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---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phi_2_twitter
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_twitter
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: 0.5104
- Accuracy: 0.7647
- F1 Macro: 0.7172
- F1 Micro: 0.7647
## 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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.5921 | 0.18 | 50 | 0.6037 | 0.6958 | 0.5054 | 0.6958 |
| 0.5904 | 0.37 | 100 | 0.5537 | 0.7188 | 0.6277 | 0.7188 |
| 0.5021 | 0.55 | 150 | 0.6041 | 0.7160 | 0.5774 | 0.7160 |
| 0.5266 | 0.74 | 200 | 0.5544 | 0.7096 | 0.6496 | 0.7096 |
| 0.5427 | 0.92 | 250 | 0.5331 | 0.7399 | 0.6915 | 0.7399 |
| 0.4715 | 1.1 | 300 | 0.5436 | 0.7399 | 0.6361 | 0.7399 |
| 0.4829 | 1.29 | 350 | 0.5217 | 0.7564 | 0.7135 | 0.7564 |
| 0.4676 | 1.47 | 400 | 0.5225 | 0.7537 | 0.6829 | 0.7537 |
| 0.5196 | 1.65 | 450 | 0.5163 | 0.7629 | 0.7096 | 0.7629 |
| 0.4815 | 1.84 | 500 | 0.5213 | 0.7656 | 0.7215 | 0.7656 |
| 0.4836 | 2.02 | 550 | 0.5221 | 0.7619 | 0.7191 | 0.7619 |
| 0.4945 | 2.21 | 600 | 0.5134 | 0.7638 | 0.7173 | 0.7638 |
| 0.4103 | 2.39 | 650 | 0.5125 | 0.7684 | 0.7211 | 0.7684 |
| 0.4191 | 2.57 | 700 | 0.5108 | 0.7684 | 0.7226 | 0.7684 |
| 0.5004 | 2.76 | 750 | 0.5104 | 0.7647 | 0.7172 | 0.7647 |
| 0.4398 | 2.94 | 800 | 0.5114 | 0.7675 | 0.7138 | 0.7675 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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