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# bert-base-cased-DreamBank

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2697
- F1: 0.8335
- Roc Auc: 0.8761
- Accuracy: 0.6703

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 185  | 0.5983          | 0.0330 | 0.5064  | 0.0162   |
| No log        | 2.0   | 370  | 0.3939          | 0.6104 | 0.7317  | 0.4649   |
| 0.4638        | 3.0   | 555  | 0.3227          | 0.7572 | 0.8154  | 0.5568   |
| 0.4638        | 4.0   | 740  | 0.2852          | 0.7902 | 0.8412  | 0.5784   |
| 0.4638        | 5.0   | 925  | 0.2720          | 0.7982 | 0.8382  | 0.6270   |
| 0.1877        | 6.0   | 1110 | 0.2795          | 0.8144 | 0.8619  | 0.6541   |
| 0.1877        | 7.0   | 1295 | 0.2575          | 0.8147 | 0.8568  | 0.6541   |
| 0.1877        | 8.0   | 1480 | 0.2556          | 0.8204 | 0.8630  | 0.6595   |
| 0.0952        | 9.0   | 1665 | 0.2668          | 0.8321 | 0.8764  | 0.6703   |
| 0.0952        | 10.0  | 1850 | 0.2697          | 0.8335 | 0.8761  | 0.6703   |


### 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}
}
```
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "widget": [{"text": "I dreamed that Hannah and Sue and I travelled back in time to meet her parents. Weird."}], "pipeline_tag": "text-classification", "model-index": [{"name": "bert-base-cased-DreamBank", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

bert-base-cased-DreamBank
=========================


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


* Loss: 0.2697
* F1: 0.8335
* Roc Auc: 0.8761
* Accuracy: 0.6703


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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### 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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### 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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### 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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### 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: