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

 ### Evaluation metrics 

 | |precision|recall|f1-score|support|
|--|--|--|--|--|
|0|0.89|0.94|0.91|217|
|1|0.68|0.39|0.50|33|
|2|0.71|0.87|0.78|45|
|3|0.75|0.52|0.62|23|
|--|--|--|--|--|
|accuracy| | |0.84|318|
|macro|avg|0.76|0.68|0.70|318|
|weighted|avg|0.83|0.84|0.83|318|


 ### 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)"
```
```