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

Quantization made by Richard Erkhov.

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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/

Name	Quant method	Size
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q2_K.gguf	Q2_K	1.32GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.IQ3_XS.gguf	IQ3_XS	1.51GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.IQ3_S.gguf	IQ3_S	1.57GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q3_K_S.gguf	Q3_K_S	1.57GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.IQ3_M.gguf	IQ3_M	1.73GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q3_K.gguf	Q3_K	1.82GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q3_K_M.gguf	Q3_K_M	1.82GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q3_K_L.gguf	Q3_K_L	1.94GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.IQ4_XS.gguf	IQ4_XS	1.93GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q4_0.gguf	Q4_0	2.03GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.IQ4_NL.gguf	IQ4_NL	2.04GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q4_K_S.gguf	Q4_K_S	2.04GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q4_K.gguf	Q4_K	2.23GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q4_K_M.gguf	Q4_K_M	2.23GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q4_1.gguf	Q4_1	2.24GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q5_0.gguf	Q5_0	2.46GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q5_K_S.gguf	Q5_K_S	2.46GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q5_K.gguf	Q5_K	2.62GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q5_K_M.gguf	Q5_K_M	2.62GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q5_1.gguf	Q5_1	2.68GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q6_K.gguf	Q6_K	2.92GB
Phi-3.5-mini-instruct-Ecommerce-Text-Classification.Q8_0.gguf	Q8_0	3.78GB

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]]