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README.md
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---
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license: apache-2.0
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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: qa_bm25_small_sample2
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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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# qa_bm25_small_sample2
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.1438
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- Rouge1: 0.1048
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- Rouge2: 0.0088
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- Rougel: 0.0949
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- Rougelsum: 0.0949
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- Gen Len: 19.0
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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: 2e-05
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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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- 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: 10
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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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| No log | 1.0 | 108 | 12.1686 | 0.014 | 0.0 | 0.0134 | 0.0132 | 6.0357 |
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| No log | 2.0 | 216 | 10.8608 | 0.0122 | 0.0 | 0.0113 | 0.0113 | 6.0 |
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| No log | 3.0 | 324 | 8.0196 | 0.044 | 0.008 | 0.041 | 0.0407 | 9.1429 |
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| No log | 4.0 | 432 | 5.8188 | 0.1099 | 0.0119 | 0.0988 | 0.0983 | 19.0 |
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| 13.9065 | 5.0 | 540 | 4.6813 | 0.1254 | 0.0138 | 0.1104 | 0.1103 | 19.0 |
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| 13.9065 | 6.0 | 648 | 4.3367 | 0.1229 | 0.0158 | 0.1094 | 0.1097 | 19.0 |
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| 13.9065 | 7.0 | 756 | 4.2485 | 0.1172 | 0.0145 | 0.1033 | 0.1037 | 19.0 |
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| 13.9065 | 8.0 | 864 | 4.1780 | 0.1048 | 0.0088 | 0.0949 | 0.0949 | 19.0 |
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| 13.9065 | 9.0 | 972 | 4.1546 | 0.1048 | 0.0088 | 0.0949 | 0.0949 | 19.0 |
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| 6.0382 | 10.0 | 1080 | 4.1438 | 0.1048 | 0.0088 | 0.0949 | 0.0949 | 19.0 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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