# Electra-base-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/)
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transformers pytorch tf jax electra text-classification emotion en dataset:emotion license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #electra #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Electra-base-emotion
====================


Model description:
------------------


Model Performance Comparision on Emotion Dataset from Twitter:
--------------------------------------------------------------



How to Use the model:
---------------------


Dataset:
--------


Twitter-Sentiment-Analysis.


Training procedure
------------------


Colab Notebook


Eval results
------------


Reference:
----------


* Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf



TAGS
#transformers #pytorch #tf #jax #electra #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #tf #jax #electra #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
