readme: ---
  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](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: 
  
   + Polish: *Fakt, Rzeczpospolita, Gazeta Wyborcza*
   + English: *The Times, The Guardian, The Sun*
   + Dutch: *De Telegraaf, NRC, Volkskrant*
   + Spanish: *El Mundo, El Pais, ABC*
   + German: *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
  
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
  
  tokenizer = AutoTokenizer.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
  m1 = TFAutoModelForSequenceClassification.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
  sentiment_classifier = TextClassificationPipeline(tokenizer=tokenizer, model=m1)
  
  sentiment_classifier('Brazylia: Bolsonaro wci±ż nie uznał porażki. Jego zwolennicy blokuj± autostrady')
  [{'label': 'negative, 0', 'score': 0.9989686012268066}]
  ```
  
  
  ### 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
  