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# t5-base-DreamBank-Generation-Emot-EmotNn

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3559
- Rouge1: 0.8119
- Rouge2: 0.2070
- Rougel: 0.8102
- Rougelsum: 0.8115

## 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: 32
- eval_batch_size: 32
- 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   | 24   | 0.5128          | 0.5154 | 0.0562 | 0.5072 | 0.5086    |
| No log        | 2.0   | 48   | 0.3782          | 0.7132 | 0.0145 | 0.7127 | 0.7159    |
| No log        | 3.0   | 72   | 0.3387          | 0.7872 | 0.1712 | 0.7745 | 0.7756    |
| No log        | 4.0   | 96   | 0.3221          | 0.7804 | 0.1598 | 0.7754 | 0.7777    |
| No log        | 5.0   | 120  | 0.3669          | 0.7453 | 0.1330 | 0.7403 | 0.7414    |
| No log        | 6.0   | 144  | 0.3559          | 0.8119 | 0.2070 | 0.8102 | 0.8115    |
| No log        | 7.0   | 168  | 0.3559          | 0.8047 | 0.1895 | 0.8036 | 0.8047    |
| No log        | 8.0   | 192  | 0.3808          | 0.7967 | 0.1925 | 0.7934 | 0.7949    |
| No log        | 9.0   | 216  | 0.3899          | 0.8047 | 0.2127 | 0.8030 | 0.8040    |
| No log        | 10.0  | 240  | 0.3991          | 0.8096 | 0.2247 | 0.8068 | 0.8074    |


### 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 had a dream that Ben was in Costa Rica. I was really nervous about something, and he tried to make me feel better. Then Delia was in the library and had turned all the lights off. I was scared to lose my books since I had a paper due the next day."}], "model-index": [{"name": "t5-base-DreamBank-Generation-Emot-EmotNn", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-base-DreamBank-Generation-Emot-EmotNn
========================================


This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3559
* Rouge1: 0.8119
* Rouge2: 0.2070
* Rougel: 0.8102
* Rougelsum: 0.8115


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: 32
* eval\_batch\_size: 32
* 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: 32
* eval\_batch\_size: 32
* 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 #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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: