hienbm/gemma-2-9b-mtaste-16bit

Usage

!pip install unsloth -q

from unsloth import FastModel
import torch, json, re

model, tokenizer = FastModel.from_pretrained(
    model_name     = "hienbm/gemma-2-9b-mtaste-16bit",
    max_seq_length = 2048,
    load_in_4bit   = True, 
)
FastModel.for_inference(model)

ASPECT_CATEGORIES = [
    "AMBIENCE#GENERAL", "DRINKS#PRICES", "DRINKS#QUALITY", "DRINKS#STYLE_OPTIONS",
    "FOOD#PRICES", "FOOD#QUALITY", "FOOD#STYLE_OPTIONS", "LOCATION#GENERAL",
    "RESTAURANT#GENERAL", "RESTAURANT#MISCELLANEOUS", "RESTAURANT#PRICES", "SERVICE#GENERAL",
]

INSTRUCTION = (
    "Given a restaurant review, extract all sentiment triplets.\n"
    "Read the ENTIRE review first to understand context, sarcasm, and irony.\n"
    "Then extract triplets SENTENCE BY SENTENCE in the ORDER they appear.\n\n"
    "Output a JSON array sorted by appearance order in the review:\n"
    '[{"target": <word/phrase or "NULL">, '
    '"aspect": <ASPECT#CATEGORY>, "polarity": <positive|negative|neutral>}]\n\n'
    "aspect must be one of: " + ", ".join(ASPECT_CATEGORIES) + "\n\n"
    "Rules:\n"
    "- Sentence order: extract from sentence 1 first, then sentence 2, etc.\n"
    "- Multiple triplets per sentence: one object per triplet, keep order\n"
    "- target: exact word/phrase from text, or NULL if implicit\n"
    "- Output ONLY the JSON array, no explanation\n\n"
    'Example:\nReview: "Food was great. Service was slow."\n'
    'Output: [{"target": "food", "aspect": "FOOD#QUALITY", "polarity": "positive"}, '
    '{"target": "NULL", "aspect": "SERVICE#GENERAL", "polarity": "negative"}]'
)

def build_prompt(text: str) -> str:
    return (
        "<start_of_turn>user\n"
        f"{INSTRUCTION}\n\nReview: {text}"
        "<end_of_turn>\n"
        "<start_of_turn>model\n"
    )

def parse_output(raw: str) -> list[dict]:
    match = re.search(r"\[.*?\]", raw, re.DOTALL)
    if match:
        try:
            return json.loads(match.group())
        except json.JSONDecodeError:
            pass
    try:
        return json.loads(raw)
    except json.JSONDecodeError:
        return []

review = "This place serves fast, it's been over 30 minutes and the dish still hasn't come out."

inputs = tokenizer(build_prompt(review), return_tensors="pt").to("cuda")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens = 1024,
        temperature    = 0.8,
        do_sample      = False,
        pad_token_id   = tokenizer.eos_token_id,
    )

raw = tokenizer.decode(
    output[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
).strip()

triplets = parse_output(raw)

print(f"Review : {review}\n")
print(f"Raw output : {raw}\n")
print("Extracted triplets:")
for i, t in enumerate(triplets, 1):
    print(f"  {i}. target={t['target']!r:20s}  aspect={t['aspect']:30s}  polarity={t['polarity']}")