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# rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3902
- Accuracy: 0.8727
- F1: 0.8720
- Precision: 0.8718
- Recall: 0.8727

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3497        | 1.0   | 69   | 1.2944          | 0.5376   | 0.4665 | 0.6374    | 0.5376 |
| 1.2023        | 2.0   | 138  | 1.0370          | 0.7056   | 0.6745 | 0.7458    | 0.7056 |
| 0.9289        | 3.0   | 207  | 0.7437          | 0.8121   | 0.8082 | 0.8117    | 0.8121 |
| 0.6932        | 4.0   | 276  | 0.5717          | 0.8445   | 0.8428 | 0.8434    | 0.8445 |
| 0.5613        | 5.0   | 345  | 0.4888          | 0.8580   | 0.8572 | 0.8573    | 0.8580 |
| 0.469         | 6.0   | 414  | 0.4401          | 0.8633   | 0.8625 | 0.8623    | 0.8633 |
| 0.4176        | 7.0   | 483  | 0.4156          | 0.8653   | 0.8646 | 0.8644    | 0.8653 |
| 0.3724        | 8.0   | 552  | 0.4001          | 0.8706   | 0.8700 | 0.8699    | 0.8706 |
| 0.3427        | 9.0   | 621  | 0.3972          | 0.8706   | 0.8698 | 0.8701    | 0.8706 |
| 0.3243        | 10.0  | 690  | 0.3898          | 0.8737   | 0.8729 | 0.8736    | 0.8737 |
| 0.3039        | 11.0  | 759  | 0.3887          | 0.8716   | 0.8710 | 0.8717    | 0.8716 |
| 0.2803        | 12.0  | 828  | 0.3841          | 0.8716   | 0.8709 | 0.8709    | 0.8716 |
| 0.264         | 13.0  | 897  | 0.3872          | 0.8758   | 0.8753 | 0.8758    | 0.8758 |
| 0.2607        | 14.0  | 966  | 0.3837          | 0.8747   | 0.8743 | 0.8741    | 0.8747 |
| 0.2437        | 15.0  | 1035 | 0.3893          | 0.8716   | 0.8710 | 0.8712    | 0.8716 |
| 0.2358        | 16.0  | 1104 | 0.3867          | 0.8695   | 0.8691 | 0.8690    | 0.8695 |
| 0.2278        | 17.0  | 1173 | 0.3886          | 0.8737   | 0.8732 | 0.8732    | 0.8737 |
| 0.2143        | 18.0  | 1242 | 0.3902          | 0.8727   | 0.8720 | 0.8718    | 0.8727 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

rubert-tiny2\_best\_finetuned\_emotion\_experiment\_augmented\_anger\_fear
==========================================================================


This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3902
* Accuracy: 0.8727
* F1: 0.8720
* Precision: 0.8718
* Recall: 0.8727


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
* lr\_scheduler\_type: linear
* num\_epochs: 40


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
* lr\_scheduler\_type: linear
* num\_epochs: 40 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
* lr\_scheduler\_type: linear
* num\_epochs: 40 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
* lr\_scheduler\_type: linear
* num\_epochs: 40### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1