evaluatorhub42/climate-mitigation-classifier

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
weighted	avg	0.81	0.81	0.81

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)"