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
agricultural_scheme: The farmer query is about schemes in Odisha
agriculture: General agri queries
pest: The farmer query is about pests
seed: The farmer query is about seed varieties
weather : The farmer query is asking about the weather for a district /place e.g. : 'What's the weather forecast for Sundargarh?'
price : The farmer query is asking about the price of some crop e.g. 'Price for paddy'
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