---
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert-large_ner
  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-large_ner

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: 0.3755
- Precision: 0.9010
- Recall: 0.8948
- F1: 0.8975
- Accuracy: 0.9521

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 438  | 0.2334          | 0.8727    | 0.8653 | 0.8649 | 0.9303   |
| 0.3598        | 2.0   | 876  | 0.2149          | 0.8885    | 0.8649 | 0.8757 | 0.9391   |
| 0.1678        | 3.0   | 1314 | 0.2257          | 0.8820    | 0.8906 | 0.8847 | 0.9461   |
| 0.1054        | 4.0   | 1752 | 0.2580          | 0.8902    | 0.8884 | 0.8884 | 0.9463   |
| 0.0645        | 5.0   | 2190 | 0.2881          | 0.8896    | 0.8820 | 0.8833 | 0.9451   |
| 0.0436        | 6.0   | 2628 | 0.2767          | 0.8922    | 0.8911 | 0.8914 | 0.9479   |
| 0.0245        | 7.0   | 3066 | 0.3190          | 0.9026    | 0.9038 | 0.9030 | 0.9534   |
| 0.0108        | 8.0   | 3504 | 0.3547          | 0.8879    | 0.8886 | 0.8876 | 0.9474   |
| 0.0108        | 9.0   | 3942 | 0.3780          | 0.8943    | 0.8886 | 0.8910 | 0.9494   |
| 0.0074        | 10.0  | 4380 | 0.3755          | 0.9010    | 0.8948 | 0.8975 | 0.9521   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1