OECD CRS Classifiers
Collection
3 items • Updated • 1
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("evaluatorhub42/gender-marker-classifier", dtype="auto")Quick Links
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)"
Model tree for evaluatorhub42/gender-marker-classifier
Base model
FacebookAI/roberta-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="evaluatorhub42/gender-marker-classifier")