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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-NER-Char |
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results: [] |
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language: |
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- en |
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widget: |
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- text: "I'm in an auditorium. Susie S is concerned at her part in this disability awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's represent us in a patronizing way. Why shouldn't we represent ourselves in a good, funny way?' I watch the video we all made. It is funny. I try to sit on a folding chair. Some guy in front talks to me. Merle is in the audience somewhere. [BL]" |
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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-NER-Char |
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the DremBan dataset to detect |
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which characters are present in a given report, following the [Hall & Van de Castle](https://dreams.ucsc.edu/Coding/) (HVDC) framework. Please note that, during training: |
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i) it was not specified to which features the characters were associated with; ii) in accordance with the HVDC system, the presence of the dreamer is not assessed. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4674 |
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- Rouge1: 0.7853 |
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- Rouge2: 0.6927 |
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- Rougel: 0.7564 |
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- Rougelsum: 0.7565 |
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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: 0.0002 |
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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 | 93 | 0.6486 | 0.5936 | 0.4495 | 0.5705 | 0.5701 | |
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| No log | 2.0 | 186 | 0.5363 | 0.7196 | 0.6020 | 0.6990 | 0.6983 | |
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| No log | 3.0 | 279 | 0.4391 | 0.7568 | 0.6459 | 0.7235 | 0.7244 | |
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| No log | 4.0 | 372 | 0.4223 | 0.7751 | 0.6748 | 0.7473 | 0.7477 | |
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| No log | 5.0 | 465 | 0.4266 | 0.7789 | 0.6746 | 0.7512 | 0.7522 | |
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| 0.6336 | 6.0 | 558 | 0.4296 | 0.7810 | 0.6790 | 0.7537 | 0.7539 | |
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| 0.6336 | 7.0 | 651 | 0.4400 | 0.7798 | 0.6808 | 0.7537 | 0.7543 | |
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| 0.6336 | 8.0 | 744 | 0.4497 | 0.7749 | 0.6821 | 0.7471 | 0.7481 | |
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| 0.6336 | 9.0 | 837 | 0.4661 | 0.7828 | 0.6910 | 0.7554 | 0.7563 | |
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| 0.6336 | 10.0 | 930 | 0.4674 | 0.7853 | 0.6927 | 0.7564 | 0.7565 | |
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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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If you use the model, please cite the pre-print. |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2302.14828, |
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doi = {10.48550/ARXIV.2302.14828}, |
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url = {https://arxiv.org/abs/2302.14828}, |
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author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
