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question-answering mask_token: [MASK]
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							$
							curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
https://api-inference.huggingface.co/models/NeuML/bert-small-cord19-squad2
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NeuML/bert-small-cord19-squad2 NeuML/bert-small-cord19-squad2
59 downloads
last 30 days

pytorch

tf

Contributed by

NeuML company
1 team member 3 models

How to use this model directly from the 馃/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("NeuML/bert-small-cord19-squad2") model = AutoModelForQuestionAnswering.from_pretrained("NeuML/bert-small-cord19-squad2")

BERT-Small CORD-19 fine-tuned on SQuAD 2.0

bert-small-cord19 model fine-tuned on SQuAD 2.0

Building the model

python run_squad.py
    --model_type bert
    --model_name_or_path bert-small-cord19
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --train_file train-v2.0.json
    --predict_file dev-v2.0.json
    --per_gpu_train_batch_size 8
    --learning_rate 3e-5
    --num_train_epochs 3.0
    --max_seq_length 384
    --doc_stride 128
    --output_dir bert-small-cord19-squad2
    --save_steps 0
    --threads 8
    --overwrite_cache
    --overwrite_output_dir