kaejo98
/

bart-base_question_generation

@@ -7,18 +7,14 @@ model-index:
   results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-# bart-base_question_generation
-This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
-## Model description
-More information needed
-## Intended uses & limitations
 The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions:
 - <b> Casual-Generation </b>:  where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/>
@@ -33,6 +29,11 @@ Example: <br/>
 The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method.
 ## Training and evaluation data
 The dataset used to train the model comprises the training datasets from:
@@ -41,8 +42,10 @@ The dataset used to train the model comprises the training datasets from:
 - DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop
 - SciQ
 ## Training procedure
 ### Training hyperparameters
 The following hyperparameters were used during training:
@@ -55,6 +58,8 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.25
 - num_epochs: 5
 ### Framework versions
 - Transformers 4.23.1

   results: []
 ---
+# BART-base Question Generation
+This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>.
+## Model description
 The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions:
 - <b> Casual-Generation </b>:  where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/>
 The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method.
+## limitations
+The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data.
 ## Training and evaluation data
 The dataset used to train the model comprises the training datasets from:
 - DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop
 - SciQ
+After preprocessing the data from the above listed datasets, we had 408372 examples for training the model and 25k for development and 18k for testing.
 ## Training procedure
+The model is trained (finetuned) for 5 epochs with the hyperparameters listed below:
 ### Training hyperparameters
 The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.25
 - num_epochs: 5
+At the end of 5 epochs, the Evaluation loss was: 1.6493076086044312 and the training loss was: 0.9671.
 ### Framework versions
 - Transformers 4.23.1