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# bert-zs-sentence-classifier

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3663
- F1: 0.8483

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.5973        | 0.01  | 500   | 0.5186          | 0.7538 |
| 0.5021        | 0.03  | 1000  | 0.4646          | 0.7996 |
| 0.4741        | 0.04  | 1500  | 0.4634          | 0.8064 |
| 0.4656        | 0.06  | 2000  | 0.4485          | 0.8142 |
| 0.4567        | 0.07  | 2500  | 0.4345          | 0.8160 |
| 0.4448        | 0.09  | 3000  | 0.4239          | 0.8228 |
| 0.4403        | 0.1   | 3500  | 0.4155          | 0.8294 |
| 0.4163        | 0.12  | 4000  | 0.4021          | 0.8290 |
| 0.4205        | 0.13  | 4500  | 0.4057          | 0.8283 |
| 0.416         | 0.14  | 5000  | 0.4049          | 0.8319 |
| 0.4115        | 0.16  | 5500  | 0.4095          | 0.8280 |
| 0.4156        | 0.17  | 6000  | 0.3927          | 0.8349 |
| 0.4042        | 0.19  | 6500  | 0.4003          | 0.8392 |
| 0.4057        | 0.2   | 7000  | 0.3929          | 0.8385 |
| 0.3977        | 0.22  | 7500  | 0.3915          | 0.8406 |
| 0.4049        | 0.23  | 8000  | 0.3785          | 0.8433 |
| 0.4027        | 0.24  | 8500  | 0.3807          | 0.8424 |
| 0.4096        | 0.26  | 9000  | 0.3768          | 0.8435 |
| 0.3958        | 0.27  | 9500  | 0.3846          | 0.8420 |
| 0.4037        | 0.29  | 10000 | 0.3808          | 0.8381 |
| 0.3813        | 0.3   | 10500 | 0.4004          | 0.8415 |
| 0.3934        | 0.32  | 11000 | 0.3821          | 0.8422 |
| 0.3895        | 0.33  | 11500 | 0.3844          | 0.8428 |
| 0.3907        | 0.35  | 12000 | 0.3847          | 0.8435 |
| 0.3862        | 0.36  | 12500 | 0.3803          | 0.8431 |
| 0.3958        | 0.37  | 13000 | 0.3739          | 0.8392 |
| 0.3845        | 0.39  | 13500 | 0.3817          | 0.8422 |
| 0.3914        | 0.4   | 14000 | 0.3857          | 0.8424 |
| 0.3814        | 0.42  | 14500 | 0.3793          | 0.8438 |
| 0.3816        | 0.43  | 15000 | 0.3843          | 0.8395 |
| 0.4022        | 0.45  | 15500 | 0.3737          | 0.8436 |
| 0.3879        | 0.46  | 16000 | 0.3750          | 0.8424 |
| 0.3794        | 0.48  | 16500 | 0.3743          | 0.8410 |
| 0.393         | 0.49  | 17000 | 0.3733          | 0.8461 |
| 0.384         | 0.5   | 17500 | 0.3765          | 0.8476 |
| 0.3782        | 0.52  | 18000 | 0.3748          | 0.8451 |
| 0.3931        | 0.53  | 18500 | 0.3807          | 0.8454 |
| 0.3889        | 0.55  | 19000 | 0.3653          | 0.8463 |
| 0.386         | 0.56  | 19500 | 0.3707          | 0.8445 |
| 0.3802        | 0.58  | 20000 | 0.3700          | 0.8474 |
| 0.3883        | 0.59  | 20500 | 0.3646          | 0.8463 |
| 0.3825        | 0.61  | 21000 | 0.3665          | 0.8513 |
| 0.382         | 0.62  | 21500 | 0.3620          | 0.8508 |
| 0.3795        | 0.63  | 22000 | 0.3692          | 0.8493 |
| 0.367         | 0.65  | 22500 | 0.3704          | 0.8479 |
| 0.3825        | 0.66  | 23000 | 0.3723          | 0.8472 |
| 0.3902        | 0.68  | 23500 | 0.3681          | 0.8465 |
| 0.3813        | 0.69  | 24000 | 0.3668          | 0.8515 |
| 0.3878        | 0.71  | 24500 | 0.3632          | 0.8506 |
| 0.3743        | 0.72  | 25000 | 0.3728          | 0.8463 |
| 0.3826        | 0.73  | 25500 | 0.3746          | 0.8465 |
| 0.3892        | 0.75  | 26000 | 0.3602          | 0.8518 |
| 0.3767        | 0.76  | 26500 | 0.3722          | 0.8513 |
| 0.3724        | 0.78  | 27000 | 0.3716          | 0.8499 |
| 0.3767        | 0.79  | 27500 | 0.3651          | 0.8483 |
| 0.3846        | 0.81  | 28000 | 0.3753          | 0.8493 |
| 0.3748        | 0.82  | 28500 | 0.3720          | 0.8458 |
| 0.3768        | 0.84  | 29000 | 0.3663          | 0.8508 |
| 0.3716        | 0.85  | 29500 | 0.3635          | 0.8531 |
| 0.3673        | 0.86  | 30000 | 0.3659          | 0.8485 |
| 0.3805        | 0.88  | 30500 | 0.3608          | 0.8518 |
| 0.3718        | 0.89  | 31000 | 0.3695          | 0.8520 |
| 0.374         | 0.91  | 31500 | 0.3631          | 0.8485 |
| 0.3871        | 0.92  | 32000 | 0.3659          | 0.8485 |
| 0.3724        | 0.94  | 32500 | 0.3584          | 0.8518 |
| 0.3756        | 0.95  | 33000 | 0.3587          | 0.8492 |
| 0.3709        | 0.97  | 33500 | 0.3700          | 0.8488 |
| 0.376         | 0.98  | 34000 | 0.3657          | 0.8492 |
| 0.372         | 0.99  | 34500 | 0.3663          | 0.8483 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

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

bert-zs-sentence-classifier
===========================


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


* Loss: 0.3663
* F1: 0.8483


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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1