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--- |
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language: |
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- hi |
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- en |
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tags: |
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- hi |
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- en |
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- codemix |
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license: "apache-2.0" |
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datasets: |
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- SAIL 2017 |
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metrics: |
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- fscore |
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- accuracy |
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--- |
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# BERT codemixed base model for hinglish (cased) |
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## Model description |
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Input for the model: Any codemixed hinglish text |
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Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive) |
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I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [SAIL 2017](http://www.dasdipankar.com/SAILCodeMixed.html) dataset. |
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Performance of this model on the SAIL 2017 dataset |
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| metric | score | |
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|------------|----------| |
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| acc | 0.588889 | |
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| f1 | 0.582678 | |
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| acc_and_f1 | 0.585783 | |
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| precision | 0.586516 | |
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| recall | 0.588889 | |
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## Intended uses & limitations |
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#### How to use |
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Here is how to use this model to get the features of a given text in *PyTorch*: |
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```python |
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# You can include sample code which will be formatted |
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from transformers import BertTokenizer, BertModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment") |
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model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment") |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in *TensorFlow*: |
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```python |
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from transformers import BertTokenizer, TFBertModel |
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tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-codemixed-uncased-sentiment') |
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model = TFBertModel.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment") |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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#### Limitations and bias |
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Coming soon! |
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## Training data |
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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). |
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## Training procedure |
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No preprocessing. |
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## Eval results |
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### BibTeX entry and citation info |
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```bibtex |
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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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