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