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