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--- |
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license: mit |
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base_model: roberta-base |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: EVALutionRelationTrain-5 |
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results: [] |
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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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# EVALutionRelationTrain-5 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6933 |
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- Accuracy: 0.5 |
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- Precision: 0.5 |
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- Recall: 1.0 |
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- F1: 0.6667 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-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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- 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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.35 | 100 | 0.7119 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| No log | 0.71 | 200 | 0.7123 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| No log | 1.06 | 300 | 0.6936 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| No log | 1.42 | 400 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6993 | 1.77 | 500 | 0.6945 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6993 | 2.13 | 600 | 0.6948 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6993 | 2.48 | 700 | 0.6999 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6993 | 2.84 | 800 | 0.6943 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6993 | 3.19 | 900 | 0.6951 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.698 | 3.55 | 1000 | 0.6945 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.698 | 3.9 | 1100 | 0.6956 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.698 | 4.26 | 1200 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.698 | 4.61 | 1300 | 0.6941 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.698 | 4.96 | 1400 | 0.6934 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6964 | 5.32 | 1500 | 0.6933 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6964 | 5.67 | 1600 | 0.6943 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6964 | 6.03 | 1700 | 0.6946 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6964 | 6.38 | 1800 | 0.6932 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6964 | 6.74 | 1900 | 0.6952 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6952 | 7.09 | 2000 | 0.6934 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6952 | 7.45 | 2100 | 0.6935 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6952 | 7.8 | 2200 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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