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--- |
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license: apache-2.0 |
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base_model: google/long-t5-tglobal-large |
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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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model-index: |
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- name: LongT5-Large-NSPCC |
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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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# LongT5-Large-NSPCC |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5481 |
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- Rouge1: 0.4597 |
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- Rouge2: 0.1665 |
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- Rougel: 0.2562 |
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- Rougelsum: 0.2557 |
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- Gen Len: 250.6383 |
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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: 0.0003 |
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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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- 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: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 6 |
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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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| 6.0521 | 1.0 | 188 | 2.8154 | 0.2268 | 0.0411 | 0.1627 | 0.1626 | 145.7447 | |
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| 2.5796 | 2.0 | 377 | 1.9961 | 0.3798 | 0.1115 | 0.2103 | 0.2101 | 220.234 | |
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| 2.0398 | 3.0 | 566 | 1.7703 | 0.4208 | 0.1319 | 0.2255 | 0.2258 | 299.6915 | |
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| 1.7329 | 4.0 | 755 | 1.5996 | 0.4427 | 0.1488 | 0.2423 | 0.2424 | 255.2553 | |
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| 1.5609 | 5.0 | 943 | 1.5510 | 0.4688 | 0.1726 | 0.2578 | 0.2576 | 289.2979 | |
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| 1.4733 | 5.98 | 1128 | 1.5481 | 0.4597 | 0.1665 | 0.2562 | 0.2557 | 250.6383 | |
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### Framework versions |
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- Transformers 4.39.2 |
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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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