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# bert-base-uncased-finetuned-surveyclassification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a custom survey dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2818
- Accuracy: 0.9097
- F1: 0.9097

## Model description

More information needed

#### Limitations and bias

This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.

#### How to use

You can use this model with Transformers *pipeline* for Text Classification.

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
example = "The agent on the phone was very helpful and nice to me."
results = text_classifier(example)
print(results)
```

## Training and evaluation data

Custom survey dataset.

## Training procedure
SageMaker notebook instance.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4136        | 1.0   | 902  | 0.2818          | 0.9097   | 0.9097 |
| 0.2213        | 2.0   | 1804 | 0.2990          | 0.9077   | 0.9077 |
| 0.1548        | 3.0   | 2706 | 0.3507          | 0.9026   | 0.9026 |
| 0.1034        | 4.0   | 3608 | 0.4692          | 0.9011   | 0.9011 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.8.1+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "widget": [{"text": "The agent on the phone was very helpful and nice to me."}], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-finetuned-surveyclassification", "results": []}]}
transformers pytorch safetensors bert text-classification generated_from_trainer en base_model:bert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-finetuned-surveyclassification
================================================


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


* Loss: 0.2818
* Accuracy: 0.9097
* F1: 0.9097


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


More information needed


#### Limitations and bias


This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.


#### How to use


You can use this model with Transformers *pipeline* for Text Classification.


Training and evaluation data
----------------------------


Custom survey dataset.


Training procedure
------------------


SageMaker notebook instance.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0



#### Limitations and bias


This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains. #### How to use


You can use this model with Transformers *pipeline* for Text Classification.


Training and evaluation data
----------------------------


Custom survey dataset.


Training procedure
------------------


SageMaker notebook instance. ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### Limitations and bias


This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains. #### How to use


You can use this model with Transformers *pipeline* for Text Classification.


Training and evaluation data
----------------------------


Custom survey dataset.


Training procedure
------------------


SageMaker notebook instance. ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### Limitations and bias


This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.#### How to use


You can use this model with Transformers *pipeline* for Text Classification.


Training and evaluation data
----------------------------


Custom survey dataset.


Training procedure
------------------


SageMaker notebook instance.### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0