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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: multilingual_sentiment_newspaper_headlines |
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results: [] |
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--- |
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# multilingual_sentiment_newspaper_headlines |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co./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: |
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+ Polish: *Fakt, Rzeczpospolita, Gazeta Wyborcza* |
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+ English: *The Times, The Guardian, The Sun* |
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+ Dutch: *De Telegraaf, NRC, Volkskrant* |
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+ Spanish: *El Mundo, El Pais, ABC* |
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+ German: *Suddeutsche Zeitung, De Welt, Bild* |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.2886 |
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- Train Sparse Categorical Accuracy: 0.8688 |
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- Validation Loss: 1.0107 |
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- Validation Sparse Categorical Accuracy: 0.6434 |
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- Epoch: 4 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |
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|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| |
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| 0.8008 | 0.6130 | 0.7099 | 0.6558 | 0 | |
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| 0.6148 | 0.6973 | 0.7559 | 0.6200 | 1 | |
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| 0.4626 | 0.7690 | 0.8233 | 0.6368 | 2 | |
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| 0.3632 | 0.8229 | 0.9609 | 0.6454 | 3 | |
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| 0.2886 | 0.8688 | 1.0107 | 0.6434 | 4 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- TensorFlow 2.9.2 |
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- Tokenizers 0.13.2 |
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