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# ibert-roberta-base-Abusive_Or_Threatening_Speech

This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0802
- Accuracy: 0.9741
- F1: 0.7773
- Recall: 0.8610
- Precision: 0.7084

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0771        | 1.0   | 1828 | 0.0802          | 0.9741   | 0.7773 | 0.8610 | 0.7084    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.8.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "ibert-roberta-base-Abusive_Or_Threatening_Speech", "results": []}]}
transformers pytorch tensorboard ibert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #ibert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

ibert-roberta-base-Abusive\_Or\_Threatening\_Speech
===================================================


This model is a fine-tuned version of kssteven/ibert-roberta-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0802
* Accuracy: 0.9741
* F1: 0.7773
* Recall: 0.8610
* Precision: 0.7084


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #ibert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.8.0
* Tokenizers 0.12.1