---
license: mit
base_model: google/gemma-2b
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: gemma-2b
  results: []
library_name: peft
datasets:
- AndersGiovanni/10-dim
pipeline_tag: text-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# gemma-2b

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2043
- Accuracy: 0.1214
- Precision: 0.5978
- Recall: 0.2784
- F1: 0.3799
- Hamming Loss: 0.1948

## 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: 0.0001
- 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: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.5.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2