458          | 0.7973 | 0.6822 | 0.7973 | 0.7974    | 0.8139 | [0.8318891557995882, 0.7666015625, 0.7682954289574421, 0.9333333333333333]       | 0.9897          | 0.9898       | 23312              | 23553            | 0.6955 | 19.1441 | 4451      | 23250.0    |
| 0.3827        | 1.6676 | 6265  | 0.1320          | 0.8109 | 0.6993 | 0.8107 | 0.8111    | 0.8294 | [0.8467426359922597, 0.7889852885703508, 0.788783355947535, 0.9453376205787781]  | 0.9873          | 0.9873       | 23255              | 23553            | 0.7111 | 17.6258 | 4098      | 23250.0    |
| 0.1238        | 2.0011 | 7518  | 0.1218          | 0.8205 | 0.7139 | 0.8205 | 0.8206    | 0.8462 | [0.8559577028885832, 0.8045084439083233, 0.8144353369763205, 0.9607843137254902] | 0.9877          | 0.9877       | 23264              | 23553            | 0.7231 | 16.5720 | 3853      | 23250.0    |
| 0.1238        | 2.3346 | 8771  | 0.1223          | 0.8246 | 0.7219 | 0.8247 | 0.8249    | 0.8506 | [0.8575583882282488, 0.8074450590521752, 0.8080267558528428, 0.9639344262295082] | 0.9925          | 0.9926       | 23378              | 23553            | 0.7298 | 16.1978 | 3766      | 23250.0    |
| 0.1238        | 2.6681 | 10024 | 0.1177          | 0.8319 | 0.7326 | 0.8320 | 0.8321    | 0.8580 | [0.8628791114908159, 0.8155853840417598, 0.8160765976397238, 0.9671052631578947] | 0.9939          | 0.9939       | 23410              | 23553            | 0.7379 | 15.6602 | 3641      | 23250.0    |
| 0.0686        | 3.0016 | 11277 | 0.1122          | 0.8388 | 0.7400 | 0.8391 | 0.8391    | 0.8623 | [0.8686514886164624, 0.8236522257848036, 0.8239625167336011, 0.9607843137254902] | 0.9940          | 0.9940       | 23411              | 23553            | 0.7462 | 15.0237 | 3493      | 23250.0    |
| 0.0686        | 3.3351 | 12530 | 0.1184          | 0.8398 | 0.7450 | 0.8397 | 0.8398    | 0.8682 | [0.8676339190741608, 0.8243353328889876, 0.8229854689564069, 0.9735099337748344] | 0.9979          | 0.9979       | 23503              | 23553            | 0.7488 | 14.9677 | 3480      | 23250.0    |
| 0.0686        | 3.6686 | 13783 | 0.1148          | 0.8440 | 0.7484 | 0.8441 | 0.8442    | 0.8716 | [0.8706277359853798, 0.8271121294995935, 0.826640333552776, 0.9735099337748344]  | 0.9990          | 0.9990       | 23529              | 23553            | 0.7533 | 14.5806 | 3390      | 23250.0    |
| 0.0383        | 4.0021 | 15036 | 0.1134          | 0.8498 | 0.7547 | 0.8498 | 0.8500    | 0.8750 | [0.8757069354084279, 0.8344307168750462, 0.8344676180021954, 0.9671052631578947] | 0.9985          | 0.9985       | 23517              | 23553            | 0.7592 | 14.0516 | 3267      | 23250.0    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1