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zeze35h/
gemma3-4b-sentiment-lora-v1

Name: gemma3-4b-sentiment-lora-v1
Availability: InStock

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🧠 Gemma 3 – 4B Sentiment & Intent Classifier (LoRA Fine-Tuned)

Model type

Framework

PEFT

📋 Overview

This model is a LoRA-fine-tuned variant of Google’s Gemma-3-4B-IT language model.
It was trained to perform sentiment and intent classification on real-world customer-support conversations.

After fine-tuning, the LoRA adapter was merged into the base weights, producing a stand-alone model that can be used directly with transformers, vLLM, or DJL without loading external adapters.

💬 Capabilities

Task	Description	Example output
Sentiment Analysis	Classifies a message’s emotional tone	`very_negative`, `negative`, `neutral`, `positive`, `very_positive`
Custom Sentiments	Identifies customer's specific needs or gibberish	`request_human`, `drop_off`, `oos_nonsense`
Intent Recognition	Extracts the customer’s goal or topic	`"report damaged item"`, `"book flight"`, `"request supervisor"`, etc.
Conversation-aware	Uses recent chat context to interpret ambiguous user turns	e.g., disambiguates “That’s fine” as neutral vs. positive

🧩 Training setup

Component	Detail
Base model	`google/gemma-3-4b-it`
Fine-tuning method	LoRA (PEFT)
Frameworks	🤗 Transformers + PEFT + Accelerate
Dataset size	≈ 10 000 messages / ~1 000 conversations
Objective	Supervised fine-tuning on labeled sentiment + intent pairs

🧠 Intended usage

Use this model to classify customer-support chats into sentiment and intent labels, or to detect handoff/drop-off cases in virtual-assistant logs.

It’s optimized for:

Short to medium-length user messages
English (primary), with reasonable multilingual generalization (PT, EN, FR)
Domains such as telecom, retail, travel, and delivery services

🚀 Example usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "zeze35h/gemma3-4b-sentiment-lora-v1"

tok = AutoTokenizer.from_pretrained(model_id)
mdl = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

prompt = """<start_of_turn>user
Hi, I’ve been waiting for 40 minutes and my internet is still down.
<end_of_turn>
Classify the most recent user message:
Output JSON only with {"sentiment": "...", "intent": "..."}."""

out = mdl.generate(**tok(prompt, return_tensors="pt").to(mdl.device), max_new_tokens=80)
print(tok.decode(out[0], skip_special_tokens=True))