README.md

---
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)"
```
```