---
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: task2_flausch_classification_gbert-large_span_classifier_with_nonspan
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# task2_flausch_classification_gbert-large_span_classifier_with_nonspan

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: 0.2307
- Accuracy: 0.9479
- F1: 0.9467

## 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.3749        | 0.2697 | 1000  | 0.3099          | 0.9149   | 0.9174 |
| 0.2804        | 0.5394 | 2000  | 0.2330          | 0.9371   | 0.9305 |
| 0.2672        | 0.8091 | 3000  | 0.2369          | 0.9391   | 0.9339 |
| 0.2248        | 1.0787 | 4000  | 0.2295          | 0.9427   | 0.9406 |
| 0.1942        | 1.3484 | 5000  | 0.2244          | 0.9452   | 0.9422 |
| 0.1873        | 1.6181 | 6000  | 0.2310          | 0.9423   | 0.9393 |
| 0.1768        | 1.8878 | 7000  | 0.2155          | 0.9469   | 0.9450 |
| 0.1443        | 2.1575 | 8000  | 0.2295          | 0.9454   | 0.9449 |
| 0.1198        | 2.4272 | 9000  | 0.2295          | 0.9484   | 0.9474 |
| 0.1238        | 2.6969 | 10000 | 0.2278          | 0.9479   | 0.9469 |
| 0.1166        | 2.9666 | 11000 | 0.2307          | 0.9479   | 0.9467 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1