poetry-combined-best

KemiOm/poetry-combined-best

KemiOm/poetry-combined-best is a LoRA-adapted google/flan-t5-large model for English poetry generation with explicit structural constraints, especially meter and rhyme.
This checkpoint represents the best-performing combined run from a multi-round fine-tuning workflow.

Model Description

• Model type: Seq2seq language model flan-t5-large with LoRA adapters
• Primary task: Constraint-aware poetry generation
• Focus constraints: Stress/meter conformity and end-rhyme pattern adherence
• Domain: Predominantly eighteenth-century English poetic style plus preprocessing for modern compatibility where applicable

Intended Uses

• Creative writing assistance for metrically constrained verse
• Research experiments in form-constrained generation
• Educational demos on meter, stress, and rhyme control in language models
• Rapid prototyping of poetry prompts requiring line-level formal structure

Training Data

This model is fine-tuned on an English poetry corpus curated for line-level form analysis.

• Genre emphasis: Formal verse with strong metrical/rhyme signals
• Data processing goals:
  • normalize punctuation and line breaks
  • extract/encode stress and meter targets
  • mark rhyme targets from line-end tokens
• Split strategy: train/validation/test

Replace dataset references with exact public/private IDs and include any additional licensing notes for upstream poem sources.

Preprocessing

The preprocessing pipeline includes:

1. text normalization (quotes, whitespace, punctuation variants),
2. line segmentation and stanza preservation,
3. stress pattern estimation (dictionary + heuristic fallback),
4. meter label assignment (iambic pentameter target strings),
5. rhyme label extraction and scheme encoding (AABB/ABAB/etc.),
6. prompt-target formatting for text-to-text training.

Task Definition

KemiOm/poetry-combined-best performs combined line-level structure evaluation for poetry.

Given an input line, the model predicts a structured label string:

stress:<pattern>|meter_type:<meter>|rhyme:<phones>|end:<0_or_1>|caesura:<index_or_->

Where:

• stress: token-level stress sequence pattern
• meter_type: predicted metrical class (e.g., iambic pentameter)
• rhyme: phonetic rhyme representation of the line ending
• end: end-of-unit flag (1 if line ends a unit, else 0)
• caesura: caesura position or - if none

Training Procedure

• Base model: google/flan-t5-large
• Adaptation method: LoRA
• Framework: Hugging Face transformers + PEFT
• Objective: Conditional generation under form constraints
• Selection criterion: Best combined validation performance on meter + rhyme objectives

Best combined run parameters (from round2/combined/run_params.json):

• Base model: google/flan-t5-large
• Adaptation: LoRA
• LoRA rank (r): 32
• LoRA alpha: 64
• LoRA dropout: 0.05
• LoRA target modules: q,k,v,o
• Epochs: 5.0
• Learning rate: 5e-05
• Weight decay: 0.01
• Warmup ratio: 0.1
• Train batch size (per device): 8
• Gradient accumulation: 2
• Effective batch size: 16 per device group (multiply by number of GPUs if distributed)
• Eval batch size (per device): 4
• Max input length: 384
• Max target length: 128
• Eval/Save interval: every 300 steps
• Seed: 42
• Precision flags: no_fp16=true, bf16=false
• Max steps: -1 (epoch-based)
• Merge adapters after training: true

Example Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "KemiOm/poetry-combined-best"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
prompt = "Tired Nature's sweet restorer, balmy Sleep!"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=180,
    do_sample=True,
    temperature=0.8,
    top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))