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
| 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:
# 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:
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 on this pretrained model.
Training procedure
No preprocessing.
Eval results
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"
}