autoevaluator
HF staff
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator
f6b546b
metadata
language:
- en
license: apache-2.0
tags:
- text-classification
- emotion
- pytorch
datasets:
- emotion
metrics:
- Accuracy, F1 Score
thumbnail: >-
https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
model-index:
- name: bhadresh-savani/roberta-base-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- type: accuracy
value: 0.931
name: Accuracy
verified: true
verifyToken: >-
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- type: precision
value: 0.9168321948556312
name: Precision Macro
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2EzYTcxNTExNGU1MmFiZjE3NGE5MDIyMDU2M2U3OGExOTdjZDE5YWU2NDhmOTJlYWMzY2NkN2U5MmRmZTE0MiIsInZlcnNpb24iOjF9.4U7vJ3ALdUUxySMhVeb4Qa1tSp3wphSIZkRYNMujz-KrOZW8kkcmCde3ioStBg3Qqyf1powYd88uk1R7DuWRBA
- type: precision
value: 0.931
name: Precision Micro
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhmZGRlYWE5ZTAzMmJiMzlmMWZiM2VlYjdiNzI0NjVmN2M2YzcxM2EzYTg0OTFiZTE1MjVmNzE5NGEzYTg2ZCIsInZlcnNpb24iOjF9.8eCHAK0rlZWnhBNQdh9kcuAeItmDUAgK3KkZ7eC-GyYhi4HT5dZiS6btcC5EjkYVOS4czcjzqxfVz4PuZgtLDQ
- type: precision
value: 0.9357445689014415
name: Precision Weighted
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhZTdkNzYzMjhjZjc4MTAxNWZiYjgzMjhhNjRiZWRmYjc5YTA0NTQ1MzllMTYxMTVkMDk4OTE0ZGEyMTNhMiIsInZlcnNpb24iOjF9.YIZfj2Eo1nMX2GVSfqJy-Cp7VBubfUh2LuOnU60sG5Lci8FdlNbAanS1IzAyxU3U29lqiTasxfS_yrwAj5cmBQ
- type: recall
value: 0.8743657671177089
name: Recall Macro
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2YTcyNzMwYzZiMmM1Yzc4YWZhNDM3ZDQyMjI1NWZhMjQyNmU5NTA0YmE2ZDBiZmY1MmUyZWRlMjRhMjFmYSIsInZlcnNpb24iOjF9.XKlFy_Cx4T4l7Otd8aAwWcI-fJ_dJ6V1Kp3uZm6OWjwCb1Do6mSdPFfwiMeBZZyfEIsNBnguegssZvHsOfTSAQ
- type: recall
value: 0.931
name: Recall Micro
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzgzN2JkNzAzZDRjNjJmZjNkY2RmYzVkMWEzYTMzZDU4NzJlYzBmOWE4MTU0MGU0MTJhM2JjZDdjODhlZDExOCIsInZlcnNpb24iOjF9.9tSVB4yNBdFXpH3equwo1ZaEnVUktO6lm93UEJ-luKhxo6wgS54OLjgDq7IpJYwa3lvYyjy-sxzQEe9ri31WAg
- type: recall
value: 0.931
name: Recall Weighted
verified: true
verifyToken: >-
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- type: f1
value: 0.8821236522209227
name: F1 Macro
verified: true
verifyToken: >-
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- type: f1
value: 0.931
name: F1 Micro
verified: true
verifyToken: >-
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- type: f1
value: 0.9300782840205046
name: F1 Weighted
verified: true
verifyToken: >-
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- type: loss
value: 0.15155883133411407
name: loss
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2M4MmVlNjAzZjhiMWJlNWQxMDg5ZTRiYjFlZGYyMGMyYzU4M2IwY2E1M2E2MzA5NmU5ZjgwZTZmMDI5YjgzMyIsInZlcnNpb24iOjF9.kjgFJohkTxLKtzHJDlBvd6qolGQDSZLbrDE7C07xNGmarhTLc_A3MmLeC4MmQGOl1DxfnHflImIkdqPylyylDA
robert-base-emotion
Model description:
roberta is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.
roberta-base 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/roberta-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': 0.002281982684507966},
{'label': 'joy', 'score': 0.9726489186286926},
{'label': 'love', 'score': 0.021365027874708176},
{'label': 'anger', 'score': 0.0026395076420158148},
{'label': 'fear', 'score': 0.0007162453257478774},
{'label': 'surprise', 'score': 0.0003483477921690792}
]]
"""
Dataset:
Training procedure
Colab Notebook follow the above notebook by changing the model name to roberta
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
}