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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Bloom-1b7-dialogsum-IT
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This model is a
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## Model description
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## Training and evaluation data
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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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### Training results
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### Framework versions
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results: []
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# Bloom-1b7-dialogsum-IT
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This model is a instruction-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on a dialog summation dataset.
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## Model description
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## Training and evaluation data
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Instruction Tuned on the dialog summation task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/dialogsum/train
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## Training procedure
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Given a set of prompts:
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``` python
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prompts = [
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"Provide a concise summary for the following dialogue:",
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"Summarize this conversation in a few sentences:",
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"Here is a dialogue. Can you summarize it briefly?",
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"Read the following dialogue and write a short summary:",
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"Condense the essence of this conversation into a summary:"
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]
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```
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Each example is concatenated with the prompt, the dialogue, and the summary as so:
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``` python
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concatenated_texts = [
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random.choice(prompts) + " " + dialogue + "<\s>" + " Summary:" + summary
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for dialogue, summary in zip(examples['dialogue'], examples['summary'])
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]
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```
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Training results
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Final epoch results: {'loss': 0.0137, 'grad_norm': 0.6599154472351074, 'learning_rate': 7.000000000000001e-07, 'epoch': 10.0}
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Average results: {'train_runtime': 1142.1524, 'train_samples_per_second': 1.751, 'train_steps_per_second': 0.438, 'train_loss': 0.37129621666669843, 'epoch': 10.0}
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### Framework versions
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