metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: sentiment-bert-base-uncased
results: []
sentiment-bert-base-uncased
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3626
- Precision: 0.8862
- Recall: 0.8829
- F1: 0.8845
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
---|---|---|---|---|---|---|
0.2839 | 0.9995 | 1024 | 0.2913 | 0.8693 | 0.8853 | 0.8663 |
0.2686 | 2.0 | 2049 | 0.2956 | 0.8914 | 0.8804 | 0.8849 |
0.2786 | 2.9985 | 3072 | 0.3626 | 0.8862 | 0.8829 | 0.8845 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1