metadata
license: apache-2.0
Base Model: google/flan-t5-large
A seq2seq event triggers tagger trained on the dataset: maven ere
Usage
Input:
triggers: I like this model and hate this sentence
Output:
like | hate
- Python
Using .generate()
from transformers import GenerationConfig, T5ForConditionalGeneration, T5Tokenizer
model_name = "ahmeshaf/maven_ere_trigger_seq2seq"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_config = GenerationConfig.from_pretrained(model_name)
tokenized_inputs = tokenizer(["I like this model and hate this sentence ."], return_tensors="pt")
outputs = model.generate(**tokenized_inputs, generation_config=generation_config)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['like | hate']
Using pipeline
from transformers import pipeline
srl = pipeline("ahmeshaf/maven_ere_trigger_seq2seq")
print(srl(["I like this model and hate this sentence ."]))
# [{'generated_text': 'like | hate'}]