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mdjrjoy/
distilbert-fine-tuned

Name: distilbert-fine-tuned
Availability: InStock

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distilbert

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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

python

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 the**odel is distributed.

mdjrjoy/distilbert-fine-tuned