
<!-- 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_3_binary

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: 1.8310
- F1: 0.7584

## 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  | 0.4560          | 0.7522 |
| 0.4008        | 2.0   | 578  | 0.4790          | 0.7567 |
| 0.4008        | 3.0   | 867  | 0.6368          | 0.7557 |
| 0.1967        | 4.0   | 1156 | 0.6854          | 0.7534 |
| 0.1967        | 5.0   | 1445 | 0.9823          | 0.7309 |
| 0.0768        | 6.0   | 1734 | 1.2531          | 0.7511 |
| 0.0202        | 7.0   | 2023 | 1.2906          | 0.7391 |
| 0.0202        | 8.0   | 2312 | 1.4025          | 0.7460 |
| 0.0087        | 9.0   | 2601 | 1.5713          | 0.7507 |
| 0.0087        | 10.0  | 2890 | 1.4212          | 0.7528 |
| 0.0162        | 11.0  | 3179 | 1.5775          | 0.7511 |
| 0.0162        | 12.0  | 3468 | 1.6361          | 0.7377 |
| 0.0048        | 13.0  | 3757 | 1.6972          | 0.7542 |
| 0.0098        | 14.0  | 4046 | 1.6569          | 0.7565 |
| 0.0098        | 15.0  | 4335 | 1.7547          | 0.7325 |
| 0.0042        | 16.0  | 4624 | 1.8108          | 0.7544 |
| 0.0042        | 17.0  | 4913 | 1.7593          | 0.7554 |
| 0.0041        | 18.0  | 5202 | 1.7582          | 0.7551 |
| 0.0041        | 19.0  | 5491 | 1.8200          | 0.7512 |
| 0.0029        | 20.0  | 5780 | 1.8310          | 0.7584 |
| 0.0018        | 21.0  | 6069 | 1.8146          | 0.7568 |
| 0.0018        | 22.0  | 6358 | 1.7870          | 0.7558 |
| 0.0029        | 23.0  | 6647 | 1.8508          | 0.7530 |
| 0.0029        | 24.0  | 6936 | 1.8327          | 0.7543 |
| 0.0001        | 25.0  | 7225 | 1.8546          | 0.7561 |


### 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_3_binary", "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\_3\_binary
========================================


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: 1.8310
* F1: 0.7584


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
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