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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-base |
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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: cnn_dailymail_350_t5-base |
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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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# cnn_dailymail_350_t5-base |
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This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8973 |
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- Rouge1: 0.2524 |
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- Rouge2: 0.1238 |
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- Rougel: 0.2084 |
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- Rougelsum: 0.2083 |
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- Gen Len: 18.9993 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_steps: 500 |
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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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| 0.8756 | 0.45 | 500 | 0.9285 | 0.2483 | 0.1206 | 0.2051 | 0.2051 | 18.9993 | |
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| 0.8719 | 0.89 | 1000 | 0.9147 | 0.2496 | 0.1221 | 0.2063 | 0.2062 | 18.9999 | |
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| 0.8407 | 1.34 | 1500 | 0.9101 | 0.2497 | 0.1217 | 0.2061 | 0.2061 | 18.9999 | |
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| 0.8433 | 1.78 | 2000 | 0.9054 | 0.2512 | 0.1225 | 0.2072 | 0.2072 | 18.9995 | |
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| 0.8346 | 2.23 | 2500 | 0.9048 | 0.2515 | 0.123 | 0.2074 | 0.2074 | 18.9998 | |
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| 0.8308 | 2.67 | 3000 | 0.9037 | 0.2504 | 0.1226 | 0.2073 | 0.2073 | 18.9996 | |
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| 0.8189 | 3.12 | 3500 | 0.9022 | 0.2517 | 0.1232 | 0.2082 | 0.2081 | 19.0 | |
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| 0.8275 | 3.57 | 4000 | 0.9011 | 0.2514 | 0.123 | 0.2076 | 0.2076 | 19.0 | |
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| 0.8272 | 4.01 | 4500 | 0.9010 | 0.2517 | 0.1236 | 0.2081 | 0.2081 | 18.9993 | |
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| 0.819 | 4.46 | 5000 | 0.8994 | 0.2517 | 0.1235 | 0.208 | 0.2079 | 18.999 | |
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| 0.8096 | 4.9 | 5500 | 0.9001 | 0.2518 | 0.1236 | 0.208 | 0.208 | 18.9992 | |
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| 0.823 | 5.35 | 6000 | 0.8976 | 0.2519 | 0.1232 | 0.208 | 0.208 | 18.9993 | |
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| 0.8205 | 5.8 | 6500 | 0.8979 | 0.2516 | 0.1234 | 0.2079 | 0.2079 | 18.9996 | |
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| 0.8136 | 6.24 | 7000 | 0.8981 | 0.2515 | 0.1232 | 0.2078 | 0.2078 | 18.9992 | |
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| 0.8117 | 6.69 | 7500 | 0.8984 | 0.2519 | 0.1236 | 0.2081 | 0.208 | 18.9996 | |
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| 0.8039 | 7.13 | 8000 | 0.8979 | 0.2524 | 0.1237 | 0.2083 | 0.2083 | 18.9993 | |
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| 0.7934 | 7.58 | 8500 | 0.8981 | 0.2517 | 0.1235 | 0.2078 | 0.2078 | 18.9992 | |
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| 0.7947 | 8.02 | 9000 | 0.8979 | 0.252 | 0.1237 | 0.2081 | 0.2081 | 18.9989 | |
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| 0.8189 | 8.47 | 9500 | 0.8974 | 0.2523 | 0.1237 | 0.2083 | 0.2083 | 18.999 | |
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| 0.8102 | 8.92 | 10000 | 0.8976 | 0.2523 | 0.1237 | 0.2084 | 0.2084 | 18.9991 | |
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| 0.8029 | 9.36 | 10500 | 0.8978 | 0.2523 | 0.1237 | 0.2083 | 0.2083 | 18.9992 | |
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| 0.8004 | 9.81 | 11000 | 0.8973 | 0.2524 | 0.1238 | 0.2084 | 0.2083 | 18.9993 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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