| license: apache-2.0 | |
| tags: | |
| - text-classification | |
| - binary-classification | |
| pipeline_tag: text-classification | |
| datasets: | |
| - custom | |
| language: | |
| - en | |
| # 🚀 My First AI Model | |
| ✅ This is my first text classification model, fine-tuned using `distilbert-base-uncased` on a binary sentiment dataset. | |
| - **Task:** Sentiment Analysis (Positive/Negative) | |
| - **Labels:** | |
| - `LABEL_1`: Positive | |
| - `LABEL_0`: Negative | |
| --- | |
| ## 📊 Training Data | |
| Custom CSV dataset with two columns: | |
| - `text`: The input text. | |
| - `label`: 0 for Negative, 1 for Positive. | |
| --- | |
| ## 🧪 Metrics | |
| | Metric | Value | | |
| |----------------|-------| | |
| | Training Loss | ~0.71 | | |
| | Epochs | 2 | | |
| --- | |
| ## 🚀 How to use | |
| ### With 🤗 `transformers` (locally): | |
| ```python | |
| from transformers import pipeline | |
| clf = pipeline("text-classification", model="BinaryMans/my-first-ai-model") | |
| clf("I love this product!") | |