---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Research_paper_MLM_Final_Label
  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. -->

# Research_paper_MLM_Final_Label

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9925
- Accuracy: 0.8591
- F1: 0.8575
- Precision: 0.8712
- Recall: 0.8591

## 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: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1091        | 0.04  | 500   | 0.9687          | 0.8633   | 0.8614 | 0.8791    | 0.8633 |
| 0.1186        | 0.08  | 1000  | 1.2620          | 0.8629   | 0.8612 | 0.8769    | 0.8629 |
| 0.2068        | 0.12  | 1500  | 2.0443          | 0.8527   | 0.8509 | 0.8659    | 0.8527 |
| 0.42          | 0.16  | 2000  | 2.1931          | 0.8595   | 0.8574 | 0.8764    | 0.8595 |
| 0.1435        | 0.2   | 2500  | 2.0973          | 0.8644   | 0.8625 | 0.8805    | 0.8644 |
| 0.4373        | 0.24  | 3000  | 2.0976          | 0.8603   | 0.8580 | 0.8785    | 0.8603 |
| 0.1527        | 0.28  | 3500  | 2.2136          | 0.8550   | 0.8532 | 0.8679    | 0.8550 |
| 0.459         | 0.32  | 4000  | 2.0543          | 0.8610   | 0.8590 | 0.8775    | 0.8610 |
| 0.2396        | 0.36  | 4500  | 2.1373          | 0.8565   | 0.8548 | 0.8690    | 0.8565 |
| 0.1641        | 0.4   | 5000  | 2.2913          | 0.8557   | 0.8539 | 0.8695    | 0.8557 |
| 0.1841        | 0.44  | 5500  | 2.1315          | 0.8539   | 0.8520 | 0.8672    | 0.8539 |
| 0.133         | 0.48  | 6000  | 2.2268          | 0.8580   | 0.8564 | 0.8695    | 0.8580 |
| 0.1659        | 0.52  | 6500  | 2.1685          | 0.8573   | 0.8557 | 0.8689    | 0.8573 |
| 0.1677        | 0.56  | 7000  | 2.1515          | 0.8576   | 0.8558 | 0.8712    | 0.8576 |
| 0.3713        | 0.6   | 7500  | 2.2057          | 0.8606   | 0.8584 | 0.8785    | 0.8606 |
| 0.1469        | 0.64  | 8000  | 1.8279          | 0.8606   | 0.8594 | 0.8698    | 0.8606 |
| 0.3673        | 0.68  | 8500  | 1.9808          | 0.8625   | 0.8603 | 0.8812    | 0.8625 |
| 0.1395        | 0.72  | 9000  | 2.0565          | 0.8603   | 0.8585 | 0.8741    | 0.8603 |
| 0.1052        | 0.76  | 9500  | 2.0813          | 0.8606   | 0.8591 | 0.8724    | 0.8606 |
| 0.3925        | 0.8   | 10000 | 2.0700          | 0.8569   | 0.8553 | 0.8687    | 0.8569 |
| 0.1886        | 0.84  | 10500 | 1.9925          | 0.8591   | 0.8575 | 0.8712    | 0.8591 |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1