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# bert-multilabel-sector-classifier

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: 0.0563
- Precision Micro: 0.9091
- Precision Weighted: 0.9080
- Precision Samples: 0.9149
- Recall Micro: 0.8553
- Recall Weighted: 0.8553
- Recall Samples: 0.8996
- Accuracy: 0.8026

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|
| 0.0601        | 1.0   | 464  | 0.0563          | 0.9091          | 0.9080             | 0.9149            | 0.8553       | 0.8553          | 0.8996         | 0.8026   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-multilabel-sector-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-multilabel-sector-classifier
=================================


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: 0.0563
* Precision Micro: 0.9091
* Precision Weighted: 0.9080
* Precision Samples: 0.9149
* Recall Micro: 0.8553
* Recall Weighted: 0.8553
* Recall Samples: 0.8996
* Accuracy: 0.8026


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


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: cosine
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2