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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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- relation-extraction |
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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: >- |
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I was skating on the outdoor ice pond that used to be across the street from |
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my house. I was not alone, but I did not recognize any of the other people |
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who were skating around. I went through my whole repertoire of jumps, |
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spires, and steps-some of which I can do and some of which I'm not yet sure |
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of. They were all executed flawlessly-some I repeated, some I did only once. |
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I seemed to know that if I went into competition, I would be sure of coming |
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in third because there were only three contestants. Up to that time I hadn't |
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considered it because I hadn't thought I was good enough, but now since |
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everything was going so well, I decided to enter. |
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example_title: Dream 1 |
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- text: >- |
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I was talking on the telephone to the father of an old friend of mine (boy, |
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21 years old). We were discussing the party the Saturday night before to |
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which I had invited his son as a guest. I asked him if his son had a good |
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time at the party. He told me not to tell his son that he had told me, but |
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that he had had a good time, except he was a little surprised that I had |
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acted the way I did. |
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example_title: Dream 2 |
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- text: I was walking alone with my dog in a forest. |
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example_title: Dream 3 |
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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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## Model description |
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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 : none)` setting. |
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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: 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 | 0.3674 | 0.4008 | 0.3122 | 0.3821 | 0.3812 | |
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| No log | 2.0 | 98 | 0.3200 | 0.4240 | 0.3433 | 0.4130 | 0.4121 | |
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| No log | 3.0 | 147 | 0.2845 | 0.4591 | 0.3883 | 0.4459 | 0.4455 | |
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| No log | 4.0 | 196 | 0.2508 | 0.4614 | 0.3930 | 0.4504 | 0.4497 | |
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| No log | 5.0 | 245 | 0.2632 | 0.4614 | 0.3929 | 0.4467 | 0.4459 | |
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| No log | 6.0 | 294 | 0.2688 | 0.4706 | 0.4036 | 0.4537 | 0.4534 | |
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| No log | 7.0 | 343 | 0.2790 | 0.4682 | 0.4043 | 0.4559 | 0.4556 | |
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| No log | 8.0 | 392 | 0.2895 | 0.4670 | 0.3972 | 0.4529 | 0.4534 | |
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| No log | 9.0 | 441 | 0.3058 | 0.4708 | 0.4040 | 0.4576 | 0.4572 | |
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| No log | 10.0 | 490 | 0.3169 | 0.4690 | 0.4001 | 0.4547 | 0.4544 | |
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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 |
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# Cite |
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Should use our models in your work, please consider citing us as: |
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```bibtex |
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@article{BERTOLINI2024406, |
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title = {DReAMy: a library for the automatic analysis and annotation of dream reports with multilingual large language models}, |
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journal = {Sleep Medicine}, |
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volume = {115}, |
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pages = {406-407}, |
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year = {2024}, |
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note = {Abstracts from the 17th World Sleep Congress}, |
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issn = {1389-9457}, |
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doi = {https://doi.org/10.1016/j.sleep.2023.11.1092}, |
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url = {https://www.sciencedirect.com/science/article/pii/S1389945723015186}, |
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author = {L. Bertolini and A. Michalak and J. Weeds} |
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} |
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``` |
