
<!-- 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. -->

# distilbert-base-uncased_fold_4_binary_v1

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: 1.5144
- F1: 0.8245

## 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   | 289  | 0.3756          | 0.8175 |
| 0.3977        | 2.0   | 578  | 0.3672          | 0.8336 |
| 0.3977        | 3.0   | 867  | 0.4997          | 0.8276 |
| 0.1972        | 4.0   | 1156 | 0.6597          | 0.8244 |
| 0.1972        | 5.0   | 1445 | 0.8501          | 0.8195 |
| 0.0824        | 6.0   | 1734 | 1.0074          | 0.8097 |
| 0.037         | 7.0   | 2023 | 1.1122          | 0.8131 |
| 0.037         | 8.0   | 2312 | 1.0963          | 0.8189 |
| 0.0182        | 9.0   | 2601 | 1.2511          | 0.8125 |
| 0.0182        | 10.0  | 2890 | 1.2255          | 0.8141 |
| 0.0121        | 11.0  | 3179 | 1.3120          | 0.8187 |
| 0.0121        | 12.0  | 3468 | 1.4182          | 0.8165 |
| 0.0079        | 13.0  | 3757 | 1.4142          | 0.8218 |
| 0.0081        | 14.0  | 4046 | 1.4765          | 0.8150 |
| 0.0081        | 15.0  | 4335 | 1.3510          | 0.8187 |
| 0.0109        | 16.0  | 4624 | 1.3455          | 0.8255 |
| 0.0109        | 17.0  | 4913 | 1.4157          | 0.8234 |
| 0.0022        | 18.0  | 5202 | 1.4651          | 0.8197 |
| 0.0022        | 19.0  | 5491 | 1.4388          | 0.8267 |
| 0.0017        | 20.0  | 5780 | 1.4552          | 0.8304 |
| 0.0005        | 21.0  | 6069 | 1.5357          | 0.8248 |
| 0.0005        | 22.0  | 6358 | 1.4924          | 0.8241 |
| 0.0009        | 23.0  | 6647 | 1.4865          | 0.8248 |
| 0.0009        | 24.0  | 6936 | 1.4697          | 0.8275 |
| 0.0013        | 25.0  | 7225 | 1.5144          | 0.8245 |


### 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_4_binary_v1", "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\_4\_binary\_v1
============================================


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: 1.5144
* F1: 0.8245


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