Rud/bigbird_qlora_bfloat16_multi_lexsum
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README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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base_model: google/bigbird-pegasus-large-bigpatent
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model-index:
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- name: bigbird_lora_multi_lexsum
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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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# bigbird_lora_multi_lexsum
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This model is a fine-tuned version of [google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 9.1007
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- Rouge1: 0.197
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- Rouge2: 0.0165
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- Rougel: 0.1446
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- Rougelsum: 0.1445
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- Gen Len: 235.208
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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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More information needed
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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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- learning_rate: 2e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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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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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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| 9.2003 | 1.0 | 850 | 9.1012 | 0.1982 | 0.0162 | 0.1439 | 0.1441 | 234.016 |
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| 9.151 | 2.0 | 1700 | 9.1007 | 0.197 | 0.0165 | 0.1446 | 0.1445 | 235.208 |
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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adapter_model.safetensors
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runs/Apr10_08-28-58_79d0368ef05d/events.out.tfevents.1712737740.79d0368ef05d.4494.0
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