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# rtmex23-pol2-cardif

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7781
- eval_f1: 0.6070
- eval_runtime: 119.5054
- eval_samples_per_second: 210.283
- eval_steps_per_second: 26.292
- epoch: 8.0
- step: 113088

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8

### Framework versions

- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "rtmex23-pol2-cardif", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# rtmex23-pol2-cardif

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7781
- eval_f1: 0.6070
- eval_runtime: 119.5054
- eval_samples_per_second: 210.283
- eval_steps_per_second: 26.292
- epoch: 8.0
- step: 113088

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8

### Framework versions

- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3

# rtmex23-pol2-cardif

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7781
- eval_f1: 0.6070
- eval_runtime: 119.5054
- eval_samples_per_second: 210.283
- eval_steps_per_second: 26.292
- epoch: 8.0
- step: 113088 ## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8 ### Framework versions

- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # rtmex23-pol2-cardif

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7781
- eval_f1: 0.6070
- eval_runtime: 119.5054
- eval_samples_per_second: 210.283
- eval_steps_per_second: 26.292
- epoch: 8.0
- step: 113088 ## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8 ### Framework versions

- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3