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cloud0day3/bert-ft-v2

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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` (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).