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# xlm-roberta-large-DreamBank

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
Best result (loaded model)
- F1: 0.8621

## 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  | 0.5949          | 0.0    | 0.5     | 0.0      |
| No log        | 2.0   | 370  | 0.3825          | 0.6052 | 0.7481  | 0.4595   |
| 0.476         | 3.0   | 555  | 0.2891          | 0.7403 | 0.8010  | 0.5730   |
| 0.476         | 4.0   | 740  | 0.2604          | 0.8425 | 0.8852  | 0.7081   |
| 0.476         | 5.0   | 925  | 0.2484          | 0.8504 | 0.8932  | 0.6649   |
| 0.1457        | 6.0   | 1110 | 0.3092          | 0.8352 | 0.8909  | 0.6703   |
| 0.1457        | 7.0   | 1295 | 0.2882          | 0.8546 | 0.8950  | 0.6919   |
| 0.1457        | 8.0   | 1480 | 0.3099          | 0.8549 | 0.9014  | 0.6865   |
| 0.0691        | 9.0   | 1665 | 0.3080          | 0.8548 | 0.9019  | 0.6811   |
| 0.0691        | 10.0  | 1850 | 0.2942          | 0.8621 | 0.9069  | 0.6973   |


### Framework versions

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

### Cite 
Should 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": "mit", "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": "xlm-roberta-large-DreamBank", "results": []}]}
transformers pytorch safetensors xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

xlm-roberta-large-DreamBank
===========================


This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:
Best result (loaded model)


* F1: 0.8621


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 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 use our models in your work, please consider citing us as:
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #license-mit #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 use our models in your work, please consider citing us as:
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #license-mit #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 use our models in your work, please consider citing us as: