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Language Models Fine-tuning on Question Generation: lmqg/bart-large-squad

This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg_squad (dataset_name: default).

Overview

Usage


from transformers import pipeline

model_path = 'lmqg/bart-large-squad'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
question = pipe(input_text)

Evaluation Metrics

Metrics

Dataset Type BLEU4 ROUGE-L METEOR BERTScore MoverScore Link
lmqg/qg_squad default 0.26168385362299557 0.5384959163821219 0.27073122286541956 0.9100413219045603 0.6499011626820898 link

Out-of-domain Metrics

Dataset Type BLEU4 ROUGE-L METEOR BERTScore MoverScore Link
lmqg/qg_squadshifts reddit 0.059525104157825456 0.22365090580055863 0.21499800504546457 0.9095144685254328 0.6059332247878408 link
lmqg/qg_squadshifts new_wiki 0.11118273173452982 0.2967546690273089 0.27315087810722966 0.9322739617807421 0.6623000084761579 link
lmqg/qg_subjqa tripadvisor 8.380171318718442e-07 0.1402922852924756 0.1372146070365174 0.8891002409937424 0.5604572211470809 link
lmqg/qg_squadshifts default 0.07839941048417529 0.25357667226247294 0.24046838149047955 0.9182198703598111 0.6274693859765924 link
lmqg/qg_squadshifts nyt 0.08117757543966063 0.25292097720734297 0.25254205113198686 0.9249009759439454 0.6406329128556304 link
lmqg/qg_subjqa restaurants 1.1301750984972448e-06 0.13083168975354642 0.12419733006916912 0.8797711839570719 0.5542757411268555 link
lmqg/qg_subjqa electronics 0.00866799444965211 0.1601628874804186 0.15348605312210778 0.8783386920680519 0.5634845371093992 link
lmqg/qg_subjqa books 0.006278914808207679 0.12368226019088967 0.11576293675813865 0.8807110440044503 0.5555905941686486 link
lmqg/qg_subjqa movies 1.0121579426501661e-06 0.12508697028506718 0.11862284941640638 0.8748829724726739 0.5528899173535703 link
lmqg/qg_subjqa grocery 0.00528043272450429 0.12343711316491492 0.15133496445452477 0.8778951253890991 0.5701949938103265 link
lmqg/qg_squadshifts amazon 0.06530369842068952 0.25030985091008146 0.2229994442645732 0.9092814804525936 0.6086538514008419 link
lmqg/qg_subjqa default 0.005121882223046874 0.1346485324169255 0.13733272662214893 0.8811488576438816 0.5614233235005509 link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: None
  • model: facebook/bart-large
  • max_length: 512
  • max_length_output: 32
  • epoch: 4
  • batch: 32
  • lr: 5e-05
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • label_smoothing: 0.15

The full configuration can be found at fine-tuning config file.

Citation

TBA

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Dataset used to train lmqg/bart-large-squad

Evaluation results