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---
license: apache-2.0
datasets:
- amazon_polarity
metrics:
- accuracy
- f1
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: 0.958
name: Accuracy
- type: loss
value: 0.119
name: Loss
- type: f1
value: 0.957
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
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- type: precision
value: 0.9321570625232675
name: Precision
verified: true
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- type: recall
value: 0.95149
name: Recall
verified: true
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- type: auc
value: 0.9849019044624999
name: AUC
verified: true
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- type: f1
value: 0.9417243188138998
name: F1
verified: true
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- type: loss
value: 0.16342754662036896
name: loss
verified: true
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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: 0.119
- Accuracy: 0.958
- F1_score: 0.957
## 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_loss | 0.119 |
| eval_accuracy | 0.958 |
| eval_f1_score | 0.957 |
### Framework versions
- Transformers 4.34.0
- Pytorch lightning 2.0.9
- Tokenizers 0.13.3 |