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# bert-base-Daichi_support

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7348
- F1: 0.5408

## Model description

This model was created for classification users requests for tech support. Users ask the same questions for tech support and operators waste a lot of time answering them. There were instructions, but users had trouble finding them, so we made the decision to make a chat interface where user can text his problem and a pretrained model will classify his question and respond with correct instruction.


## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 7    | 1.7976          | 0.3806 |
| No log        | 2.0   | 14   | 1.6849          | 0.3806 |
| No log        | 3.0   | 21   | 1.5963          | 0.3806 |
| No log        | 4.0   | 28   | 1.4947          | 0.3806 |
| No log        | 5.0   | 35   | 1.4645          | 0.3806 |
| No log        | 6.0   | 42   | 1.4063          | 0.3806 |
| No log        | 7.0   | 49   | 1.4314          | 0.4935 |
| No log        | 8.0   | 56   | 1.2979          | 0.5274 |
| No log        | 9.0   | 63   | 1.3582          | 0.4626 |
| No log        | 10.0  | 70   | 1.5711          | 0.5164 |
| No log        | 11.0  | 77   | 1.2483          | 0.5881 |
| No log        | 12.0  | 84   | 1.1974          | 0.5860 |
| No log        | 13.0  | 91   | 1.2582          | 0.5426 |
| No log        | 14.0  | 98   | 1.7688          | 0.4504 |
| No log        | 15.0  | 105  | 1.3278          | 0.5557 |
| No log        | 16.0  | 112  | 1.6230          | 0.5119 |
| No log        | 17.0  | 119  | 1.4229          | 0.5536 |
| No log        | 18.0  | 126  | 1.4000          | 0.5536 |
| No log        | 19.0  | 133  | 1.4614          | 0.5408 |
| No log        | 20.0  | 140  | 1.4676          | 0.5536 |
| No log        | 21.0  | 147  | 1.7174          | 0.555  |
| No log        | 22.0  | 154  | 1.5338          | 0.5536 |
| No log        | 23.0  | 161  | 1.6979          | 0.6179 |
| No log        | 24.0  | 168  | 1.7075          | 0.5408 |
| No log        | 25.0  | 175  | 1.6655          | 0.5408 |
| No log        | 26.0  | 182  | 1.6043          | 0.6179 |
| No log        | 27.0  | 189  | 1.6945          | 0.6051 |
| No log        | 28.0  | 196  | 1.7289          | 0.5408 |
| 1.1079        | 29.0  | 203  | 1.7329          | 0.5408 |
| 1.1079        | 30.0  | 210  | 1.7348          | 0.5408 |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bert-base-Daichi_support", "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-base-Daichi\_support
=========================


This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7348
* F1: 0.5408


Model description
-----------------


This model was created for classification users requests for tech support. Users ask the same questions for tech support and operators waste a lot of time answering them. There were instructions, but users had trouble finding them, so we made the decision to make a chat interface where user can text his problem and a pretrained model will classify his question and respond with correct instruction.


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2