---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: hebert-finetuned-hebrew-metaphor
  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. -->

# hebert-finetuned-hebrew-metaphor

This model is a fine-tuned version of [avichr/heBERT](https://huggingface.co/avichr/heBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4682
- Accuracy: 0.9510

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 389  | 0.1813          | 0.9379   |
| 0.2546        | 2.0   | 778  | 0.2309          | 0.9479   |
| 0.08          | 3.0   | 1167 | 0.3342          | 0.9492   |
| 0.0298        | 4.0   | 1556 | 0.4076          | 0.9460   |
| 0.0298        | 5.0   | 1945 | 0.3803          | 0.9485   |
| 0.0105        | 6.0   | 2334 | 0.3674          | 0.9454   |
| 0.0077        | 7.0   | 2723 | 0.5356          | 0.9410   |
| 0.0088        | 8.0   | 3112 | 0.4776          | 0.9422   |
| 0.0044        | 9.0   | 3501 | 0.4258          | 0.9504   |
| 0.0044        | 10.0  | 3890 | 0.4305          | 0.9523   |
| 0.001         | 11.0  | 4279 | 0.4357          | 0.9548   |
| 0.0031        | 12.0  | 4668 | 0.4770          | 0.9473   |
| 0.0015        | 13.0  | 5057 | 0.4604          | 0.9523   |
| 0.0015        | 14.0  | 5446 | 0.4670          | 0.9510   |
| 0.0022        | 15.0  | 5835 | 0.4682          | 0.9510   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3