---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: gbert-base-amdi-synset
  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. -->

# gbert-base-amdi-synset

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6415
- Accuracy: 0.8330
- F1: 0.6477
- Precision: 0.6550
- Recall: 0.6579

## 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: 48
- eval_batch_size: 48
- 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 | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 3.3175        | 0.4587 | 50   | 2.2214          | 0.5594   | 0.2286 | 0.2022    | 0.2872 |
| 1.5824        | 0.9174 | 100  | 1.1227          | 0.6867   | 0.3880 | 0.4003    | 0.4337 |
| 0.9358        | 1.3761 | 150  | 0.8457          | 0.7866   | 0.5421 | 0.5293    | 0.5807 |
| 0.7884        | 1.8349 | 200  | 0.7147          | 0.7762   | 0.5535 | 0.5538    | 0.5913 |
| 0.6245        | 2.2936 | 250  | 0.6656          | 0.8055   | 0.5663 | 0.5539    | 0.6033 |
| 0.5484        | 2.7523 | 300  | 0.6216          | 0.7986   | 0.5762 | 0.5789    | 0.6072 |
| 0.462         | 3.2110 | 350  | 0.5902          | 0.8227   | 0.6267 | 0.6206    | 0.6518 |
| 0.4089        | 3.6697 | 400  | 0.6369          | 0.8072   | 0.5902 | 0.5842    | 0.6126 |
| 0.368         | 4.1284 | 450  | 0.6189          | 0.8158   | 0.6296 | 0.6384    | 0.6613 |
| 0.3232        | 4.5872 | 500  | 0.6415          | 0.8330   | 0.6477 | 0.6550    | 0.6579 |
| 0.2836        | 5.0459 | 550  | 0.6373          | 0.8124   | 0.6341 | 0.6491    | 0.6609 |
| 0.2212        | 5.5046 | 600  | 0.6843          | 0.8090   | 0.6315 | 0.6471    | 0.6501 |
| 0.2228        | 5.9633 | 650  | 0.5933          | 0.8365   | 0.6625 | 0.6898    | 0.6686 |
| 0.1838        | 6.4220 | 700  | 0.6382          | 0.8313   | 0.6452 | 0.6472    | 0.6626 |
| 0.1527        | 6.8807 | 750  | 0.6471          | 0.8330   | 0.6601 | 0.6751    | 0.6772 |
| 0.1393        | 7.3394 | 800  | 0.6751          | 0.8227   | 0.6279 | 0.6339    | 0.6434 |
| 0.1082        | 7.7982 | 850  | 0.6689          | 0.8382   | 0.6608 | 0.6836    | 0.6772 |
| 0.0812        | 8.2569 | 900  | 0.7124          | 0.8296   | 0.6670 | 0.6785    | 0.6802 |
| 0.0836        | 8.7156 | 950  | 0.7201          | 0.8244   | 0.6446 | 0.6597    | 0.6574 |
| 0.0816        | 9.1743 | 1000 | 0.7253          | 0.8296   | 0.6478 | 0.6722    | 0.6567 |
| 0.0645        | 9.6330 | 1050 | 0.7236          | 0.8262   | 0.6425 | 0.6655    | 0.6521 |


### Framework versions

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