<!-- 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. -->
mangersFeedback-V1.0.2
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: 0.2372
- F1: 0.9290
- Recall: 0.9290
- Precision: 0.9290
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
---|---|---|---|---|---|---|
0.2398 | 1.0 | 385 | 0.2383 | 0.9294 | 0.9294 | 0.9294 |
0.0931 | 2.0 | 770 | 0.2404 | 0.9192 | 0.9192 | 0.9192 |
0.0761 | 3.0 | 1155 | 0.2458 | 0.9263 | 0.9263 | 0.9263 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3