| --- |
| license: apache-2.0 |
| --- |
| # Finance Document Classifier |
|
|
| This repository contains a classifier for determining whether a document is finance-related. |
|
|
| ## Model Overview |
| - A regression-based classifier with two classes: financial (1) and non-financial (0). |
| - Uses `Snowflake/snowflake-arctic-embed-m` as the embedding model with a classification head. During the training, we train the model in a regression way. |
| - We used `Qwen/Qwen2.5-72B-Instruct` to annotate 110k CulturaX documents with a note between 0 and 5, for the training, scores between [0,2] are converted to 0, [3,5] to 1. Then trained on 108k and test on 2k. |
|
|
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| # Load tokenizer and model |
| tokenizer = AutoTokenizer.from_pretrained("DragonLLM/ClassiFin") |
| model = AutoModelForSequenceClassification.from_pretrained("DragonLLM/ClassiFin") |
| |
| # Example text |
| text = "This is a test sentence." |
| |
| # Tokenize input |
| inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True) |
| |
| # Get model outputs |
| outputs = model(**inputs) |
| logits = outputs.logits.float().detach().cpu().numpy() |
| logits = logits.ravel().tolist() |
| |
| # Convert logits to class labels |
| int_scores = [int(round(max(0, min(logit, 1)))) for logit in logits] # 0 for non-financial, 1 for financial |
| ``` |
|
|
| ## Model Performance |
| - Evaluated on the test set of 2000 samples. |
|
|
| ``` |
| precision recall f1-score support |
| |
| 0 0.95 0.99 0.97 1750 |
| 1 0.92 0.62 0.74 250 |
| accuracy 0.95 2000 |
| macro avg 0.93 0.81 0.85 2000 |
| weighted avg 0.94 0.95 0.94 2000 |
| ``` |
| ## Citation |
|
|
| If you use this model in your research or applications, please cite this repository. |
|
|
| ``` |
| @misc{ClassiFin, |
| title={ClassiFin: Finance Document Classifier}, |
| author={Liu, Jingshu and Qader, Raheel and Caillaut, Gaëtan and Nakhle, Mariam and Barthelemy, Jean-Gabriel and Sadoune, Arezki and Foly, Sabine}, |
| url={https://huggingface.co/DragonLLM/ClassiFin}, |
| year={2025} |
| } |
| ``` |
|
|
