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Update README.md

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  ---
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  datasets:
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  - hotpot_qa
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- - canard
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  ---
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  # Model Card for T5-LM-Large_Canard-HotpotQA-rephrase
@@ -15,8 +15,8 @@ The best checkpoint was picked by a minimal loss on all (3) training objectives.
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  ```python
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  import datasets
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- canard_train_augm = datasets.load_from_disk("canard_train_augm_full.hf") # constructed in notebook: 2.1_construct_qa_dataset.ipynb
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- canard_test_augm = datasets.load_from_disk("canard_test_augm_full.hf")
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  canard_df = canard_train_augm.to_pandas()
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  canard_test_df = canard_train_augm.to_pandas()
@@ -105,6 +105,9 @@ rephrasing_val_labels = canard_test_df.Rewrite.values
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  print(rephrasing_labels[0])
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  # Training
 
 
 
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  from adaptor.lang_module import LangModule
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  lang_module = LangModule("google/t5-large-lm-adapt")
@@ -191,4 +194,4 @@ See the prompting templates used in training to infer the optimal prompting form
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  #### Contact
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- Feel free to ask questions at stefanik{at} gaussalgo.com
 
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  ---
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  datasets:
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  - hotpot_qa
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+ - gaussalgo/Canard_Wiki-augmented
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  ---
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  # Model Card for T5-LM-Large_Canard-HotpotQA-rephrase
 
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  ```python
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  import datasets
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+ canard_train_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="train") # see the dataset card for details
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+ canard_test_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="test")
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  canard_df = canard_train_augm.to_pandas()
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  canard_test_df = canard_train_augm.to_pandas()
 
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  print(rephrasing_labels[0])
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  # Training
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+ # see Adaptor's homepage for details:
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+ # https://github.com/gaussalgo/adaptor
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+
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  from adaptor.lang_module import LangModule
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  lang_module = LangModule("google/t5-large-lm-adapt")
 
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  #### Contact
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+ Feel free to ask questions here, or at stefanik{at} gaussalgo.com