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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:

  • 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
import torch
from transformers import AutoTokenizer, TextClassificationPipeline,TFAutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
m1 = TFAutoModelForSequenceClassification.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines", from_tf=True)
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