slvDev
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esp32-ai
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它展示了清晰可复现的流程，通过加权损失和宏 F1 处理类别不平衡，并使用诚实的基线验证了 Transformer 在少数类上的提升。
适合谁使用
- 对金融文本分类感兴趣的 NLP 从业者
- 构建交易信号情感分析的数据科学家
- 学习迁移学习和模型评估的学生
- 寻求自动化新闻情感工具的金融分析师
典型使用场景
- 将金融新闻标题或推文分类为看涨/看跌/中性
- 增强交易策略的实时情感信号
- 在不平衡数据集中对比 Transformer 模型与传统基线
- 在 Apple Silicon 上使用加权损失微调 DistilBERT 的教学示例
项目优势
- 清晰的问题定义和宏 F1 评估，有效处理类别不平衡
- 诚实的基线（TF-IDF 二元组+类别权重）确保 Transformer 改进是真实的
- 可复现的流程，包含文档步骤、代码结构和图表
- 在损失函数和评估指标两个层面处理不平衡
使用前须知
- 仅在约 1.2 万条推文的单一数据集上测试，对其他金融文本源的泛化性未验证
- 仅使用 DistilBERT，未与其他 Transformer 模型或更大架构比较
- 未提供部署或推理 API，需本地 Python 环境运行预测
README 快速开始
Financial Sentiment Analysis with DistilBERT
Fine-tuning a transformer to classify the sentiment of financial-news tweets as Bearish, Bullish, or Neutral, and benchmarking it against a classical TF-IDF + Logistic Regression baseline. Fine-tuning runs on Apple-Silicon GPU (MPS).
Problem
Given a short financial statement (e.g. "$AAPL smashes revenue estimates, guidance raised"), predict market sentiment. This is a 3-class, imbalanced text classification task (65% of tweets are Neutral), so the project reports macro-F1 alongside accuracy so the minority classes actually count.
Results (validation set, 2,388 tweets)
| Model | Accuracy | Macro-F1 |
|---|---|---|
| TF-IDF + Logistic Regression (baseline) | 0.812 | 0.755 |
| Fine-tuned DistilBERT | 0.851 | 0.818 |
Fine-tuning lifts macro-F1 by +6.3 points — the gain concentrates on the minority Bearish/Bullish classes, where the transformer's contextual understanding beats bag-of-words. Per-class DistilBERT F1: Bearish 0.76, Bullish 0.81, Neutral 0.89.
Live inference examples (src/predict.py):
"Tesla shares tumble after disappointing quarterly earnings" -> Bearish (98%)
"$AAPL smashes revenue estimates, guidance raised next quarter" -> Bullish (98%)
"The Federal Reserve will announce its rate decision Wednesday" -> Neutral (98%)
Approach
- Data + EDA (
src/data.py) — 9,543 train / 2,388 validation tweets; light cleaning that keeps$TICKERSand%since they carry sentiment signal. - Baseline (
src/baseline.py) — TF-IDF (1–2 grams) + Logistic Regression with balanced class weights. A strong baseline so the transformer has something real to beat. - Fine-tuning (
src/train.py) — DistilBERT with a class-weighted cross-entropy loss to handle imbalance; best checkpoint selected by macro-F1; 3 epochs, lr 2e-5, on MPS. - Evaluation (
src/evaluate.py) — classification report, confusion matrix, and a baseline-vs-transformer comparison chart. - Inference (
src/predict.py) — sentiment + class probabilities for any text.
Figures (reports/figures/)
01_class_distribution.png | class imbalance in the training set |
02_tweet_length.png | tweet-length distribution |
03_confusion_matrix.png | DistilBERT errors by class |
04_model_comparison.png | baseline vs. fine-tuned m |
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