T5-base data to text model specialized for Finance NLG
simple version
This model was trained on a limited number of indicators, values and dates
Usage (HuggingFace Transformers)
Call the model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yseop/FNP_T5_D2T_simple")
model = AutoModelForSeq2SeqLM.from_pretrained("yseop/FNP_T5_D2T_simple")
text = ["Group profit | valIs | $ 10 && € $10 | dTime | in 2019"]
Choose a generation method
input_ids = tokenizer.encode(": {}".format(text), return_tensors="pt")
p=0.72
k=40
outputs = model.generate(input_ids,
do_sample=True,
top_p=p,
top_k=k,
early_stopping=True)
print(tokenizer.decode(outputs[0]))
input_ids = tokenizer.encode(": {}".format(text), return_tensors="pt")
outputs = model.generate(input_ids,
max_length=200,
num_beams=2, repetition_penalty=2.5,
top_k=50, top_p=0.98,
length_penalty=1.0,
early_stopping=True)
print(tokenizer.decode(outputs[0]))
Created by: Yseop | Pioneer in Natural Language Generation (NLG) technology. Scaling human expertise through Natural Language Generation.