
# 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)
```
{"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "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": [{"type": "exact_match", "value": 0, "name": "Exact Match", "verified": false}, {"type": "f1", "value": 0, "name": "F1", "verified": false}, {"type": "total", "value": 11869, "name": "total", "verified": false}]}]}]}
transformers pytorch roberta question-answering en dataset:squad_v2 license:cc-by-4.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 


# roberta-base for QA finetuned over community safety domain data

We fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering. 


### Using model in Transformers

# roberta-base for QA finetuned over community safety domain data

We fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering. ### Using model in Transformers
TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
 # roberta-base for QA finetuned over community safety domain data

We fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering. ### Using model in Transformers
passage: TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
# roberta-base for QA finetuned over community safety domain data

We fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.### Using model in Transformers