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multilingualbert-for-eqa-finetuned
primasr/multilingualbert-for-eqa-finetuned
multilingualbert-for-eqa-finetuned is a question answering model from primasr. Use it when the input is a question plus a passage. It is set up for transformers.
This model is an experiment I and my friend did as a researcher internship at the National University of Singapore (NUS). We finetuned the model to our datasets in Finance and Healthcare domain, in the Malay Language.
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At a glance
- Task
- Question Answering
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Feb 23, 2023
- Updated
- Feb 24, 2023
- SHA
- 83de0fc3
Datasets
Model card
From the Hugging Face model README
Overview
This model is an experiment I and my friend did as a researcher internship at the National University of Singapore (NUS). We finetuned the model to our datasets in Finance and Healthcare domain, in the Malay Language.
Details
- Finetuned from the base model by timpal0l
- The base datasets from SQuAD2.0
- Our datasets in Finance and Healthcare domain
Finetuned Detail
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir='test_trainer',
evaluation_strategy='epoch',
num_train_epochs=20,
optim='adamw_torch',
report_to='all',
logging_steps=1,
)
How to use the Model
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "primasr/multilingualbert-for-eqa-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
- Task
- Question Answering
- Library
- transformers
- Type
- deberta-v2
- Languages
- ms
- Created
- Feb 23, 2023
- Updated
- Feb 24, 2023