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--- |
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license: apache-2.0 |
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base_model: Rijgersberg/GEITje-7B-chat-v2 |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: AmsterdamDocClassificationGEITje200T2Epochs |
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results: [] |
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datasets: |
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- FemkeBakker/AmsterdamBalancedFirst200Tokens |
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language: |
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- nl |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# AmsterdamDocClassificationGEITje200T2Epochs |
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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. |
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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. |
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In our research, we evaluate the fine-tuning of these LLMs across one, two, and three epochs. |
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This model is a fine-tuned version of [Rijgersberg/GEITje-7B-chat-v2](https://huggingface.co/Rijgersberg/GEITje-7B-chat-v2) and has been fine-tuned for two epochs. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5796 |
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## Training and evaluation data |
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- The training data consists of 9900 documents and their labels formatted into conversations. |
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- The evaluation data consists of 1100 documents and their labels formatted into conversations. |
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## Training procedure |
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See the [GitHub](https://github.com/Amsterdam-Internships/document-classification-using-large-language-models) for specifics about the training and the code. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.7664 | 0.1988 | 123 | 0.6890 | |
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| 0.6617 | 0.3976 | 246 | 0.6347 | |
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| 0.3825 | 0.5964 | 369 | 0.6028 | |
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| 0.4427 | 0.7952 | 492 | 0.5913 | |
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| 0.6739 | 0.9939 | 615 | 0.5906 | |
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| 0.4407 | 1.1939 | 738 | 0.5918 | |
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| 0.6671 | 1.3927 | 861 | 0.5835 | |
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| 0.4845 | 1.5915 | 984 | 0.5802 | |
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| 0.4699 | 1.7903 | 1107 | 0.5796 | |
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| 0.5434 | 1.9891 | 1230 | 0.5796 | |
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Training time: it took in total 1 hour and 36 minutes to fine-tune the model for two epochs. |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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### Acknowledgements |
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This model was trained as part of [insert thesis info] in collaboration with Amsterdam Intelligence for the City of Amsterdam. |
