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--- |
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library_name: peft |
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license: llama3.2 |
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base_model: meta-llama/Llama-3.2-1B |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: Llama-3.2-1B-binary-citation-classifier |
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results: [] |
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--- |
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# Llama-3.2-1B-binary-citation-classifier |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on a dataset of scientific abstracts and citation counts. |
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Its aim is to predict, based on an article abstract, if an article will be cited within five years or not. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5450 |
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- Accuracy: 0.746 |
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- F1: 0.7460 |
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- Precision: 0.7460 |
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- Recall: 0.746 |
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## Model description |
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Llama-3.2-1B architecture, modified with a rank 8 LORA adapter. |
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## Intended uses & limitations |
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Intended use is binary classification. The training set consists of PubMed indexed neuroscience-related articles exclusively. |
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## Training and evaluation data |
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[Training and evalutation data](https://huggingface.co/datasets/rudyvdbrink/CitationDatabase) |
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## Training procedure |
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Pre-training following Meta's procedures. |
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LORA fine tuning with PEFT on 16k abstracts (8k cited, 8k uncited) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6249 | 1.0 | 500 | 0.5853 | 0.716 | 0.7160 | 0.7161 | 0.716 | |
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| 0.5585 | 2.0 | 1000 | 0.5523 | 0.748 | 0.7478 | 0.7487 | 0.748 | |
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| 0.6066 | 3.0 | 1500 | 0.5303 | 0.7535 | 0.7535 | 0.7535 | 0.7535 | |
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| 0.5447 | 4.0 | 2000 | 0.5202 | 0.761 | 0.7609 | 0.7615 | 0.761 | |
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| 0.4709 | 5.0 | 2500 | 0.5168 | 0.7645 | 0.7645 | 0.7645 | 0.7645 | |
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| 0.5002 | 6.0 | 3000 | 0.5137 | 0.7695 | 0.7695 | 0.7696 | 0.7695 | |
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### Framework versions |
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- PEFT 0.15.2 |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.2 |
