CPU-Paper
Collection
Explore the use of NLP as a tool for policy advisors to efficiently track and assess climate policy documents (CPU: Climate Policy Understanding)
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12 items
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Updated
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3
This model is a fine-tuned version of BAAI/bge-base-en-v1.5 on the on the Policy-Classification dataset. It achieves the following results on the evaluation set:
The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 2 labels - AdaptationLabel, MitigationLabel - that are relevant to a particular task or application
More information needed
Training Dataset: 12538
Class | Positive Count of Class |
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AdaptationLabel | 5439 |
MitigationLabel | 6659 |
Validation Dataset: 1190
Class | Positive Count of Class |
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AdaptationLabel | 533 |
MitigationLabel | 604 |
The following hyperparameters were used during training:
Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted |
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0.3368 | 1.0 | 784 | 0.2917 | 0.8651 | 0.8450 | 0.8664 | 0.9138 | 0.8542 | 0.9138 | 0.8888 | 0.8437 | 0.8890 |
0.1807 | 2.0 | 1568 | 0.2549 | 0.9092 | 0.8643 | 0.9094 | 0.9156 | 0.8571 | 0.9156 | 0.9124 | 0.8571 | 0.9123 |
0.0955 | 3.0 | 2352 | 0.2988 | 0.9069 | 0.8660 | 0.9072 | 0.9252 | 0.8655 | 0.9252 | 0.9160 | 0.8613 | 0.9160 |
0.0495 | 4.0 | 3136 | 0.3101 | 0.9058 | 0.8647 | 0.9058 | 0.9305 | 0.8693 | 0.9305 | 0.9180 | 0.8622 | 0.9180 |
label | precision | recall | f1-score | support | ||||||||
:-------------: | :---------: | :-----: | :------: | :------: | ||||||||
AdaptationLabel | 0.910 | 0.928 | 0.919 | 533.0 | ||||||||
MitigationLabel | 0.902 | 0.932 | 0.917 | 604.0 |
Carbon emissions were measured using CodeCarbon.
Base model
BAAI/bge-base-en-v1.5