---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Phi-3-mini-4k-instruct-mbti-2
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/0x5scv56)
# Phi-3-mini-4k-instruct-mbti-2

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6372
- Accuracy: 0.6461
- F1: 0.7150

## 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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.7097        | 0.1582 | 2000  | 0.6888          | 0.6080   | 0.7463 |
| 0.6833        | 0.3164 | 4000  | 0.6675          | 0.5969   | 0.6512 |
| 0.6713        | 0.4745 | 6000  | 0.6596          | 0.6138   | 0.6748 |
| 0.6592        | 0.6327 | 8000  | 0.6540          | 0.6389   | 0.7222 |
| 0.6498        | 0.7909 | 10000 | 0.6456          | 0.5914   | 0.5887 |
| 0.6362        | 0.9491 | 12000 | 0.6372          | 0.6461   | 0.7150 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1