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base_model: FacebookAI/roberta-large
language: en
license: apache-2.0
model_name: gender-marker-classifier
pipeline_tag: text-classification
tags:
- CRS
- OECD CRS
- text-classification
- lora
- transformers
funded_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
tasks:
- text-classification
shared_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
---
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|357|
|weighted|avg|0.89|0.89|0.89|357|
### Usage
```python## How to Use
```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
```python
from transformers import TextClassificationPipeline
model = TextClassificationPipeline("namespace/my-model")
outputs = model("Hello World!")
print(outputs)"
```
```
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