| # Topic Classifier | |
| This repository contains the Topic Classifier model developed by DAXA.AI. The Topic Classifier is a machine learning model designed to categorize text documents across various domains, such as corporate documents, financial texts, harmful content, and medical documents. | |
| ## Model Details | |
| ### Model Description | |
| The Topic Classifier is a BERT-based model, fine-tuned from the `distilbert-base-uncased` model. It is intended for categorizing text into specific topics, including "CORPORATE_DOCUMENTS," "FINANCIAL," "HARMFUL," and "MEDICAL." This model streamlines text classification tasks across multiple sectors, making it suitable for various business use cases. | |
| - **Developed by:** DAXA.AI | |
| - **Funded by:** Open Source | |
| - **Model type:** Text classification | |
| - **Language(s):** English | |
| - **License:** MIT | |
| - **Fine-tuned from:** `distilbert-base-uncased` | |
| ### Model Sources | |
| - **Repository:** [https://huggingface.co/daxa-ai/topic-classifier](https://huggingface.co/daxa-ai/Topic-Classifier-2) | |
| - **Demo:** [https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2](https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2) | |
| ## Usage | |
| ### How to Get Started with the Model | |
| To use the Topic Classifier in your Python project, you can follow the steps below: | |
| ```python | |
| # Import necessary libraries | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import joblib | |
| from huggingface_hub import hf_hub_url, cached_download | |
| # Load the tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained("daxa-ai/topic-classifier") | |
| model = AutoModelForSequenceClassification.from_pretrained("daxa-ai/topic-classifier") | |
| # Example text | |
| text = "Please enter your text here." | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| # Apply softmax to the logits | |
| probabilities = torch.nn.functional.softmax(output.logits, dim=-1) | |
| # Get the predicted label | |
| predicted_label = torch.argmax(probabilities, dim=-1) | |
| # URL of your Hugging Face model repository | |
| REPO_NAME = "daxa-ai/topic-classifier" | |
| # Path to the label encoder file in the repository | |
| LABEL_ENCODER_FILE = "label_encoder.joblib" | |
| # Construct the URL to the label encoder file | |
| url = hf_hub_url(REPO_NAME, filename=LABEL_ENCODER_FILE) | |
| # Download and cache the label encoder file | |
| filename = cached_download(url) | |
| # Load the label encoder | |
| label_encoder = joblib.load(filename) | |
| # Decode the predicted label | |
| decoded_label = label_encoder.inverse_transform(predicted_label.numpy()) | |
| print(decoded_label) | |
| ``` | |
| ## Training Details | |
| ### Training Data | |
| The training dataset consists of 29,286 entries, categorized into four distinct labels. The distribution of these labels is presented below: | |
| | Document Type | Instances | | |
| | ------------------- | --------- | | |
| | CORPORATE_DOCUMENTS | 17,649 | | |
| | FINANCIAL | 3,385 | | |
| | HARMFUL | 2,388 | | |
| | MEDICAL | 5,864 | | |
| ### Evaluation | |
| #### Testing Data & Metrics | |
| The model was evaluated on a dataset consisting of 4,565 entries. The distribution of labels in the evaluation set is shown below: | |
| | Document Type | Instances | | |
| | ------------------- | --------- | | |
| | CORPORATE_DOCUMENTS | 3,051 | | |
| | FINANCIAL | 409 | | |
| | HARMFUL | 246 | | |
| | MEDICAL | 859 | | |
| The evaluation metrics include precision, recall, and F1-score, calculated for each label: | |
| | Document Type | Precision | Recall | F1-Score | Support | | |
| | ------------------- | --------- | ------ | -------- | ------- | | |
| | CORPORATE_DOCUMENTS | 1.00 | 1.00 | 1.00 | 3,051 | | |
| | FINANCIAL | 0.95 | 0.96 | 0.96 | 409 | | |
| | HARMFUL | 0.95 | 0.95 | 0.95 | 246 | | |
| | MEDICAL | 0.99 | 1.00 | 0.99 | 859 | | |
| | Accuracy | | | 0.99 | 4,565 | | |
| | Macro Avg | 0.97 | 0.98 | 0.97 | 4,565 | | |
| | Weighted Avg | 0.99 | 0.99 | 0.99 | 4,565 | | |
| #### Test Data Evaluation Results | |
| The model's evaluation results are as follows: | |
| - **Evaluation Loss:** 0.0233 | |
| - **Accuracy:** 0.9908 | |
| - **Precision:** 0.9909 | |
| - **Recall:** 0.9908 | |
| - **F1-Score:** 0.9908 | |
| - **Evaluation Runtime:** 30.1149 seconds | |
| - **Evaluation Samples Per Second:** 151.586 | |
| - **Evaluation Steps Per Second:** 2.391 | |
| ## Conclusion | |
| The Topic Classifier achieves high accuracy, precision, recall, and F1-score, making it a reliable model for categorizing text across the domains of corporate documents, financial content, harmful content, and medical texts. The model is optimized for immediate deployment and works efficiently in real-world applications. | |
| For more information or to try the model yourself, check out the public space [here](https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2). | |