README.md

---
language: en
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
tags:
- sagemaker
- roberta-base
- text classification
license: apache-2.0
datasets:
- emotion
model-index:
- name: sagemaker-roberta-base-emotion
  results:
  - task:
      name: Multi Class Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
    metrics:
    - name: Validation Accuracy
      type: accuracy
      value: 94.1
    - name: Validation F1
      type: f1
      value: 94.13
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.931
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8833042147663716
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.931
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9337002742192515
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9087144572668905
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.931
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.931
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8949974527433656
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.931
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9318434300647934
      verified: true
    - name: loss
      type: loss
      value: 0.17379647493362427
      verified: true
---
## 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
```json
{
    "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 |