distilbert-base-uncased-finetuned-emotion-classification-10_epochs
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1464
- Accuracy: 0.9375
- F1 score: 0.9376
Model description
This model is fine-tuned on the 'emotion' dataset to classify text into six emotion categories: sadness, joy, love, anger, fear, and surprise.
Use the Model
from transformers import pipeline
import pandas as pd
emt_clf = pipeline("text-classification", model="Swoodplays/emotion-classification")
text = "I saw a movie today and it was really good."
preds = emt_clf(text, return_all_scores=True)
labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
print(preds)
preds_df = pd.DataFrame(preds[0])
plt.bar(labels, 100 * preds_df["score"])
plt.title(f'"{text}"')
plt.xlabel("Classfied emotions")
plt.ylabel("Class probability (%)")
plt.show()
Training and evaluation data
dair-ai/emotion
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score |
---|---|---|---|---|---|
No log | 1.0 | 125 | 0.4449 | 0.873 | 0.8654 |
No log | 2.0 | 250 | 0.2166 | 0.9265 | 0.9270 |
No log | 3.0 | 375 | 0.1726 | 0.933 | 0.9339 |
No log | 4.0 | 500 | 0.1552 | 0.9385 | 0.9386 |
No log | 5.0 | 625 | 0.1439 | 0.938 | 0.9383 |
No log | 6.0 | 750 | 0.1435 | 0.937 | 0.9370 |
No log | 7.0 | 875 | 0.1481 | 0.9355 | 0.9356 |
No log | 8.0 | 1000 | 0.1402 | 0.935 | 0.9352 |
No log | 9.0 | 1125 | 0.1491 | 0.9355 | 0.9355 |
No log | 10.0 | 1250 | 0.1464 | 0.9375 | 0.9376 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for Swoodplays/emotion-classification
Base model
distilbert/distilbert-base-uncased