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--- |
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base_model: haryoaw/scenario-TCR-NER_data-univner_half |
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library_name: transformers |
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license: mit |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-kd-scr-ner-full-mdeberta_data-univner_half44 |
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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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# scenario-kd-scr-ner-full-mdeberta_data-univner_half44 |
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 364.7336 |
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- Precision: 0.3918 |
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- Recall: 0.4292 |
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- F1: 0.4096 |
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- Accuracy: 0.9267 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 44 |
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- gradient_accumulation_steps: 4 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 637.6988 | 0.5828 | 500 | 570.9927 | 0.6154 | 0.0012 | 0.0023 | 0.9241 | |
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| 541.2212 | 1.1655 | 1000 | 524.6858 | 0.3571 | 0.0353 | 0.0643 | 0.9251 | |
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| 490.5141 | 1.7483 | 1500 | 492.2816 | 0.3048 | 0.1754 | 0.2227 | 0.9310 | |
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| 455.7006 | 2.3310 | 2000 | 474.9406 | 0.3064 | 0.2626 | 0.2828 | 0.9273 | |
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| 430.062 | 2.9138 | 2500 | 452.2111 | 0.3632 | 0.3073 | 0.3329 | 0.9298 | |
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| 408.0248 | 3.4965 | 3000 | 434.8791 | 0.3994 | 0.3220 | 0.3566 | 0.9341 | |
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| 390.2744 | 4.0793 | 3500 | 424.2673 | 0.3727 | 0.3444 | 0.3580 | 0.9307 | |
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| 374.3932 | 4.6620 | 4000 | 411.0975 | 0.4020 | 0.3979 | 0.3999 | 0.9328 | |
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| 362.2752 | 5.2448 | 4500 | 403.7659 | 0.3614 | 0.3963 | 0.3781 | 0.9239 | |
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| 350.9508 | 5.8275 | 5000 | 392.9673 | 0.3736 | 0.3855 | 0.3795 | 0.9296 | |
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| 341.7654 | 6.4103 | 5500 | 385.3136 | 0.4030 | 0.3972 | 0.4001 | 0.9302 | |
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| 334.4205 | 6.9930 | 6000 | 380.2038 | 0.3773 | 0.4142 | 0.3949 | 0.9263 | |
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| 327.3654 | 7.5758 | 6500 | 375.4951 | 0.3694 | 0.4276 | 0.3964 | 0.9227 | |
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| 322.1269 | 8.1585 | 7000 | 372.3464 | 0.3650 | 0.4338 | 0.3965 | 0.9209 | |
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| 318.558 | 8.7413 | 7500 | 366.4694 | 0.3970 | 0.4191 | 0.4078 | 0.9295 | |
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| 315.4182 | 9.3240 | 8000 | 365.6752 | 0.3861 | 0.4409 | 0.4117 | 0.9260 | |
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| 314.0958 | 9.9068 | 8500 | 364.7336 | 0.3918 | 0.4292 | 0.4096 | 0.9267 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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