---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotions-text-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-finetuned-emotions-text-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2130
- Accuracy: 0.928
- F1: 0.9278

## Model description

More information needed

## Intended uses & limitations

```python
import pandas as pd
from transformers import pipeline
import pandas as pd
import matplotlib.pyplot as plt


model_id = 'ithattieu/distilbert-base-uncased-finetuned-emotions-text-classification'
classifier = pipeline('text-classification', model=model_id)

labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']

text = 'I am not a really smart lady, which makes it challenging to grasp certain concepts, but you made it super easy to understand NLP and relevant applications. So, thank you!'

preds_df = pd.DataFrame(preds[0])
preds = classifier(text, return_all_scores=True)

preds_df = pd.DataFrame(preds[0])
plt.bar(labels, 100 * preds_df["score"], color='C0')
plt.title(f'"{text}"')
plt.ylabel("Class probability (%)")
plt.show()
```


## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8161        | 1.0   | 250  | 0.3051          | 0.912    | 0.9114 |
| 0.2464        | 2.0   | 500  | 0.2130          | 0.928    | 0.9278 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.5.0.dev20240815
- Datasets 2.19.1
- Tokenizers 0.19.1