README.md

metadata

language: en
datasets:
  - squad_v2
license: cc-by-4.0
model-index:
  - name: plm_qa
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad_v2
          type: squad_v2
          config: squad_v2
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 0
            verified: false
          - name: F1
            type: f1
            value: 0
            verified: false
          - name: total
            type: total
            value: 11869
            verified: false

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)