---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-NER-Char
  results: []
language:
- en
widget:
- 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]"

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# t5-base-DreamBank-Generation-NER-Char

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the DremBan dataset to detect
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:
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.

It achieves the following results on the evaluation set:
- Loss: 0.4674
- Rouge1: 0.7853
- Rouge2: 0.6927
- Rougel: 0.7564
- Rougelsum: 0.7565

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 1.0   | 93   | 0.6486          | 0.5936 | 0.4495 | 0.5705 | 0.5701    |
| No log        | 2.0   | 186  | 0.5363          | 0.7196 | 0.6020 | 0.6990 | 0.6983    |
| No log        | 3.0   | 279  | 0.4391          | 0.7568 | 0.6459 | 0.7235 | 0.7244    |
| No log        | 4.0   | 372  | 0.4223          | 0.7751 | 0.6748 | 0.7473 | 0.7477    |
| No log        | 5.0   | 465  | 0.4266          | 0.7789 | 0.6746 | 0.7512 | 0.7522    |
| 0.6336        | 6.0   | 558  | 0.4296          | 0.7810 | 0.6790 | 0.7537 | 0.7539    |
| 0.6336        | 7.0   | 651  | 0.4400          | 0.7798 | 0.6808 | 0.7537 | 0.7543    |
| 0.6336        | 8.0   | 744  | 0.4497          | 0.7749 | 0.6821 | 0.7471 | 0.7481    |
| 0.6336        | 9.0   | 837  | 0.4661          | 0.7828 | 0.6910 | 0.7554 | 0.7563    |
| 0.6336        | 10.0  | 930  | 0.4674          | 0.7853 | 0.6927 | 0.7564 | 0.7565    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1

### Cite 
If you use the model, please cite the pre-print.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.14828,
  doi = {10.48550/ARXIV.2302.14828},
  url = {https://arxiv.org/abs/2302.14828},
  author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}
```