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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_pair
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_pair
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: 0.4826
- Accuracy: 0.8551
- F1: 0.8501
- Precision: 0.8590
- Recall: 0.8551
- F1 Macro: 0.7419
- Precision Macro: 0.7334
- Recall Macro: 0.7673
- F1 Micro: 0.8551
- Precision Micro: 0.8551
- Recall Micro: 0.8551
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|
| 2.7743 | 0.3891 | 100 | 1.0270 | 0.7995 | 0.7723 | 0.7757 | 0.7995 | 0.5034 | 0.5166 | 0.5282 | 0.7995 | 0.7995 | 0.7995 |
| 0.8957 | 0.7782 | 200 | 0.6587 | 0.8291 | 0.8176 | 0.8179 | 0.8291 | 0.5871 | 0.5863 | 0.6064 | 0.8291 | 0.8291 | 0.8291 |
| 0.6278 | 1.1673 | 300 | 0.5440 | 0.8457 | 0.8363 | 0.8395 | 0.8457 | 0.6407 | 0.6380 | 0.6622 | 0.8457 | 0.8457 | 0.8457 |
| 0.5132 | 1.5564 | 400 | 0.5295 | 0.8362 | 0.8275 | 0.8355 | 0.8362 | 0.6593 | 0.6525 | 0.6857 | 0.8362 | 0.8362 | 0.8362 |
| 0.4843 | 1.9455 | 500 | 0.4930 | 0.8511 | 0.8439 | 0.8500 | 0.8511 | 0.6777 | 0.6675 | 0.7028 | 0.8511 | 0.8511 | 0.8511 |
| 0.39 | 2.3346 | 600 | 0.4827 | 0.8564 | 0.8521 | 0.8555 | 0.8564 | 0.7073 | 0.6989 | 0.7287 | 0.8564 | 0.8564 | 0.8564 |
| 0.3536 | 2.7237 | 700 | 0.4818 | 0.8551 | 0.8492 | 0.8576 | 0.8551 | 0.7314 | 0.7421 | 0.7476 | 0.8551 | 0.8551 | 0.8551 |
| 0.3462 | 3.1128 | 800 | 0.4826 | 0.8551 | 0.8501 | 0.8590 | 0.8551 | 0.7419 | 0.7334 | 0.7673 | 0.8551 | 0.8551 | 0.8551 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
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