README.md

metadata

title: Financial Sentiment API
emoji: 📈
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.0.0
python_version: '3.10'
app_file: app.py
pinned: false

Financial Sentiment Analysis API

An API and web interface for analyzing financial text sentiment using FinBERT.

Evaluation

Our model is continuously evaluated to ensure high accuracy in identifying financial sentiments, particularly on ambiguous language and financial jargon.

• Test Suite: 30 comprehensive diverse test cases (pytest)
• Pipeline: Automated evaluation via GitHub Actions
• Full Report: See docs/eval_report.md for detailed metrics including Precision, Recall, and F1 per class.

ATS Use Case Prototype

The /ats/score endpoint evaluates candidate text for Applicant Tracking Systems based on financial and analytical keywords.

Example 1

Input: (Clear Positive)

{"text": "Directed a cross-functional team to reduce operational costs by 15%."}

Output:

{
  "score": 85,
  "match_level": "high",
  "keywords_found": ["reduce", "costs"]
}

Example 2

Input: (Neutral)

{"text": "Assisted with weekly reporting and internal audits."}

Output:

{
  "score": 45,
  "match_level": "medium",
  "keywords_found": ["reporting", "audits"]
}

Example 3

Input: (Financial Jargon)

{"text": "Managed a $5M portfolio and optimized the EBITDA margins through aggressive restructuring."}

Output:

{
  "score": 92,
  "match_level": "high",
  "keywords_found": ["portfolio", "EBITDA", "margins", "restructuring"]
}

Known Limitations

• Model may occasionally misclassify highly ambiguous or sarcasm-heavy sentences.
• Performance on mixed-language (e.g. Turkish + English) heavily depends on the translation layer.
• Some niche financial jargon might not be fully captured if it was not present in the FinBERT pre-training corpus.