Create README.md

README.md

@@ -0,0 +1,150 @@
+# SOLD - A Benchmark for Sinhala Offensive Language Identification
+
+In this repository, we introduce the {S}inhala {O}ffensive {L}anguage {D}ataset **(SOLD)** and present multiple experiments on this dataset. **SOLD** is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level. **SOLD** is the largest offensive language dataset compiled for Sinhala. We also introduce **SemiSOLD**, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach.
+
+:warning: This repository contains texts that may be offensive and harmful.
+
+## Annotation
+We use an annotation scheme split into two levels deciding (a) Offensiveness of a tweet (sentence-level) and (b) Tokens that contribute to the offence at sentence-level (token-level).
+
+### Sentence-level 
+Our sentence-level offensive language detection follows level A in OLID [(Zampieri et al., 2019)](https://aclanthology.org/N19-1144/). We asked annotators to discriminate between the following types of tweets:
+* **Offensive (OFF)**: Posts containing any form of non-acceptable language (profanity) or a targeted offence, which can be veiled or direct. This includes insults, threats, and posts containing profane language or swear words.
+* **Not Offensive (NOT)**: Posts that do not contain offense or profanity.
+
+Each tweet was annotated with one of the above labels, which we used as the labels in sentence-level offensive language identification.
+
+### Token-level
+To provide a human explanation of labelling, we collect rationales for the offensive language. Following HateXplain [(Mathew et al., 2021)](https://ojs.aaai.org/index.php/AAAI/article/view/17745), we define a rationale as a specific text segment that justifies the human annotator’s decision of the sentence-level labels. Therefore, We ask the annotators to highlight particular tokens in a tweet that supports their judgement about the sentence-level label (offensive, not offensive). Specifically, if a tweet is offensive, we guide the annotators to highlight tokens from the text that supports the judgement while including non-verbal expressions such as emojis and morphemes that are used to convey the intention as well. We use this as token-level offensive labels in SOLD.
+
+
+![Alt text](https://github.com/Sinhala-NLP/SOLD/blob/master/images/SOLD_Annotation.png?raw=true "Annotation Process")
+
+## Data
+SOLD is released in HuggingFace. It can be loaded in to pandas dataframes using the following code. 
+
+```python
+from datasets import Dataset
+from datasets import load_dataset
+
+sold_train = Dataset.to_pandas(load_dataset('sinhala-nlp/SOLD', split='train'))
+sold_test = Dataset.to_pandas(load_dataset('sinhala-nlp/SOLD', split='test'))
+```
+The dataset contains of the following columns. 
+* **post_id** - Twitter ID
+* **text** - Post text
+* **tokens** - Tokenised text. Each token is seperated by a space. 
+* **rationals** - Offensive tokens. If a token is offensive it is shown as 1 and 0 otherwise.
+* **label** - Sentence-level label, offensive or not-offensive. 
+
+![Alt text](https://github.com/Sinhala-NLP/SOLD/blob/master/images/SOLD_Examples.png?raw=true "Four examples from the SOLD dataset")
+
+SemiSOLD is also released HuggingFace and can be loaded to a pandas dataframe using the following code. 
+
+```python
+from datasets import Dataset
+from datasets import load_dataset
+
+semi_sold = Dataset.to_pandas(load_dataset('sinhala-nlp/SemiSOLD', split='train'))
+```
+The dataset contains following columns 
+* **post_id** - Twitter ID
+* **text** - Post text
+
+Furthermore it contains predicted offensiveness scores from nine classifiers trained on SOLD train; xlmr, xlmt, mbert, sinbert, lstm_ft, cnn_ft, lstm_cbow, cnn_cbow, lstm_sl, cnn_sl and svm
+
+
+## Experiments
+Clone the repository and install the libraries using the following command (preferably inside a conda environment)
+
+~~~
+pip install -r requirements.txt
+~~~
+
+### Sentence-level
+Sentence-level transformer based experiments can be executed using the following command. 
+
+~~~
+python -m experiments.sentence_level.sinhala_deepoffense
+~~~
+
+The command takes the following arguments;
+
+~~~
+--model_type : Type of the transformer model (bert, xlmroberta, roberta etc ).
+--model_name : The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.
+--transfer : Whether to perform transfer learning or not (true or false).
+--transfer_language : The initial language if transfer learning is performed (hi, en or si).
+    * hi - Perform transfer learning from HASOC 2019 Hindi dataset (Modha et al., 2019).
+    * en - Perform transfer learning from Offenseval English dataset (Zampieri et al., 2019).
+    * si - Perform transfer learning from CCMS Sinhala dataset (Rathnayake et al., 2021).
+--augment : Perform semi supervised data augmentation.
+--std : Standard deviation of the models to cut down data augmentation.
+--augment_type: The type of the data augmentation.
+    * off - Augment only the offensive instances.
+    * normal - Augment both offensive and non-offensive instances.
+~~~
+
+Sentence-level CNN and LSTM based experiments can be executed using the following command. 
+
+~~~
+python -m experiments.sentence_level.sinhala_offensive_nn
+~~~
+
+The command takes the following arguments;
+
+~~~
+--model_type : Type of the architecture (cnn2D, lstm).
+--model_name : The exact word embeddings to use. This may be a gensim model, or the path to a word embeddinng files.
+--augment : Perform semi supervised data augmentation.
+--std : Standard deviation of the models to cut down data augmentation.
+--augment_type: The type of the data augmentation.
+    * off - Augment only the offensive instances.
+    * normal - Augment both offensive and non-offensive instances.
+~~~
+
+### Token-level
+Token-level transformer based experiments can be executed using the following command. 
+
+~~~
+python -m experiments.sentence_level.sinhala_mudes
+~~~
+
+The command takes the following arguments;
+
+~~~
+--model_type : Type of the transformer model (bert, xlmroberta, roberta etc ).
+--model_name : The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.
+--transfer : Whether to perform transfer learning or not (true or false).
+--transfer_language : The initial language if transfer learning is performed (hatex or tsd).
+    * hatex - Perform transfer learning from HateXplain dataset (Mathew et al., 2021).
+    * tsd - Perform transfer learning from TSD dataset (Pavlopoulos  et al., 2021).
+~~~
+
+Token-level LIME experiments can be executed using the following command. 
+
+~~~
+python -m experiments.sentence_level.sinhala_lime
+~~~
+
+The command takes the following arguments;
+
+~~~
+--model_type : Type of the transformer model (bert, xlmroberta, roberta etc ).
+--model_name : The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.
+~~~
+
+
+## Acknowledgments
+We want to acknowledge Janitha Hapuarachchi, Sachith Suraweera, Chandika Udaya Kumara and Ridmi Randima, the team of volunteer annotators that provided their free time and efforts to help us produce SOLD.
+
+## Citation
+If you are using the dataset or the models please cite the following paper
+~~~
+@article{ranasinghe2022sold,
+  title={SOLD: Sinhala Offensive Language Dataset},
+  author={Ranasinghe, Tharindu and Anuradha, Isuri and Premasiri, Damith and Silva, Kanishka and Hettiarachchi, Hansi and Uyangodage, Lasitha and Zampieri, Marcos},
+  journal={arXiv preprint arXiv:2212.00851},
+  year={2022}
+}
+~~~