---
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: 0.5622
- Loc: {'precision': 0.9222857142857143, 'recall': 0.9449648711943794, 'f1': 0.9334875650665124, 'number': 854}
- Org: {'precision': 0.8973561430793157, 'recall': 0.8876923076923077, 'f1': 0.8924980665119876, 'number': 650}
- Per: {'precision': 0.9014373716632443, 'recall': 0.9440860215053763, 'f1': 0.9222689075630252, 'number': 465}
- Overall Precision: 0.9092
- Overall Recall: 0.9259
- Overall F1: 0.9175
- Overall Accuracy: 0.9582

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                      | Org                                                                                                      | Per                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1729        | 10.0  | 5000  | 0.4248          | {'precision': 0.9111361079865017, 'recall': 0.9484777517564403, 'f1': 0.9294320137693631, 'number': 854} | {'precision': 0.9027113237639554, 'recall': 0.8707692307692307, 'f1': 0.8864526233359435, 'number': 650} | {'precision': 0.9010309278350516, 'recall': 0.9397849462365592, 'f1': 0.92, 'number': 465}               | 0.9060            | 0.9208         | 0.9134     | 0.9584           |
| 0.0068        | 20.0  | 10000 | 0.5622          | {'precision': 0.9222857142857143, 'recall': 0.9449648711943794, 'f1': 0.9334875650665124, 'number': 854} | {'precision': 0.8973561430793157, 'recall': 0.8876923076923077, 'f1': 0.8924980665119876, 'number': 650} | {'precision': 0.9014373716632443, 'recall': 0.9440860215053763, 'f1': 0.9222689075630252, 'number': 465} | 0.9092            | 0.9259         | 0.9175     | 0.9582           |


### Framework versions

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