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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- multi_news |
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model-index: |
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- name: t5-small_multinews_model |
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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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# t5-small_multinews_model |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6269 |
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- Rouge Rouge1: 0.1471 |
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- Rouge Rouge2: 0.0483 |
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- Rouge Rougel: 0.1131 |
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- Rouge Rougelsum: 0.1131 |
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- Bleu Bleu: 0.0003 |
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- Bleu Precisions: [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968] |
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- Bleu Brevity Penalty: 0.0022 |
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- Bleu Length Ratio: 0.1408 |
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- Bleu Translation Length: 191567 |
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- Bleu Reference Length: 1360656 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge Rouge1 | Rouge Rouge2 | Rouge Rougel | Rouge Rougelsum | Bleu Bleu | Bleu Precisions | Bleu Brevity Penalty | Bleu Length Ratio | Bleu Translation Length | Bleu Reference Length | |
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|:-------------:|:-----:|:-----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:---------:|:-----------------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------------:|:---------------------:| |
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| 2.9189 | 1.0 | 7870 | 2.6869 | 0.1448 | 0.0474 | 0.1117 | 0.1117 | 0.0003 | [0.5827522821123012, 0.1820493433028088, 0.08242051182628926, 0.04574874477953644] | 0.0023 | 0.1411 | 192037 | 1360656 | |
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| 2.8435 | 2.0 | 15740 | 2.6535 | 0.1460 | 0.0474 | 0.1122 | 0.1122 | 0.0003 | [0.5809636959568958, 0.18126278620071182, 0.08254004826406995, 0.04636911719064694] | 0.0023 | 0.1410 | 191907 | 1360656 | |
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| 2.7922 | 3.0 | 23610 | 2.6389 | 0.1461 | 0.0477 | 0.1124 | 0.1124 | 0.0003 | [0.581669805398619, 0.18257649098318213, 0.08343485040444401, 0.0471782007379682] | 0.0022 | 0.1405 | 191160 | 1360656 | |
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| 2.814 | 4.0 | 31480 | 2.6280 | 0.1468 | 0.0478 | 0.1129 | 0.1129 | 0.0003 | [0.5844809737428239, 0.18360803285143726, 0.08381524001996615, 0.04753093788548009] | 0.0022 | 0.1406 | 191262 | 1360656 | |
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| 2.7869 | 5.0 | 39350 | 2.6269 | 0.1471 | 0.0483 | 0.1131 | 0.1131 | 0.0003 | [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968] | 0.0022 | 0.1408 | 191567 | 1360656 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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