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
+---
+# Distilbert-base-uncased-emotion
+
+## Model description:
+
+## Model Performance Comparision on Emotion Dataset from Twitter:
+
+| Model | Accuracy | F1 Score |  Test Sample per Second |
+| --- | --- | --- | --- |
+| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |
+| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |
+| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |
+| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |
+| [Electra-base-emotion](https://huggingface.co/bhadresh-savani/electra-base-emotion) | 91.95 | 91.90 | 472.72 |
+
+## How to Use the model:
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification",model='bhadresh-savani/electra-base-emotion', return_all_scores=True)
+prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
+print(prediction)
+
+"""
+Output:
+[[
+{'label': 'sadness', 'score': 0.0006792712374590337}, 
+{'label': 'joy', 'score': 0.9959300756454468}, 
+{'label': 'love', 'score': 0.0009452480007894337}, 
+{'label': 'anger', 'score': 0.0018055217806249857}, 
+{'label': 'fear', 'score': 0.00041110432357527316}, 
+{'label': 'surprise', 'score': 0.0002288572577526793}
+]]
+"""
+```
+
+## Dataset:
+[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
+
+## Training procedure
+[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
+
+## Eval results
+```json
+{
+ 'epoch': 8.0,
+ 'eval_accuracy': 0.9195,
+ 'eval_f1': 0.918975455617076,
+ 'eval_loss': 0.3486028015613556,
+ 'eval_runtime': 4.2308,
+ 'eval_samples_per_second': 472.726,
+ 'eval_steps_per_second': 7.564
+ }
+ ```
+
+## Reference:
+* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)