metadata
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 |
| weighted | avg | 0.83 | 0.84 | 0.83 |
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)"