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hongpingjun98
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run1

Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: michiyasunaga/BioLinkBERT-base
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - sem_eval_2024_task_2
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: run1
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: sem_eval_2024_task_2
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+ type: sem_eval_2024_task_2
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+ config: sem_eval_2024_task_2_source
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+ split: validation
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+ args: sem_eval_2024_task_2_source
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.64
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+ - name: Precision
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+ type: precision
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+ value: 0.6582994120307553
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+ - name: Recall
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+ type: recall
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+ value: 0.64
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+ - name: F1
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+ type: f1
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+ value: 0.6292863762743282
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+ ---
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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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+
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+ # run1
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+
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+ This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the sem_eval_2024_task_2 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2153
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+ - Accuracy: 0.64
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+ - Precision: 0.6583
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+ - Recall: 0.64
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+ - F1: 0.6293
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 16
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | No log | 0.99 | 53 | 0.6971 | 0.515 | 0.5272 | 0.515 | 0.4537 |
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+ | 0.7029 | 2.0 | 107 | 0.6899 | 0.535 | 0.5413 | 0.535 | 0.5166 |
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+ | 0.7029 | 2.99 | 160 | 0.6855 | 0.535 | 0.5399 | 0.5350 | 0.5203 |
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+ | 0.6955 | 4.0 | 214 | 0.6698 | 0.565 | 0.5686 | 0.5650 | 0.5592 |
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+ | 0.6955 | 4.99 | 267 | 0.6722 | 0.57 | 0.5703 | 0.5700 | 0.5696 |
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+ | 0.6581 | 6.0 | 321 | 0.6367 | 0.61 | 0.6104 | 0.61 | 0.6096 |
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+ | 0.6581 | 6.99 | 374 | 0.6973 | 0.58 | 0.5905 | 0.58 | 0.5675 |
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+ | 0.5796 | 8.0 | 428 | 0.6925 | 0.625 | 0.6348 | 0.625 | 0.6180 |
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+ | 0.5796 | 8.99 | 481 | 0.7539 | 0.61 | 0.6364 | 0.61 | 0.5902 |
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+ | 0.4636 | 10.0 | 535 | 0.9313 | 0.575 | 0.6043 | 0.575 | 0.5429 |
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+ | 0.4636 | 10.99 | 588 | 0.9028 | 0.615 | 0.6227 | 0.615 | 0.6089 |
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+ | 0.3577 | 12.0 | 642 | 0.8694 | 0.615 | 0.6227 | 0.615 | 0.6089 |
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+ | 0.3577 | 12.99 | 695 | 0.9201 | 0.635 | 0.6494 | 0.635 | 0.6260 |
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+ | 0.3041 | 14.0 | 749 | 0.9186 | 0.645 | 0.6583 | 0.645 | 0.6374 |
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+ | 0.3041 | 14.99 | 802 | 1.1683 | 0.63 | 0.6578 | 0.63 | 0.6129 |
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+ | 0.2344 | 16.0 | 856 | 1.1405 | 0.625 | 0.6383 | 0.625 | 0.6158 |
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+ | 0.2344 | 16.99 | 909 | 1.2451 | 0.625 | 0.6474 | 0.625 | 0.6102 |
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+ | 0.208 | 18.0 | 963 | 1.1640 | 0.65 | 0.6671 | 0.65 | 0.6408 |
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+ | 0.208 | 18.99 | 1016 | 1.2081 | 0.64 | 0.6583 | 0.64 | 0.6293 |
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+ | 0.1757 | 19.81 | 1060 | 1.2153 | 0.64 | 0.6583 | 0.64 | 0.6293 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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