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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_tiny
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_tiny
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: 0.6469
- Accuracy: 0.8376
- F1: 0.8366
- Precision: 0.8446
- Recall: 0.8376
- F1 Macro: 0.8202
- Precision Macro: 0.7900
- Recall Macro: 0.8625
- F1 Micro: 0.8376
- Precision Micro: 0.8376
- Recall Micro: 0.8376
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|
| 2.7014 | 0.8584 | 100 | 1.1834 | 0.6919 | 0.6499 | 0.6573 | 0.6919 | 0.4964 | 0.5670 | 0.4997 | 0.6919 | 0.6919 | 0.6919 |
| 0.7696 | 1.7167 | 200 | 0.5970 | 0.8241 | 0.8188 | 0.8227 | 0.8241 | 0.7784 | 0.7603 | 0.8062 | 0.8241 | 0.8241 | 0.8241 |
| 0.4739 | 2.5751 | 300 | 0.5408 | 0.8321 | 0.8290 | 0.8381 | 0.8321 | 0.8082 | 0.7815 | 0.8508 | 0.8321 | 0.8321 | 0.8321 |
| 0.3743 | 3.4335 | 400 | 0.5343 | 0.8426 | 0.8385 | 0.8480 | 0.8426 | 0.8269 | 0.8046 | 0.8598 | 0.8426 | 0.8426 | 0.8426 |
| 0.2931 | 4.2918 | 500 | 0.5188 | 0.8469 | 0.8465 | 0.8516 | 0.8469 | 0.8312 | 0.8133 | 0.8552 | 0.8469 | 0.8469 | 0.8469 |
| 0.2175 | 5.1502 | 600 | 0.5697 | 0.8426 | 0.8419 | 0.8506 | 0.8426 | 0.8295 | 0.8093 | 0.8572 | 0.8426 | 0.8426 | 0.8426 |
| 0.1689 | 6.0086 | 700 | 0.5781 | 0.8426 | 0.8421 | 0.8470 | 0.8426 | 0.8322 | 0.8164 | 0.8540 | 0.8426 | 0.8426 | 0.8426 |
| 0.1174 | 6.8670 | 800 | 0.6469 | 0.8376 | 0.8366 | 0.8446 | 0.8376 | 0.8202 | 0.7900 | 0.8625 | 0.8376 | 0.8376 | 0.8376 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
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