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from transformers import FlaxAutoModelForSeq2SeqLM
from transformers import AutoTokenizer
MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)
prefix = "items: "
# generation_kwargs = {
# "max_length": 512,
# "min_length": 64,
# "no_repeat_ngram_size": 3,
# "early_stopping": True,
# "num_beams": 5,
# "length_penalty": 1.5,
# }
generation_kwargs = {
"max_length": 512,
"min_length": 64,
"no_repeat_ngram_size": 3,
"do_sample": True,
"top_k": 60,
"top_p": 0.95
}
special_tokens = tokenizer.all_special_tokens
tokens_map = {
"<sep>": "--",
"<section>": "\n"
}
def skip_special_tokens(text, special_tokens):
for token in special_tokens:
text = text.replace(token, "")
return text
def target_postprocessing(texts, special_tokens):
if not isinstance(texts, list):
texts = [texts]
new_texts = []
for text in texts:
text = skip_special_tokens(text, special_tokens)
for k, v in tokens_map.items():
text = text.replace(k, v)
new_texts.append(text)
return new_texts
def generation_function(texts):
_inputs = texts if isinstance(texts, list) else [texts]
inputs = [prefix + inp for inp in _inputs]
inputs = tokenizer(
inputs,
max_length=256,
padding="max_length",
truncation=True,
return_tensors="jax"
)
input_ids = inputs.input_ids
attention_mask = inputs.attention_mask
output_ids = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
**generation_kwargs
)
generated = output_ids.sequences
generated_recipe = target_postprocessing(
tokenizer.batch_decode(generated, skip_special_tokens=False),
special_tokens
)
return generated_recipe
items = [
"chicken", "salt", "cumin"
]
generated = generation_function(items)
for text in generated:
sections = text.split("\n")
for section in sections:
section = section.strip()
if section.startswith("title:"):
section = section.replace("title:", "")
headline = "TITLE"
elif section.startswith("ingredients:"):
section = section.replace("ingredients:", "")
headline = "INGREDIENTS"
elif section.startswith("directions:"):
section = section.replace("directions:", "")
headline = "DIRECTIONS"
if headline == "TITLE":
print(f"[{headline}]: {section.strip().capitalize()}")
else:
section_info = [f" - {i+1}: {info.strip().capitalize()}" for i, info in enumerate(section.split("--"))]
print(f"[{headline}]:")
print("\n".join(section_info))
print("-" * 130) |