README.md

---
tags:
- adapterhub:sentiment/hinglish-twitter-sentiment
- text-classification
- bert
- adapter-transformers
license: "apache-2.0"
---

# Adapter `bert-base-multilingual-uncased-hinglish-sentiment` for bert-base-multilingual-uncased

**Note: This adapter was not trained by the AdapterHub team, but by these author(s): Meghana Bhange, Nirant K.
See author details below.**

Adapter for Hinglish Sentiment Analysis, based on SemEval 2020 Task 9

**This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.**

## Usage

First, install `adapters`:

```
pip install -U adapters
```

Now, the adapter can be loaded and activated like this:

```python
from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("bert-base-multilingual-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment")
model.set_active_adapters(adapter_name)
```

## Architecture & Training

- Adapter architecture: pfeiffer
- Prediction head: classification
- Dataset: [Hinglish Sentiment](https://ritual-uh.github.io/sentimix2020/hinglish_res)

## Author Information

- Author name(s): Meghana Bhange, Nirant K
- Author email: hinglish@nirantk.com
- Author links: [Website](https://github.com/NirantK), [GitHub](https://github.com/NirantK), [Twitter](https://twitter.com/@NirantK)



## Citation

```bibtex
@article{Hinglish,
    title={HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection},
    author={Meghana Bhange,
            Nirant Kasliwal,
    journal={ArXiv},
    year={2020}
}

```

*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/nirantk/bert-base-multilingual-uncased-hinglish-sentiment.yaml*.