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library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: gbert_synset_classifier_amdi_small
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_synset_classifier_amdi_small
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: 0.6371
- Accuracy: 0.8443
- F1: 0.8414
- Precision: 0.8523
- Recall: 0.8443
- F1 Macro: 0.7742
- Precision Macro: 0.7539
- Recall Macro: 0.8118
- F1 Micro: 0.8443
- Precision Micro: 0.8443
- Recall Micro: 0.8443
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|
| 3.1817 | 0.6483 | 100 | 1.7424 | 0.6200 | 0.5455 | 0.5576 | 0.6200 | 0.2894 | 0.3465 | 0.2954 | 0.6200 | 0.6200 | 0.6200 |
| 1.0711 | 1.2966 | 200 | 0.7171 | 0.8140 | 0.7971 | 0.7992 | 0.8140 | 0.5958 | 0.5870 | 0.6238 | 0.8140 | 0.8140 | 0.8140 |
| 0.649 | 1.9449 | 300 | 0.6003 | 0.8275 | 0.8184 | 0.8282 | 0.8275 | 0.6797 | 0.6812 | 0.7138 | 0.8275 | 0.8275 | 0.8275 |
| 0.4903 | 2.5932 | 400 | 0.5668 | 0.8336 | 0.8268 | 0.8375 | 0.8336 | 0.6942 | 0.6869 | 0.7271 | 0.8336 | 0.8336 | 0.8336 |
| 0.4095 | 3.2415 | 500 | 0.5511 | 0.8387 | 0.8351 | 0.8398 | 0.8387 | 0.7224 | 0.7198 | 0.7414 | 0.8387 | 0.8387 | 0.8387 |
| 0.3586 | 3.8898 | 600 | 0.5313 | 0.8415 | 0.8360 | 0.8452 | 0.8415 | 0.7188 | 0.7075 | 0.7481 | 0.8415 | 0.8415 | 0.8415 |
| 0.2813 | 4.5381 | 700 | 0.5442 | 0.8485 | 0.8451 | 0.8502 | 0.8485 | 0.7290 | 0.7355 | 0.7419 | 0.8485 | 0.8485 | 0.8485 |
| 0.2543 | 5.1864 | 800 | 0.5736 | 0.8494 | 0.8461 | 0.8515 | 0.8494 | 0.7812 | 0.7708 | 0.8047 | 0.8494 | 0.8494 | 0.8494 |
| 0.1928 | 5.8347 | 900 | 0.5791 | 0.8448 | 0.8419 | 0.8484 | 0.8448 | 0.7646 | 0.7536 | 0.7899 | 0.8448 | 0.8448 | 0.8448 |
| 0.1645 | 6.4830 | 1000 | 0.6371 | 0.8443 | 0.8414 | 0.8523 | 0.8443 | 0.7742 | 0.7539 | 0.8118 | 0.8443 | 0.8443 | 0.8443 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
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