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metadata
license: mit
language:
  - en
metrics:
  - accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
  - agriculture
widget:
  - text: paddy pest
    example_title: Example- pest
  - text: how do I apply for PM-Kisan
    example_title: Example- scheme
  - text: Will it rain today
    example_title: Example- weather

Agri-flow Classification Model

This model classifies grievances into five distinct buckets:

  • Label 0: agricultural_scheme
  • Label 1: agriculture
  • Label 2: pest
  • Label 3: seed
  • Label 4: weather
  • Label 5: price
  • Label 6: non_agri

Description of the Buckets

  1. agricultural_scheme: The farmer query is about schemes in Odisha

  2. agriculture: General agri queries

  3. pest: The farmer query is about pests

  4. seed: The farmer query is about seed varieties

  5. weather : The farmer query is asking about the weather for a district /place e.g. : 'What's the weather forecast for Sundargarh?'

  6. price : The farmer query is asking about the price of some crop e.g. 'Price for paddy'

  7. non_agri : The farmer query is just some salutation or unrelated to agri

Training Metrics

The following training metrics were observed over 10 epochs: Epoch 1/1000 - Loss: 0.8210 - Accuracy: 0.7443 - F1 Score: 0.7360 Validation Accuracy: 0.9037 Validation F1 Score: 0.9022 Epoch 2/1000 - Loss: 0.2868 - Accuracy: 0.9199 - F1 Score: 0.9197 Validation Accuracy: 0.9241 Validation F1 Score: 0.9236 Epoch 3/1000 - Loss: 0.1620 - Accuracy: 0.9536 - F1 Score: 0.9534 Validation Accuracy: 0.9408 Validation F1 Score: 0.9407 Epoch 4/1000 - Loss: 0.0975 - Accuracy: 0.9698 - F1 Score: 0.9698 Validation Accuracy: 0.9457 Validation F1 Score: 0.9461 Epoch 5/1000 - Loss: 0.0722 - Accuracy: 0.9777 - F1 Score: 0.9777 Validation Accuracy: 0.9518 Validation F1 Score: 0.9520 Epoch 6/1000 - Loss: 0.0570 - Accuracy: 0.9801 - F1 Score: 0.9801 Validation Accuracy: 0.9574 Validation F1 Score: 0.9573 Epoch 7/1000 - Loss: 0.0426 - Accuracy: 0.9838 - F1 Score: 0.9838 Validation Accuracy: 0.9601 Validation F1 Score: 0.9601 Epoch 8/1000 - Loss: 0.0403 - Accuracy: 0.9850 - F1 Score: 0.9850 Validation Accuracy: 0.9646 Validation F1 Score: 0.9646 Epoch 9/1000 - Loss: 0.0340 - Accuracy: 0.9853 - F1 Score: 0.9853 Validation Accuracy: 0.9623 Validation F1 Score: 0.9624 Epoch 10/1000 - Loss: 0.0307 - Accuracy: 0.9857 - F1 Score: 0.9857 Validation Accuracy: 0.9640 Validation F1 Score: 0.9640 Epoch 11/1000 - Loss: 0.0297 - Accuracy: 0.9873 - F1 Score: 0.9873 Validation Accuracy: 0.9618 Validation F1 Score: 0.9618 Epoch 12/1000 - Loss: 0.0279 - Accuracy: 0.9867 - F1 Score: 0.9867 Validation Accuracy: 0.9607 Validation F1 Score: 0.9607