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# rtmex23-pol4-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:
- Loss: 0.6787
- F1: 0.8463

## 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: 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

### Training results

| Training Loss | Epoch | Step   | Validation Loss | F1     |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.7006        | 1.0   | 17996  | 0.6293          | 0.6758 |
| 0.5558        | 2.0   | 35992  | 0.5515          | 0.7590 |
| 0.4566        | 3.0   | 53988  | 0.5066          | 0.7939 |
| 0.3855        | 4.0   | 71984  | 0.4959          | 0.8217 |
| 0.3258        | 5.0   | 89980  | 0.5075          | 0.8200 |
| 0.2744        | 6.0   | 107976 | 0.5251          | 0.8409 |
| 0.2322        | 7.0   | 125972 | 0.5889          | 0.8461 |
| 0.2029        | 8.0   | 143968 | 0.6787          | 0.8463 |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "rtmex23-pol4-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-pol4-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:


* Loss: 0.6787
* F1: 0.8463


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: 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


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 1.13.1
* 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: 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 ### Training results ### 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 
 ### Training hyperparameters


The following hyperparameters were used during training:


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


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