HRVibeCheck-BERT / README.md
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HRVibeCheck-Hire-Recommendation-Model
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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: HRVibeCheck-BERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# HRVibeCheck-BERT
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4412
- Accuracy: 0.8040
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.3513 | 1.0 | 310 | 0.6149 | 0.6935 |
| 4.5175 | 2.0 | 620 | 0.4793 | 0.7653 |
| 3.9709 | 3.0 | 930 | 0.4676 | 0.7685 |
| 2.9567 | 4.0 | 1240 | 0.4412 | 0.8040 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2