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---
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
```python
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)
``` |