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# da-discourse-coherence-base

This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the [DDisco](https://huggingface.co/datasets/ajders/ddisco) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7487
- Accuracy: 0.6915

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 703
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3422        | 0.4   | 5    | 1.0166          | 0.5721   |
| 0.9645        | 0.8   | 10   | 0.8966          | 0.5721   |
| 0.9854        | 1.24  | 15   | 0.8499          | 0.5721   |
| 0.8628        | 1.64  | 20   | 0.8379          | 0.6517   |
| 0.9046        | 2.08  | 25   | 0.8228          | 0.5721   |
| 0.8361        | 2.48  | 30   | 0.7980          | 0.5821   |
| 0.8158        | 2.88  | 35   | 0.8095          | 0.5821   |
| 0.8689        | 3.32  | 40   | 0.7989          | 0.6169   |
| 0.8125        | 3.72  | 45   | 0.7730          | 0.6965   |
| 0.843         | 4.16  | 50   | 0.7566          | 0.6418   |
| 0.7421        | 4.56  | 55   | 0.7840          | 0.6517   |
| 0.7949        | 4.96  | 60   | 0.7531          | 0.6915   |
| 0.828         | 5.4   | 65   | 0.7464          | 0.6816   |
| 0.7438        | 5.8   | 70   | 0.7487          | 0.6915   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.0a0+d0d6b1f
- Datasets 2.9.0
- Tokenizers 0.13.2

### Contributor

[ajders](https://github.com/AJDERS)
{"language": ["da"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ajders/ddisco"], "metrics": ["accuracy"], "base_model": "NbAiLab/nb-bert-base", "model-index": [{"name": "ddisco_classifier", "results": []}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer da dataset:ajders/ddisco base_model:NbAiLab/nb-bert-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #dataset-ajders/ddisco #base_model-NbAiLab/nb-bert-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

da-discourse-coherence-base
===========================


This model is a fine-tuned version of NbAiLab/nb-bert-base on the DDisco dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7487
* Accuracy: 0.6915


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 703
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 6.0


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0a0+d0d6b1f
* Datasets 2.9.0
* Tokenizers 0.13.2


### Contributor


ajders



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 703
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 6.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0a0+d0d6b1f
* Datasets 2.9.0
* Tokenizers 0.13.2 ### Contributor


ajders
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #dataset-ajders/ddisco #base_model-NbAiLab/nb-bert-base #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: 4
* eval\_batch\_size: 4
* seed: 703
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 6.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0a0+d0d6b1f
* Datasets 2.9.0
* Tokenizers 0.13.2 ### Contributor


ajders
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #dataset-ajders/ddisco #base_model-NbAiLab/nb-bert-base #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: 4
* eval\_batch\_size: 4
* seed: 703
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 6.0### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0a0+d0d6b1f
* Datasets 2.9.0
* Tokenizers 0.13.2### Contributor


ajders