---
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: flausch_span_gbert-large
  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. -->

# flausch_span_gbert-large

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6725
- Model Preparation Time: 0.0057
- Precision: 0.5075
- Recall: 0.6548
- F1: 0.5718

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:------:|:------:|
| 0.5882        | 1.0   | 517  | 0.5842          | 0.0057                 | 0.4539    | 0.6280 | 0.5270 |
| 0.3879        | 2.0   | 1034 | 0.5720          | 0.0057                 | 0.5194    | 0.6646 | 0.5831 |
| 0.256         | 3.0   | 1551 | 0.6075          | 0.0057                 | 0.4985    | 0.6445 | 0.5622 |
| 0.1477        | 4.0   | 2068 | 0.6725          | 0.0057                 | 0.5075    | 0.6548 | 0.5718 |


### Framework versions

- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1