Phi-4-mini-instruct-Ecommerce-Text-Classification

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

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Direct Use

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Downstream Use [optional]

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Classifiy a product description among 4 categories: "Electronics", "Household", "Books", "Clothing"

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Source: https://www.kaggle.com/datasets/saurabhshahane/ecommerce-text-classification

2k samples:

• Training: 0.8
• Validation: 0.1
• Test: 0.2

Training Procedure

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Preprocessing [optional]

[More Information Needed] to speed up the training: max_seq_length = 200

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

trainer = SFTTrainer(
    model = model,
    train_dataset=train_data,
    eval_dataset=eval_data,
    processing_class = tokenizer,
    # data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),
    peft_config=peft_config,
    args = SFTConfig(
        output_dir = output_dir,
        num_train_epochs = 1, # Set this for 1 full training run.
        per_device_train_batch_size=1,
        per_device_eval_batch_size=2,
        gradient_accumulation_steps=4,
        gradient_checkpointing=True,
        optim = "paged_adamw_8bit",
        logging_steps=1,
        learning_rate = 2e-5,
        weight_decay = 0.001,
        fp16=False,
        bf16=False,
        max_grad_norm=0.3,
        max_steps = -1,
        # warmup_steps = 5,
        warmup_ratio=0.03, 
        group_by_length=False,
        lr_scheduler_type = "cosine",
        # seed = 3407,
        report_to = "wandb",
        eval_strategy="steps",              # save checkpoint every epoch
        eval_steps = 0.2,
        max_seq_length = 200,
        dataset_text_field="text",
        # dataset_num_proc = 4,
        # packing = False, # Can make training 5x faster for short sequences.
        dataset_kwargs={
            "add_special_tokens": False,
            "append_concat_token": False,
        }
    ),
)

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Before fine-tuning:

Accuracy: 0.530
Accuracy for label Electronics: 0.875
Accuracy for label Household: 0.049
Accuracy for label Books: 0.780
Accuracy for label Clothing: 0.921

Classification Report:
              precision    recall  f1-score   support

 Electronics       0.56      0.88      0.69        40
   Household       1.00      0.05      0.09        81
       Books       0.73      0.78      0.75        41
    Clothing       0.56      0.92      0.69        38

   micro avg       0.61      0.53      0.57       200
   macro avg       0.71      0.66      0.56       200
weighted avg       0.77      0.53      0.46       200


Confusion Matrix:
[[35  0  1  3]
 [25  4 11 20]
 [ 1  0 32  5]
 [ 1  0  0 35]]

After fine-tuning:

Accuracy: 0.850
Accuracy for label Electronics: 0.775
Accuracy for label Household: 0.852
Accuracy for label Books: 0.829
Accuracy for label Clothing: 0.947

Classification Report:
              precision    recall  f1-score   support

 Electronics       0.82      0.78      0.79        40
   Household       0.84      0.85      0.85        81
       Books       0.92      0.83      0.87        41
    Clothing       0.86      0.95      0.90        38

   micro avg       0.85      0.85      0.85       200
   macro avg       0.86      0.85      0.85       200
weighted avg       0.86      0.85      0.85       200


Confusion Matrix:
[[31  8  1  0]
 [ 7 69  2  3]
 [ 0  4 34  3]
 [ 0  1  0 36]]

Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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