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--- |
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language: en |
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tags: |
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- transformers |
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- text-classification |
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- taxonomy |
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license: other |
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license_name: link-attribution |
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license_link: https://dejanmarketing.com/link-attribution/ |
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model_name: Taxonomy Classifier |
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pipeline_tag: text-classification |
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base_model: albert-base-v2 |
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--- |
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# Taxonomy Classifier |
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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. |
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## Model Details |
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- **Model Developers:** [DEJAN.AI](https://dejan.ai/) |
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- **Model Type:** Hierarchical Text Classification |
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- **Base Model:** [`albert/albert-base-v2`](https://huggingface.co/albert/albert-base-v2) |
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- **Taxonomy Structure:** |
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| Level | Unique Classes | |
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|---|---| |
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| 1 | 21 | |
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| 2 | 193 | |
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| 3 | 1350 | |
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| 4 | 2205 | |
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| 5 | 1387 | |
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| 6 | 399 | |
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| 7 | 50 | |
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- **Model Architecture:** |
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- **Level 1:** Standard sequence classification using `AlbertForSequenceClassification`. |
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- **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. |
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- **Language(s):** English |
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- **Library:** [Transformers](https://huggingface.co/docs/transformers/index) |
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- **License:** [link-attribution](https://dejanmarketing.com/link-attribution/) |
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## Uses |
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### Direct Use |
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The model is intended for categorizing text into a predefined 7-level taxonomy. |
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### Downstream Uses |
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Potential applications include: |
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- Automated content tagging |
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- Product categorization |
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- Information organization |
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### Out-of-Scope Use |
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The model's performance on text outside the domain of the training data or for classifying into taxonomies with different structures is not guaranteed. |
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## Limitations |
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- Performance is dependent on the quality and coverage of the training data. |
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- Errors in earlier levels of the hierarchy can propagate to subsequent levels. |
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- The model's performance on unseen categories is limited. |
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- The model may exhibit biases present in the training data. |
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- 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). |
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## Training Data |
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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. |
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## Training Procedure |
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- **Levels:** Seven separate models were trained, one for each level of the taxonomy. |
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- **Level 1 Training:** Trained as a standard sequence classification task. |
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- **Levels 2-7 Training:** Trained with a custom architecture incorporating the predicted parent ID. |
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- **Input Format:** |
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- **Level 1:** Text response. |
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- **Levels 2-7:** Text response concatenated with a one-hot encoded vector of the predicted ID from the previous level. |
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- **Objective Function:** CrossEntropyLoss |
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- **Optimizer:** AdamW |
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- **Learning Rate:** Initially 5e-5, adjusted to 1e-5 for Level 4. |
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- **Training Hyperparameters:** |
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- **Epochs:** 10 |
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- **Validation Split:** 0.1 |
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- **Validation Frequency:** Every 1000 steps |
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- **Batch Size:** 38 |
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- **Max Sequence Length:** 512 |
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- **Early Stopping Patience:** 3 |
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## Evaluation |
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Validation loss was used as the primary evaluation metric during training. The following validation loss trends were observed: |
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- **Level 1, 2, and 3:** Showed a relatively rapid decrease in validation loss during training. |
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- **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. |
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Further evaluation on downstream tasks is recommended to assess the model's practical performance. |
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## How to Use |
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Inference can be performed using the provided Streamlit application. |
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1. **Input Text:** Enter the text you want to classify. |
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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`). |
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3. **Run Inference:** Click the "Run Inference" button. |
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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. |
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## Visualizations |
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### Level 1: Training Loss |
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This graph shows the training loss over the steps for Level 1, demonstrating a significant drop in loss during the initial training period. |
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### Level 1: Validation Loss |
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This graph illustrates the validation loss progression over training steps for Level 1, showing steady improvement. |
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### Level 2: Training Loss |
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Here we see the training loss for Level 2, which also shows a significant decrease early on in training. |
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### Level 2: Validation Loss |
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The validation loss for Level 2 shows consistent reduction as training progresses. |
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### Level 3: Training Loss |
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This graph displays the training loss for Level 3, where training stabilizes after an initial drop. |
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### Level 3: Validation Loss |
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The validation loss for Level 3, demonstrating steady improvements as the model converges. |
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### Level 4: Training Loss |
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The training loss for Level 4 is plotted here, showing the effects of high-dimensional input features at this level. |
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### Level 4: Validation Loss |
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Finally, the validation loss for Level 4 is shown, where training seems to stabilize after a longer period. |
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