metadata
license: apache-2.0
pipeline_tag: text-classification
tags:
- text-classification
- distilbert
- sentiment-analysis
- sales-leads
DistilBERT Sales Lead Classification Model
This model is a fine-tuned version of distilbert-base-uncased trained on the Rotten Tomatoes sentiment analysis dataset (as a proxy for sales lead quality) to perform binary classification.
Training Results (2 Epochs)
The training was successful, showing a significant reduction in loss and gain in accuracy:
| Epoch | Validation Loss | Accuracy | F1 |
|---|---|---|---|
| 1 | 0.396396 | 0.8274 | 0.8261 |
| 2 | 0.403630 | 0.8546 | 0.8542 |
The model achieved 85.46% validation accuracy on the Rotten Tomatoes dataset, which is a very strong result for a base DistilBERT model.
Usage
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="kaizen696/my_lead_model"
)
# Example: High Quality
print(classifier("Budget is approved, excited to move forward."))
# Example: Low Quality
print(classifier("Not interested at all, too expensive."))
---
## 3. 📊 Analysis: Was Your Training Fine?
**Yes, your training was very good!**
* **Final Validation Accuracy:** **85.46%** is an excellent result for this dataset, especially in just two epochs. This is competitive with published results for traditional models on similar datasets (often cited in the 80%-85% range, see search results 4.2-4.4).
* **Losses:** Your **Training Loss** dropped consistently (from 0.40 to 0.26), and your **Validation Loss** remained low (around 0.40). This shows the model was effectively learning without immediately overfitting.
You now have a **verified, trained model** with a great accuracy score.
---
Your next step is to run the **`force_download=True`** script to ensure the model on your laptop is the new, correct version.
... [Guide to clearing Hugging Face cache](https://www.youtube.com/shorts/auIhrVclrng) ...
This video gives a quick visual guide on locating and deleting files from your local Hugging Face cache, which is the necessary next step after your upload.