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---
license: mit
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---
# News Relevancy Classifiers
## bert-ft-v2
### Model Description
- **Purpose**: This model is trained for a specific task in research, it is not a commmercial product and should not be used in for-profit.
- **Architecture**: `bert-base-uncased`
- **Fine-tuning task**: Four-class English healthcare and AI news-headline relevancy classification
- **Dataset**: ~254 English headlines (2024–2025) manually labeled into:
- 0 — Not Relevant
- 1 — Least Relevant
- 2 — Highly Relevant
- 3 — Most Relevant
- **HF Repo**: [`cloud0day3/bert-ft-v2`](https://huggingface.co/cloud0day3/bert-ft-v2) (latest v3 checkpoint, 6 June 2025)
- **Date Trained**: 2025-06-06
#### Model Inputs
- A raw English headline (string), truncated/padded to 96 tokens.
- Tokenization handled by the bundled `vocab.txt` + `tokenizer_config.json` + `special_tokens_map.json`.
#### Model Outputs
- A single integer label (0–3). Mapped to human-readable categories:
```python
LABELS = {
0: "Not Relevant",
1: "Least Relevant",
2: "Highly Relevant",
3: "Most Relevant"
}
#### Intended Use
- **Primary**: Automatically assign a relevancy score to healthcare and AI English news headlines so that downstream pipelines (e.g., filtering, ranking) can operate without manual triage.
#### Examples of use:
- Pre-filtering a news aggregation feed to capture healthcare and AI news.
- Prioritizing headlines for editorial review.
- Input to summarization/retrieval pipelines.
#### Out-of-Scope Uses
- Any non-English text.
- Multi-sentence inputs or full articles (this model is tuned on single-sentence headlines).
- Tasks other than healthcare-tech relevancy (e.g., sentiment analysis, topic modeling).
- High-risk decision making without human oversight (e.g., emergency alerts). |