bert-base-italian-xxl-uncased-italian-finetuned-emotions

This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-uncased on an unknown dataset. This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-uncased specifically for emotion classification in Italian text. The model is trained to classify text into seven emotions:

• Joy
• Sadness
• Anger
• Fear
• Disgust
• Surprise
• Neutral
• It achieves high performance on the evaluation dataset, making it suitable for tasks requiring emotional tone analysis of Italian text.

It achieves the following results on the evaluation set:

• Loss: 0.5546
• Accuracy: 0.9877
• F1: 0.9876

Model description

Model description The model is based on the BERT architecture with an uncased vocabulary and is fine-tuned for emotion detection in Italian texts. It uses the transformer architecture, which relies on attention mechanisms for context comprehension in sequences. This fine-tuned model improves performance for tasks like social media sentiment analysis, customer feedback interpretation, and conversational agents.

Intended uses & limitations

Intended uses & limitations Intended uses: - Emotion analysis of text in Italian. - Sentiment classification for customer service or social media posts. - Research in natural language understanding related to emotions in Italian. Limitations: - The model is fine-tuned on an undisclosed dataset and may not generalize well to certain domains. - Emotion detection models might struggle with texts containing sarcasm, irony, or complex multi-emotion sentences.

Training and evaluation data This model was fine-tuned on a custom dataset labeled with seven emotions (joy, sadness, anger, fear, disgust, surprise, neutral). The dataset includes Italian text samples, likely drawn from conversational or written contexts where emotion detection is relevant.

How To Use It

You can use this model with the transformers library in Python:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-uncased")

model = AutoModelForSequenceClassification.from_pretrained("MelmaGrigia/bert-base-italian-xxl-uncased-italian-finetuned-emotions")

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("Questo è un testo di esempio.")
print(result)

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
No log	1.0	29	0.9305	0.9571	0.9561
No log	2.0	58	0.5546	0.9877	0.9876

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1