---
base_model: FacebookAI/roberta-large
language: en
license: apache-2.0
model_name: climate-mitigation-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 climate-change mitigation. It is trained on manually annotated CRS data using the standard Rio Marker classification. Labels 0, 1, and 2 indicate whether a project has no, significant, or primary focus on climate-change mitigation. (RIO Marker)

 ### Evaluation metrics 

 | |precision|recall|f1-score|support|
|--|--|--|--|--|
|0|0.92|0.90|0.91|311|
|1|0.53|0.66|0.59|65|
|2|0.75|0.85|0.80|87|
|3|0.59|0.37|0.46|51|
|--|--|--|--|--|
|accuracy| | |0.81|514|
|macro|avg|0.70|0.70|0.69|514|
|weighted|avg|0.81|0.81|0.81|514|


 ### 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)"
```
```