
# BERT codemixed base model for hinglish (cased)

## Model description

Input for the model: Any codemixed hinglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)

I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [SAIL 2017](http://www.dasdipankar.com/SAILCodeMixed.html) dataset.  

Performance of this model on the SAIL 2017 dataset

| metric     |    score |
|------------|----------|
| acc        | 0.588889 |
| f1         | 0.582678 |
| acc_and_f1 | 0.585783 |
| precision  | 0.586516 |
| recall     | 0.588889 |

## Intended uses & limitations

#### How to use

Here is how to use this model to get the features of a given text in *PyTorch*:

```python
# You can include sample code which will be formatted
from transformers import BertTokenizer, BertModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in *TensorFlow*:

```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-codemixed-uncased-sentiment')
model = TFBertModel.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

#### Limitations and bias

Coming soon!

## Training data

I trained on the SAIL 2017 dataset [link](http://amitavadas.com/SAIL/Data/SAIL_2017.zip) on this [pretrained model](https://huggingface.co/bert-base-multilingual-cased).

## Training procedure

No preprocessing.

## Eval results

### BibTeX entry and citation info

```bibtex
@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"
}
```

{"language": ["hi", "en"], "license": "apache-2.0", "tags": ["hi", "en", "codemix"], "datasets": ["SAIL 2017"], "metrics": ["fscore", "accuracy"]}
transformers pytorch tf jax bert text-classification hi en codemix license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BERT codemixed base model for hinglish (cased)
==============================================


Model description
-----------------


Input for the model: Any codemixed hinglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)


I took a bert-base-multilingual-cased model from Huggingface and finetuned it on SAIL 2017 dataset.


Performance of this model on the SAIL 2017 dataset



Intended uses & limitations
---------------------------


#### How to use


Here is how to use this model to get the features of a given text in *PyTorch*:


and in *TensorFlow*:


#### Limitations and bias


Coming soon!


Training data
-------------


I trained on the SAIL 2017 dataset link on this pretrained model.


Training procedure
------------------


No preprocessing.


Eval results
------------


### BibTeX entry and citation info



#### How to use


Here is how to use this model to get the features of a given text in *PyTorch*:


and in *TensorFlow*: #### Limitations and bias


Coming soon!


Training data
-------------


I trained on the SAIL 2017 dataset link on this pretrained model.


Training procedure
------------------


No preprocessing.


Eval results
------------ ### BibTeX entry and citation info
TAGS
#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### How to use


Here is how to use this model to get the features of a given text in *PyTorch*:


and in *TensorFlow*: #### Limitations and bias


Coming soon!


Training data
-------------


I trained on the SAIL 2017 dataset link on this pretrained model.


Training procedure
------------------


No preprocessing.


Eval results
------------ ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### How to use


Here is how to use this model to get the features of a given text in *PyTorch*:


and in *TensorFlow*:#### Limitations and bias


Coming soon!


Training data
-------------


I trained on the SAIL 2017 dataset link on this pretrained model.


Training procedure
------------------


No preprocessing.


Eval results
------------### BibTeX entry and citation info