transformers pytorch bert text-classification generated_from_trainer base_model:deepset/gbert-large license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FragZONFactMetaRecommendation
  results: []
widget:
- text: "Wer arbeitet im Ressort PWG von ZEIT ONLINE?"
  example_title: "Meta"
- text: "Wann ist Helmut Schmidt gestorben?"
  example_title: "Fakt"
- text: "Was kann die ZEIT-KI?"
  example_title: "Meta"
- text: "Wer hat 2021/22 die Meisterschaft der Fussballbundesliga gewonnen?"
  example_title: "Fakt"
- text: "Wen soll ich bei der nächsten Bundestagswahl wählen?"
  example_title: "Empfehlung"

---

# FragZONFactMetaRecommendation

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the beta dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4128
- Accuracy: 0.9205

## Model description

Fine-tuned gbert on queries

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.171         | 1.0   | 76   | 0.4003          | 0.9139   |
| 0.1154        | 2.0   | 152  | 0.4128          | 0.9205   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
