
<!-- 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_small_summarized

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1652
- Accuracy: 0.82
- Precision: 0.4667
- Recall: 0.2
- F1: 0.2800
- D-index: 1.5200

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 200  | 0.4533          | 0.825    | 0.0       | 0.0    | 0.0    | 1.4529  |
| No log        | 2.0   | 400  | 0.4694          | 0.825    | 0.0       | 0.0    | 0.0    | 1.4529  |
| 0.5094        | 3.0   | 600  | 0.6237          | 0.825    | 0.0       | 0.0    | 0.0    | 1.4529  |
| 0.5094        | 4.0   | 800  | 0.7898          | 0.81     | 0.4286    | 0.2571 | 0.3214 | 1.5270  |
| 0.3984        | 5.0   | 1000 | 0.9268          | 0.83     | 0.5556    | 0.1429 | 0.2273 | 1.5127  |
| 0.3984        | 6.0   | 1200 | 1.3541          | 0.8      | 0.4074    | 0.3143 | 0.3548 | 1.5339  |
| 0.3984        | 7.0   | 1400 | 1.4264          | 0.805    | 0.375     | 0.1714 | 0.2353 | 1.4893  |
| 0.0939        | 8.0   | 1600 | 1.8870          | 0.8      | 0.4194    | 0.3714 | 0.3939 | 1.5539  |
| 0.0939        | 9.0   | 1800 | 1.8734          | 0.825    | 0.5       | 0.1143 | 0.1860 | 1.4955  |
| 0.0061        | 10.0  | 2000 | 1.8938          | 0.825    | 0.5       | 0.1714 | 0.2553 | 1.5164  |
| 0.0061        | 11.0  | 2200 | 2.0755          | 0.825    | 0.5       | 0.1143 | 0.1860 | 1.4955  |
| 0.0061        | 12.0  | 2400 | 2.1068          | 0.805    | 0.4231    | 0.3143 | 0.3607 | 1.5406  |
| 0.0134        | 13.0  | 2600 | 2.0895          | 0.82     | 0.4444    | 0.1143 | 0.1818 | 1.4887  |
| 0.0134        | 14.0  | 2800 | 2.0520          | 0.815    | 0.4545    | 0.2857 | 0.3509 | 1.5439  |
| 0.0011        | 15.0  | 3000 | 2.0795          | 0.81     | 0.4211    | 0.2286 | 0.2963 | 1.5168  |
| 0.0011        | 16.0  | 3200 | 2.1177          | 0.815    | 0.4444    | 0.2286 | 0.3019 | 1.5235  |
| 0.0011        | 17.0  | 3400 | 2.1396          | 0.815    | 0.4444    | 0.2286 | 0.3019 | 1.5235  |
| 0.0003        | 18.0  | 3600 | 2.1605          | 0.825    | 0.5       | 0.2286 | 0.3137 | 1.5370  |
| 0.0003        | 19.0  | 3800 | 2.1677          | 0.825    | 0.5       | 0.2286 | 0.3137 | 1.5370  |
| 0.0           | 20.0  | 4000 | 2.1652          | 0.82     | 0.4667    | 0.2    | 0.2800 | 1.5200  |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert_small_summarized", "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\_small\_summarized
=======================


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


* Loss: 2.1652
* Accuracy: 0.82
* Precision: 0.4667
* Recall: 0.2
* F1: 0.2800
* D-index: 1.5200


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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3