Create README.md (#8096)

* Create README.md

* Fix model card
Co-authored-by: Julien Chaumond <julien@huggingface.co>

model_cards/ganeshkharad/gk-hinglish-sentiment/README.md

+---
+language:
+- hi-en
+
+tags:
+- sentiment
+- multilingual
+- hindi codemix
+- hinglish 
+license: apache-2.0
+datasets:
+- sail
+---
+
+# Sentiment Classification for hinglish text: `gk-hinglish-sentiment`
+
+## Model description
+
+Trained small amount of reviews dataset
+
+## Intended uses & limitations
+
+I wanted something to work well with hinglish data as it is being used in India mostly.
+The training data was not much as expected
+
+#### How to use
+
+```python
+#sample code 
+from transformers import BertTokenizer, BertForSequenceClassification
+tokenizerg = BertTokenizer.from_pretrained("/content/model")
+modelg = BertForSequenceClassification.from_pretrained("/content/model")
+
+text = "kuch bhi type karo hinglish mai"
+encoded_input = tokenizerg(text, return_tensors='pt')
+output = modelg(**encoded_input)
+print(output)
+#output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive
+```
+
+#### Limitations and bias
+
+The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data
+
+## Training data
+
+Training data contains labeled data for 3 labels
+
+link to the pre-trained model card with description of the pre-training data.
+I have Tuned below model
+
+https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment
+
+
+### 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"
+}
+```