
<!-- 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. -->

# bert-base-ehddnr-ynat

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3587
- F1: 0.8721

## 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: 256
- eval_batch_size: 256
- seed: 42
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 179  | 0.4398          | 0.8548 |
| No log        | 2.0   | 358  | 0.3587          | 0.8721 |
| 0.3859        | 3.0   | 537  | 0.3639          | 0.8707 |
| 0.3859        | 4.0   | 716  | 0.3592          | 0.8692 |
| 0.3859        | 5.0   | 895  | 0.3646          | 0.8717 |


### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model_index": [{"name": "bert-base-ehddnr-ynat", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metric": {"name": "F1", "type": "f1", "value": 0.8720568553403009}}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:klue autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us 

bert-base-ehddnr-ynat
=====================


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


* Loss: 0.3587
* F1: 0.8721


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


### Training results



### Framework versions


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3