File size: 4,552 Bytes
b1fd899 af13b5a b1fd899 af13b5a b1fd899 af13b5a b1fd899 c3c5ee0 b1fd899 0cd7de3 b1fd899 af13b5a c3c5ee0 b1fd899 e2dee29 88935a4 b1fd899 9f47e3d b1fd899 bdb95af b1fd899 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
---
language:
- en
- pt
license: mit
library_name: transformers
tags:
- biology
- science
- text-classification
- nlp
- biomedical
- filter
- roberta
- medical
metrics:
- f1
- accuracy
- recall
datasets:
- Madras1/BioClass80k
base_model: roberta-base
widget:
- text: The mitochondria is the powerhouse of the cell and generates ATP.
example_title: Biology Example 🧬
- text: The stock market crashed today due to high inflation rates.
example_title: Finance Example 💰
- text: CRISPR-Cas9 technology allows for precise gene editing.
example_title: Genetics Example 🔬
pipeline_tag: text-classification
---
[](https://opensource.org/licenses/MIT)
[](https://pytorch.org/)
[](https://huggingface.co/tasks/text-classification)
[](https://www.python.org/)
# 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.)
## Training Data & Procedure
### Data Overview
The dataset consists of approximately **80,000 text samples** aggregated from multiple sources.
* **Total Samples:** ~79,700
* **Class Balance:** The dataset was imbalanced, with ~71% belonging to the "Non-Bio" class and ~29% to the "Bio" class.
* **Preprocessing:** Scripts were used to clean delimiter issues in CSVs, remove duplicates, and perform a stratified split for validation.
### Training Procedure
To address the class imbalance without discarding valuable data (undersampling), we employed a custom **Weighted Cross-Entropy Loss**.
* **Class Weights:** Calculated using `sklearn.utils.class_weight`. The model was penalized significantly more for missing a Biology sample than for misclassifying a general text, which directly contributed to the high Recall score.
### Hyperparameters
The model was fine-tuned using the Hugging Face `Trainer` with the following configuration:
* **Optimizer:** AdamW
* **Learning Rate:** 2e-5
* **Batch Size:** 16
* **Epochs:** 2
* **Weight Decay:** 0.01
* **Hardware:** Trained on a NVIDIA T4 GPU
## 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. |