---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phi_2_ledgar
  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. -->

# phi_2_ledgar

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6120
- Accuracy: 0.826
- F1 Macro: 0.7355
- F1 Micro: 0.826

## 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 3.6034        | 0.11  | 100  | 3.2114          | 0.337    | 0.1236   | 0.337    |
| 2.2678        | 0.21  | 200  | 1.9837          | 0.5623   | 0.3331   | 0.5623   |
| 1.4927        | 0.32  | 300  | 1.3369          | 0.6712   | 0.4884   | 0.6712   |
| 1.1518        | 0.43  | 400  | 1.0526          | 0.7243   | 0.5613   | 0.7243   |
| 1.1041        | 0.53  | 500  | 0.9305          | 0.7521   | 0.6206   | 0.7521   |
| 1.0144        | 0.64  | 600  | 0.9068          | 0.7574   | 0.6294   | 0.7574   |
| 0.9892        | 0.75  | 700  | 0.8712          | 0.7669   | 0.6430   | 0.7669   |
| 0.9972        | 0.85  | 800  | 0.8591          | 0.7675   | 0.6369   | 0.7675   |
| 0.8439        | 0.96  | 900  | 0.7895          | 0.7848   | 0.6835   | 0.7848   |
| 0.7409        | 1.07  | 1000 | 0.7614          | 0.7944   | 0.6809   | 0.7944   |
| 0.7627        | 1.17  | 1100 | 0.7539          | 0.7946   | 0.6810   | 0.7946   |
| 0.8065        | 1.28  | 1200 | 0.7289          | 0.8008   | 0.6945   | 0.8008   |
| 0.7359        | 1.39  | 1300 | 0.7254          | 0.8034   | 0.6976   | 0.8034   |
| 0.6525        | 1.49  | 1400 | 0.7073          | 0.8065   | 0.7050   | 0.8065   |
| 0.7359        | 1.6   | 1500 | 0.7206          | 0.8033   | 0.6949   | 0.8033   |
| 0.7291        | 1.71  | 1600 | 0.6924          | 0.8089   | 0.7066   | 0.8089   |
| 0.7072        | 1.81  | 1700 | 0.6764          | 0.8102   | 0.7070   | 0.8102   |
| 0.6688        | 1.92  | 1800 | 0.6546          | 0.814    | 0.7128   | 0.814    |
| 0.6253        | 2.03  | 1900 | 0.6506          | 0.8158   | 0.7059   | 0.8158   |
| 0.6044        | 2.13  | 2000 | 0.6603          | 0.8155   | 0.7165   | 0.8155   |
| 0.6414        | 2.24  | 2100 | 0.6435          | 0.8138   | 0.7185   | 0.8138   |
| 0.6115        | 2.35  | 2200 | 0.6368          | 0.8216   | 0.7280   | 0.8216   |
| 0.6331        | 2.45  | 2300 | 0.6273          | 0.8208   | 0.7251   | 0.8208   |
| 0.608         | 2.56  | 2400 | 0.6252          | 0.8232   | 0.7286   | 0.8232   |
| 0.5879        | 2.67  | 2500 | 0.6172          | 0.8241   | 0.7308   | 0.8241   |
| 0.6056        | 2.77  | 2600 | 0.6157          | 0.8257   | 0.7346   | 0.8257   |
| 0.5711        | 2.88  | 2700 | 0.6129          | 0.8253   | 0.7341   | 0.8253   |
| 0.5802        | 2.99  | 2800 | 0.6120          | 0.826    | 0.7355   | 0.826    |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2