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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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widget: |
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- text: >- |
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Every woman wants to be a model. It's codeword for 'I get everything for |
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free and people want me' |
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pipeline_tag: text-classification |
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--- |
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### BERTweet-large-sexism-detector |
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This is a fine-tuned model of BERTweet-large 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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More information about the original pre-trained model can be found [here](https://huggingface.co/docs/transformers/model_doc/bertweet) |
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Our model accuracy was 89.72 using the test set and 86.13 F1-score. |
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Classification examples: |
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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/bertweet-large-sexism-detector') |
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tokenizer = AutoTokenizer.from_pretrained('NLP-LTU/bertweet-large-sexism-detector') |
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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(prediction) |
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``` |
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our system rank 10 out of 84 teams, and our results on the test set was: |
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``` |
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precision recall f1-score support |
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not sexsit 0.9355 0.9284 0.9319 3030 |
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sexist 0.7815 0.8000 0.7906 970 |
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accuracy 0.8972 4000 |
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macro avg 0.8585 0.8642 0.8613 4000 |
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weighted avg 0.8981 0.8972 0.8977 4000 |
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``` |
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tn 2813, fp 217, fn 194, tp 776``` |
