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README.md

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+---
+language:
+  - he
+  - el
+license: mit
+tags:
+  - biblical-hebrew
+  - biblical-greek
+  - morphology
+  - parsing
+  - mt5
+  - seq2seq
+datasets:
+  - LoveJesus/biblical-tutor-dataset-chirho
+pipeline_tag: text2text-generation
+---
+
+# Biblical Morphological Parser (mT5-small)
+
+*For God so loved the world that he gave his only begotten Son, that whoever believes in him should not perish but have eternal life. - John 3:16*
+
+## What This Does
+
+This model parses biblical Hebrew and Greek words into their morphological components: part of speech, stem, lemma, tense, person, gender, number, and English gloss.
+
+## Usage
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("LoveJesus/biblical-parser-chirho")
+model = AutoModelForSeq2SeqLM.from_pretrained("LoveJesus/biblical-parser-chirho")
+
+# Parse a Hebrew word
+input_text = 'parse [hebrew]: בָּרָא [GEN 1:1] context: בְּרֵאשִׁית אֱלֹהִים'
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Expected: "class:verb | stem:qal | lemma:ברא | morph:... | person:3 | gender:m | number:s | gloss:he created"
+
+# Parse a Greek word
+input_text = 'parse [greek]: λόγος [JHN 1:1] context: ἐν ἀρχῇ ἦν'
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Input Format
+
+```
+parse [{language}]: {word} [{verse_ref}] context: {surrounding_words}
+```
+
+- `{language}`: `hebrew` or `greek`
+- `{word}`: The biblical word in original script
+- `{verse_ref}`: Book chapter:verse reference
+- `{surrounding_words}`: 2 words before and after for disambiguation
+
+## Output Format
+
+Pipe-separated morphological tags:
+```
+class:{pos} | stem:{stem} | lemma:{lemma} | morph:{code} | person:{p} | gender:{g} | number:{n} | gloss:{english}
+```
+
+## Training Data
+
+- **Macula Hebrew** (Clear-Bible): ~425K OT words with morphology and glosses
+- **Macula Greek SBLGNT** (Clear-Bible): ~138K NT words with morphology and glosses
+- Subsampled to ~200K words (100K per language), stratified by book
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base model | google/mt5-small (300M params) |
+| Architecture | Encoder-decoder (Seq2Seq) |
+| Languages | Biblical Hebrew, Koine Greek |
+| Training | 5 epochs, lr=3e-4, batch=32 |
+| Hardware | NVIDIA A100/H200 GPU |
+
+## Limitations
+
+- Trained on Macula morphological annotations — may not match all scholarly traditions
+- Handles individual words, not full syntactic analysis
+- Performance may vary on words not well-represented in training data
+
+---
+
+Built with love for Jesus. Published by [LoveJesus](https://huggingface.co/LoveJesus).
+Part of the [bible.systems](https://bible.systems) project.