---
license: llama2
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: AmsterdamDocClassificationLlama200T3Epochs
  results: []
datasets:
- FemkeBakker/AmsterdamBalancedFirst200Tokens
language:
- nl
---

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# AmsterdamDocClassificationLlama200T3Epochs

As part of the Assessing Large Language Models for Document Classification project by the Municipality of Amsterdam, we fine-tune Mistral, Llama, and GEITje for document classification. 
The fine-tuning is performed using the [AmsterdamBalancedFirst200Tokens](https://huggingface.co/datasets/FemkeBakker/AmsterdamBalancedFirst200Tokens) dataset, which consists of documents truncated to the first 200 tokens. 
In our research, we evaluate the fine-tuning of these LLMs across one, two, and three epochs. 
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and has been fine-tuned for three epochs.

It achieves the following results on the evaluation set:
- Loss: 0.8116


## Training and evaluation data

- The training data consists of 9900 documents and their labels formatted into conversations. 
- The evaluation data consists of 1100 documents and their labels formatted into conversations. 

## Training procedure

See the [GitHub](https://github.com/Amsterdam-Internships/document-classification-using-large-language-models) for specifics about the training and the code.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0345        | 0.1988 | 123  | 0.9800          |
| 0.8537        | 0.3976 | 246  | 0.8808          |
| 0.5807        | 0.5964 | 369  | 0.8503          |
| 0.7419        | 0.7952 | 492  | 0.8413          |
| 0.9967        | 0.9939 | 615  | 0.8406          |
| 0.7252        | 1.1939 | 738  | 0.8301          |
| 0.9605        | 1.3927 | 861  | 0.8214          |
| 0.7785        | 1.5915 | 984  | 0.8186          |
| 0.7233        | 1.7903 | 1107 | 0.8178          |
| 0.8389        | 1.9891 | 1230 | 0.8173          |
| 0.976         | 2.1891 | 1353 | 0.8148          |
| 0.6826        | 2.3879 | 1476 | 0.8127          |
| 0.7712        | 2.5867 | 1599 | 0.8117          |
| 0.9744        | 2.7855 | 1722 | 0.8116          |
| 1.0399        | 2.9842 | 1845 | 0.8116          |

Training time: in total it took 2 hours and 3 minutes to fine-tune the model for three epochs.


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1



### Acknowledgements
This model was trained as part of [insert thesis info] in collaboration with Amsterdam Intelligence for the City of Amsterdam.