---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large
  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-large

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2253
- Precision: 0.6260
- Recall: 0.6749
- F1: 0.6495
- Accuracy: 0.9376

## 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: 4
- eval_batch_size: 8
- seed: 42
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4658        | 1.0   | 746  | 0.2568          | 0.5354    | 0.5597 | 0.5473 | 0.9154   |
| 0.245         | 2.0   | 1492 | 0.2295          | 0.6059    | 0.6708 | 0.6367 | 0.9297   |
| 0.0948        | 3.0   | 2238 | 0.2253          | 0.6260    | 0.6749 | 0.6495 | 0.9376   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1