---
library_name: transformers
license: mit
language:
- en
base_model:
- microsoft/Phi-4-mini-instruct
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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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.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below 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

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

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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 -->

```python
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

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

[More Information Needed]

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]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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