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--- |
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license: apache-2.0 |
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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: mt5-base-finetuned-stocks-event-all |
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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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# mt5-base-finetuned-stocks-event-all |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6143 |
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- Rouge1: 0.4336 |
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- Rouge2: 0.3906 |
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- Rougel: 0.4328 |
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- Rougelsum: 0.4317 |
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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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| 6.4821 | 1.0 | 97 | 2.0502 | 0.1648 | 0.0891 | 0.1587 | 0.1588 | |
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| 2.3093 | 2.0 | 194 | 0.9278 | 0.3059 | 0.2526 | 0.3063 | 0.3048 | |
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| 1.5634 | 3.0 | 291 | 0.7646 | 0.3348 | 0.2917 | 0.3334 | 0.3334 | |
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| 1.2392 | 4.0 | 388 | 0.7398 | 0.3721 | 0.3193 | 0.3725 | 0.3721 | |
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| 1.1755 | 5.0 | 485 | 0.6872 | 0.3498 | 0.2990 | 0.3496 | 0.3507 | |
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| 1.0981 | 6.0 | 582 | 0.6685 | 0.3579 | 0.3256 | 0.3591 | 0.3566 | |
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| 1.0238 | 7.0 | 679 | 0.6379 | 0.4261 | 0.3882 | 0.4278 | 0.4253 | |
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| 1.0265 | 8.0 | 776 | 0.6143 | 0.4336 | 0.3906 | 0.4328 | 0.4317 | |
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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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