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# Kjøretid
{'train_runtime': 291.2967, 'train_samples_per_second': 51.494, 'train_steps_per_second': 0.189, 'train_loss': 0.6998663252050227, 'epoch': 4.94}

Time: 291.30

Samples/second: 51.49

GPU memory occupied: 6267 MB.

# norbert2_sentiment_norec_en_gpu_500_rader_9

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: 0.6255
- Compute Metrics: :
- Accuracy: 0.694
- Balanced Accuracy: 0.5023
- F1 Score: 0.8185
- Recall: 0.9914
- Precision: 0.6970

## 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: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Compute Metrics | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:--------:|:-----------------:|:--------:|:------:|:---------:|
| No log        | 1.0   | 2    | 0.6208          | :               | 0.684    | 0.5006            | 0.8101   | 0.9684 | 0.6963    |
| No log        | 2.0   | 4    | 0.6262          | :               | 0.692    | 0.4990            | 0.8175   | 0.9914 | 0.6956    |
| No log        | 3.0   | 6    | 0.6338          | :               | 0.672    | 0.4846            | 0.8034   | 0.9626 | 0.6893    |
| No log        | 4.0   | 8    | 0.6240          | :               | 0.698    | 0.5051            | 0.8213   | 0.9971 | 0.6982    |
| 0.6681        | 5.0   | 10   | 0.6255          | :               | 0.694    | 0.5023            | 0.8185   | 0.9914 | 0.6970    |


### 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_en_gpu_500_rader_9", "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 

Kjøretid
========


{'train\_runtime': 291.2967, 'train\_samples\_per\_second': 51.494, 'train\_steps\_per\_second': 0.189, 'train\_loss': 0.6998663252050227, 'epoch': 4.94}


Time: 291.30


Samples/second: 51.49


GPU memory occupied: 6267 MB.


norbert2\_sentiment\_norec\_en\_gpu\_500\_rader\_9
==================================================


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: 0.6255
* Compute Metrics: :
* Accuracy: 0.694
* Balanced Accuracy: 0.5023
* F1 Score: 0.8185
* Recall: 0.9914
* Precision: 0.6970


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: 32
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


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