Create README.md

README.md

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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
+
+![BERTft Badge](https://img.shields.io/badge/Model-BERT--ft--v2-blue)
+
+### 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).