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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment/README.md

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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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+
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+ # BERT codemixed base model for hinglish (cased)
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+
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+ ## Model description
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+
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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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+
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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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+
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+ Performance of this model on the SAIL 2017 dataset
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+
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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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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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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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+
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+ and in *TensorFlow*:
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+
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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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+
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+ #### Limitations and bias
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+
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+ Coming soon!
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+
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+ ## Training data
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+
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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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+
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+ ## Training procedure
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+
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+ No preprocessing.
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+
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+ ## Eval results
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+
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+ ### BibTeX entry and citation info
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+
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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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+ ```