---
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