bert-base-finnish-cased-toxicity

Model Card for Model ID

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This is a toxicity identification model which classifies a text as either "toxic" or "non-toxic".

Model Details

Model Description

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This is a toxicity identification model which classifies a text as either "toxic" or "non-toxic".

• Developed by: Anni Eskelinen
• Model type: Text classification
• Language(s) (NLP): Finnish
• Finetuned from model: TurkuNLP/bert-base-finnish-cased-v1

Use

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This model is intended to be used to as a helpful tool for content moderation.

Bias, Risks, and Limitations

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The model is sometimes very sensitive to toxicity and might classify non-toxic texts as toxic.

How to Get Started with the Model

Use the code below to get started with the model.

>>> model = transformers.AutoModelForSequenceClassification.from_pretrained("annieske/bert-base-finnish-cased-toxicity")
>>> tokenizer = transformers.AutoTokenizer.from_pretrained("TurkuNLP/bert-base-finnish-cased-v1")
>>> pipe = transformers.pipeline(task="text-classification", model=model, tokenizer=tokenizer)
>>> pipe("This text is neutral!")
>>> pipe("You suck!")

Training Details

Training Data

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Training data included ten different toxicity and related task datasets that were machine translated to Finnish and the labels were unified.

The datasets can be found in GitHub.

Training Procedure

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Preprocessing

No preprocessing was done on the training data.

Training Hyperparameters

• learning rate 1e-05
• batch size 8
• sequence length 512
• 5 epochs with early stopping
• evaluation every 25,000 steps

Evaluation

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Testing Data

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A manually annotated Finnish dataset consisting of 600 examples which is a sample of the "TurkuNLP/Suomi24-toxicity-annotated" dataset. Includes 299 non-toxic examples and 301 toxic examples.

The dataset can be foung in GitHub.

Metrics

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• Accuracy (corresponds to micro F1)
• Precision (macro)
• Recall (macro)
• F1 (macro)

Results

• Accuracy: 0.71
• Precision: 0.73
• Recall: 0.71
• F1: 0.71

Citation

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BibTeX:

Citation information coming later.