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--- |
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license: bigscience-bloom-rail-1.0 |
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base_model: bigscience/bloom-1b7 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Bloom-1b7-dialogsum-IT |
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results: [] |
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--- |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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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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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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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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- Transformers 4.38.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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