evaluatorhub42/climate-mitigation-classifier

1---2base_model: FacebookAI/roberta-large3language: en4license: apache-2.05model_name: climate-mitigation-classifier6pipeline_tag: text-classification7tags:8- CRS9- OECD CRS10- text-classification11- lora12- transformers13funded_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH14tasks:15- text-classification16shared_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH17---18This 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)19 20 ### Evaluation metrics 21 22 | |precision|recall|f1-score|support|23|--|--|--|--|--|24|0|0.92|0.90|0.91|311|25|1|0.53|0.66|0.59|65|26|2|0.75|0.85|0.80|87|27|3|0.59|0.37|0.46|51|28|--|--|--|--|--|29|accuracy| | |0.81|514|30|macro|avg|0.70|0.70|0.69|514|31|weighted|avg|0.81|0.81|0.81|514|32 33 34 ### Usage 35 36 ```python## How to Use37 38```python 39from transformers import AutoModelForSequenceClassification, AutoTokenizer40 41model = AutoModelForSequenceClassification.from_pretrained("namespace/my-model")42tokenizer = AutoTokenizer.from_pretrained("namespace/my-model")43 44inputs = tokenizer("hello world", return_tensors="pt")45outputs = model(**inputs)46print(outputs)"47```48or49 50```python 51from transformers import TextClassificationPipeline52 53model = TextClassificationPipeline("namespace/my-model")54outputs = model("Hello World!")55print(outputs)"56```57```58 59