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# bertbasecasedfinancialphrasebank

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: 0.5046
- Accuracy: 0.8660

## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9921        | 0.04  | 5    | 0.9266          | 0.6082   |
| 0.8989        | 0.08  | 10   | 0.8833          | 0.6082   |
| 0.8563        | 0.12  | 15   | 0.8287          | 0.6371   |
| 0.8316        | 0.16  | 20   | 0.7626          | 0.6866   |
| 0.808         | 0.2   | 25   | 0.7398          | 0.6845   |
| 0.8219        | 0.25  | 30   | 0.7234          | 0.6969   |
| 0.7402        | 0.29  | 35   | 0.7080          | 0.6990   |
| 0.749         | 0.33  | 40   | 0.7261          | 0.6948   |
| 0.8777        | 0.37  | 45   | 0.7253          | 0.7072   |
| 0.7057        | 0.41  | 50   | 0.6736          | 0.7196   |
| 0.6968        | 0.45  | 55   | 0.6326          | 0.7320   |
| 0.6264        | 0.49  | 60   | 0.6121          | 0.7443   |
| 0.703         | 0.53  | 65   | 0.6008          | 0.7505   |
| 0.6788        | 0.57  | 70   | 0.6421          | 0.7278   |
| 0.6458        | 0.61  | 75   | 0.5801          | 0.7546   |
| 0.605         | 0.66  | 80   | 0.5601          | 0.7588   |
| 0.5428        | 0.7   | 85   | 0.5480          | 0.7546   |
| 0.6025        | 0.74  | 90   | 0.5632          | 0.7402   |
| 0.5761        | 0.78  | 95   | 0.5053          | 0.7794   |
| 0.4919        | 0.82  | 100  | 0.4745          | 0.8289   |
| 0.5217        | 0.86  | 105  | 0.4731          | 0.8268   |
| 0.5517        | 0.9   | 110  | 0.4512          | 0.8474   |
| 0.4131        | 0.94  | 115  | 0.4280          | 0.8392   |
| 0.5244        | 0.98  | 120  | 0.4222          | 0.8433   |
| 0.6407        | 1.02  | 125  | 0.4474          | 0.8392   |
| 0.3661        | 1.07  | 130  | 0.4101          | 0.8474   |
| 0.4148        | 1.11  | 135  | 0.3810          | 0.8619   |
| 0.296         | 1.15  | 140  | 0.4003          | 0.8371   |
| 0.3579        | 1.19  | 145  | 0.3738          | 0.8598   |
| 0.3718        | 1.23  | 150  | 0.3635          | 0.8598   |
| 0.307         | 1.27  | 155  | 0.3900          | 0.8515   |
| 0.3055        | 1.31  | 160  | 0.3881          | 0.8454   |
| 0.2789        | 1.35  | 165  | 0.3679          | 0.8495   |
| 0.2541        | 1.39  | 170  | 0.3709          | 0.8557   |
| 0.3263        | 1.43  | 175  | 0.3815          | 0.8557   |
| 0.305         | 1.48  | 180  | 0.3757          | 0.8495   |
| 0.2215        | 1.52  | 185  | 0.3643          | 0.8639   |
| 0.3642        | 1.56  | 190  | 0.3887          | 0.8495   |
| 0.4179        | 1.6   | 195  | 0.3736          | 0.8536   |
| 0.3126        | 1.64  | 200  | 0.3928          | 0.8392   |
| 0.2625        | 1.68  | 205  | 0.3648          | 0.8577   |
| 0.2155        | 1.72  | 210  | 0.3612          | 0.8577   |
| 0.2526        | 1.76  | 215  | 0.3781          | 0.8433   |
| 0.3033        | 1.8   | 220  | 0.4334          | 0.8412   |
| 0.2752        | 1.84  | 225  | 0.4039          | 0.8392   |
| 0.3466        | 1.89  | 230  | 0.3800          | 0.8412   |
| 0.4447        | 1.93  | 235  | 0.3575          | 0.8412   |
| 0.257         | 1.97  | 240  | 0.3732          | 0.8515   |
| 0.2647        | 2.01  | 245  | 0.3542          | 0.8454   |
| 0.1795        | 2.05  | 250  | 0.3599          | 0.8495   |
| 0.1697        | 2.09  | 255  | 0.3694          | 0.8515   |
| 0.1907        | 2.13  | 260  | 0.3985          | 0.8495   |
| 0.2515        | 2.17  | 265  | 0.3753          | 0.8660   |
| 0.1921        | 2.21  | 270  | 0.3674          | 0.8598   |
| 0.1342        | 2.25  | 275  | 0.3712          | 0.8577   |
| 0.1424        | 2.3   | 280  | 0.3595          | 0.8680   |
| 0.2071        | 2.34  | 285  | 0.3650          | 0.8680   |
| 0.2224        | 2.38  | 290  | 0.3881          | 0.8557   |
| 0.2556        | 2.42  | 295  | 0.3598          | 0.8536   |
| 0.1518        | 2.46  | 300  | 0.3506          | 0.8619   |
| 0.1427        | 2.5   | 305  | 0.3621          | 0.8639   |
| 0.1599        | 2.54  | 310  | 0.4495          | 0.8371   |
| 0.163         | 2.58  | 315  | 0.4812          | 0.8124   |
| 0.1462        | 2.62  | 320  | 0.4012          | 0.8680   |
| 0.2213        | 2.66  | 325  | 0.4047          | 0.8557   |
| 0.1967        | 2.7   | 330  | 0.4008          | 0.8701   |
| 0.2305        | 2.75  | 335  | 0.3838          | 0.8722   |
| 0.1097        | 2.79  | 340  | 0.3743          | 0.8577   |
| 0.1521        | 2.83  | 345  | 0.3700          | 0.8701   |
| 0.0833        | 2.87  | 350  | 0.3831          | 0.8680   |
| 0.2099        | 2.91  | 355  | 0.3947          | 0.8722   |
| 0.2086        | 2.95  | 360  | 0.4184          | 0.8515   |
| 0.1294        | 2.99  | 365  | 0.4214          | 0.8536   |
| 0.0992        | 3.03  | 370  | 0.4087          | 0.8639   |
| 0.0973        | 3.07  | 375  | 0.4073          | 0.8557   |
| 0.0519        | 3.11  | 380  | 0.4085          | 0.8639   |
| 0.1418        | 3.16  | 385  | 0.4109          | 0.8639   |
| 0.081         | 3.2   | 390  | 0.4426          | 0.8619   |
| 0.0506        | 3.24  | 395  | 0.4733          | 0.8454   |
| 0.0962        | 3.28  | 400  | 0.5100          | 0.8412   |
| 0.1045        | 3.32  | 405  | 0.4408          | 0.8639   |
| 0.1236        | 3.36  | 410  | 0.4395          | 0.8619   |
| 0.0962        | 3.4   | 415  | 0.4515          | 0.8557   |
| 0.1611        | 3.44  | 420  | 0.4819          | 0.8474   |
| 0.1044        | 3.48  | 425  | 0.4336          | 0.8598   |
| 0.0715        | 3.52  | 430  | 0.4574          | 0.8639   |
| 0.0302        | 3.57  | 435  | 0.4617          | 0.8619   |
| 0.1234        | 3.61  | 440  | 0.4710          | 0.8577   |
| 0.1439        | 3.65  | 445  | 0.5082          | 0.8433   |
| 0.1113        | 3.69  | 450  | 0.4596          | 0.8639   |
| 0.1336        | 3.73  | 455  | 0.4631          | 0.8598   |
| 0.072         | 3.77  | 460  | 0.4372          | 0.8660   |
| 0.0621        | 3.81  | 465  | 0.4381          | 0.8619   |
| 0.1703        | 3.85  | 470  | 0.4337          | 0.8680   |
| 0.0284        | 3.89  | 475  | 0.4321          | 0.8742   |
| 0.0647        | 3.93  | 480  | 0.4575          | 0.8680   |
| 0.0897        | 3.98  | 485  | 0.4676          | 0.8680   |
| 0.0646        | 4.02  | 490  | 0.4550          | 0.8722   |
| 0.0554        | 4.06  | 495  | 0.4721          | 0.8598   |
| 0.0259        | 4.1   | 500  | 0.5045          | 0.8454   |
| 0.0186        | 4.14  | 505  | 0.4635          | 0.8577   |
| 0.0391        | 4.18  | 510  | 0.5362          | 0.8639   |
| 0.0505        | 4.22  | 515  | 0.5122          | 0.8722   |
| 0.1101        | 4.26  | 520  | 0.5046          | 0.8660   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bertbasecasedfinancialphrasebank", "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 

bertbasecasedfinancialphrasebank
================================


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: 0.5046
* Accuracy: 0.8660


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: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+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: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1