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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+base_model:
+- google/flan-t5-base
+pipeline_tag: feature-extraction
+library_name: transformers
+tags:
+- biology
+language:
+- en
+---
+Model Card: Core Schema Parsing LLM (Microbiology)
+Model Overview
+
+This model is a domain-adapted sequence-to-sequence language model designed to parse free-text microbiology phenotype descriptions into a structured core schema of laboratory test results and traits.
+
+The model is intended to augment deterministic rule-based and extended parsers by recovering fields that may be missed due to complex phrasing, implicit descriptions, or uncommon linguistic constructions. It is not designed to operate as a standalone classifier or diagnostic system.
+
+Base Model
+
+Base architecture: google/flan-t5-base
+
+Model type: Encoder–decoder (Seq2Seq), instruction-tuned
+
+The FLAN-T5 base model was selected due to its strong instruction-following behaviour, stability during fine-tuning, and suitability for structured text generation tasks on limited hardware.
+
+Training Data
+
+The model was fine-tuned on 8,700 curated microbiology phenotype examples, each consisting of:
+
+A free-text phenotype description
+
+A deterministic target serialization of core schema fields and values
+
+Data preprocessing:
+
+The name field and all non-core schema fields were explicitly removed to prevent label leakage.
+
+Target outputs were serialized deterministically using sorted schema keys (Field: Value format).
+
+Inputs and targets were constrained to schema-relevant content only.
+
+The dataset was split 80/20 into training and validation subsets.
+
+Training Procedure
+
+Epochs: 3
+
+Optimizer: AdamW (default Hugging Face Trainer)
+
+Learning rate: 1e-5
+
+Batching:
+
+Per-device batch size: 1
+
+Gradient accumulation: 8 (effective batch size = 8)
+
+Sequence lengths:
+
+Max input length: 2048 tokens
+
+Max output length: 2048 tokens
+
+Precision:
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+bf16 on supported hardware (A100), otherwise fp16
+
+Stability measures:
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+Gradient checkpointing enabled
+
+Gradient clipping (max_grad_norm = 1.0)
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+Warmup ratio of 0.03
+
+The model was trained using the Hugging Face Trainer API and saved after completion of all epochs.
+
+Intended Use
+
+This model is intended for:
+
+Structured parsing of microbiology phenotype text into predefined schema fields
+
+Use as a third-stage parser alongside rule-based and extended parsers
+
+Supporting downstream deterministic scoring, ranking, and retrieval systems
+
+Not intended for:
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+Standalone clinical diagnosis
+
+Autonomous decision-making
+
+Use without additional validation layers
+
+Integration Context
+
+In production, the model is used as a fallback and recovery mechanism within a hybrid parsing pipeline:
+
+Rule-based parser (high precision)
+
+Extended parser (schema-aware)
+
+LLM parser (coverage and robustness)
+
+Outputs are reconciled and validated downstream before being used for identification or explanation.
+
+Limitations
+
+Performance depends on coverage of the training schema and cannot generalize beyond it.
+
+The model may hallucinate field values if used outside its intended constrained pipeline.
+
+It is sensitive to extreme deviations in input style or unsupported terminology.
+
+Ethical and Safety Considerations
+
+The model does not provide medical advice or diagnoses.
+
+Outputs should always be reviewed in conjunction with deterministic logic and domain expertise.
+
+Training data was curated to minimize leakage and unintended inference.
+
+Author
+
+Developed and fine-tuned by Zain Asad as part of the BactAI-D project.