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