metadata
language:
- en
license: apache-2.0
base_model:
- FacebookAI/roberta-base
pipeline_tag: token-classification
Usage
- Importing the libraries and loading the models and the pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model_id = "gauneg/roberta-base-absa-ate-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)
ate_sent_pipeline = pipeline(task='ner',
aggregation_strategy='simple',
tokenizer=tokenizer,
model=model)
- Using the pipeline object:
text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
ate_sent_pipeline(text_input)
- pipeline output:
[{'entity_group': 'pos',
'score': 0.8447307,
'word': ' food',
'start': 26,
'end': 30},
{'entity_group': 'neg',
'score': 0.81927896,
'word': ' service',
'start': 56,
'end': 63}]