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# minirbt-h256-fin-finetuned

This model is a fine-tuned version of [hfl/minirbt-h256](https://huggingface.co/hfl/minirbt-h256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4252
- Accuracy: 0.8808
- F1: 0.8813

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4226        | 1.0   | 1096 | 0.4544          | 0.8569   | 0.8576 |
| 0.3963        | 2.0   | 2192 | 0.3878          | 0.8800   | 0.8806 |
| 0.324         | 3.0   | 3288 | 0.4218          | 0.8779   | 0.8784 |
| 0.3088        | 4.0   | 4384 | 0.4252          | 0.8808   | 0.8813 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0.dev20230212
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "minirbt-h256-fin-finetuned", "results": []}]}
transformers pytorch onnx bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

minirbt-h256-fin-finetuned
==========================


This model is a fine-tuned version of hfl/minirbt-h256 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4252
* Accuracy: 0.8808
* F1: 0.8813


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230212
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230212
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #onnx #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230212
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #onnx #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230212
* Datasets 2.9.0
* Tokenizers 0.13.2