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--- |
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tags: |
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- generated_from_trainer |
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- summarization |
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- book summary |
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dataset: |
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- kmfoda/booksum |
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metrics: |
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- rouge |
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model-index: |
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- name: long-t5-tglobal-large-booksum-WIP |
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results: [] |
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--- |
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# tglobal-large-booksum-WIP |
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> this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference** |
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This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the `kmfoda/booksum` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.9519 |
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- Rouge1: 21.8058 |
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- Rouge2: 2.9343 |
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- Rougel: 10.3717 |
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- Rougelsum: 20.1537 |
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- Gen Len: 106.055 |
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## Model description |
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Testing fine-tuning only on booksum with 16384/1024 the whole time (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint) |
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## Intended uses & limitations |
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this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference** |
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## Training and evaluation data |
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This is **only** fine-tuned on booksum (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint) |
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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: 0.0004 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 31060 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- total_eval_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: cosine |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:| |
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| 5.0389 | 0.99 | 37 | 219.03 | 5.1884 | 29.995 | 4.4045 | 12.8837 | 27.557 | |
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| 4.8986 | 1.0 | 75 | 5.1286 | 26.921 | 3.7193 | 11.3605| 25.3492 | 276.005 | |
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| 4.5928 | 2.0 | 150 | 4.9900 | 26.6667 | 3.7342 | 11.8223| 24.7087 | 178.775 | |
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| 4.6159 | 3.0 | 225 | 4.9519 | 21.8058 | 2.9343 | 10.3717| 20.1537 | 106.055 | |
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#### eval in bf16 |
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``` |
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***** eval metrics ***** |
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epoch = 3.0 |
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eval_gen_len = 103.075 |
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eval_loss = 4.9501 |
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eval_rouge1 = 21.6345 |
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eval_rouge2 = 2.877 |
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eval_rougeL = 10.386 |
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eval_rougeLsum = 20.0148 |
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eval_runtime = 0:06:02.75 |
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eval_samples = 200 |
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eval_samples_per_second = 0.551 |
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eval_steps_per_second = 0.138 |
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[INFO|trainer.py:2724] 2022-11-27 01:00: |
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``` |
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### Framework versions |
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- Transformers 4.25.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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