metadata
language:
- ht
license: mit
library_name: peft
tags:
- peft
- text2text-generation
- text-generation
base_model: google/mt5-large
PTHQL_language_Haitian_Creole
This is the Haitian Creole (hat_Latn) Phylogenetic Tree Hierarquical QLoRAs (PTHQL) adapter from Generating from AMRs into High and Low-Resource Languages using Phylogenetic Knowledge and Hierarchical QLoRA Training (HQL) used for AMR-to-Text generation.
Use
This model is the last of 4 hierarquical LoRAs. It is strongly adviseable to load all 4 LoRAs in order.
The following is minimal code to generate Haitian Creole text from an AMR graph:
from transformers import MT5ForConditionalGeneration, AutoTokenizer
from peft import PeftModel
model = MT5ForConditionalGeneration.from_pretrained('google/mt5-large')
tokennizer = AutoTokenizer.from_pretrained('google/mt5-large')
model = PeftModel.from_pretrained(model, 'WilliamSotoM/PTHQL_level0_Indo_European')
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, 'WilliamSotoM/PTHQL_level1_Romance')
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, 'WilliamSotoM/PTHQL_level2_Gallo_Romance')
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, 'WilliamSotoM/PTHQL_language_Haitian_Creole')
model = model.merge_and_unload()
graph = '''
(c / contrast-01
:ARG2 (t / thing
:quant (l2 / lot)
:ARG0-of (l / look-02
:ARG1 (d / dinosaur)
:mod (s / still))
:topic (b / bird)))
'''
tokenized_input = tokenizer(graph, return_tensors='pt')
with torch.inference_mode():
prediction = model.generate(**tokenized_input)
generated_text = tokenizer.batch_decode(prediction, skip_special_tokens=True)[0]
print(f'Generated text:', generated_text)
Expected outpu:
Men, en ce qui concerne les oiseaux, il y a beaucoup de coses qui toujou semblen desinòxes.