---
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