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# norbert2_sentiment_norec_8

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5046
- Start: ------------------------------------------------------------
- Accuracy: 0.8
- Balanced Accuracy: 0.5
- F1 Score: 0.8889
- Recall: 1.0
- Precision: 0.8
- End: ------------------------------------------------------------

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Start                                                        | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision | End                                                          |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------:|:--------:|:-----------------:|:--------:|:------:|:---------:|:------------------------------------------------------------:|
| 0.4534        | 1.0   | 5    | 0.6422          | ------------------------------------------------------------ | 0.8      | 0.5               | 0.8889   | 1.0    | 0.8       | ------------------------------------------------------------ |
| 0.5493        | 2.0   | 10   | 0.5159          | ------------------------------------------------------------ | 0.8      | 0.5               | 0.8889   | 1.0    | 0.8       | ------------------------------------------------------------ |
| 0.3452        | 3.0   | 15   | 0.5453          | ------------------------------------------------------------ | 0.8      | 0.5               | 0.8889   | 1.0    | 0.8       | ------------------------------------------------------------ |
| 0.2336        | 4.0   | 20   | 0.5046          | ------------------------------------------------------------ | 0.8      | 0.5               | 0.8889   | 1.0    | 0.8       | ------------------------------------------------------------ |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall", "precision"], "model-index": [{"name": "norbert2_sentiment_norec_8", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

norbert2\_sentiment\_norec\_8
=============================


This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5046
* Start: ------------------------------------------------------------
* Accuracy: 0.8
* Balanced Accuracy: 0.5
* F1 Score: 0.8889
* Recall: 1.0
* Precision: 0.8
* End: ------------------------------------------------------------


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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2