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https://api-inference.huggingface.co/models/rohanrajpal/bert-base-codemixed-uncased-sentiment
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rohanrajpal/bert-base-codemixed-uncased-sentiment rohanrajpal/bert-base-codemixed-uncased-sentiment
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pytorch

tf

Contributed by

rohanrajpal Rohan Rajpal
4 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")

Model name

Model description

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

Intended uses & limitations

How to use

# You can include sample code which will be formatted
#Coming soon!

Limitations and bias

Provide examples of latent issues and potential remediations.

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"
}