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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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datasets: |
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- winograd_wsc |
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metrics: |
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- rouge |
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widget: |
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- text: Sam has a Parker pen. He loves writing with it. |
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example_title: Example 1 |
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- text: Coronavirus quickly spread worldwide in 2020. The virus mostly affects elderly |
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people. They can easily catch it. |
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example_title: Example 2 |
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- text: First, the manager evaluates the candidates. Afterwards, he notifies the candidates |
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regarding the evaluation. |
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example_title: Example 3 |
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base_model: google/flan-t5-small |
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model-index: |
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- name: flan-t5-small-coref |
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results: |
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- task: |
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type: text2text-generation |
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name: Sequence-to-sequence Language Modeling |
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dataset: |
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name: winograd_wsc |
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type: winograd_wsc |
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config: wsc285 |
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split: test |
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args: wsc285 |
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metrics: |
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- type: rouge |
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value: 0.906 |
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name: Rouge1 |
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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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# flan-t5-small-coref |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the winograd_wsc dataset. |
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The model was trained on the task of coreference resolution. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5656 |
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- Rouge1: 0.906 |
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- Rouge2: 0.8192 |
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- Rougel: 0.9016 |
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- Rougelsum: 0.9026 |
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- Gen Len: 23.1724 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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 | 16 | 1.0901 | 0.6849 | 0.561 | 0.6734 | 0.6746 | 18.4483 | |
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| No log | 2.0 | 32 | 0.9083 | 0.8512 | 0.7509 | 0.8438 | 0.8437 | 21.1379 | |
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| No log | 3.0 | 48 | 0.8132 | 0.8638 | 0.7728 | 0.8588 | 0.8595 | 21.8276 | |
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| No log | 4.0 | 64 | 0.7590 | 0.8786 | 0.7842 | 0.8744 | 0.876 | 22.2069 | |
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| No log | 5.0 | 80 | 0.7225 | 0.8846 | 0.7928 | 0.8805 | 0.8817 | 22.3793 | |
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| No log | 6.0 | 96 | 0.6920 | 0.886 | 0.7942 | 0.8821 | 0.8827 | 22.4483 | |
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| No log | 7.0 | 112 | 0.6660 | 0.8861 | 0.7922 | 0.8816 | 0.8827 | 22.5172 | |
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| No log | 8.0 | 128 | 0.6470 | 0.8879 | 0.7953 | 0.8836 | 0.8849 | 22.6897 | |
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| No log | 9.0 | 144 | 0.6318 | 0.8968 | 0.806 | 0.8923 | 0.8933 | 23.069 | |
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| No log | 10.0 | 160 | 0.6160 | 0.8968 | 0.806 | 0.8923 | 0.8933 | 23.069 | |
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| No log | 11.0 | 176 | 0.6055 | 0.9056 | 0.822 | 0.9014 | 0.9021 | 23.1724 | |
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| No log | 12.0 | 192 | 0.5962 | 0.9056 | 0.822 | 0.9014 | 0.9021 | 23.1724 | |
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| No log | 13.0 | 208 | 0.5884 | 0.9074 | 0.8246 | 0.9033 | 0.9042 | 23.2069 | |
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| No log | 14.0 | 224 | 0.5825 | 0.9049 | 0.8182 | 0.9005 | 0.9016 | 23.2414 | |
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| No log | 15.0 | 240 | 0.5769 | 0.9049 | 0.8182 | 0.9005 | 0.9016 | 23.2414 | |
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| No log | 16.0 | 256 | 0.5727 | 0.903 | 0.8132 | 0.8991 | 0.8997 | 23.1724 | |
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| No log | 17.0 | 272 | 0.5698 | 0.906 | 0.8192 | 0.9016 | 0.9026 | 23.1724 | |
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| No log | 18.0 | 288 | 0.5673 | 0.906 | 0.8192 | 0.9016 | 0.9026 | 23.1724 | |
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| No log | 19.0 | 304 | 0.5661 | 0.906 | 0.8192 | 0.9016 | 0.9026 | 23.1724 | |
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| No log | 20.0 | 320 | 0.5656 | 0.906 | 0.8192 | 0.9016 | 0.9026 | 23.1724 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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