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# finetuned-endpoints_classif_test-4_13_1246

This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4666
- F1: 0.8717
- Accuracy: 0.8667
- Precision: 0.9019
- Recall: 0.8667

## 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: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 2.0932        | 1.0   | 13   | 1.8419          | 0.3116 | 0.3667   | 0.3823    | 0.3667 |
| 1.683         | 2.0   | 26   | 1.5969          | 0.3338 | 0.4      | 0.4632    | 0.4    |
| 1.3516        | 3.0   | 39   | 1.3390          | 0.5505 | 0.5667   | 0.6117    | 0.5667 |
| 1.0476        | 4.0   | 52   | 1.0331          | 0.6773 | 0.7      | 0.7741    | 0.7    |
| 0.6697        | 5.0   | 65   | 0.8544          | 0.7635 | 0.7667   | 0.8483    | 0.7667 |
| 0.417         | 6.0   | 78   | 0.5855          | 0.8068 | 0.8      | 0.8722    | 0.8    |
| 0.2449        | 7.0   | 91   | 0.5300          | 0.8409 | 0.8333   | 0.89      | 0.8333 |
| 0.1387        | 8.0   | 104  | 0.5291          | 0.8717 | 0.8667   | 0.9019    | 0.8667 |
| 0.0898        | 9.0   | 117  | 0.4517          | 0.8717 | 0.8667   | 0.9019    | 0.8667 |
| 0.0605        | 10.0  | 130  | 0.4855          | 0.8717 | 0.8667   | 0.9019    | 0.8667 |
| 0.0474        | 11.0  | 143  | 0.4727          | 0.8717 | 0.8667   | 0.9019    | 0.8667 |
| 0.0436        | 12.0  | 156  | 0.4666          | 0.8717 | 0.8667   | 0.9019    | 0.8667 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "model-index": [{"name": "finetuned-endpoints_classif_test-4_13_1246", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

finetuned-endpoints\_classif\_test-4\_13\_1246
==============================================


This model is a fine-tuned version of ProsusAI/finbert on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4666
* F1: 0.8717
* Accuracy: 0.8667
* Precision: 0.9019
* Recall: 0.8667


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: 5e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3