
# FeverCodeChallenge
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on a a simplified version of [Amazon 2018](https://jmcauley.ucsd.edu/data/amazon/), only containing products and their descriptions.

# Model description
SequenceClassification to predict amazon product's main category (22 categories):
```json
{0: 'All Electronics',
 1: 'Amazon Fashion',
 2: 'Amazon Home',
 3: 'Arts, Crafts & Sewing',
 4: 'Automotive',
 5: 'Books',
 6: 'Camera & Photo',
 7: 'Cell Phones & Accessories',
 8: 'Computers',
 9: 'Digital Music',
 10: 'Grocery',
 11: 'Health & Personal Care',
 12: 'Home Audio & Theater',
 13: 'Industrial & Scientific',
 14: 'Movies & TV',
 15: 'Musical Instruments',
 16: 'Office Products',
 17: 'Pet Supplies',
 18: 'Sports & Outdoors',
 19: 'Tools & Home Improvement',
 20: 'Toys & Games',
 21: 'Video Games'}

```


# Data

Example of a product in the dataset
```json
{
 "also_buy": ["B071WSK6R8", "B006K8N5WQ", "B01ASDJLX0", "B00658TPYI"],
 "also_view": [],
 "asin": "B00N31IGPO",
 "brand": "Speed Dealer Customs",
 "category": ["Automotive", "Replacement Parts", "Shocks, Struts & Suspension", "Tie Rod Ends & Parts", "Tie Rod Ends"],
 "description": ["Universal heim joint tie rod weld in tube adapter bung. Made in the USA by Speed Dealer Customs. Tube adapter measurements are as in the title, please contact us about any questions you 
may have."],
 "feature": ["Completely CNC machined 1045 Steel", "Single RH Tube Adapter", "Thread: 3/4-16", "O.D.: 1-1/4", "Fits 1-1/4\" tube with .120\" wall thickness"],
 "image": [],
 "price": "",
 "title": "3/4-16 RH Weld In Threaded Heim Joint Tube Adapter Bung for 1-1/4&quot; Dia by .120 Wall Tube",
 "main_cat": "Automotive"
}
```

## Fields used
* [Used for the split] **also_buy/also_view**: IDs of related products
* **description**: description of the product
* **feature**: bullet point format features of the product
* **title**: name of the product
* [label] **main_cat**: main category of the product

## Split of the data
|            | # Samples |
|------------|-----------|
| Train      | 317662    |
| Validation | 53890     |
| Test       | 54716     |


# Evaluation results
| TEST         | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| 0            | 0.56      | 0.42   | 0.48     | 5327    |
| 1            | 0.81      | 0.86   | 0.83     | 1595    |
| 2            | 0.75      | 0.76   | 0.76     | 2224    |
| 3            | 0.80      | 0.82   | 0.81     | 1190    |
| 4            | 0.93      | 0.92   | 0.93     | 2632    |
| 5            | 0.99      | 0.97   | 0.98     | 4775    |
| 6            | 0.74      | 0.80   | 0.77     | 1024    |
| 7            | 0.71      | 0.64   | 0.67     | 1111    |
| 8            | 0.79      | 0.80   | 0.80     | 9765    |
| 9            | 0.94      | 0.93   | 0.94     | 840     |
| 10           | 0.94      | 0.98   | 0.96     | 1639    |
| 11           | 0.62      | 0.52   | 0.56     | 539     |
| 12           | 0.57      | 0.74   | 0.64     | 3802    |
| 13           | 0.79      | 0.84   | 0.81     | 2476    |
| 14           | 0.83      | 0.94   | 0.88     | 813     |
| 15           | 0.88      | 0.87   | 0.87     | 3004    |
| 16           | 0.76      | 0.61   | 0.68     | 2031    |
| 17           | 0.88      | 0.88   | 0.88     | 577     |
| 18           | 0.73      | 0.71   | 0.72     | 1813    |
| 19           | 0.79      | 0.85   | 0.82     | 3840    |
| 20           | 0.89      | 0.91   | 0.90     | 3253    |
| 21           | 0.69      | 0.75   | 0.72     | 446     |
| accuracy     |           |        | 0.79     | 54716   |
| macro avg    | 0.79      | 0.80   | 0.79     | 54716   |
| weighted avg | 0.79      | 0.79   | 0.79     | 54716   |

| VALIDATION   | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| 0            | 0.55      | 0.32   | 0.40     | 1034    |
| 1            | 0.79      | 0.85   | 0.82     | 1747    |
| 2            | 0.75      | 0.78   | 0.76     | 2273    |
| 3            | 0.84      | 0.88   | 0.86     | 2982    |
| 4            | 0.93      | 0.92   | 0.93     | 2236    |
| 5            | 0.97      | 0.98   | 0.97     | 2893    |
| 6            | 0.88      | 0.76   | 0.81     | 1335    |
| 7            | 0.77      | 0.74   | 0.75     | 837     |
| 8            | 0.76      | 0.73   | 0.74     | 790     |
| 9            | 0.95      | 0.96   | 0.95     | 839     |
| 10           | 0.96      | 0.98   | 0.97     | 13182   |
| 11           | 0.50      | 0.30   | 0.37     | 907     |
| 12           | 0.55      | 0.74   | 0.64     | 965     |
| 13           | 0.83      | 0.86   | 0.85     | 2780    |
| 14           | 0.93      | 0.94   | 0.93     | 1245    |
| 15           | 0.89      | 0.92   | 0.91     | 930     |
| 16           | 0.87      | 0.85   | 0.86     | 3226    |
| 17           | 0.96      | 0.97   | 0.96     | 2633    |
| 18           | 0.75      | 0.71   | 0.73     | 2518    |
| 19           | 0.74      | 0.81   | 0.77     | 2303    |
| 20           | 0.92      | 0.91   | 0.92     | 6032    |
| 21           | 0.72      | 0.89   | 0.79     | 203     |
| accuracy     |           |        | 0.87     | 53890   |
| macro avg    | 0.81      | 0.81   | 0.81     | 53890   |
| weighted avg | 0.87      | 0.87   | 0.87     | 53890   |

# Training results
| train_runtime | train_samples_per_second | train_steps_per_second | eval_loss         | epoch |
|---------------|--------------------------|------------------------|--------------------|-------|
| 48601.2302    | 13.072                   | 1.634                  | 0.5335464077893132 | 2     |
{"language": ["en"], "license": "apache-2.0", "metrics": ["accuracy", "precision", "recall"], "pipeline_tag": "text-classification"}
transformers pytorch deberta-v2 text-classification en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

FeverCodeChallenge
==================


This model is a fine-tuned version of microsoft/deberta-v3-small on a a simplified version of Amazon 2018, only containing products and their descriptions.


Model description
=================


SequenceClassification to predict amazon product's main category (22 categories):


Data
====


Example of a product in the dataset


Fields used
-----------


* [Used for the split] also\_buy/also\_view: IDs of related products
* description: description of the product
* feature: bullet point format features of the product
* title: name of the product
* [label] main\_cat: main category of the product


Split of the data
-----------------



Evaluation results
==================




Training results
================




TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
