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--- |
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license: bigscience-bloom-rail-1.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bloom-560m-finetuned-cdn_law |
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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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# Canadian Appellate Judgement Model |
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This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on Canadian appellate decisions (Ontario Court of Appeal and the British Columbia Court of Appeal) found in the [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0135 |
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## Intended uses & limitations |
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This model is intended to facilitate research into large language models and legal reasoning. It is not intended for use in any legal domain or to support legal work . |
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## Training procedure |
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This model was trained using the methodology set out in this [notebook](https://huggingface.co/docs/transformers/training). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 2.1285 | 1.0 | 8298 | 2.0347 | |
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| 1.7999 | 2.0 | 16596 | 1.9876 | |
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| 1.6069 | 3.0 | 24894 | 2.0135 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.5.2 |
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- Tokenizers 0.13.1 |
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