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# distilbert-base-uncased_fold_5_ternary

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8096
- F1: 0.7352

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 291  | 0.6322          | 0.6742 |
| 0.5533        | 2.0   | 582  | 0.5861          | 0.7285 |
| 0.5533        | 3.0   | 873  | 0.6893          | 0.7117 |
| 0.2576        | 4.0   | 1164 | 1.0393          | 0.7124 |
| 0.2576        | 5.0   | 1455 | 1.1506          | 0.6988 |
| 0.1097        | 6.0   | 1746 | 1.3005          | 0.7166 |
| 0.0487        | 7.0   | 2037 | 1.5242          | 0.7124 |
| 0.0487        | 8.0   | 2328 | 1.5705          | 0.7010 |
| 0.0253        | 9.0   | 2619 | 1.5180          | 0.7194 |
| 0.0253        | 10.0  | 2910 | 1.6251          | 0.7062 |
| 0.022         | 11.0  | 3201 | 1.6299          | 0.7169 |
| 0.022         | 12.0  | 3492 | 1.7322          | 0.7091 |
| 0.0065        | 13.0  | 3783 | 1.8441          | 0.7044 |
| 0.0093        | 14.0  | 4074 | 1.9063          | 0.7097 |
| 0.0093        | 15.0  | 4365 | 1.8096          | 0.7352 |
| 0.0037        | 16.0  | 4656 | 1.8589          | 0.7321 |
| 0.0037        | 17.0  | 4947 | 1.9687          | 0.7211 |
| 0.0036        | 18.0  | 5238 | 1.9244          | 0.7285 |
| 0.0045        | 19.0  | 5529 | 1.9835          | 0.7299 |
| 0.0045        | 20.0  | 5820 | 2.0766          | 0.7139 |
| 0.0024        | 21.0  | 6111 | 2.1118          | 0.7144 |
| 0.0024        | 22.0  | 6402 | 2.0544          | 0.7197 |
| 0.0006        | 23.0  | 6693 | 2.0914          | 0.7217 |
| 0.0006        | 24.0  | 6984 | 2.1028          | 0.7195 |
| 0.0006        | 25.0  | 7275 | 2.1174          | 0.7224 |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_5_ternary", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased\_fold\_5\_ternary
=========================================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8096
* F1: 0.7352


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25


### Training results



### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1