metadata
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: []
bert-finetuned-twitter_sentiment_analysis
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4175
- F1: 0.7741
- Roc Auc: 0.8301
- Accuracy: 0.7639
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 | 0.3546 | 0.7613 | 0.8172 | 0.7210 |
| No log | 2.0 | 394 | 0.3312 | 0.7622 | 0.8151 | 0.6924 |
| 0.3121 | 3.0 | 591 | 0.3511 | 0.7699 | 0.8244 | 0.7368 |
| 0.3121 | 4.0 | 788 | 0.4018 | 0.7833 | 0.8355 | 0.7654 |
| 0.3121 | 5.0 | 985 | 0.4175 | 0.7741 | 0.8301 | 0.7639 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3