| 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](https://huggingface.co/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 | |
| ```python | |
| 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) |