metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: multilingual_sentiment_newspaper_headlines
results: []
multilingual_sentiment_newspaper_headlines
This model is a fine-tuned version of bert-base-multilingual-cased on a dataset of 30k newspaper headlines in German, Polish, English, Dutch and Spanish. The dataset contains 6k headlines in each of the five languages. The newspapers used are as follows:
['fakt', 'Rzeczpospolita', 'gazeta_wyborcza', 'UK_times', 'guardian', 'UK_sun', 'NRC', 'de_telegraaf', 'volkskrant', 'el_mundo', 'el_pais', 'ABC_spain', 'suddeutsche_zeitung', 'De_Welt', 'Bild']
It achieves the following results on the evaluation set:
- Train Loss: 0.2886
- Train Sparse Categorical Accuracy: 0.8688
- Validation Loss: 1.0107
- Validation Sparse Categorical Accuracy: 0.6434
- Epoch: 4
Model description
More information needed
Intended uses & limitations
Newpaper headline classification
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|---|---|---|---|---|
| 0.8008 | 0.6130 | 0.7099 | 0.6558 | 0 |
| 0.6148 | 0.6973 | 0.7559 | 0.6200 | 1 |
| 0.4626 | 0.7690 | 0.8233 | 0.6368 | 2 |
| 0.3632 | 0.8229 | 0.9609 | 0.6454 | 3 |
| 0.2886 | 0.8688 | 1.0107 | 0.6434 | 4 |
Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2