tooshlegacy/distilbert-financial-tweet-sentiment

DistilBERT Financial-Tweet Sentiment

Fine-tuned distilbert-base-uncased that classifies a financial tweet as Bearish, Bullish, or Neutral. Trained entirely on a laptop CPU as the sentiment factor for a combined sentiment + price stock-movement model (GitHub repo).

Labels

id	label
0	Bearish
1	Bullish
2	Neutral

Training data

zeroshot/twitter-financial-news-sentiment — 11,932 finance-related tweets, pre-labeled, MIT licensed. Used with its own splits: ~9,543 train / ~2,388 validation. Tweets are short, informal, and cashtag-heavy ($AAPL), with URLs/mentions replaced by placeholder tokens.

Training procedure

• Base model: distilbert-base-uncased (66M params)
• Max sequence length: 128 tokens
• Batch size: 16, learning rate: 2e-5, epochs: 3
• Optimizer/loss: HuggingFace Trainer defaults (AdamW, cross-entropy)
• Hardware: CPU only (no GPU)
• Seed: 42

Evaluation

On the dataset's own validation split:

metric	value
accuracy	0.8807
macro-F1	0.8464

(From sentiment_metrics.txt, 3 epochs on CPU.)

Intended use & limitations

• Intended: research/education — scoring short financial-tweet text for coarse bull/bear/neutral tone, e.g. as one factor in a movement-prediction model.
• Not intended: trading decisions, non-English text, long-form news articles, or any high-stakes use. Sentiment ≠ price direction; on the downstream StockNet task, adding this factor moves accuracy only modestly above chance (see the repo's comparison table), which is consistent with the published literature.
• Biases: reflects the training tweets' era (financial Twitter, ~2020s labeling) and domain; cashtag/ticker conventions matter. May misread sarcasm or mixed-signal posts.

How to use

from transformers import pipeline
clf = pipeline("text-classification", model="<your-hf-username>/distilbert-financial-tweet-sentiment")
clf("$AAPL breaking out to new highs, strong buy")
# -> [{'label': 'Bullish', 'score': ...}]

Citation

Downstream task and dataset:

Yumo Xu and Shay B. Cohen. 2018. Stock Movement Prediction from Tweets and Historical Prices. ACL 2018.