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# bert-base-cased-english-sentweet-Derogatory

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: 0.9504
- Accuracy: 0.7917
- Precision: 0.7961
- Recall: 0.7982
- F1: 0.7916

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 81   | 0.4578          | 0.8160   | 0.8363    | 0.8289 | 0.8157 |
| No log        | 2.0   | 162  | 0.4759          | 0.8264   | 0.8459    | 0.8391 | 0.8262 |
| No log        | 3.0   | 243  | 0.5410          | 0.8160   | 0.8317    | 0.8275 | 0.8159 |
| No log        | 4.0   | 324  | 0.5789          | 0.7569   | 0.7545    | 0.7557 | 0.7549 |
| No log        | 5.0   | 405  | 0.9143          | 0.7847   | 0.7891    | 0.7911 | 0.7846 |
| No log        | 6.0   | 486  | 0.9504          | 0.7917   | 0.7961    | 0.7982 | 0.7916 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-cased-english-sentweet-Derogatory", "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-cased-english-sentweet-Derogatory
===========================================


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: 0.9504
* Accuracy: 0.7917
* Precision: 0.7961
* Recall: 0.7982
* F1: 0.7916


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0
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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0