peyterho
/

macro-sentiment-finbert

@@ -1,252 +1,66 @@
 ---
 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

 ---
 library_name: transformers
+base_model: ProsusAI/finbert
 tags:
+- generated_from_trainer
 metrics:
 - accuracy
 model-index:
 - name: macro-sentiment-finbert
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# macro-sentiment-finbert
+This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3450
+- Accuracy: 0.8486
+- F1 Macro: 0.8325
+- F1 Weighted: 0.8515
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 31
+- num_epochs: 2
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
+| 1.6807        | 1.0   | 314  | 0.3761          | 0.8246   | 0.8058   | 0.8300      |
+| 1.1679        | 2.0   | 628  | 0.3450          | 0.8486   | 0.8325   | 0.8515      |
+### Framework versions
+- Transformers 5.6.2
+- Pytorch 2.11.0+cu130
+- Datasets 4.8.4
+- Tokenizers 0.22.2