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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-twitter_sentiment_analysis
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. -->
# bert-finetuned-twitter_sentiment_analysis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4113
- F1: 0.7556
- Roc Auc: 0.8165
- Accuracy: 0.7454
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log | 1.0 | 197 | 0.3564 | 0.7485 | 0.8072 | 0.6981 |
| No log | 2.0 | 394 | 0.3285 | 0.7686 | 0.8197 | 0.7010 |
| 0.3302 | 3.0 | 591 | 0.3463 | 0.7810 | 0.8315 | 0.7425 |
| 0.3302 | 4.0 | 788 | 0.3806 | 0.7730 | 0.8276 | 0.7496 |
| 0.3302 | 5.0 | 985 | 0.4113 | 0.7556 | 0.8165 | 0.7454 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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