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---
license: other
base_model: google/gemma-2b
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gemma_2b_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. -->
# gemma_2b_twitter
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4890
- Accuracy: 0.7702
- F1 Macro: 0.7272
- F1 Micro: 0.7702
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.5354 | 0.18 | 50 | 0.5565 | 0.7307 | 0.6303 | 0.7307 |
| 0.5439 | 0.37 | 100 | 0.5230 | 0.7491 | 0.6894 | 0.7491 |
| 0.4586 | 0.55 | 150 | 0.5234 | 0.7390 | 0.6373 | 0.7390 |
| 0.4596 | 0.74 | 200 | 0.4985 | 0.7647 | 0.7211 | 0.7647 |
| 0.4886 | 0.92 | 250 | 0.4890 | 0.7702 | 0.7272 | 0.7702 |
| 0.3321 | 1.1 | 300 | 0.5282 | 0.7665 | 0.7135 | 0.7665 |
| 0.3257 | 1.29 | 350 | 0.5488 | 0.7583 | 0.7216 | 0.7583 |
| 0.2753 | 1.47 | 400 | 0.5489 | 0.7463 | 0.6899 | 0.7463 |
| 0.3038 | 1.65 | 450 | 0.5646 | 0.7610 | 0.7128 | 0.7610 |
| 0.3198 | 1.84 | 500 | 0.5672 | 0.7546 | 0.7165 | 0.7546 |
| 0.2154 | 2.02 | 550 | 0.5663 | 0.7619 | 0.7196 | 0.7619 |
| 0.1491 | 2.21 | 600 | 0.6345 | 0.7546 | 0.7079 | 0.7546 |
| 0.1073 | 2.39 | 650 | 0.7069 | 0.7564 | 0.7091 | 0.7564 |
| 0.1364 | 2.57 | 700 | 0.7230 | 0.7546 | 0.7133 | 0.7546 |
| 0.1545 | 2.76 | 750 | 0.7099 | 0.7518 | 0.7038 | 0.7518 |
| 0.1718 | 2.94 | 800 | 0.7138 | 0.7472 | 0.6925 | 0.7472 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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