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gender-marker-classifier
evaluatorhub42/gender-marker-classifier
gender-marker-classifier is a text classification model from evaluatorhub42. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
This model identifies the relevance of CRS projects to feminist development policy. It is trained on manually annotated CRS data and uses the Gender Marker classification. Labels 0, 1, and 2 represent whether a projec…
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Public
Updated Jan 23, 2026
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13.7 MB
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jan 20, 2026
- Updated
- Jan 23, 2026
- SHA
- c50ec86d
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Base models
Model card
From the Hugging Face model README
This model identifies the relevance of CRS projects to feminist development policy. It is trained on manually annotated CRS data and uses the Gender Marker classification. Labels 0, 1, and 2 represent whether a project has no, significant, or primary focus on feminist policy objectives, such as strengthening rights, resources, and representation (“3R”), advancing gender-transformative and intersectional approaches, or supporting the broader goals of feminist development policy. (CRS Gender Marker)
Evaluation metrics
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.93 | 0.95 | 0.94 | 234 |
| 1 | 0.82 | 0.68 | 0.74 | 34 |
| 2 | 0.88 | 0.95 | 0.91 | 55 |
| 3 | 0.70 | 0.62 | 0.66 | 34 |
| -- | -- | -- | -- | -- |
| accuracy | 0.89 | 357 | ||
| macro | avg | 0.83 | 0.80 | 0.81 |
| weighted | avg | 0.89 | 0.89 | 0.89 |
Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("namespace/my-model")
tokenizer = AutoTokenizer.from_pretrained("namespace/my-model")
inputs = tokenizer("hello world", return_tensors="pt")
outputs = model(**inputs)
print(outputs)"
or
from transformers import TextClassificationPipeline
model = TextClassificationPipeline("namespace/my-model")
outputs = model("Hello World!")
print(outputs)"
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Jan 20, 2026
- Updated
- Jan 23, 2026