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# SloBertAA_Top5_WithOOC

This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5616
- Accuracy: 0.8946

## Other related models

Models fine-tuned on the RTV datasets:

| Base model          | Includes the OOC class? | 5 classes | 10 classes | 20 classes | 50 classes | 100 classes |
| ------------------- | ----------------------- |:---------:|:----------:|:----------:|:----------:|:-----------:|
| SloBERTa            | Yes                     | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top5_WithOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top10_WithOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top20_WithOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top50_WithOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top100_WithOOC) |
| SloBERTa            | No                      | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top5_WithoutOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top10_WithoutOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top20_WithoutOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top50_WithoutOOC) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top100_WithoutOOC) | 
| BERT Multilingual   | Yes                     | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top5_WithOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top10_WithOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top20_WithOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top50_WithOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top100_WithOOC_MultilingualBertBase) |
| BERT Multilingual   | No                      | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top5_WithoutOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top10_WithoutOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top20_WithoutOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top50_WithoutOOC_MultilingualBertBase) | [link](https://huggingface.co/gregorgabrovsek/SloBertAA_Top100_WithoutOOC_MultilingualBertBase) |

Models fine-tuned on the IMDb datasets:

| Base model          | Includes the OOC class? | 5 classes | 10 classes | 25 classes | 50 classes | 100 classes |
| ------------------- | ----------------------- |:---------:|:----------:|:----------:|:----------:|:-----------:|
| BERT Multilingual   | No                      | [link](https://huggingface.co/gregorgabrovsek/BERT_AA_IMDB_Top5_WithoutOOC_MultilingualBertBase) |[link](https://huggingface.co/gregorgabrovsek/BERT_AA_IMDB_Top10_WithoutOOC_MultilingualBertBase) |[link](https://huggingface.co/gregorgabrovsek/BERT_AA_IMDB_Top25_WithoutOOC_MultilingualBertBase) |[link](https://huggingface.co/gregorgabrovsek/BERT_AA_IMDB_Top50_WithoutOOC_MultilingualBertBase) |[link](https://huggingface.co/gregorgabrovsek/BERT_AA_IMDB_Top100_WithoutOOC_MultilingualBertBase) |


## 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4211        | 1.0   | 10508 | 0.3823          | 0.8700   |
| 0.3163        | 2.0   | 21016 | 0.3917          | 0.8772   |
| 0.257         | 3.0   | 31524 | 0.3771          | 0.8925   |
| 0.1874        | 4.0   | 42032 | 0.5059          | 0.8931   |
| 0.129         | 5.0   | 52540 | 0.5616          | 0.8946   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.8.0
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["sl"], "license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["gregorgabrovsek/RTVCommentsTop5UsersWithOOC"], "metrics": ["accuracy"], "model-index": [{"name": "SloBertAA_Top5_WithOOC", "results": []}]}
transformers pytorch tensorboard camembert text-classification generated_from_trainer sl dataset:gregorgabrovsek/RTVCommentsTop5UsersWithOOC license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #sl #dataset-gregorgabrovsek/RTVCommentsTop5UsersWithOOC #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

SloBertAA\_Top5\_WithOOC
========================


This model is a fine-tuned version of EMBEDDIA/sloberta on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5616
* Accuracy: 0.8946


Other related models
--------------------


Models fine-tuned on the RTV datasets:



Models fine-tuned on the IMDb datasets:



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: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.8.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.8.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #sl #dataset-gregorgabrovsek/RTVCommentsTop5UsersWithOOC #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.8.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #sl #dataset-gregorgabrovsek/RTVCommentsTop5UsersWithOOC #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.8.0
* Datasets 2.10.1
* Tokenizers 0.13.2