Update README.md

README.md

@@ -19,10 +19,11 @@ language:
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model is for Hindi-English code-mixed hate detection.
 
-- **Developed by:** Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro
+- **Developed by:** Debajyoti Mazumder, Aakash Kumar
 - **Model type:** Text Classification
 - **Language(s) :** Hindi-English code-mixed
 - **Parent Model:** See the [BERT multilingual base model (cased)](https://huggingface.co/google-bert/bert-base-multilingual-cased) for more information about the model.
+- **Paper:** [https://dl.acm.org/doi/full/10.1145/3726866](https://dl.acm.org/doi/full/10.1145/3726866)
 
 ## How to Get Started with the Model
 
@@ -39,7 +40,26 @@ prediction= model(input_ids=inputs['input_ids'],attention_mask=inputs['attention
 print(prediction.logits)
 ```
 
-#### Metrics
+## Citation
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-We use the F1 score of positive class as the evaluation metric for training of our model because it takes into account the Acc, Pre and Rec values.
+```bibtex
+@article{10.1145/3726866,
+author = {Mazumder, Debajyoti and Kumar, Aakash and Patro, Jasabanta},
+title = {Improving Code-Mixed Hate Detection by Native Sample Mixing: A Case Study for Hindi-English Code-Mixed Scenario},
+year = {2025},
+issue_date = {May 2025},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {24},
+number = {5},
+issn = {2375-4699},
+url = {https://doi.org/10.1145/3726866},
+doi = {10.1145/3726866},
+abstract = {Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the hate expressed through language alteration. Compared to the monolingual setup, we see much less work on code-mixed hate as large-scale annotated hate corpora are unavailable for the study. To overcome this bottleneck, we propose using native language hate samples (native language samples/ native samples hereafter). We hypothesise that in the era of multilingual language models (MLMs), hate in code-mixed settings can be detected by majorly relying on the native language samples. Even though the NLP literature reports the effectiveness of MLMs on hate detection in many cross-lingual settings, their extensive evaluation in a code-mixed scenario is yet to be done. This article attempts to fill this gap through rigorous empirical experiments. We considered the Hindi-English code-mixed setup as a case study as we have the linguistic expertise for the same. Some of the interesting observations we got are: (i) adding native hate samples in the code-mixed training set, even in small quantity, improved the performance of MLMs for code-mixed hate detection, (ii) MLMs trained with native samples alone observed to be detecting code-mixed hate to a large extent, (iii) the visualisation of attention scores revealed that, when native samples were included in training, MLMs could better focus on the hate emitting words in the code-mixed context, and (iv) finally, when hate is subjective or sarcastic, naively mixing native samples doesn’t help much to detect code-mixed hate. We have released the data and code repository to reproduce the reported results.1},
+journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
+month = apr,
+articleno = {47},
+numpages = {21},
+keywords = {Code-mixed hate detection, cross-lingual learning, native sample mixing}
+}
+```