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# bert-base-uncased-english-sentweet-derogatory

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1640
- Accuracy: 0.7917
- Precision: 0.8058
- Recall: 0.8025
- 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   | 0.4757          | 0.8021   | 0.8300    | 0.8171 | 0.8014 |
| No log        | 2.0   | 162  | 0.5035          | 0.8194   | 0.8412    | 0.8328 | 0.8191 |
| No log        | 3.0   | 243  | 0.5446          | 0.8021   | 0.8220    | 0.8149 | 0.8018 |
| No log        | 4.0   | 324  | 0.7602          | 0.7465   | 0.7482    | 0.7507 | 0.7462 |
| No log        | 5.0   | 405  | 1.0083          | 0.7743   | 0.7793    | 0.7810 | 0.7742 |
| No log        | 6.0   | 486  | 1.1640          | 0.7917   | 0.8058    | 0.8025 | 0.7916 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-uncased-english-sentweet-derogatory", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-english-sentweet-derogatory
=============================================


This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1640
* Accuracy: 0.7917
* Precision: 0.8058
* Recall: 0.8025
* 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-mit #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