# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("yirenl2/plm_qa")
model = AutoModelForQuestionAnswering.from_pretrained("yirenl2/plm_qa")Quick Links
roberta-base for QA finetuned over community safety domain data
We fine-tuned the roBERTa-based model (https://huggingface.co/deepset/roberta-base-squad2) over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.
Using model in Transformers
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "yirenl2/plm_qa"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the location of the incident?',
'context': 'I was attacked by someone in front of the bus station.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
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Dataset used to train yirenl2/plm_qa
Evaluation results
- Exact Match on squad_v2validation set self-reported0.000
- F1 on squad_v2validation set self-reported0.000
- total on squad_v2validation set self-reported11869.000
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="yirenl2/plm_qa")