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--- |
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language_bcp47: |
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- hi-en |
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tags: |
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- sentiment |
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- multilingual |
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- hindi codemix |
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- hinglish |
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license: apache-2.0 |
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datasets: |
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- sail |
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--- |
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# Sentiment Classification for hinglish text: `gk-hinglish-sentiment` |
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## Model description |
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Trained small amount of reviews dataset |
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## Intended uses & limitations |
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I wanted something to work well with hinglish data as it is being used in India mostly. |
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The training data was not much as expected |
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#### How to use |
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```python |
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#sample code |
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from transformers import BertTokenizer, BertForSequenceClassification |
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tokenizerg = BertTokenizer.from_pretrained("/content/model") |
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modelg = BertForSequenceClassification.from_pretrained("/content/model") |
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text = "kuch bhi type karo hinglish mai" |
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encoded_input = tokenizerg(text, return_tensors='pt') |
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output = modelg(**encoded_input) |
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print(output) |
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#output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive |
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``` |
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#### Limitations and bias |
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The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data |
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## Training data |
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Training data contains labeled data for 3 labels |
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link to the pre-trained model card with description of the pre-training data. |
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I have Tuned below model |
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https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment |
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### BibTeX entry and citation info |
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```@inproceedings{khanuja-etal-2020-gluecos, |
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title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}", |
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author = "Khanuja, Simran and |
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Dandapat, Sandipan and |
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Srinivasan, Anirudh and |
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Sitaram, Sunayana and |
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Choudhury, Monojit", |
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booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", |
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month = jul, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.acl-main.329", |
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pages = "3575--3585" |
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} |
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``` |
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