added implementation for loss function #7
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Sharwin24
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Apr 22, 2024
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Sharwin24
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Sharwin24
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Just reading over, looks like the loss function depends on GPTDataGenerator so we should add some logging or checks to make sure that module has done whatever it needs to before running this function
train.py
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| super(CustomLossFunction, self).__init__() | ||
| # Initialize your loss components here, if any | ||
| self.margin = margin | ||
| self.model = SentenceTransformer('distilbert-base-uncased') |
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I don't know if this parameter is ever something else but this string should probably be passed into the init function as a parameter with a default value
train.py
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| # Initialize your loss components here, if any | ||
| self.margin = margin | ||
| self.model = SentenceTransformer('distilbert-base-uncased') | ||
| self.training_examples = gptdatagenerator.generate_qa() # Assuming gptdatagenerator generates input examples |
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We might want to create a function here or in gptdatagenerator to determine if it has generated examples or not, log appropriately, and then either continue or not if the obtained examples exist or not
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updated loss implementation -> this function relies on getting labels from the data generator