
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:A100m40:1

# norbert2_sentiment_norec_en_gpu_500_rader_max_1

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: 0.6269
- Compute Metrics: :
- Accuracy: 0.682
- Balanced Accuracy: 0.5048
- F1 Score: 0.8073
- Recall: 0.9569
- Precision: 0.6981

## 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    | 0.6311          | :               | 0.688    | 0.4943            | 0.8152   | 0.9885 | 0.6935    |
| No log        | 2.0   | 4    | 0.6316          | :               | 0.674    | 0.5268            | 0.7939   | 0.9023 | 0.7088    |
| No log        | 3.0   | 6    | 0.6199          | :               | 0.686    | 0.5002            | 0.8120   | 0.9741 | 0.6961    |
| No log        | 4.0   | 8    | 0.6475          | :               | 0.652    | 0.5277            | 0.7717   | 0.8448 | 0.7101    |
| 0.6559        | 5.0   | 10   | 0.6269          | :               | 0.682    | 0.5048            | 0.8073   | 0.9569 | 0.6981    |


### 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_max_1", "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


#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:A100m40:1


norbert2\_sentiment\_norec\_en\_gpu\_500\_rader\_max\_1
=======================================================


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: 0.6269
* Compute Metrics: :
* Accuracy: 0.682
* Balanced Accuracy: 0.5048
* F1 Score: 0.8073
* Recall: 0.9569
* Precision: 0.6981


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