README.md

---
language:
  - en
  - es
  - pt
  - fr
  - ar
  - zh
license: cc-by-nc-4.0
library_name: sentence-transformers
pipeline_tag: text-classification
tags:
  - central-bank-communication
  - multi-dimensional-classification
  - multi_task_gist
  - qwen3-embedding
  - training-artefact
model-index:
  - name: v29NA-openvino-int8-serving
    results:
      - task:
          type: text-classification
          name: topic classification
        dataset:
          name: CBC Held-out Eval
          type: private
        metrics:
          - type: f1_macro
            value: 0.8008
      - task:
          type: text-classification
          name: temporal_orientation classification
        dataset:
          name: CBC Held-out Eval
          type: private
        metrics:
          - type: f1_macro
            value: 0.8957
      - task:
          type: text-classification
          name: audience classification
        dataset:
          name: CBC Held-out Eval
          type: private
        metrics:
          - type: f1_macro
            value: 0.7590
      - task:
          type: text-classification
          name: sentiment classification
        dataset:
          name: CBC Held-out Eval
          type: private
        metrics:
          - type: f1_macro
            value: 0.7511
base_model: Qwen/Qwen3-Embedding-4B
---

# v29NA-openvino-int8-serving

Multi-dimensional classifier for central-bank communications produced by
the CBCommunication training pipeline (`multi_task_gist` rung).

## Provenance

| Field | Value |
|---|---|
| Trainer | `multi_task_gist` |
| Model kind | `multi_task` |
| Encoder body | `Qwen/Qwen3-Embedding-4B` |
| Loss | `cached_gist` |
| Taxonomy version | `2026-04-rev2` (sha256 `e7c237aac8db66ca`) |
| Training examples | 3584 |
| Validation examples | 1809 |
| Git commit | `9d90b862` (dirty) |
| Created | 2026-04-28T02:41:30.780109+00:00 |

## Dimensions and labels

### `topic` (21 classes)

- `Climate change`
- `Crisis management`
- `Currency circulation and management`
- `Financial inclusion`
- `Financial stability`
- `Fiscal policy`
- `Governance`
- `MP - balance sheet size and asset purchase programs`
- `MP - credit`
- `MP - economic activity`
- `MP - exchange rate`
- `MP - inflation`
- `MP - interest rate`
- `MP - labor market`
- `MP - open market operations`
- `MP - reserve requirements`
- `Metadata`
- `Payment system`
- `Structural economic reform`
- `Supervision and regulation`
- `Technological innovation and fintech`
### `temporal_orientation` (2 classes)

- `Backward-looking`
- `Forward-looking`
### `audience` (6 classes)

- `Business Sector`
- `Financial Sector`
- `General Public`
- `Government`
- `International Stakeholders`
- `Metadata`
### `sentiment` (6 classes)

- `Confidence-building`
- `Dovish`
- `Hawkish`
- `Neutral/Balanced`
- `Not applicable`
- `Risk-highlighting`

## Evaluation (held-out validation set)

| Dimension | Macro F1 |
| --- | --- |
| `topic` | 0.8008 |
| `temporal_orientation` | 0.8957 |
| `audience` | 0.7590 |
| `sentiment` | 0.7511 |

## Intended use

Classify sentences from central-bank speeches, press releases, and
financial-stability reports along the four CBC taxonomy dimensions
(topic, temporal orientation, audience, sentiment). Produced for
research and policy analysis at the IMF.

## Limitations

- Trained on a small labeled set; tail classes (low support) carry less
  reliable per-class metrics.
- Multilingual coverage depends on the encoder and labeled-set coverage;
  the current Qwen3-Embedding family is strong cross-lingually, but
  performance still varies on low-resource languages and OCR-heavy inputs.
- Sentiment / temporal labels reflect the taxonomy decision rules in
  the source workbook; downstream consumers should re-read those rules
  before interpreting per-class deltas.

## How to load

```python
# Recommended after registering this revision in config/classifiers.toml:
from cb_communication.processing.classification import load_named_classifier

# Auto-resolves the artefact via the registry's pinned hub_revision.
# Private collaborators authenticate with their own HF_TOKEN.
with load_named_classifier("v29NA-openvino-int8-serving") as clf:
    results = clf.classify_chunk([
        "Inflation expectations remain anchored at 2 percent.",
    ])
```

Alternative — explicit tag-pinned Hub load:

```python
from huggingface_hub import snapshot_download
from cb_communication.processing.classification import load_classifier

local = snapshot_download(repo_id="thiagochris/cbcc", revision="v29NA-openvino-int8-serving")
with load_classifier(local) as clf:
    ...
```

Both call paths satisfy the canonical ``MultiTaskClassifier`` Protocol
(see ``cb_communication/processing/classification/multi_task_classifier.py``)
— the runtime dispatches on ``model_kind`` from ``manifest.json``.