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---
language: en
tags:
- transformers
- text-classification
- taxonomy
license: other
license_name: link-attribution
license_link: https://dejanmarketing.com/link-attribution/
model_name: Taxonomy Classifier
pipeline_tag: text-classification
base_model: albert-base-v2
---
# Taxonomy Classifier
This model is a hierarchical text classifier designed to categorize text into a 7-level taxonomy. It utilizes a chain of models, where the prediction at each level informs the prediction at the subsequent level. This approach reduces the classification space at each step.
## Model Details
- **Model Developers:** [DEJAN.AI](https://dejan.ai/)
- **Model Type:** Hierarchical Text Classification
- **Base Model:** [`albert/albert-base-v2`](https://huggingface.co/albert/albert-base-v2)
- **Taxonomy Structure:**
| Level | Unique Classes |
|---|---|
| 1 | 21 |
| 2 | 193 |
| 3 | 1350 |
| 4 | 2205 |
| 5 | 1387 |
| 6 | 399 |
| 7 | 50 |
- **Model Architecture:**
- **Level 1:** Standard sequence classification using `AlbertForSequenceClassification`.
- **Levels 2-7:** Custom architecture (`TaxonomyClassifier`) where the ALBERT pooled output is concatenated with a one-hot encoded representation of the predicted ID from the previous level before being fed into a linear classification layer.
- **Language(s):** English
- **Library:** [Transformers](https://huggingface.co/docs/transformers/index)
- **License:** [link-attribution](https://dejanmarketing.com/link-attribution/)
## Uses
### Direct Use
The model is intended for categorizing text into a predefined 7-level taxonomy.
### Downstream Uses
Potential applications include:
- Automated content tagging
- Product categorization
- Information organization
### Out-of-Scope Use
The model's performance on text outside the domain of the training data or for classifying into taxonomies with different structures is not guaranteed.
## Limitations
- Performance is dependent on the quality and coverage of the training data.
- Errors in earlier levels of the hierarchy can propagate to subsequent levels.
- The model's performance on unseen categories is limited.
- The model may exhibit biases present in the training data.
- The reliance on one-hot encoding for parent IDs can lead to high-dimensional input features at deeper levels, potentially impacting training efficiency and performance (especially observed at Level 4).
## Training Data
The model was trained on a dataset of 374,521 samples. Each row in the training data represents a full taxonomy path from the root level to a leaf node.
## Training Procedure
- **Levels:** Seven separate models were trained, one for each level of the taxonomy.
- **Level 1 Training:** Trained as a standard sequence classification task.
- **Levels 2-7 Training:** Trained with a custom architecture incorporating the predicted parent ID.
- **Input Format:**
- **Level 1:** Text response.
- **Levels 2-7:** Text response concatenated with a one-hot encoded vector of the predicted ID from the previous level.
- **Objective Function:** CrossEntropyLoss
- **Optimizer:** AdamW
- **Learning Rate:** Initially 5e-5, adjusted to 1e-5 for Level 4.
- **Training Hyperparameters:**
- **Epochs:** 10
- **Validation Split:** 0.1
- **Validation Frequency:** Every 1000 steps
- **Batch Size:** 38
- **Max Sequence Length:** 512
- **Early Stopping Patience:** 3
## Evaluation
Validation loss was used as the primary evaluation metric during training. The following validation loss trends were observed:
- **Level 1, 2, and 3:** Showed a relatively rapid decrease in validation loss during training.
- **Level 4:** Exhibited a slower decrease in validation loss, potentially due to the significant increase in the dimensionality of the parent ID one-hot encoding and the larger number of unique classes at this level.
Further evaluation on downstream tasks is recommended to assess the model's practical performance.
## How to Use
Inference can be performed using the provided Streamlit application.
1. **Input Text:** Enter the text you want to classify.
2. **Select Checkpoints:** Choose the desired checkpoint for each level's model. Checkpoints are saved in the respective `level{n}` directories (e.g., `level1/model` or `level4/level4_step31000`).
3. **Run Inference:** Click the "Run Inference" button.
The application will output the predicted ID and the corresponding text description for each level of the taxonomy, based on the provided `mapping.csv` file.
## Visualizations
### Level 1: Training Loss
This graph shows the training loss over the steps for Level 1, demonstrating a significant drop in loss during the initial training period.
### Level 1: Validation Loss
This graph illustrates the validation loss progression over training steps for Level 1, showing steady improvement.
### Level 2: Training Loss
Here we see the training loss for Level 2, which also shows a significant decrease early on in training.
### Level 2: Validation Loss
The validation loss for Level 2 shows consistent reduction as training progresses.
### Level 3: Training Loss
This graph displays the training loss for Level 3, where training stabilizes after an initial drop.
### Level 3: Validation Loss
The validation loss for Level 3, demonstrating steady improvements as the model converges.
### Level 4: Training Loss
The training loss for Level 4 is plotted here, showing the effects of high-dimensional input features at this level.
### Level 4: Validation Loss
Finally, the validation loss for Level 4 is shown, where training seems to stabilize after a longer period.
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