File size: 1,844 Bytes
7818195 1ca6bd8 7818195 267a51f e56b297 1ca6bd8 7818195 7d378dd 7818195 7d378dd 7818195 7d378dd 7818195 7d378dd e56b297 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
language:
- en
library_name: transformers
tags:
- bert
- multilabel
- classification
- finetune
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: 'Where an FI employs a technological solution provided by an external party
to conduct screening of virtual asset transactions and the associated wallet addresses,
the FI remains responsible for discharging its AML/CFT obligations. The FI should
conduct due diligence on the solution before deploying it, taking into account
relevant factors such as:'
---
This model is a fine-tuned version of the BERT language model, specifically adapted for multi-label classification tasks in the
financial regulatory domain. It is built upon the pre-trained ProsusAI/finbert model, which has been further fine-tuned using a diverse
dataset of financial regulatory texts. This allows the model to accurately classify text into multiple relevant categories simultaneously.
# Model Architecture
- **Base Model**: BERT
- **Pre-trained Model**: ProsusAI/finbert
- **Task**: Multi-label classification
## Performance
Performance metrics on the validation set:
- F1 Score: 0.8637
- ROC AUC: 0.9044
- Accuracy: 0.6155
## Limitations and Ethical Considerations
- This model's performance may vary depending on the specific nature of the text data and label distribution.
- Class imbalance in the dataset.
## Dataset Information
- **Training Dataset**: Number of samples: 6562
- **Validation Dataset**: Number of samples: 929
- **Test Dataset**: Number of samples: 1884
## Training Details
- **Training Strategy**: Fine-tuning BERT with a randomly initialized classification head.
- **Optimizer**: Adam
- **Learning Rate**: 1e-4
- **Batch Size**: 16
- **Number of Epochs**: 2
- **Evaluation Strategy**: Epoch
- **Weight Decay**: 0.01
- **Metric for Best Model**: F1 Score |