Create README.md

README.md

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+---
+language: 
+- en
+thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
+tags:
+- text-classification
+- emotion
+- pytorch
+license: apache-2.0
+datasets:
+- emotion
+metrics:
+- Accuracy, F1 Score
+---
+# robert-base-emotion
+
+## Model description:
+`robert-base` finetuned on the emotion dataset using HuggingFace Trainer.
+```
+ learning rate 2e-5, 
+ batch size 64,
+ num_train_epochs=8,
+```
+
+## How to Use the model:
+```python
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis",model='bhadresh-savani/robert-base-emotion')
+prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use")
+```
+
+## Dataset:
+[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
+
+## Training procedure
+[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
+follow the above notebook by changing the model name to robert
+
+## Eval results
+```
+{
+ 'test_accuracy': 0.9395,
+ 'test_f1': 0.9397328860104454,
+ 'test_loss': 0.14367154240608215,
+ 'test_runtime': 10.2229,
+ 'test_samples_per_second': 195.639,
+ 'test_steps_per_second': 3.13
+ }
+```
+
+## Reference:
+* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)