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---
license: mit
datasets:
- dair-ai/emotion
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
## Model Description
<!-- Provide a longer summary of what this model is. -->
Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective
[bert-base-uncased-emotion-fituned](https://huggingface.co/sonia12138/bert-base-uncased-emotion-fituned) finetuned on the emotion dataset using HuggingFace Trainer with below training parameters
```
num_train_epochs=8,
train_batch_size=32,
eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01
```
## Dataset
[emotion](https://huggingface.co/datasets/dair-ai/emotion)
## Model Performance Comparision on Emotion Dataset
| Model | Accuracy | Recall | F1 Score |
| ------------ | -------- | -------- | -------- |
| Bert-base-uncased-emotion **(SOTA)**| 92.6 | 87.9 | 88.2
| **Bert-base-uncased-emotion-fintuned**| 92.9 | 88 | 88.5
## How to Use the Model:
```
from transformers import pipeline
classifier = pipeline("text-classification",model='sonia12138/bert-base-uncased-emotion-fituned', 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)
```
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed](https://github.com/SoniaWang121/lyrics-emo-bert/tree/main)
## Eval Results
```
{
'eval_accuracy': 0.929,
'eval_f1': 0.9405920712282673,
'eval_loss': 0.15769127011299133,
'eval_loss': 0.37796708941459656,
"eval_runtime': 8.0514,
'eval_samples_per_second': 248.403,
'eval_steps_per_second': 3.974,
}
```
## Compute Infrastructure
### Hardware
NVIDIA GeForce RTX 4090
### Software
22.04.1-Ubuntu
## Model Card Authors
[Xiaohan Wang](https://github.com/SoniaWang121), [Kun Peng](https://github.com/Eric-Pk)
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