| --- |
| language: en |
| license: apache-2.0 |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - text-classification |
| - sentiment-analysis |
| - distilbert |
| datasets: |
| - imdb |
| metrics: |
| - loss |
| --- |
| |
| # DistilBERT Sentiment Classifier (IMDB) |
|
|
| Fine-tuned [distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) for binary sentiment classification (positive/negative) on a subset of the IMDB movie review dataset. |
|
|
| ## Model Details |
|
|
| - **Base model:** distilbert/distilbert-base-uncased |
| - **Task:** Sentiment analysis (binary classification) |
| - **Labels:** `0` = negative, `1` = positive |
| - **Max sequence length:** 128 tokens |
|
|
| ## Training |
|
|
| | Hyperparameter | Value | |
| |---|---| |
| | Dataset | IMDB (500 samples, 80/20 split) | |
| | Epochs | 2 | |
| | Batch size | 8 | |
| | Learning rate | 5e-5 (linear decay) | |
|
|
| **Final eval loss:** 0.0008 |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="chinmaygarde/hello") |
| classifier("This movie was absolutely fantastic!") |
| # [{'label': 'LABEL_1', 'score': 0.999}] |
| ``` |
|
|
