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# xlm-roberta-base-english-sentweet-sentiment

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: 0.5227
- Accuracy: 0.8090
- Precision: 0.8294
- Recall: 0.8176
- F1: 0.8082

## 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: 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 81   | 0.5108          | 0.7708   | 0.7955    | 0.7804 | 0.7692 |
| No log        | 2.0   | 162  | 0.5028          | 0.7882   | 0.8054    | 0.7961 | 0.7875 |
| No log        | 3.0   | 243  | 0.4881          | 0.7847   | 0.8102    | 0.7943 | 0.7832 |
| No log        | 4.0   | 324  | 0.4914          | 0.8090   | 0.8248    | 0.8166 | 0.8085 |
| No log        | 5.0   | 405  | 0.5390          | 0.8090   | 0.8248    | 0.8166 | 0.8085 |
| No log        | 6.0   | 486  | 0.5227          | 0.8090   | 0.8294    | 0.8176 | 0.8082 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "xlm-roberta-base-english-sentweet-sentiment", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-english-sentweet-sentiment
===========================================


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: 0.5227
* Accuracy: 0.8090
* Precision: 0.8294
* Recall: 0.8176
* F1: 0.8082


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: 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: 6


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 6 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #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: 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: 6 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.1+cu117
* Datasets 2.6.1
* Tokenizers 0.11.0