Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- en
+- pt
+tags:
+- biology
+- classification
+- text-classification
+- roberta
+metrics:
+- f1
+- accuracy
+- recall
+base_model: roberta-base
+license: mit
+pipeline_tag: text-classification
+---
+
+# RobertaBioClass 🧬
+
+**RobertaBioClass** is a fine-tuned RoBERTa model designed to distinguish biological texts from other general topics. It was trained to filter large datasets, prioritizing high recall to ensure relevant biological content is captured.
+
+## Model Details
+
+- **Model Architecture:** RoBERTa Base
+- **Task:** Binary Text Classification
+- **Language:** English (and Portuguese capabilities depending on training data mix)
+- **Author:** Madras1
+
+## Performance Metrics 📊
+
+The model was evaluated on a held-out validation set of ~16k samples. It is optimized for **High Recall**, making it excellent for filtering pipelines where missing a biological text is worse than including a false positive.
+
+| Metric | Score | Description |
+| :--- | :--- | :--- |
+| **Accuracy** | **86.8%** | Overall correctness |
+| **F1-Score** | **78.5%** | Harmonic mean of precision and recall |
+| **Recall (Bio)** | **83.1%** | Ability to find biological texts (Sensitivity) |
+| **Precision** | **74.4%** | Correctness when predicting "Bio" |
+
+## Label Mapping
+
+The model outputs the following labels:
+* `LABEL_0`: **Non-Biology** (General text, News, Finance, Sports, etc.)
+* `LABEL_1`: **Biology** (Genetics, Medicine, Anatomy, Ecology, etc.)
+
+## How to Use 🚀
+
+You can use this model directly with the Hugging Face `pipeline`:
+
+```python
+from transformers import pipeline
+
+# Load the pipeline
+classifier = pipeline("text-classification", model="Madras1/RobertaBioClass")
+
+# Test strings
+examples = [
+    "The mitochondria is the powerhouse of the cell.",
+    "The stock market crashed yesterday due to inflation."
+]
+
+# Get predictions
+predictions = classifier(examples)
+print(predictions)
+# Output:
+# [{'label': 'LABEL_1', 'score': 0.99...},  <- Biology
+#  {'label': 'LABEL_0', 'score': 0.98...}]  <- Non-Biology
+
+```
+
+Intended Use
+This model is ideal for:
+
+Filtering biological data from Common Crawl or other web datasets.
+
+Categorizing academic papers.
+
+Tagging educational content.
+
+Limitations
+Since the model prioritizes Recall (83%), it may generate some False Positives (Precision ~74%). It might occasionally classify related scientific fields (like Chemistry or Physics) as Biology depending on the context.