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climate-adaptation-classifier
evaluatorhub42/climate-adaptation-classifier
climate-adaptation-classifier is a text classification model from evaluatorhub42. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
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 projec…
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Updated Jan 23, 2026
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jan 20, 2026
- Updated
- Jan 23, 2026
- SHA
- d5b6646f
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Base models
Model card
From the Hugging Face model README
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)"
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Jan 20, 2026
- Updated
- Jan 23, 2026