mdjrjoy/distilbert-fine-tuned

DistilBERT Sentiment Analysis Model

Overview

This model is a fine-tuned DistilBERT model for customer review sentiment analysis.

The model classifies customer reviews into three sentiment categories:

• Positive
• Neutral
• Negative

Model Details

• Base Model: distilbert-base-uncased
• Task: Text Classification
• Number of Classes: 3
• Labels:
  • 0: Negative
  • 1: Neutral
  • 2: Positive

Training Data

The model was fine-tuned on a dataset of customer reviews annotated with sentiment labels.

Intended Use

This model can be used to:

• Analyze customer feedback
• Monitor consumer sentiment
• Classify reviews automatically
• Support business intelligence applications

Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "mdjrjoy/distilbert-my-task"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "The product quality is excellent."

inputs = tokenizer(text, return_tensors="pt", truncation=True)

with torch.no_grad():
    outputs = model(**inputs)

prediction = torch.argm**(outputs.logits, dim=1).item()

l**els = {
    0: "Negative",
    1:**Neutral",
    2: "Positive"
}

pr**t(labels[prediction])

Lim**ations

• Performance dependsn the quality andiversity of the training data.
• e model may perform lessccurately on reviews from domainshat differ significantly from theraining data.
• Sentiment cane subjective and contextependent.

License

Specify thlicensender which theodel is distributed. ````