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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-glue-mrpc-IT-baseline |
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results: [] |
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--- |
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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-glue-mrpc-IT-baseline |
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This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on an unknown 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 glue-mrpc task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/glue-mrpc |
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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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"Determine if the following sentences are equivalent: Sentence 1: {sentence1} Sentence 2: {sentence2}. Answer: ", |
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"Are these sentences saying the same thing? First: {sentence1} Second: {sentence2}. Response: ", |
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"Check sentence equivalence: \"{sentence1}\" versus \"{sentence2}\". Result: ", |
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] |
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``` |
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Concatenate the prompts, the two sentences and the label as so: |
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```python |
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input_text = prompt.format(sentence1=sentence1, sentence2=sentence2) |
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input_text += " " + responses[label] |
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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 results: {'loss': 0.0949, 'grad_norm': 5.0146379470825195, 'learning_rate': 6.000000000000001e-07, 'epoch': 10.0} |
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Average results: {'train_runtime': 363.2148, 'train_samples_per_second': 5.506, 'train_steps_per_second': 1.377, 'train_loss': 0.4939311617612839, '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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