Hugging Face's logo

bhadresh-savani
/
bert-base-uncased-emotion

Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
emotion
Eval Results
Inference Endpoints
Model card Files Community
4
bert-base-uncased-emotion / README.md
bhadresh-savani's picture
bhadresh-savani
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2)
79a0190
preview code
|
raw
history blame contribute delete
6.84 kB
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/bert-base-uncased-emotion
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9265
            name: Accuracy
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWQzNzA2MTFkY2RkNDMxYTFhOGUzMTdiZTgwODA3ODdmZTVhNTVjOTAwMGM5NjU1OGY0MjMzZWU0OTU2MzY1YiIsInZlcnNpb24iOjF9.f6iWK0iyU8_g32W2oMfh1ChevMsl0StI402cB6DNzJCYj9xywTnFltBY36jAJFDRK41HXdMnPMl64Bynr-Q9CA
          - type: precision
            value: 0.8859601677706858
            name: Precision Macro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc2ZjRmMzYzNTE0ZDQ1ZDdkYWViYWNhZDhkOTE2ZDhmMDFjZmZiZjRkZWVlMzQ3MWE4NDNlYzlmM2I4ZGM2OCIsInZlcnNpb24iOjF9.jR-gFrrBIAfiYV352RDhK3nzgqIgNCPd55OhIcCfVdVAWHQSZSJXhFyg8yChC7DwoVmUQy1Ya-d8Hflp7Wi-AQ
          - type: precision
            value: 0.9265
            name: Precision Micro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDAyMWZjZTM5NWNjNTcyMWQzMWQyNDcyN2RlZTQyZTM4ZDQ4Y2FlNzM2OTZkMzM3YzI4YTAwNzg4MGNjZmZjZCIsInZlcnNpb24iOjF9.cmkuDmhhETKIKAL81K28oiO889sZ0hvEpZ6Ep7dW_KB9VOTFs15BzFY9vwcpdXQDugWBbB2g7r3FUgRLwIEpAg
          - type: precision
            value: 0.9265082039990273
            name: Precision Weighted
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA2NzY2NTJmZTExZWM3OGIzYzg3ZDM3Y2I5MTU3Mjg3Y2NmZGEyMjFmNjExZWM3ZDFjNzdhOTZkNTYwYWQxYyIsInZlcnNpb24iOjF9.DJgeA6ZovHoxgCqhzilIzafet8uN3-Xbx1ZYcEEc4jXzFbRtErE__QHGaaSaUQEzPp4BAztp1ageOaBoEmXSDg
          - type: recall
            value: 0.879224648382427
            name: Recall Macro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGU3MmQ1Yjg5OGJlYTE1NWJmNGVjY2ExMDZiZjVjYmVkOGYxYWFkOTVlMDVjOWVhZGFjOGFkYzcwMGIyMTAyZCIsInZlcnNpb24iOjF9.jwgaNEBSQENlx3vojBi1WKJOQ7pSuP4Iyw4kKPsq9IUaW-Ah8KdgPV9Nm2DY1cwEtMayvVeIVmQ3Wo8PORDRAg
          - type: recall
            value: 0.9265
            name: Recall Micro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDE3OWQ0ZGZjNzAxY2I0NGMxNDU0OWE1OGM2N2Q3OTUwYWI0NmZjMDQ3MDc0NDA4YTc2NDViM2Y0ZTMyMjYyZCIsInZlcnNpb24iOjF9.Ihc61PSO3K63t5hUSAve4Gt1tC8R_ZruZo492dTD9CsKOF10LkvrCskJJaOATjFJgqb3FFiJ8-nDL9Pa3HF-Dg
          - type: recall
            value: 0.9265
            name: Recall Weighted
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzJkYTg5YjA0YTBlNDY3ZjFjZWIzOWVhYjI4Y2YxM2FhMmUwMDZlZTE0NTIzNjMxMjE3NzgwNGFjYTkzOWM1YyIsInZlcnNpb24iOjF9.LlBX4xTjKuTX0NPK0jYzYDXRVnUEoUKVwIHfw5xUzaFgtF4wuqaYV7F0VKoOd3JZxzxNgf7JzeLof0qTquE9Cw
          - type: f1
            value: 0.8821398657055098
            name: F1 Macro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTE4OThiMmE0NDEzZjBkY2RmZWNjMGI3YWNmNTFjNTY5NjIwNjFkZjk1ZjIxMjI4M2ZiZGJhYzJmNzVhZTU1NSIsInZlcnNpb24iOjF9.gzYyUbO4ycvP1RXnrKKZH3E8ym0DjwwUFf4Vk9j0wrg2sWIchjmuloZz0SLryGqwHiAV8iKcSBWWy61Q480XAw
          - type: f1
            value: 0.9265
            name: F1 Micro
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM2Y2E0NjMyNmJhMTE4NjYyMjI2MTJlZjUzNmRmY2U3Yjk3ZGUyYzU2OWYzMWM2ZjY4ZTg0OTliOTY3YmI2MSIsInZlcnNpb24iOjF9.hEz_yExs6LV0RBpFBoUbnAQZHitxN57HodCJpDx0yyW6dQwWaza0JxdO-kBf8JVBK8JyISkNgOYskBY5LD4ZDQ
          - type: f1
            value: 0.9262425173620311
            name: F1 Weighted
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmMyY2NhNTRhOGMwM2M5OTQxNDQ0NjRkZDdiMDExMWFkMmI4MmYwZGQ1OGRiYmRjMmE2YTc0MGZmMWMwN2Q4MSIsInZlcnNpb24iOjF9.ljbb2L4R08NCGjcfuX1878HRilJ_p9qcDJpWhsu-5EqWCco80e9krb7VvIJV0zBfmi7Z3C2qGGRsfsAIhtQ5Dw
          - type: loss
            value: 0.17315374314785004
            name: loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQwN2I2Nzg4OWU1ODE5NTBhMTZiMjljMjJhN2JiYmY0MTkzMTA1NmVhMGU0Y2Y0NjgyOTU3ZjgyYTc3ODE5NCIsInZlcnNpb24iOjF9.EEp3Gxm58ab-9335UGQEk-3dFQcMRgJgViI7fpz7mfY2r5Pg-AOel5w4SMzmBM-hiUFwStgxe5he_kG2yPGFCw

bert-base-uncased-emotion

Model description:

Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective

bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below training parameters

 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/bert-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': 0.0005138228880241513}, 
{'label': 'joy', 'score': 0.9972520470619202}, 
{'label': 'love', 'score': 0.0007443308713845909}, 
{'label': 'anger', 'score': 0.0007404946954920888}, 
{'label': 'fear', 'score': 0.00032938539516180754}, 
{'label': 'surprise', 'score': 0.0004197491507511586}
]]
"""

Dataset:

Twitter-Sentiment-Analysis.

Training procedure

Colab Notebook follow the above notebook by changing the model name from distilbert to bert

Eval results 

{
 'test_accuracy': 0.9405,
 'test_f1': 0.9405920712282673,
 'test_loss': 0.15769127011299133,
 'test_runtime': 10.5179,
 'test_samples_per_second': 190.152,
 'test_steps_per_second': 3.042
 }

Reference: