metadata
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
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.927
verified: true
- name: Precision Macro
type: precision
value: 0.8880230732280744
verified: true
- name: Precision Micro
type: precision
value: 0.927
verified: true
- name: Precision Weighted
type: precision
value: 0.9272902840835793
verified: true
- name: Recall Macro
type: recall
value: 0.8790126653780703
verified: true
- name: Recall Micro
type: recall
value: 0.927
verified: true
- name: Recall Weighted
type: recall
value: 0.927
verified: true
- name: F1 Macro
type: f1
value: 0.8825061528287809
verified: true
- name: F1 Micro
type: f1
value: 0.927
verified: true
- name: F1 Weighted
type: f1
value: 0.926876082854655
verified: true
- name: loss
type: loss
value: 0.17403268814086914
verified: true
Distilbert-base-uncased-emotion
Model description:
Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
Distilbert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
learning rate 2e-5,
batch size 64,
num_train_epochs=8,
Model Performance Comparision on Emotion Dataset from Twitter:
| Model | Accuracy | F1 Score | Test Sample per Second |
|---|---|---|---|
| Distilbert-base-uncased-emotion | 93.8 | 93.79 | 398.69 |
| Bert-base-uncased-emotion | 94.05 | 94.06 | 190.152 |
| Roberta-base-emotion | 93.95 | 93.97 | 195.639 |
| Albert-base-v2-emotion | 93.6 | 93.65 | 182.794 |
How to Use the model:
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-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:
Training procedure
Eval results
{
'test_accuracy': 0.938,
'test_f1': 0.937932884041714,
'test_loss': 0.1472451239824295,
'test_mem_cpu_alloc_delta': 0,
'test_mem_cpu_peaked_delta': 0,
'test_mem_gpu_alloc_delta': 0,
'test_mem_gpu_peaked_delta': 163454464,
'test_runtime': 5.0164,
'test_samples_per_second': 398.69
}