metadata
license: apache-2.0
datasets:
- stanfordnlp/imdb
language:
- en
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
Sentiment Analysis Model
This model is a fine-tuned version of distilbert-base-uncased on the IMDb dataset for sentiment analysis.
Model Details
- Base model: distilbert-base-uncased
- Fine-tuning dataset: IMDb
- Task: Sentiment analysis (text classification)
Intended Use
The model is designed to classify text into positive or negative sentiment. You can use it for tasks such as:
- Analyzing product reviews.
- Social media sentiment analysis.
- General text classification tasks involving sentiment.
Limitations
- The model is fine-tuned on the IMDb dataset and may not generalize well to all domains or datasets.
- It may inherit biases from the IMDb dataset.
Example Usage
from transformers import pipeline
# Load the model
model_pipeline = pipeline("text-classification", model="proc015/sentiment-model")
# Run sentiment analysis
result = model_pipeline("I love this product!")
print(result)