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--- |
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language: "en" |
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license: "cc-by-sa-4.0" |
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tags: |
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- text-classification |
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- hate-speech |
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widget: |
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- text: "Gay is okay." |
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--- |
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# roberta-base-frenk-hate |
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Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). |
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## Fine-tuning hyperparameters |
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Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: |
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```python |
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model_args = { |
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"num_train_epochs": 6, |
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"learning_rate": 3e-6, |
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"train_batch_size": 69} |
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``` |
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## Performance |
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The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. |
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| model | average accuracy | average macro F1| |
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|---|---|---| |
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|roberta-base-frenk-hate|0.7915|0.7785| |
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|xlm-roberta-large |0.7904|0.77876| |
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|xlm-roberta-base |0.7577|0.7402| |
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|fasttext|0.725 |0.707 | |
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From recorded accuracies and macro F1 scores p-values were also calculated: |
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Comparison with `xlm-roberta-base`: |
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| test | accuracy p-value | macro F1 p-value| |
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| --- | --- | --- | |
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|Wilcoxon|0.00781|0.00781| |
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|Mann Whithney U-test|0.00108|0.00108| |
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|Student t-test | 1.35e-08 | 1.05e-07| |
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Comparison with `xlm-roberta-large` yielded inconclusive results. `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below. |
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| test | accuracy p-value | macro F1 p-value| |
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| --- | --- | --- | |
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|Wilcoxon|0.188|0.406| |
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|Mann Whithey|0.375|0.649| |
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|Student t-test | 0.681| 0.934| |
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With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another. |
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## Use examples |
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```python |
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from simpletransformers.classification import ClassificationModel |
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model_args = { |
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"num_train_epochs": 6, |
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"learning_rate": 3e-6, |
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"train_batch_size": 69} |
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model = ClassificationModel( |
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"roberta", "5roop/roberta-base-frenk-hate", use_cuda=True, |
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args=model_args |
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) |
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predictions, logit_output = model.predict(["Build the wall", |
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"Build the wall of trust"] |
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) |
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predictions |
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### Output: |
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### array([1, 0]) |
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``` |
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## Citation |
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If you use the model, please cite the following paper on which the original model is based: |
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``` |
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@article{DBLP:journals/corr/abs-1907-11692, |
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author = {Yinhan Liu and |
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Myle Ott and |
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Naman Goyal and |
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Jingfei Du and |
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Mandar Joshi and |
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Danqi Chen and |
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Omer Levy and |
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Mike Lewis and |
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Luke Zettlemoyer and |
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Veselin Stoyanov}, |
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title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, |
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journal = {CoRR}, |
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volume = {abs/1907.11692}, |
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year = {2019}, |
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url = {http://arxiv.org/abs/1907.11692}, |
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archivePrefix = {arXiv}, |
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eprint = {1907.11692}, |
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timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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and the dataset used for fine-tuning: |
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``` |
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@misc{ljubešić2019frenk, |
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title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, |
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author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, |
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year={2019}, |
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eprint={1906.02045}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/1906.02045} |
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} |
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``` |
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