metadata
language: en
license: apache-2.0
tags:
- sagemaker
- roberta-base
- text classification
datasets:
- emotion
widget:
- text: >-
I am really upset that I have to call up to three times to the number on
the back of my insurance card for my call to be answer
model-index:
- name: sagemaker-roberta-base-emotion
results:
- task:
type: text-classification
name: Multi Class Text Classification
dataset:
name: emotion
type: emotion
metrics:
- type: accuracy
value: 94.1
name: Validation Accuracy
- type: f1
value: 94.13
name: Validation F1
- 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
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- type: precision
value: 0.8833042147663716
name: Precision Macro
verified: true
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- type: precision
value: 0.931
name: Precision Micro
verified: true
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- type: precision
value: 0.9337002742192515
name: Precision Weighted
verified: true
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- type: recall
value: 0.9087144572668905
name: Recall Macro
verified: true
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- type: recall
value: 0.931
name: Recall Micro
verified: true
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- type: recall
value: 0.931
name: Recall Weighted
verified: true
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- type: f1
value: 0.8949974527433656
name: F1 Macro
verified: true
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- type: f1
value: 0.931
name: F1 Micro
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- type: f1
value: 0.9318434300647934
name: F1 Weighted
verified: true
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- type: loss
value: 0.17379647493362427
name: loss
verified: true
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roberta-base
This model is a fine-tuned model that was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Multi Class Text Classification (emotion detection).
It achieves the following results on the evaluation set:
- Loss: 0.1613253802061081
- f1: 0.9413321705151999
Hyperparameters
{
"epochs": 10,
"train_batch_size": 16,
"learning_rate": 3e-5,
"weight_decay":0.01,
"load_best_model_at_end": true,
"model_name":"roberta-base",
"do_eval": True,
"load_best_model_at_end":True
}
Validation Metrics
| key | value |
|---|---|
| eval_accuracy | 0.941 |
| eval_f1 | 0.9413321705151999 |
| eval_loss | 0.1613253802061081 |
| eval_recall | 0.941 |
| eval_precision | 0.9419519436781406 |