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mr-checker/
distilbert-sentimentx-twitter

Name: distilbert-sentimentx-twitter
Availability: InStock

distilbert/distilbert-base-uncased

finetune

apache-2.0

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SentimentX-DistilBERT

This model is a fine-tuned version of distilbert-base-uncased trained on the Sentiment140 dataset. It is designed to classify the sentiment of short-form text (specifically tweets) into two categories: Positive and Negative.

Model Description

Developed by: [Kalyan Sai]
Model type: Transformer-based Text Classification
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: distilbert-base-uncased

Intended Uses & Limitations

Intended Use

This model is intended for real-time sentiment monitoring of social media feeds, specifically Twitter (X). It is optimized for high-throughput inference environments.

Limitations

The model was trained on a binary dataset (Positive/Negative). It may not perform accurately on neutral text.
Performance may decrease on text with heavy use of modern slang or emojis not present in the 2009 Sentiment140 dataset.

Training and Evaluation Data

The model was trained on a sample of 100,000 tweets from the Sentiment140 dataset, which contains 1.6 million tweets automatically labeled (0 = negative, 4 = positive) based on emoticons.

Training Procedure

Hardware: Trained on Kaggle NVIDIA Tesla T4 GPU.
Optimizer: AdamW
Learning Rate: Default (5e-5)
Epochs: 2
Mixed Precision: FP16 enabled for acceleration.

Evaluation Results

The model achieved the following results on the evaluation set:

Accuracy: 82.44%
F1-Score: 82.50%
Precision: 83.01%
Recall: 81.99%

How to Get Started with the Model

Use the code below to get started with the model locally:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("mr-checker/sentimentX-distilbert")
model = AutoModelForSequenceClassification.from_pretrained("mr-checker/sentimentX-distilbert")

text = "SentimentX is working perfectly!"
inputs = tokenizer(text, return_tensors="pt")

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

predicted_class_id = logits.argmax().item()
# 0: Negative, 1: Positive

mr-checker/distilbert-sentimentx-twitter