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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-large |
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model-index: |
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- name: flan-t5-large-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: 0.9495 |
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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-large-coref |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) 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: 0.2404 |
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- Rouge1: 0.9495 |
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- Rouge2: 0.9107 |
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- Rougel: 0.9494 |
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- Rougelsum: 0.9494 |
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- Gen Len: 23.4828 |
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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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| 1.0169 | 1.0 | 16 | 0.6742 | 0.7918 | 0.6875 | 0.7836 | 0.7847 | 18.2414 | |
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| 0.6275 | 2.0 | 32 | 0.5093 | 0.8776 | 0.7947 | 0.8734 | 0.8732 | 21.5517 | |
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| 0.596 | 3.0 | 48 | 0.4246 | 0.9104 | 0.8486 | 0.9085 | 0.9091 | 22.5172 | |
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| 0.743 | 4.0 | 64 | 0.3632 | 0.9247 | 0.8661 | 0.9235 | 0.9231 | 22.8621 | |
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| 0.5007 | 5.0 | 80 | 0.3301 | 0.9353 | 0.8845 | 0.9357 | 0.9353 | 22.8621 | |
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| 0.2567 | 6.0 | 96 | 0.3093 | 0.9388 | 0.8962 | 0.9392 | 0.9388 | 22.9655 | |
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| 0.4146 | 7.0 | 112 | 0.2978 | 0.9449 | 0.907 | 0.9455 | 0.9458 | 23.1034 | |
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| 0.1991 | 8.0 | 128 | 0.2853 | 0.9454 | 0.9064 | 0.946 | 0.9462 | 23.069 | |
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| 0.1786 | 9.0 | 144 | 0.2794 | 0.9475 | 0.9097 | 0.9475 | 0.9477 | 23.069 | |
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| 0.3559 | 10.0 | 160 | 0.2701 | 0.9424 | 0.9013 | 0.9428 | 0.9426 | 23.0345 | |
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| 0.2059 | 11.0 | 176 | 0.2636 | 0.9472 | 0.9069 | 0.9472 | 0.9472 | 23.0345 | |
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| 0.199 | 12.0 | 192 | 0.2592 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.1634 | 13.0 | 208 | 0.2553 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.2006 | 14.0 | 224 | 0.2518 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.1419 | 15.0 | 240 | 0.2487 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.2089 | 16.0 | 256 | 0.2456 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.1007 | 17.0 | 272 | 0.2431 | 0.9523 | 0.9141 | 0.9521 | 0.9524 | 23.4483 | |
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| 0.1598 | 18.0 | 288 | 0.2415 | 0.9495 | 0.9107 | 0.9494 | 0.9494 | 23.4828 | |
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| 0.3088 | 19.0 | 304 | 0.2407 | 0.9495 | 0.9107 | 0.9494 | 0.9494 | 23.4828 | |
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| 0.2003 | 20.0 | 320 | 0.2404 | 0.9495 | 0.9107 | 0.9494 | 0.9494 | 23.4828 | |
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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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