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# Clickbait3

This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0248

## 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: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.05  | 50   | 0.0373          |
| No log        | 0.1   | 100  | 0.0320          |
| No log        | 0.15  | 150  | 0.0295          |
| No log        | 0.21  | 200  | 0.0302          |
| No log        | 0.26  | 250  | 0.0331          |
| No log        | 0.31  | 300  | 0.0280          |
| No log        | 0.36  | 350  | 0.0277          |
| No log        | 0.41  | 400  | 0.0316          |
| No log        | 0.46  | 450  | 0.0277          |
| 0.0343        | 0.51  | 500  | 0.0276          |
| 0.0343        | 0.56  | 550  | 0.0282          |
| 0.0343        | 0.62  | 600  | 0.0280          |
| 0.0343        | 0.67  | 650  | 0.0271          |
| 0.0343        | 0.72  | 700  | 0.0264          |
| 0.0343        | 0.77  | 750  | 0.0265          |
| 0.0343        | 0.82  | 800  | 0.0260          |
| 0.0343        | 0.87  | 850  | 0.0263          |
| 0.0343        | 0.92  | 900  | 0.0259          |
| 0.0343        | 0.97  | 950  | 0.0277          |
| 0.0278        | 1.03  | 1000 | 0.0281          |
| 0.0278        | 1.08  | 1050 | 0.0294          |
| 0.0278        | 1.13  | 1100 | 0.0256          |
| 0.0278        | 1.18  | 1150 | 0.0258          |
| 0.0278        | 1.23  | 1200 | 0.0254          |
| 0.0278        | 1.28  | 1250 | 0.0265          |
| 0.0278        | 1.33  | 1300 | 0.0252          |
| 0.0278        | 1.38  | 1350 | 0.0251          |
| 0.0278        | 1.44  | 1400 | 0.0264          |
| 0.0278        | 1.49  | 1450 | 0.0262          |
| 0.023         | 1.54  | 1500 | 0.0272          |
| 0.023         | 1.59  | 1550 | 0.0278          |
| 0.023         | 1.64  | 1600 | 0.0255          |
| 0.023         | 1.69  | 1650 | 0.0258          |
| 0.023         | 1.74  | 1700 | 0.0262          |
| 0.023         | 1.79  | 1750 | 0.0250          |
| 0.023         | 1.85  | 1800 | 0.0253          |
| 0.023         | 1.9   | 1850 | 0.0271          |
| 0.023         | 1.95  | 1900 | 0.0248          |
| 0.023         | 2.0   | 1950 | 0.0258          |
| 0.0224        | 2.05  | 2000 | 0.0252          |
| 0.0224        | 2.1   | 2050 | 0.0259          |
| 0.0224        | 2.15  | 2100 | 0.0254          |
| 0.0224        | 2.21  | 2150 | 0.0260          |
| 0.0224        | 2.26  | 2200 | 0.0254          |
| 0.0224        | 2.31  | 2250 | 0.0266          |
| 0.0224        | 2.36  | 2300 | 0.0258          |
| 0.0224        | 2.41  | 2350 | 0.0258          |
| 0.0224        | 2.46  | 2400 | 0.0256          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait3", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Clickbait3
==========


This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0248


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: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.0.0
* Tokenizers 0.11.6



### 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: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.0.0
* Tokenizers 0.11.6