Qwen3.5-2B Financial Sentiment Extractor
This model is a Tuned Tensor fine-tune of Qwen/Qwen3.5-2B for extracting a structured market sentiment signal from finance-related tweets and social posts.
Given one post, it returns compact JSON:
{"sentiment":"bearish","label":0,"rationale":"The post expresses a bearish market signal."}
Label mapping:
0: bearish1: bullish2: neutral
Training
- Base model:
Qwen/Qwen3.5-2B - Training dataset:
zeroshot/twitter-financial-news-sentiment - Dataset license: MIT
- Base model license: Apache-2.0
- Tuned Tensor run ID:
61d64e3e-b9a1-48e5-8803-c7c30a4df5a8 - Tuned Tensor model ID:
fd09ae23-1935-48b4-b838-9e563df59b49 - Training rows used by trainer:
4,080 - Precision:
bf16 - Epochs:
1 - Final reported training loss:
0.5898758276
The source dataset was converted into strict input / output supervised rows with balanced labels. The behavior spec used during training is included as tunedtensor.json.
Evaluation
Tuned Tensor LLM-judge evaluation, capped at 120 examples per split:
| Split | Base avg score | Tuned avg score | Delta | Base pass rate | Tuned pass rate | Delta |
|---|---|---|---|---|---|---|
| Validation | 0.819 | 0.903 | +0.084 | 79.2% | 86.7% | +7.5 pp |
| Test | 0.834 | 0.875 | +0.041 | 80.0% | 85.8% | +5.8 pp |
Output format diagnostics:
- Valid JSON: 100%
- Strict JSON: 100%
- Expected schema keys: 100%
- Non-JSON prefix: 0%
Local hand-curated smoke tests are included:
local_real_tests_fd09ae23.jsonlocal_real_tests_fd09ae23_batch2.json
Usage
The model was locally served and tested with Tuned Tensor's OpenAI-compatible serving runtime:
tt models serve fd09ae23-1935-48b4-b838-9e563df59b49 \
--spec tunedtensor.json \
--device auto \
--temperature 0 \
--max-tokens 96
Example prompt:
Extract the market sentiment signal from this finance-related social post. Return only strict JSON with exactly these keys: sentiment, label, rationale. sentiment must be one of bearish, bullish, neutral; label must be one of 0, 1, 2.
Post: $NVDA shares jump after analysts raise price targets on stronger AI chip demand.
Expected response shape:
{"sentiment":"bullish","label":1,"rationale":"The post expresses a bullish market signal."}
Limitations
This model classifies short market-social posts into coarse sentiment categories. It is not an investment advisor, trading system, or factual market-data source.
Known caveat from evaluation: some ambiguous mixed-signal posts may still be difficult, especially when a post contains both a clearly negative primary event and a secondary contrarian or factual framing.