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# 20230429-002-baseline-xlmr-no-qa-ft-clickbait-spoiling

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8065

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 200  | 3.1638          |
| No log        | 2.0   | 400  | 3.0033          |
| 2.1763        | 3.0   | 600  | 3.3458          |
| 2.1763        | 4.0   | 800  | 3.5899          |
| 1.3037        | 5.0   | 1000 | 3.7460          |
| 1.3037        | 6.0   | 1200 | 4.1879          |
| 1.3037        | 7.0   | 1400 | 4.4619          |
| 0.6515        | 8.0   | 1600 | 4.5780          |
| 0.6515        | 9.0   | 1800 | 4.8045          |
| 0.3926        | 10.0  | 2000 | 4.8065          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "20230429-002-baseline-xlmr-no-qa-ft-clickbait-spoiling", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

20230429-002-baseline-xlmr-no-qa-ft-clickbait-spoiling
======================================================


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


* Loss: 4.8065


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: 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: 10


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3