# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("franklu/pubmed_bert_squadv2")
model = AutoModelForQuestionAnswering.from_pretrained("franklu/pubmed_bert_squadv2")Quick Links
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Check out the documentation for more information.
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext fine-tuned on SQuAD V2 using run_qa.py
Tunning script:
BASE_MODEL=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
OUTPUT_DIR=~/Documents/projects/tunned_models/ms_pubmed_bert_squadv2/
python run_qa.py \
--model_name_or_path $BASE_MODEL\
--dataset_name squad_v2 \
--do_train \
--do_eval \
--version_2_with_negative \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir $OUTPUT_DIR
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="franklu/pubmed_bert_squadv2")