---
license: apache-2.0
datasets:
- amazon_polarity
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-emotion-balanced
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: amazon_polarity
      type: sentiment
      args: default
    metrics:
    - type: accuracy
      value: 0.94112
      name: Accuracy
    - type: loss
      value: 0.1634
      name: Loss
    - type: f1
      value: 0.9417
      name: F1
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: amazon_polarity
      type: amazon_polarity
      config: amazon_polarity
      split: test
    metrics:
    - type: accuracy
      value: 0.94112
      name: Accuracy
      verified: true
      verifyToken: >-
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    - type: precision
      value: 0.9321570625232675
      name: Precision
      verified: true
      verifyToken: >-
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    - type: recall
      value: 0.95149
      name: Recall
      verified: true
      verifyToken: >-
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    - type: auc
      value: 0.9849019044624999
      name: AUC
      verified: true
      verifyToken: >-
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    - type: f1
      value: 0.9417243188138998
      name: F1
      verified: true
      verifyToken: >-
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    - type: loss
      value: 0.16342754662036896
      name: loss
      verified: true
      verifyToken: >-
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---

# distilbert-sentiment

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of the [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity).
It achieves the following results on the evaluation set:
- Loss: 0.163
- Accuracy: 0.941
- F1_score: 0.942

## Model description

This sentiment classifier has been trained on 180_000 samples for the training set, 20_000 samples for the validation set and 20_000 samples for the test set.

## Intended uses & limitations
```python
from transformers import pipeline

# Create the pipeline
sentiment_classifier = pipeline('text-classification', model='AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon')

# Now you can use the pipeline to classify emotions
result = sentiment_classifier("This product doesn't fit me at all.")
print(result)
#[{'label': 'negative', 'score': 0.9994848966598511}]
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1270
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 2
- weight_decay: 0.01

### Training results

| key | value |
| --- | ----- |
| eval_accuracy | 0.94112 |
| eval_auc | 0.9849 |
| eval_f1_score | 0.9417 |
| eval_precision | 0.9321 |
| eval_recall | 0.95149 |
### Framework versions

- Transformers 4.34.0
- Pytorch lightning 2.0.9
- Tokenizers 0.14.0