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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: pszemraj/tFINE-base-300m |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: tFINE-base-300m-samsum |
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results: |
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- task: |
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name: Summarization |
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type: summarization |
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dataset: |
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name: samsum |
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type: samsum |
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config: samsum |
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split: None |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 42.3629 |
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library_name: transformers |
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pipeline_tag: summarization |
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--- |
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# tFINE-base-300m-samsum |
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An example fine-tune of [pszemraj/tFINE-base-300m](https://hf.co/pszemraj/tFINE-base-300m) for summarization using the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9820 |
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- Rouge1: 42.3629 |
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- Rouge2: 18.4285 |
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- Rougel: 34.6339 |
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- Rougelsum: 38.7792 |
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- Gen Len: 27.8033 |
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> [!NOTE] |
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> The base model was pre-trained with CTX 1024 and fine-tuned on samsum with 1024 CTX inputs. |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 17868 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 4.0 |
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### Training results |
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> keep epoch 3 checkpt as final |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.9528 | 0.9989 | 115 | 1.9189 | 40.093 | 18.2018 | 33.9749 | 36.9071 | 29.3333 | |
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| 1.5346 | 1.9978 | 230 | 1.8827 | 41.4676 | 18.3467 | 34.1909 | 38.2131 | 27.6633 | |
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| 1.1696 | 2.9967 | 345 | 1.9820 | 42.3629 | 18.4285 | 34.6339 | 38.7792 | 27.8033 | |
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| 0.9359 | 3.9957 | 460 | 2.1588 | 41.2237 | 17.8161 | 33.7101 | 37.9569 | 30.18 | |
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