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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
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