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--- |
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language: |
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- en |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: text-classification |
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widget: |
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- text: "Every woman wants to be a model. It's codeword for 'I get everything for free and people want me'" |
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--- |
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### distilbert-base-sexism-detector |
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This is a fine-tuned model of distilbert-base on the Explainable Detection of Online Sexism (EDOS) dataset. It is intended to be used as a classification model for identifying tweets (0 - not sexist; 1 - sexist). |
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**This is a light model with an 81.2 F1 score. Use this model for fase prediction using the online API, if you like to see our best model with 86.3 F1 score , use this [link](https://huggingface.co/NLP-LTU/BERTweet-large-sexism-detector).** |
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Classification examples (use these example in the Hosted Inference API in the right panel ): |
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|Prediction|Tweet| |
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|-----|--------| |
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|sexist |Every woman wants to be a model. It's codeword for "I get everything for free and people want me" | |
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|not sexist |basically I placed more value on her than I should then?| |
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# More Details |
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For more details about the datasets and eval results, see (we will updated the page with our paper link) |
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# How to use |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer,pipeline |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained('NLP-LTU/distilbert-sexism-detector') |
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tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') |
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classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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prediction=classifier("Every woman wants to be a model. It's codeword for 'I get everything for free and people want me' ") |
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label_pred = 'not sexist' if prediction == 0 else 'sexist' |
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print(label_pred) |
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``` |
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``` |
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precision recall f1-score support |
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not sexsit 0.9000 0.9264 0.9130 3030 |
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sexist 0.7469 0.6784 0.7110 970 |
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accuracy 0.8662 4000 |
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macro avg 0.8234 0.8024 0.8120 4000 |
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weighted avg 0.8628 0.8662 0.8640 4000 |
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``` |
