kingabzpro_-_Phi-3.5-mini-instruct-Ecommerce-Text-Classification-gguf

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Phi-3.5-mini-instruct-Ecommerce-Text-Classification - GGUF

• Model creator: https://huggingface.co/kingabzpro/
• Original model: https://huggingface.co/kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification/

Original model description:

datasets:

• saurabhshahane/ecommerce-text-classification language:
• en library_name: transformers license: apache-2.0 metrics:
• accuracy
• f1 pipeline_tag: text-generation tags:
• Ecommerce
• Phi-3.5
• Fine-tuned

---

Phi-3.5-mini-instruct-Ecommerce-Text-Classification

This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on an saurabhshahane/ecommerce-text-classification dataset.

Tutorial

Customize Phi-3.5-mini-instruct model to predict various Ecommerce Categories from the text.

Use with Transformers

from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline
import torch

model_id = "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
        model_id,
        return_dict=True,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
)

text = "Inalsa Dazzle Glass Top, 3 Burner Gas Stove with Rust Proof Powder Coated Body, Black Toughened Glass Top, 2 Medium and 1 Small High Efficiency Brass Burners, Aluminum Mixing Tubes, Powder Coated Body, Inbuilt Stainless Steel Drip Trays, 360 degree Swivel Nozzle,Bigger Legs to Facilitate Cleaning Under Cooktop"
prompt = f"""Classify the E-commerce text into Electronics, Household, Books and Clothing.
text: {text}
label: """.strip()

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipe(prompt, max_new_tokens=4, do_sample=True, temperature=0.1)

print(outputs[0]["generated_text"].split("label: ")[-1].strip())

# Household

Results

Accuracy: 0.860
Accuracy for label Electronics: 0.825
Accuracy for label Household: 0.926
Accuracy for label Books: 0.683
Accuracy for label Clothing: 0.947

Classification Report:

              precision    recall  f1-score   support

 Electronics       0.97      0.82      0.89        40
   Household       0.88      0.93      0.90        81
       Books       0.90      0.68      0.78        41
    Clothing       0.88      0.95      0.91        38

   micro avg       0.90      0.86      0.88       200
   macro avg       0.91      0.85      0.87       200
weighted avg       0.90      0.86      0.88       200

Confusion Matrix:

[[33  6  1  0]
 [ 1 75  2  3]
 [ 0  3 28  2]
 [ 0  1  0 36]]