---
library_name: transformers
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- finance
- finbert
- roberta
- climate
- esg
- macroeconomics
- multilingual
datasets:
- nickmuchi/financial-classification
- zeroshot/twitter-financial-news-sentiment
- FinanceInc/auditor_sentiment
- pauri32/fiqa-2018
- climatebert/climate_sentiment
metrics:
- accuracy
- f1
language:
- en
- ar
- de
- es
- fr
- hi
- it
- pt
pipeline_tag: text-classification
model-index:
- name: macro-sentiment-finbert
  results:
  - task:
      type: text-classification
      name: Financial Sentiment Analysis
    metrics:
    - name: Accuracy (RoBERTa-Large)
      type: accuracy
      value: 0.913
    - name: F1 Macro (RoBERTa-Large)
      type: f1
      value: 0.902
    - name: Accuracy (FinBERT)
      type: accuracy
      value: 0.897
    - name: F1 Macro (FinBERT)
      type: f1
      value: 0.881
---

# 🏦 Macroeconomic Sentiment Analysis

**What it does:** Takes any financial or economic text and tells you whether the sentiment is positive, negative, or neutral — plus whether the tone is hawkish/dovish, whether it signals a crisis, and how much it relates to climate/ESG. Now supports **multilingual text** (v0.3.0).

**Who it's for:** Anyone analyzing news articles, central bank statements, earnings reports, financial tweets, or climate/ESG disclosures — in English or other languages.

---

## The Simple Way (3 lines of Python)

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="peyterho/finbert-macro-sentiment")

classifier("Tesla shares surged 15% after beating earnings expectations.")
# → [{'label': 'positive', 'score': 0.998}]

classifier("Markets crashed amid recession fears and massive layoffs.")
# → [{'label': 'negative', 'score': 0.997}]
```

### Which model should I pick?

| Model | Size | Speed | Accuracy | Best for |
|-------|------|-------|----------|----------|
| [`peyterho/finbert-macro-sentiment`](https://huggingface.co/peyterho/finbert-macro-sentiment) | 110M | Fast | 89.7% | General financial text — good default choice |
| [`peyterho/financial-roberta-large-macro-sentiment`](https://huggingface.co/peyterho/financial-roberta-large-macro-sentiment) | 355M | Slower | **91.3%** | When accuracy matters most |
| [`peyterho/climatebert-macro-sentiment`](https://huggingface.co/peyterho/climatebert-macro-sentiment) | 82M | Fastest | 88.9% | Climate/ESG text |

---

## Multilingual Support (v0.3.0)

The pipeline now auto-detects non-English text and routes it to an XLM-RoBERTa multilingual sentiment model.

**Supported languages:** English, Arabic, French, German, Hindi, Italian, Portuguese, Spanish — plus reasonable performance on many others.

```python
from macro_sentiment import TransformerEnsemble

ensemble = TransformerEnsemble(device="cpu")

# German — auto-routed to multilingual head
result = ensemble.score_routed("EZB signalisiert Geduld bei Zinssenkungen.")
print(result["head_used"])           # "multilingual"
print(result["detected_language"])   # "de"
print(result["sentiment_score"])     # [-1, +1]

# French
result = ensemble.score_routed("La BCE maintient ses taux directeurs inchangés.")
print(result["head_used"])           # "multilingual"

# English — still routed to domain-specific heads as before
result = ensemble.score_routed("Fed raised rates by 75bps.")
print(result["head_used"])           # "policy" (RoBERTa-Large)
```

For better language detection accuracy, install `langdetect`:
```bash
pip install langdetect
```

To disable multilingual routing (v0.2.0 behavior):
```python
ensemble = TransformerEnsemble(multilingual_model=None)
```

---

## Fine-Tune on Your Own Data

Got your own labelled financial text? Fine-tune any head in one command:

```bash
python -m macro_sentiment.finetune \
    --data my_labels.csv \
    --text-column headline \
    --label-column sentiment \
    --base-model peyterho/finbert-macro-sentiment \
    --output my-org/my-custom-model \
    --push-to-hub
```

**Your CSV/TSV/JSON/JSONL just needs two columns:**

| headline | sentiment |
|----------|-----------|
| Company profits soared to record highs | positive |
| Stock prices crashed amid panic selling | negative |
| Revenue remained flat quarter over quarter | neutral |

Labels accept: `positive`/`negative`/`neutral` (or `bullish`/`bearish`, `pos`/`neg`, `risk`/`opportunity`, or integers `0`/`1`/`2`).

**Options:**
```
--base-model   Which model to start from (default: peyterho/finbert-macro-sentiment)
--epochs       Training epochs (default: 4)
--lr           Learning rate (default: 2e-5)
--batch-size   Batch size (default: 32)
--max-length   Max token length (default: 128)
--push-to-hub  Push result to Hugging Face Hub
--output       Local dir or Hub model ID
```

The script auto-detects label remapping from the model's config, applies class weighting for imbalanced data, and selects the best checkpoint by macro F1.

---

## The Full Pipeline

For hawkish/dovish stance, crisis detection, climate exposure, and uncertainty scoring:

```bash
pip install transformers torch pysentiment2 scikit-learn numpy datasets huggingface_hub
python -c "from huggingface_hub import snapshot_download; snapshot_download('peyterho/macro-sentiment-finbert', local_dir='macro-sentiment-finbert')"
cd macro-sentiment-finbert
```

```python
from macro_sentiment import MacroSentimentPipeline

pipe = MacroSentimentPipeline(device="cpu")

result = pipe("The Federal Reserve raised rates by 75bps citing persistent inflation.")
print(result.summary())
# → Sentiment: ... | Policy: Very Hawkish (+0.999) | Crisis: Normal | Domain: policy

result.macro_sentiment   # -1.0 to +1.0
result.policy_stance     # -1.0 (dovish) to +1.0 (hawkish)
result.crisis_signal     # 0.0 (calm) to 1.0 (crisis)
result.uncertainty       # 0.0 (certain) to 1.0 (uncertain)
result.climate_exposure  # 0.0 to 1.0
```

---

## Accuracy

### In-domain (4,333 test samples from training datasets)

| Model | Accuracy | F1 Macro |
|-------|----------|----------|
| RoBERTa-Large (fine-tuned) | **91.3%** | **0.902** |
| FinBERT (fine-tuned) | 89.7% | 0.881 |
| ClimateBERT (fine-tuned) | 88.9% | 0.872 |

### Out-of-domain (datasets NOT in training)

| Model | Stock News (30K) | JB PhraseBank (785) |
|-------|------------------|---------------------|
| RoBERTa-Large | **72.1% acc / 0.727 F1** | **94.1% acc / 0.936 F1** |
| FinBERT | 67.8% / 0.677 | 92.4% / 0.913 |
| ClimateBERT | 64.7% / 0.644 | 92.5% / 0.921 |

The Stock News OOD dataset ([ic-fspml/stock_news_sentiment](https://huggingface.co/datasets/ic-fspml/stock_news_sentiment)) uses 5-class labels mapped to 3-class, with different annotation conventions — the 20pp accuracy drop is expected and honest. The JB PhraseBank OOD dataset ([Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75](https://huggingface.co/datasets/Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75)) is a similar domain and holds up well.

---

## All Output Fields

| Field | Range | What it means |
|-------|-------|---------------|
| `macro_sentiment` | -1 to +1 | Overall sentiment |
| `policy_stance` | -1 to +1 | Dovish ← 0 → Hawkish |
| `financial_sentiment` | -1 to +1 | Raw sentiment from active head |
| `climate_sentiment` | -1 to +1 | Climate risk ← 0 → opportunity |
| `crisis_signal` | 0 to 1 | Crisis intensity |
| `uncertainty` | 0 to 1 | Uncertainty level |
| `confidence` | 0 to 1 | Model confidence |
| `climate_exposure` | 0 to 1 | Climate topic density |
| `detected_domain` | text | policy / climate / financial_news / social |
| `head_used` | text | Which model was activated |

## File Structure

```
macro_sentiment/
├── __init__.py                 # Package entry point (v0.3.0)
├── dictionaries.py             # Four financial dictionaries
├── transformers_ensemble.py    # Four AI heads + language-aware router
├── pipeline.py                 # Full pipeline combining everything
├── finetune.py                 # Custom fine-tuning CLI
├── data_prep.py                # Dataset loading
└── train_meta.py               # Meta-classifier training
eval_results.json               # In-domain metrics
eval_ood_results.json           # Out-of-domain metrics
```

## References

- Araci (2019). [FinBERT](https://arxiv.org/abs/1908.10063)
- Loughran & McDonald (2011). *Journal of Finance*
- Henry (2008). *Journal of Business Communication*
- Sautner et al. (2023). *Review of Financial Studies*
- Barbieri et al. (2022). [XLM-T: Multilingual Language Models for Twitter](https://arxiv.org/abs/2104.12250)

## License
Apache 2.0