Deploy named entity recogintion model to sagemaker grouping
Hi all,I’m currently trying to deploy one of the HuggingFace models into sagemaker, specificallyBlockquote “hub”: {“HF_MODEL_ID”:“Jean-Baptiste/roberta-large-ner-english”,“HF_TASK”:“token-classification”}with a docker image uriBlockquote“docker_image_uri”:“763104351884.dkr.ecr.eu-west-2.amazonaws.com/huggingface-pytorch-inference:1.9.0-transformers4.10.2-gpu-py38-cu111-ubuntu20.04”,The deployment works successfully. However, when I try to get some results, they don’t seem to properly group the results together.For example, when I useBlockquote nlp_jean_baptiste = pipeline(‘ner’, model=jean_model, tokenizer=jean_tokenizer, aggregation_strategy=“simple”)Blockquote nlp_jean_baptiste("Great food, great staff, great Italian atmosphere! Gimmi made amazing our dinner. Can’t wait to come back ")The above yieldsBlockquote {‘entity_group’: ‘PER’,‘score’: 0.998259,‘word’: ’ Gimmi’,‘start’: 51,‘end’: 56}]However, on AWS, the result is{‘entity’: ‘PER’,‘score’: 0.9982286691665649,‘index’: 11,‘word’: ‘ĠG’,‘start’: 51,‘end’: 52},{‘entity’: ‘PER’,‘score’: 0.9983111023902893,‘index’: 12,‘word’: ‘imm’,‘start’: 52,‘end’: 55},{‘entity’: ‘PER’,‘score’: 0.9982370734214783,‘index’: 13,‘word’: ‘i’,‘start’: 55,‘end’: 56}]Using the aggregation_strategy doesn’t yield any results when I use it as a parameter with the inputs. I’m currently wondering if there is a way to properly use the aggregation_strategy or another way to properly group the results or is there another strategy that I can use to properly group the results together.
2022-09-07T14:22:12Z
Nvm, I just had to update to a more recent version of transformers