emotion-classification

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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

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1