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--- |
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license: mit |
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tags: |
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- summarization |
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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: mbart-large-50-finetuned-stocks-event-1 |
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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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# mbart-large-50-finetuned-stocks-event-1 |
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This model is a fine-tuned version of [jiaoqsh/mbart-large-50-finetuned-stock-dividend](https://huggingface.co/jiaoqsh/mbart-large-50-finetuned-stock-dividend) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1419 |
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- Rouge1: 0.9120 |
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- Rouge2: 0.8056 |
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- Rougel: 0.9120 |
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- Rougelsum: 0.9120 |
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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: 5.6e-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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 0.3179 | 1.0 | 20 | 0.1784 | 0.8727 | 0.7639 | 0.8704 | 0.8727 | |
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| 0.0569 | 2.0 | 40 | 0.0822 | 0.9167 | 0.8333 | 0.9167 | 0.9144 | |
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| 0.0284 | 3.0 | 60 | 0.1842 | 0.9120 | 0.8194 | 0.9144 | 0.9120 | |
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| 0.0153 | 4.0 | 80 | 0.1448 | 0.9236 | 0.8472 | 0.9213 | 0.9236 | |
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| 0.0066 | 5.0 | 100 | 0.1271 | 0.9444 | 0.875 | 0.9421 | 0.9444 | |
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| 0.0013 | 6.0 | 120 | 0.1381 | 0.9190 | 0.8194 | 0.9213 | 0.9213 | |
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| 0.0083 | 7.0 | 140 | 0.1414 | 0.9190 | 0.8194 | 0.9213 | 0.9213 | |
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| 0.0002 | 8.0 | 160 | 0.1419 | 0.9120 | 0.8056 | 0.9120 | 0.9120 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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