Instructions to use ahotrod/albert_xxlargev1_squad2_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahotrod/albert_xxlargev1_squad2_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ahotrod/albert_xxlargev1_squad2_512")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ahotrod/albert_xxlargev1_squad2_512") model = AutoModelForQuestionAnswering.from_pretrained("ahotrod/albert_xxlargev1_squad2_512") - Notebooks
- Google Colab
- Kaggle
Update albert_xxlargev1_sqd2_512.sh
Browse files- albert_xxlargev1_sqd2_512.sh +34 -0
albert_xxlargev1_sqd2_512.sh
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#!/bin/bash
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export OMP_NUM_THREADS=12
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EXAMPLES=/media/dn/work2/nlpu/transformers/examples
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SQUAD=${EXAMPLES}/scripts/squad2.0
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MODEL_PATH=${EXAMPLES}/scripts/runs/albert_xxlargev1_squad2_512_bs48_3ep_fp16
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python ${EXAMPLES}/question-answering/run_squad.py \
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--model_type albert \
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--model_name_or_path albert-xxlarge-v1 \
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--do_train \
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--do_eval \
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--train_file ${SQUAD}/train-v2.0.json \
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--predict_file ${SQUAD}/dev-v2.0.json \
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--version_2_with_negative \
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--do_lower_case \
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--num_train_epochs 3 \
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--max_steps 8144 \
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--warmup_steps 814 \
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--learning_rate 3e-5 \
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--max_seq_length 512 \
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--doc_stride 128 \
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--per_gpu_train_batch_size 6 \
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--gradient_accumulation_steps 8 \
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--per_gpu_eval_batch_size 48 \
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--fp16 \
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--fp16_opt_level O1 \
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--threads 12 \
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--logging_steps 50 \
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--save_steps 3000 \
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--overwrite_output_dir \
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--output_dir ${MODEL_PATH}
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$@
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