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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: t5-base-DreamBank-Generation-Act-Char |
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results: [] |
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language: |
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- en |
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inference: |
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parameters: |
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max_length: 128 |
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widget: |
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- text: "I was skating on the outdoor ice pond that used to be across the street from my house. I was not alone, but I did not recognize any of the other people who were skating around. I went through my whole repertoire of jumps, spires, and steps-some of which I can do and some of which I'm not yet sure of. They were all executed flawlessly-some I repeated, some I did only once. I seemed to know that if I went into competition, I would be sure of coming in third because there were only three contestants. Up to that time I hadn't considered it because I hadn't thought I was good enough, but now since everything was going so well, I decided to enter." |
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example_title: "Dream" |
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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-base-DreamBank-Generation-Act-Char |
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This model is a fine-tuned version of [DReAMy-lib/t5-base-DreamBank-Generation-NER-Char](https://huggingface.co/DReAMy-lib/t5-base-DreamBank-Generation-NER-Char) on the DreamBank dataset. |
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The uploaded model contains the weights of the best-performing model (see table below), tune to annotate a given |
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dream report according to [Hall and Van de Castle the Activity feature](https://dreams.ucsc.edu/Coding/activities.html) |
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## Training procedure |
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The model is trained end-to-end using a text2text solution to annotate dream reports following the Activity feature |
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from the Hall and Van de Castle scoring framework. Given a report, the model generates texts of the form |
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`[(initialiser : activity type : receiver)]`. For those cases where `initialiser` and `receiver` are the same |
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entity, the output will follow the `[(initialiser : alone : activity type)]` setting. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| No log | 1.0 | 49 | 0.4061 | 0.3684 | 0.2537 | 0.3495 | 0.3484 | |
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| No log | 2.0 | 98 | 0.3563 | 0.4151 | 0.3185 | 0.4043 | 0.4030 | |
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| No log | 3.0 | 147 | 0.3005 | 0.4456 | 0.3588 | 0.4294 | 0.4281 | |
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| No log | 4.0 | 196 | 0.2693 | 0.4743 | 0.3903 | 0.4586 | 0.4574 | |
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| No log | 5.0 | 245 | 0.2627 | 0.4751 | 0.3939 | 0.4564 | 0.4549 | |
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| No log | 6.0 | 294 | 0.2739 | 0.4744 | 0.3920 | 0.4612 | 0.4596 | |
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| No log | 7.0 | 343 | 0.2733 | 0.4702 | 0.3940 | 0.4557 | 0.4549 | |
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| No log | 8.0 | 392 | 0.2861 | 0.4739 | 0.3950 | 0.4614 | 0.4608 | |
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| No log | 9.0 | 441 | 0.3115 | 0.4645 | 0.3868 | 0.4524 | 0.4517 | |
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| No log | 10.0 | 490 | 0.3212 | 0.4655 | 0.3886 | 0.4524 | 0.4518 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
