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license: apache-2.0 |
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license: apache-2.0 |
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model base: https://huggingface.co/google-bert/bert-base-multilingual-uncased |
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dataset: https://github.com/ramybaly/Article-Bias-Prediction |
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training parameters: |
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- batch_size: 100 |
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- epochs: 5 |
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- dropout: 0.05 |
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- max_length: 512 |
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- learning_rate: 3e-5 |
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- warmup_steps: 100 |
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- random_state: 239 |
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training methodology: |
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- sanitize dataset following specific rule-set, utilize random split as provided in the dataset |
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- train on train split and evaluate on validation split in each epoch |
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- evaluate test split only on the model that performed best on validation loss |
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result summary: |
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- throughout the five training epochs, model of second epoch achieved the lowest validation loss of 0.3003 |
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- on test split second epoch model achieved f1 score of 0.8842 |
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usage: |
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``` |
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model = AutoModelForSequenceClassification.from_pretrained("premsa/political-bias-prediction-allsides-mBERT") |
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tokenizer = AutoTokenizer.from_pretrained(premsa/"political-bias-prediction-allsides-mBERT") |
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nlp = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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print(nlp("die massen werden von den medien kontrolliert.")) |
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``` |
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