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# 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
Should you use our models in your work, please consider citing us as:
```bibtex
@article{BERTOLINI2024406,
title = {DReAMy: a library for the automatic analysis and annotation of dream reports with multilingual large language models},
journal = {Sleep Medicine},
volume = {115},
pages = {406-407},
year = {2024},
note = {Abstracts from the 17th World Sleep Congress},
issn = {1389-9457},
doi = {https://doi.org/10.1016/j.sleep.2023.11.1092},
url = {https://www.sciencedirect.com/science/article/pii/S1389945723015186},
author = {L. Bertolini and A. Michalak and J. Weeds}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "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]"}], "model-index": [{"name": "t5-base-DreamBank-Generation-NER-Char", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

t5-base-DreamBank-Generation-NER-Char
=====================================


This model is a fine-tuned version of t5-base on the DremBan dataset to detect
which characters are present in a given report, following the Hall & Van de Castle (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



### Framework versions


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


Cite
====


Should you use our models in your work, please consider citing us as:



### 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 ### Framework versions


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


Cite
====


Should you use our models in your work, please consider citing us as:
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### 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 ### Framework versions


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


Cite
====


Should you use our models in your work, please consider citing us as:
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### 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### Framework versions


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


Cite
====


Should you use our models in your work, please consider citing us as: