qwen3.5-2b-financial-sentiment

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: bearish
• 1: bullish
• 2: 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:

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.json
• local_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.