dejanseo/ecommerce-taxonomy-classifier

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

• Model Type: Hierarchical Text Classification

• Base Model: 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

• License: 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

Level 4: Training Loss

The training loss for Level 4 is plotted here, showing the effects of high-dimensional input features at this level.

Epoch	Average Training Loss
1	5.2803
2	2.8285
3	1.5707
4	0.8696
5	0.5164
6	0.3384
7	0.2408
8	0.1813
9	0.1426

Level 4: Validation Loss

Finally, the validation loss for Level 4 is shown, where training seems to stabilize after a longer period.

Level 5

Level 5: Training and Validation Loss

Level 5 training loss.

Average training loss / epoch.

Epoch	Average Training Loss
1	5.9700
2	3.9396
3	2.5609
4	1.6004
5	1.0196
6	0.6372
7	0.4410
8	0.3169
9	0.2389
10	0.1895
11	0.1635
12	0.1232
13	0.1075
14	0.0939
15	0.0792
16	0.0632
17	0.0549

Level 5 validation loss.

Level 6

Level 6: Training and Validation Loss

Epoch	Average Training Loss
1	5.5855
2	4.1836
3	3.0299
4	2.1331
5	1.4587
6	0.9847
7	0.6774
8	0.4990
9	0.3637
10	0.2688
11	0.2121
12	0.1697
13	0.1457
14	0.1139
15	0.1186
16	0.0753
17	0.0612
18	0.0676
19	0.0527
20	0.0399
21	0.0342
22	0.0304
23	0.0421
24	0.0280
25	0.0211
26	0.0189
27	0.0207
28	0.0337
29	0.0194

Level 7

Level 7: Training and Validation Loss

Epoch	Average Training Loss
1	3.8413
2	3.5653
3	3.1193
4	2.5189
5	1.9640
6	1.4992
7	1.1322
8	0.8627
9	0.6674
10	0.5232
11	0.4235
12	0.3473
13	0.2918
14	0.2501
15	0.2166

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