README.md

---
license: apache-2.0
base_model:
- distilbert/distilbert-base-uncased
tags: 
- text-classification 
- sentiment-analysis 
- pytorch 
datasets: 
- imdb 
---

# sentio-model: A Fine-Tuned Sentiment Analysis Model 

`sentio-model` is a distilled version of a larger language model, fine-tuned for the task of sentiment analysis. This model has been optimized for performance and efficiency, making it suitable for a wide range of applications where understanding user sentiment is key. 

## Model Description 

This model is a `DistilBERT-base-uncased` model fine-tuned on the `imdb` dataset for sentiment analysis. DistilBERT is a smaller, faster, and lighter version of BERT, which is ideal for production environments with limited computational resources. The `imdb` dataset contains movie reviews labeled as either positive or negative, making it a standard benchmark for sentiment analysis tasks. 

**Base Model:** `distilbert-base-uncased` 
**Fine-Tuning Dataset:** `imdb` 
**Task:** Sentiment Analysis (Text Classification) 
**Language:** English 

## Intended Uses & Limitations 

### Intended Uses 

This model is primarily intended for binary sentiment classification of English text. It can be used in a variety of scenarios, including: 

*   **Customer Feedback Analysis:** Automatically classify customer reviews, social media comments, and support tickets as positive or negative. 
*   **Brand Monitoring:** Track brand sentiment across various online platforms. 
*   **Content Recommendation:** Filter or recommend content based on user sentiment. 

### Limitations and Bias 

While `sentio-model` is a powerful tool, it's important to be aware of its limitations: 

*   **Domain Specificity:** The model was fine-tuned on movie reviews. Its performance may vary on text from different domains (e.g., legal or medical documents). 
*   **Nuanced Language:** The model might struggle with sarcasm, irony, or other forms of nuanced language. 
*   **Bias in Data:** The `imdb` dataset may contain biases present in the original reviews, which could be reflected in the model's predictions. It's recommended to evaluate the model for fairness and potential biases before deploying it in a sensitive application. 

## How to Get Started with the Model 

You can easily use this model with the `transformers` library. 

### Installation 

First, make sure you have the `transformers` library installed: 

```bash 
pip install transformers 
``` 

### Usage 

Here's how you can use the model for inference in Python: 

```python 
from transformers import pipeline 

# Initialize the sentiment analysis pipeline 
sentiment_pipeline = pipeline("sentiment-analysis", model="louijiec/sentio-model") 

# Example texts 
texts = [ 
    "This movie was absolutely fantastic! The acting was superb.", 
    "I was really disappointed with the plot. It was boring and predictable." 
] 

# Get predictions 
results = sentiment_pipeline(texts) 
print(results) 
``` 

## Training Procedure 

The model was fine-tuned using the following hyperparameters: 

*   **Learning Rate:** 2e-5 
*   **Batch Size:** 16 
*   **Number of Epochs:** 3 
*   **Weight Decay:** 0.01 

The training was performed on a single NVIDIA T4 GPU. 

## Evaluation Results 

The model achieves the following performance on the `imdb` evaluation set: 

TODO