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# BERT_model_new

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1206
- F1: 0.8301

## Model description

train_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/train.csv')\
validation_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/validation.csv')\
#test_df = pd.read_csv('/content/drive/My Drive/wiki_toxic/test.csv')\
frac = 0.9\
#TRAIN\
print(train_df.shape[0]) # get the number of rows in the dataframe\
rows_to_delete = train_df.sample(frac=frac, random_state=1)\
train_df = train_df.drop(rows_to_delete.index)\
print(train_df.shape[0])\

#VALIDATION\
print(validation_df.shape[0]) # get the number of rows in the dataframe\
rows_to_delete = validation_df.sample(frac=frac, random_state=1)\
validation_df = validation_df.drop(rows_to_delete.index)\
print(validation_df.shape[0])\

## 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: 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 399  | 0.0940          | 0.8273 |
| 0.1262        | 2.0   | 798  | 0.1206          | 0.8301 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BERT_model_new", "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\_model\_new
================


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


* Loss: 0.1206
* F1: 0.8301


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


train\_df = pd.read\_csv('/content/drive/My Drive/DATASETS/wiki\_toxic/URL')  

validation\_df = pd.read\_csv('/content/drive/My Drive/DATASETS/wiki\_toxic/URL')  

#test\_df = pd.read\_csv('/content/drive/My Drive/wiki\_toxic/URL')  

frac = 0.9  

#TRAIN  

print(train\_df.shape[0]) # get the number of rows in the dataframe  

rows\_to\_delete = train\_df.sample(frac=frac, random\_state=1)  

train\_df = train\_df.drop(rows\_to\_delete.index)  

print(train\_df.shape[0])\


#VALIDATION  

print(validation\_df.shape[0]) # get the number of rows in the dataframe  

rows\_to\_delete = validation\_df.sample(frac=frac, random\_state=1)  

validation\_df = validation\_df.drop(rows\_to\_delete.index)  

print(validation\_df.shape[0])\


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: 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: 2


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



# get the number of rows in the dataframe  

rows\_to\_delete = train\_df.sample(frac=frac, random\_state=1)  

train\_df = train\_df.drop(rows\_to\_delete.index)  

print(train\_df.shape[0])\ # get the number of rows in the dataframe  

rows\_to\_delete = validation\_df.sample(frac=frac, random\_state=1)  

validation\_df = validation\_df.drop(rows\_to\_delete.index)  

print(validation\_df.shape[0])\


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: 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: 2 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # get the number of rows in the dataframe  

rows\_to\_delete = train\_df.sample(frac=frac, random\_state=1)  

train\_df = train\_df.drop(rows\_to\_delete.index)  

print(train\_df.shape[0])\ # get the number of rows in the dataframe  

rows\_to\_delete = validation\_df.sample(frac=frac, random\_state=1)  

validation\_df = validation\_df.drop(rows\_to\_delete.index)  

print(validation\_df.shape[0])\


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: 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: 2 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# get the number of rows in the dataframe  

rows\_to\_delete = train\_df.sample(frac=frac, random\_state=1)  

train\_df = train\_df.drop(rows\_to\_delete.index)  

print(train\_df.shape[0])\# get the number of rows in the dataframe  

rows\_to\_delete = validation\_df.sample(frac=frac, random\_state=1)  

validation\_df = validation\_df.drop(rows\_to\_delete.index)  

print(validation\_df.shape[0])\


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: 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: 2### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3