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| metric | score |
|---|---|
| acc | 0.588889 |
| f1 | 0.582678 |
| acc_and_f1 | 0.585783 |
| precision | 0.586516 |
| recall | 0.588889 |
1# You can include sample code which will be formatted
2from transformers import BertTokenizer, BertModelForSequenceClassification
3tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
4model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
5text = "Replace me by any text you'd like."
6encoded_input = tokenizer(text, return_tensors='pt')
7output = model(**encoded_input)1from transformers import BertTokenizer, TFBertModel
2tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-codemixed-uncased-sentiment')
3model = TFBertModel.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
4text = "Replace me by any text you'd like."
5encoded_input = tokenizer(text, return_tensors='tf')
6output = model(encoded_input)1@inproceedings{khanuja-etal-2020-gluecos,
2 title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
3 author = "Khanuja, Simran and
4 Dandapat, Sandipan and
5 Srinivasan, Anirudh and
6 Sitaram, Sunayana and
7 Choudhury, Monojit",
8 booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
9 month = jul,
10 year = "2020",
11 address = "Online",
12 publisher = "Association for Computational Linguistics",
13 url = "https://www.aclweb.org/anthology/2020.acl-main.329",
14 pages = "3575--3585"
15}