---
license: apache-2.0
base_model: google/mt5-large
tags:
- generated_from_trainer
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9627
- Loc: {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854}
- Org: {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650}
- Per: {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465}
- Overall Precision: 0.0567
- Overall Recall: 0.1285
- Overall F1: 0.0787
- Overall Accuracy: 0.3287

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Loc                                                                                                         | Org                                                                                                         | Per                                                                                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 3.8187        | 2.0   | 10   | 3.1219          | {'precision': 0.06360022714366836, 'recall': 0.13114754098360656, 'f1': 0.08565965583173997, 'number': 854} | {'precision': 0.05763688760806916, 'recall': 0.15384615384615385, 'f1': 0.08385744234800839, 'number': 650} | {'precision': 0.027879677182685254, 'recall': 0.08172043010752689, 'f1': 0.04157549234135668, 'number': 465} | 0.0515            | 0.1270         | 0.0732     | 0.2983           |
| 3.2942        | 4.0   | 20   | 2.9627          | {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854} | {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650}  | {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465} | 0.0567            | 0.1285         | 0.0787     | 0.3287           |


### Framework versions

- Transformers 4.39.3
- Pytorch 1.11.0a0+17540c5
- Datasets 2.20.0
- Tokenizers 0.15.2