117 lines
4.4 KiB
Markdown
117 lines
4.4 KiB
Markdown
---
|
|
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
|
|
tags:
|
|
- text-generation-inference
|
|
- transformers
|
|
- unsloth
|
|
- qwen3
|
|
license: apache-2.0
|
|
language:
|
|
- en
|
|
library_name: transformers
|
|
datasets:
|
|
- RAS1981/turn-detection-probability-balanced
|
|
---
|
|
|
|
# 🇷🇺 Qwen3-0.6B Turn Detection (Probability-Based)
|
|
|
|
This model is a **specialized conversational boundary detector** for Russian real-estate dialogues.
|
|
|
|
It predicts the **probability** that a user has finished their turn (`<|im_end|>`) versus continuing their sentence. It is fine-tuned using **Single-Token Loss Masking** on a balanced dataset of ~20k complete and incomplete conversational turns.
|
|
|
|
## 🚀 Key Features
|
|
|
|
- **Base Model:** `unsloth/Qwen3-0.6B` (fast, efficient, good Russian support).
|
|
- **Method:** Probability-based Turn Detection. Instead of a binary classifier head, it uses the model's intrinsic next-token prediction.
|
|
- **Performance:**
|
|
- **Complete Turns:** Predicts `<|im_end|>` with high confidence (>90%).
|
|
- **Incomplete Turns:** Predicts the *continuation word* (next token), assigning near-zero probability to `<|im_end|>`.
|
|
- **Latency:** Extremely fast inference on CPU/GPU due to 0.6B size.
|
|
|
|
## 📊 Training Data
|
|
|
|
Trained on **[RAS1981/turn-detection-probability-balanced](https://huggingface.co/datasets/RAS1981/turn-detection-probability-balanced)**.
|
|
- **Contrastive Pairs:** Each complete sentence has a corresponding incomplete version.
|
|
- **Balanced:** 50% complete turns, 50% incomplete turns.
|
|
- **Domain:** Russian real-estate inquiries (renting, buying, viewing).
|
|
|
|
## 🛠️ How to Use (Inference)
|
|
|
|
### 1. Load Model & Tokenizer
|
|
```python
|
|
from unsloth import FastLanguageModel
|
|
import torch
|
|
|
|
model_name = "RAS1981/qwen3-0.6b-turn-detection-probability-balanced"
|
|
|
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
|
model_name=model_name,
|
|
max_seq_length=2048,
|
|
dtype=None,
|
|
load_in_4bit=True,
|
|
)
|
|
EOS_ID = tokenizer.eos_token_id # 151645 for Qwen
|
|
```
|
|
|
|
### 2. Predict Turn Completion Probability
|
|
The core idea is to check the probability of the **End-of-Sequence (EOS)** token.
|
|
|
|
```python
|
|
@torch.no_grad()
|
|
def get_eos_prob(text):
|
|
# Prepare chat template
|
|
messages = [
|
|
{"role": "system", "content": "Ты определяешь конец реплики пользователя по смыслу."},
|
|
{"role": "user", "content": text}
|
|
]
|
|
|
|
# Format prompt WITHOUT generation prompt
|
|
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
|
|
|
|
# Tokenize and STRIP trailing EOS if present (critical step!)
|
|
prompt_ids = tokenizer(prompt, add_special_tokens=False).input_ids
|
|
|
|
# Qwen adds <|im_end|>\n automatically. Strip them to predict the boundary.
|
|
if len(prompt_ids) > 2 and prompt_ids[-1] == 198 and prompt_ids[-2] == 151645:
|
|
prompt_ids = prompt_ids[:-2]
|
|
elif len(prompt_ids) > 1 and prompt_ids[-1] == 151645:
|
|
prompt_ids = prompt_ids[:-1]
|
|
|
|
inputs = torch.tensor([prompt_ids]).to("cuda")
|
|
|
|
# Get logits for the LAST token position
|
|
logits = model(inputs).logits[:, -1, :]
|
|
|
|
# Calculate probability of EOS token
|
|
prob = torch.softmax(logits, dim=-1)[0, EOS_ID].item()
|
|
return prob
|
|
|
|
# Example Usage
|
|
print(get_eos_prob("До свидания.")) # High Prob (e.g., 0.96) -> Turn Complete
|
|
print(get_eos_prob("Я хотел бы узнать...")) # Low Prob (e.g., 0.00) -> Turn Incomplete
|
|
```
|
|
|
|
## 📈 Evaluation Results
|
|
|
|
| Phrase | Type | EOS Probability | Interpretation |
|
|
|---|---|---|---|
|
|
| `"До свидания."` | **Complete** | **0.9626** | **CONFIDENT END** |
|
|
| `"Алло, здравствуйте"` | Ambiguous | 0.2599 | WAIT (User likely continues) |
|
|
| `"Я хотел бы узнать про"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
|
|
| `"Нет, вы знаете, я наверное"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
|
|
|
|
### Threshold Recommendation
|
|
- **Turn Complete:** `prob > 0.5` (Safe default)
|
|
- **Turn Incomplete:** `prob <= 0.5`
|
|
|
|
## 🧠 Methodology: Single-Token Loss Masking
|
|
|
|
We trained the model to optimize the loss **only on the final token**.
|
|
- For **complete** examples, the target label is `<|im_end|>`.
|
|
- For **incomplete** examples, the target label is the *actual next word*.
|
|
- All previous tokens are masked with `-100` in the loss function.
|
|
|
|
This forces the model to focus purely on the boundary condition: *"Given this context, does the turn end here or continue?"*
|
|
|
|
## 📜 License
|
|
Apache 2.0 |