---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: flanT5_large_MT
  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. -->

# flanT5_large_MT

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9444
- Accuracy: 0.815
- Precision: 0.8339
- Recall: 0.7867
- F1 score: 0.8096

## 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: 1
- eval_batch_size: 1
- 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 | Accuracy | Precision | Recall | F1 score |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.2742        | 0.3910 | 2500  | 1.6400          | 0.69     | 0.7096    | 0.6433 | 0.6748   |
| 1.2294        | 0.7820 | 5000  | 0.9952          | 0.725    | 0.7872    | 0.6167 | 0.6916   |
| 1.0532        | 1.1730 | 7500  | 1.1585          | 0.69     | 0.6875    | 0.6967 | 0.6921   |
| 0.9314        | 1.5640 | 10000 | 0.8117          | 0.7417   | 0.7580    | 0.71   | 0.7332   |
| 0.8698        | 1.9550 | 12500 | 0.7872          | 0.7367   | 0.7817    | 0.6567 | 0.7138   |
| 0.7603        | 2.3459 | 15000 | 0.9449          | 0.77     | 0.7470    | 0.8167 | 0.7803   |
| 0.7207        | 2.7369 | 17500 | 0.9339          | 0.7917   | 0.7850    | 0.8033 | 0.7941   |
| 0.6447        | 3.1279 | 20000 | 1.0756          | 0.8017   | 0.8198    | 0.7733 | 0.7959   |
| 0.5358        | 3.5189 | 22500 | 1.0722          | 0.79     | 0.7788    | 0.81   | 0.7941   |
| 0.5169        | 3.9099 | 25000 | 1.0001          | 0.8067   | 0.8407    | 0.7567 | 0.7965   |
| 0.4011        | 4.3009 | 27500 | 1.0915          | 0.805    | 0.8144    | 0.79   | 0.8020   |
| 0.3177        | 4.6919 | 30000 | 1.2966          | 0.8083   | 0.8157    | 0.7967 | 0.8061   |
| 0.2783        | 5.0829 | 32500 | 1.2640          | 0.8117   | 0.7913    | 0.8467 | 0.8180   |
| 0.1352        | 5.4739 | 35000 | 1.3695          | 0.82     | 0.8333    | 0.8    | 0.8163   |
| 0.2168        | 5.8649 | 37500 | 1.3541          | 0.8133   | 0.8287    | 0.79   | 0.8089   |
| 0.1186        | 6.2559 | 40000 | 1.4063          | 0.8167   | 0.8276    | 0.8    | 0.8136   |
| 0.1205        | 6.6469 | 42500 | 1.6920          | 0.8033   | 0.7993    | 0.81   | 0.8046   |
| 0.0957        | 7.0378 | 45000 | 1.5681          | 0.815    | 0.8225    | 0.8033 | 0.8128   |
| 0.0406        | 7.4288 | 47500 | 1.9015          | 0.8083   | 0.8269    | 0.78   | 0.8027   |
| 0.0558        | 7.8198 | 50000 | 1.8359          | 0.8017   | 0.7967    | 0.81   | 0.8033   |
| 0.0549        | 8.2108 | 52500 | 1.8649          | 0.8083   | 0.8201    | 0.79   | 0.8048   |
| 0.0515        | 8.6018 | 55000 | 1.8556          | 0.8067   | 0.7949    | 0.8267 | 0.8105   |
| 0.0378        | 8.9928 | 57500 | 1.9273          | 0.7983   | 0.7806    | 0.83   | 0.8045   |
| 0.0182        | 9.3838 | 60000 | 1.9497          | 0.8133   | 0.8287    | 0.79   | 0.8089   |
| 0.0264        | 9.7748 | 62500 | 1.9444          | 0.815    | 0.8339    | 0.7867 | 0.8096   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1