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asfahanjaved126
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Model Details
Model Provider
asfahanjaved126
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on a held-out test set of 300 examples:
| Metric | Score |
|---|---|
| Accuracy | 97% |
| Weighted F1 | 0.97 |
| Positive precision / recall | 0.98 / 0.96 |
| Negative precision / recall | 0.97 / 0.98 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-3B-Instruct", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "asfahanjaved126/sentiment-classifier-v1")tokenizer = AutoTokenizer.from_pretrained("asfahanjaved126/sentiment-classifier-v1") messages = [ {"role": "system", "content": "Classify as positive or negative. One word only."}, {"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=5, 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 classifying customer reviews, feedback, and similar short-form text into positive/negative sentiment categories. Suitable for e-commerce review analysis, customer feedback triage, and similar use cases.
Fine-tuned using QLoRA (4-bit quantization) on a single T4 GPU via Google Colab. Training used the TRL SFTTrainer with completion-only loss masking, cosine learning rate schedule, and 3 epochs over the training set.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-3B-Instruct", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "asfahanjaved126/sentiment-classifier-v1")tokenizer = AutoTokenizer.from_pretrained("asfahanjaved126/sentiment-classifier-v1") messages = [ {"role": "system", "content": "Classify as positive or negative. One word only."}, {"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=5, temperature=0.0, do_sample=False)result = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(result)