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# finetuned-endpoints_classif_test_2-4_14_1259

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: 0.1479
- F1: 0.9533
- Accuracy: 0.9545
- Precision: 0.9593
- Recall: 0.9545

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 1.8576        | 1.0   | 19   | 1.7069          | 0.2234 | 0.3864   | 0.1585    | 0.3864 |
| 1.4072        | 2.0   | 38   | 1.3519          | 0.4570 | 0.5227   | 0.4489    | 0.5227 |
| 1.0111        | 3.0   | 57   | 0.9178          | 0.7552 | 0.7955   | 0.7300    | 0.7955 |
| 0.6001        | 4.0   | 76   | 0.5478          | 0.7958 | 0.8409   | 0.7684    | 0.8409 |
| 0.3433        | 5.0   | 95   | 0.4193          | 0.8220 | 0.8409   | 0.8864    | 0.8409 |
| 0.1889        | 6.0   | 114  | 0.3115          | 0.8435 | 0.8636   | 0.9048    | 0.8636 |
| 0.1132        | 7.0   | 133  | 0.2479          | 0.9533 | 0.9545   | 0.9593    | 0.9545 |
| 0.071         | 8.0   | 152  | 0.1863          | 0.9531 | 0.9545   | 0.9601    | 0.9545 |
| 0.0437        | 9.0   | 171  | 0.1709          | 0.9533 | 0.9545   | 0.9593    | 0.9545 |
| 0.0324        | 10.0  | 190  | 0.1559          | 0.9533 | 0.9545   | 0.9593    | 0.9545 |
| 0.0233        | 11.0  | 209  | 0.1514          | 0.9533 | 0.9545   | 0.9593    | 0.9545 |
| 0.021         | 12.0  | 228  | 0.1479          | 0.9533 | 0.9545   | 0.9593    | 0.9545 |


### 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_2-4_14_1259", "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\_2-4\_14\_1259
=================================================


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


* Loss: 0.1479
* F1: 0.9533
* Accuracy: 0.9545
* Precision: 0.9593
* Recall: 0.9545


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