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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/ktrapeznikov/albert-xlarge-v2-squad-v2
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ktrapeznikov/albert-xlarge-v2-squad-v2 ktrapeznikov/albert-xlarge-v2-squad-v2
3,858 downloads
last 30 days

pytorch

tf

Contributed by

ktrapeznikov Kirill Trapeznikov
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("ktrapeznikov/albert-xlarge-v2-squad-v2") model = AutoModelForQuestionAnswering.from_pretrained("ktrapeznikov/albert-xlarge-v2-squad-v2")

Model

albert-xlarge-v2 fine-tuned on SQuAD V2 using run_squad.py

Training Parameters

Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb

BASE_MODEL=albert-xlarge-v2
python run_squad.py \
  --version_2_with_negative \
  --model_type albert \
  --model_name_or_path $BASE_MODEL \
  --output_dir $OUTPUT_MODEL \
  --do_eval \
  --do_lower_case \
  --train_file $SQUAD_DIR/train-v2.0.json \
  --predict_file $SQUAD_DIR/dev-v2.0.json \
  --per_gpu_train_batch_size 3 \
  --per_gpu_eval_batch_size 64 \
  --learning_rate 3e-5 \
  --num_train_epochs 3.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --save_steps 2000 \
  --threads 24 \
  --warmup_steps 814 \
  --gradient_accumulation_steps 4 \
  --fp16 \
  --do_train

Evaluation

Evaluation on the dev set. I did not sweep for best threshold.

val
exact 84.41842836688285
f1 87.4628460501696
total 11873.0
HasAns_exact 80.68488529014844
HasAns_f1 86.78245127423482
HasAns_total 5928.0
NoAns_exact 88.1412952060555
NoAns_f1 88.1412952060555
NoAns_total 5945.0
best_exact 84.41842836688285
best_exact_thresh 0.0
best_f1 87.46284605016956
best_f1_thresh 0.0

Usage

See huggingface documentation. Training on SQuAD V2 allows the model to score if a paragraph contains an answer:

start_scores, end_scores = model(input_ids) 
span_scores = start_scores.softmax(dim=1).log()[:,:,None] + end_scores.softmax(dim=1).log()[:,None,:]
ignore_score = span_scores[:,0,0] #no answer scores