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+---
+language:
+- es
+- en
+tags:
+- es
+- en
+- codemix
+license: "apache-2.0"
+datasets:
+- SAIL 2017
+metrics:
+- fscore
+- accuracy
+- precision
+- recall
+---
+
+# BERT codemixed base model for spanglish (cased)
+
+This model was built using [lingualytics](https://github.com/lingualytics/py-lingualytics), an open-source library that supports code-mixed analytics.
+
+## Model description
+
+Input for the model: Any codemixed spanglish 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 [CS-EN-ES-CORPUS](http://www.grupolys.org/software/CS-CORPORA/cs-en-es-corpus-wassa2015.txt) dataset.  
+
+Performance of this model on the dataset
+
+| metric     |    score |
+|------------|----------|
+| acc        | 0.718615 |
+| f1         | 0.71759 |
+| acc_and_f1 | 0.718103 |
+| precision  | 0.719302 |
+| recall     | 0.718615 |
+
+## Intended uses & limitations
+
+Make sure to preprocess your data using [these methods](https://github.com/microsoft/GLUECoS/blob/master/Data/Preprocess_Scripts/preprocess_sent_en_es.py) before using this model.
+
+#### 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-en-es-codemix-cased')
+model = AutoModelForSequenceClassification.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
+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-en-es-codemix-cased')
+model = TFBertModel.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+#### Limitations and bias
+
+Since I dont know spanish, I cant verify the quality of annotations or the dataset itself. This is a very simple transfer learning approach and I'm open to discussions to improve upon this.
+
+## Training data
+
+I trained on the dataset on the [bert-base-multilingual-cased model](https://huggingface.co/bert-base-multilingual-cased).
+
+## Training procedure
+
+Followed the preprocessing techniques followed [here](https://github.com/microsoft/GLUECoS/blob/master/Data/Preprocess_Scripts/preprocess_sent_en_es.py)
+
+## 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"
+}
+```