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# distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear

This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3760
- Accuracy: 0.8758
- F1: 0.8750
- Precision: 0.8753
- Recall: 0.8758

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2636        | 1.0   | 69   | 1.0914          | 0.6013   | 0.5599 | 0.5780    | 0.6013 |
| 1.029         | 2.0   | 138  | 0.9180          | 0.6514   | 0.6344 | 0.6356    | 0.6514 |
| 0.904         | 3.0   | 207  | 0.8235          | 0.6827   | 0.6588 | 0.6904    | 0.6827 |
| 0.8084        | 4.0   | 276  | 0.7272          | 0.7537   | 0.7477 | 0.7564    | 0.7537 |
| 0.7242        | 5.0   | 345  | 0.6435          | 0.7860   | 0.7841 | 0.7861    | 0.7860 |
| 0.6305        | 6.0   | 414  | 0.5543          | 0.8173   | 0.8156 | 0.8200    | 0.8173 |
| 0.562         | 7.0   | 483  | 0.4860          | 0.8392   | 0.8383 | 0.8411    | 0.8392 |
| 0.5042        | 8.0   | 552  | 0.4474          | 0.8528   | 0.8514 | 0.8546    | 0.8528 |
| 0.4535        | 9.0   | 621  | 0.4213          | 0.8580   | 0.8579 | 0.8590    | 0.8580 |
| 0.4338        | 10.0  | 690  | 0.4106          | 0.8591   | 0.8578 | 0.8605    | 0.8591 |
| 0.4026        | 11.0  | 759  | 0.4064          | 0.8622   | 0.8615 | 0.8632    | 0.8622 |
| 0.3861        | 12.0  | 828  | 0.3874          | 0.8737   | 0.8728 | 0.8733    | 0.8737 |
| 0.3709        | 13.0  | 897  | 0.3841          | 0.8706   | 0.8696 | 0.8701    | 0.8706 |
| 0.3592        | 14.0  | 966  | 0.3841          | 0.8716   | 0.8709 | 0.8714    | 0.8716 |
| 0.3475        | 15.0  | 1035 | 0.3834          | 0.8737   | 0.8728 | 0.8732    | 0.8737 |
| 0.3537        | 16.0  | 1104 | 0.3805          | 0.8727   | 0.8717 | 0.8722    | 0.8727 |
| 0.3317        | 17.0  | 1173 | 0.3775          | 0.8747   | 0.8739 | 0.8741    | 0.8747 |
| 0.323         | 18.0  | 1242 | 0.3759          | 0.8727   | 0.8718 | 0.8721    | 0.8727 |
| 0.3327        | 19.0  | 1311 | 0.3776          | 0.8758   | 0.8750 | 0.8756    | 0.8758 |
| 0.3339        | 20.0  | 1380 | 0.3760          | 0.8758   | 0.8750 | 0.8753    | 0.8758 |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

distilrubert-tiny-cased-conversational-v1\_finetuned\_emotion\_experiment\_augmented\_anger\_fear
=================================================================================================


This model is a fine-tuned version of DeepPavlov/distilrubert-tiny-cased-conversational-v1 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3760
* Accuracy: 0.8758
* F1: 0.8750
* Precision: 0.8753
* Recall: 0.8758


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: 20


### 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: 20 ### 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 #distilbert #text-classification #generated_from_trainer #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: 20 ### 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 #distilbert #text-classification #generated_from_trainer #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: 20### Training results### Framework versions


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