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Model: hienbm/gemma-2-9b-mtaste-16bit Source: Original Platform
2026-04-29 10:59:12 +08:00
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+## Usage
+
+```python
+!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']}")
+
+```