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---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: result
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5662
- Accuracy: 0.8065
## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5234 | 1.0 | 6463 | 0.5311 | 0.7852 |
| 0.4135 | 2.0 | 12926 | 0.5020 | 0.8039 |
| 0.3246 | 3.0 | 19389 | 0.5662 | 0.8065 |
### Testing results
precision recall f1-score support
0 0.815 0.821 0.818 4449
1 0.752 0.773 0.762 4071
2 0.852 0.823 0.837 4245
accuracy 0.806 12765
macro avg 0.806 0.806 0.806 12765
weighted avg 0.807 0.806 0.807 12765
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.14.1
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