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BERT-Banking77

Text Classification Transformers PyTorch
English bert Trained with AutoTrain Eval Results Carbon Emissions Inference Endpoints
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BERT-Banking77 / README.md
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metadata
language: en
tags:
  - autotrain
datasets:
  - banking77
widget:
  - text: I am still waiting on my card?
co2_eq_emissions: 0.03330651014155927
model-index:
  - name: BERT-Banking77
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: BANKING77
          type: banking77
        metrics:
          - type: accuracy
            value: 92.64
            name: Accuracy
          - type: macro-f1
            value: 92.64
            name: Macro F1
          - type: weighted-f1
            value: 92.6
            name: Weighted F1
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: banking77
          type: banking77
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9275974025974026
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9305185253845069
            name: Precision Macro
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9275974025974026
            name: Precision Micro
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9305185253845071
            name: Precision Weighted
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9275974025974028
            name: Recall Macro
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9275974025974026
            name: Recall Micro
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9275974025974026
            name: Recall Weighted
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.927623314966026
            name: F1 Macro
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9275974025974026
            name: F1 Micro
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.927623314966026
            name: F1 Weighted
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.3199225962162018
            name: loss
            verified: true
            verifyToken: >-
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BERT-Banking77 Model Trained Using AutoTrain

  • Problem type: Multi-class Classification
  • Model ID: 940131041
  • CO2 Emissions (in grams): 0.03330651014155927

Validation Metrics

  • Loss: 0.3505457043647766
  • Accuracy: 0.9263261296660118
  • Macro F1: 0.9268371013605569
  • Micro F1: 0.9263261296660118
  • Weighted F1: 0.9259954221865809
  • Macro Precision: 0.9305746406646502
  • Micro Precision: 0.9263261296660118
  • Weighted Precision: 0.929031563971418
  • Macro Recall: 0.9263724620088746
  • Micro Recall: 0.9263261296660118
  • Weighted Recall: 0.9263261296660118

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/philschmid/autotrain-does-it-work-940131041

Or Python API:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/BERT-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')