I fine-tuned Qwen2.5-1.5B for multi-class CEFR English proficiency classification using QLoRA (4-bit NF4).

The goal was to classify English text into one of the 6 CEFR levels (A1 → C2), which can be useful for:

• adaptive language learning systems,
• placement testing,
• readability estimation,
• educational NLP applications.

Dataset

The dataset contains 1,785 English texts balanced across:

• 6 CEFR levels,
• 10 domains/topics.

The samples were synthetically generated using:

• Groq API
• Llama-3.3-70B

Generation constraints were designed to preserve:

• vocabulary complexity,
• grammatical progression,
• sentence structure variation,
• CEFR-specific linguistic patterns.

Training Setup

Base model:

• Qwen2.5-1.5B

Fine-tuning method:

• QLoRA
• 4-bit NF4 quantization
• LoRA adapters

Only ~0.28% of model parameters were trained.

Results

Held-out test set:

• 179 samples

Metrics:

• Accuracy: 84.9%
• Macro F1: 84.9%

Per-level recall:

Level	Recall
A1	96.6%
A2	90.0%
B1	90.0%
B2	86.7%
C1	86.7%
C2	60.0%

Most errors come from C1/C2 confusion, which is expected due to the subtle linguistic boundary between those levels.

Deployment

I also built:

• a FastAPI inference API,
• Docker deployment setup.

Example Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer import torch model = AutoModelForSequenceClassification.from_pretrained( "yanou16/cefr-english-classifier" ) tokenizer = AutoTokenizer.from_pretrained( "yanou16/cefr-english-classifier" ) text = "Artificial intelligence is transforming many industries." inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) pred = outputs.logits.argmax(dim=-1).item() print(pred) 

Feedback is welcome, especially regarding:

• evaluation methodology,
• synthetic data quality,
• improving C2 classification performance,
• better benchmarking approaches.