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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- t5-large
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- text2text-generation
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- conversational question rewriting
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datasets:
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- CANARD
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metrics:
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- BLEU
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model-index:
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- name: t5-large-coqr-canard
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results:
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- task:
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type: text2text-generation
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name: conversational question rewriting
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dataset:
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type: CANARD
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name: CANARD
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split: test
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metrics:
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- type: BLEU
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value: 77.8
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name: BLEU
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widget:
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- text: "Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.\n\nquestion: What else happened during 1977-1981 other than Superstar Billy Graham's return?\n\ncontext: Superstar Billy Graham\nReturn to WWWF (1977-1981)\nWhy did he return to the WWWF?\nan agreement with promoter Vincent J. McMahon (Senior\nWhat was his agreement with McMahon?\nI don't know.\nHow did people respond to his return?\nI don't know."
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- text: "Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.\n\nquestion: why did Billy Graham personally sued Zahorian and the WWF?\n\ncontext: Superstar Billy Graham\nDisputes with the McMahons\nwhat disputes did he have?\nGraham personally sued Zahorian and the WWF,"
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inference:
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parameters:
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max_length: 100
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---
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# t5-large-coqr-canard
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This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the [CANARD](https://sites.google.com/view/qanta/projects/canard) dataset.
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It achieves the following results on the test set:
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- Loss: 0.3064
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- Bleu: 77.1979
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- Generation Length: 9.576
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## Model description
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CANARD dataset rewrites the original questions in conversations to make them context-independent (understandable w/o context).
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On the contrary, this model is trained to rewrite context-independent questions to conversational questions, aiming to create fluent dialog with anaphora and ellipsis.
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Input:
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```
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Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.
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question: How did people respond to Superstar Billy Graham's return?
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context: Superstar Billy Graham
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Return to WWWF (1977-1981)
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Why did he return to the WWWF?
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an agreement with promoter Vincent J. McMahon (Senior
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What was his agreement with McMahon?
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I don't know.
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```
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Target:
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```
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How did people respond to his return?
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```
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 512
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- total_eval_batch_size: 512
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- optimizer: Adafactor
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- lr_scheduler_type: linear
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- num_epochs: 1.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
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| No log | 1.0 | 62 | 0.2987 | 77.2361 | 9.4534 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.11.0+cu113
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- Datasets 2.6.1
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- Tokenizers 0.12.1
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