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asfahanjaved126
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Model Details
Model Provider
asfahanjaved126
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Input: "This blender is absolutely terrible. It broke after one day."
Output:
Evaluated on a held-out test set (sample of 50 examples):
| Metric | Score |
|---|---|
| Sentiment classification accuracy | 94% |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torchbase_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct",torch_dtype=torch.float16,device_map={"": 0})model = PeftModel.from_pretrained(base_model, "asfahanjaved126/reasoning-sentiment-classifier-v1")tokenizer = AutoTokenizer.from_pretrained("asfahanjaved126/reasoning-sentiment-classifier-v1")system_prompt = "Classify the sentiment as positive or negative, and give one brief reason why. Format your answer as:\nSentiment: <label>\nReason: <reason>"messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": "This product completely changed how I work, love it!"}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)output = model.generate(**inputs, max_new_tokens=60, temperature=0.0, do_sample=False)result = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(result)
This model is designed for businesses that need not just sentiment classification but actionable insight into customer feedback — identifying specific recurring complaint themes or praise points across large volumes of reviews, without manually reading each one.
Suitable for:
Fine-tuned using QLoRA (4-bit quantization) on Kaggle's free T4 GPU. Training used the TRL SFTTrainer with completion-only loss masking, cosine learning rate schedule, over 3 epochs. This checkpoint reflects training through 425 of 450 total steps (94%) due to a session interruption near the end of the final epoch.