---
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
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 [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5650
- Accuracy: 0.8403
- F1: 0.8328
- Precision: 0.8416
- Recall: 0.8403
- F1 Macro: 0.6886
- Precision Macro: 0.6871
- Recall Macro: 0.7119
- F1 Micro: 0.8403
- Precision Micro: 0.8403
- Recall Micro: 0.8403

## 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: 2e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|
| 3.1687        | 0.3891 | 100  | 1.7221          | 0.6317   | 0.5563 | 0.5358    | 0.6317 | 0.2779   | 0.2937          | 0.2938       | 0.6317   | 0.6317          | 0.6317       |
| 1.2239        | 0.7782 | 200  | 0.8836          | 0.7856   | 0.7633 | 0.7696    | 0.7856 | 0.5175   | 0.5077          | 0.5567       | 0.7856   | 0.7856          | 0.7856       |
| 0.7758        | 1.1673 | 300  | 0.7089          | 0.8107   | 0.7922 | 0.7939    | 0.8107 | 0.5917   | 0.5889          | 0.6185       | 0.8107   | 0.8107          | 0.8107       |
| 0.6436        | 1.5564 | 400  | 0.6498          | 0.8250   | 0.8136 | 0.8220    | 0.8250 | 0.6330   | 0.6331          | 0.6563       | 0.8250   | 0.8250          | 0.8250       |
| 0.5815        | 1.9455 | 500  | 0.6037          | 0.8300   | 0.8227 | 0.8338    | 0.8300 | 0.6583   | 0.6478          | 0.6890       | 0.8300   | 0.8300          | 0.8300       |
| 0.4695        | 2.3346 | 600  | 0.5771          | 0.8389   | 0.8319 | 0.8409    | 0.8389 | 0.6729   | 0.6688          | 0.6984       | 0.8389   | 0.8389          | 0.8389       |
| 0.4336        | 2.7237 | 700  | 0.5724          | 0.8362   | 0.8280 | 0.8395    | 0.8362 | 0.6753   | 0.6682          | 0.7038       | 0.8362   | 0.8362          | 0.8362       |
| 0.4135        | 3.1128 | 800  | 0.5650          | 0.8403   | 0.8328 | 0.8416    | 0.8403 | 0.6886   | 0.6871          | 0.7119       | 0.8403   | 0.8403          | 0.8403       |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3