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--- |
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base_model: haryoaw/scenario-TCR-NER_data-univner_en |
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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-non-kd-pre-ner-full-mdeberta_data-univner_en44 |
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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-non-kd-pre-ner-full-mdeberta_data-univner_en44 |
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1490 |
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- Precision: 0.8349 |
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- Recall: 0.8271 |
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- F1: 0.8310 |
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- Accuracy: 0.9846 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 44 |
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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: 30 |
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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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| 0.0023 | 1.2755 | 500 | 0.1120 | 0.8158 | 0.8344 | 0.8250 | 0.9851 | |
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| 0.0019 | 2.5510 | 1000 | 0.1055 | 0.7790 | 0.8468 | 0.8115 | 0.9840 | |
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| 0.0019 | 3.8265 | 1500 | 0.1115 | 0.8134 | 0.8395 | 0.8263 | 0.9847 | |
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| 0.0013 | 5.1020 | 2000 | 0.1248 | 0.7938 | 0.8251 | 0.8091 | 0.9835 | |
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| 0.0016 | 6.3776 | 2500 | 0.1281 | 0.8302 | 0.8147 | 0.8224 | 0.9845 | |
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| 0.0012 | 7.6531 | 3000 | 0.1241 | 0.8179 | 0.8137 | 0.8158 | 0.9841 | |
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| 0.001 | 8.9286 | 3500 | 0.1191 | 0.8184 | 0.8302 | 0.8243 | 0.9842 | |
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| 0.0005 | 10.2041 | 4000 | 0.1335 | 0.7951 | 0.8395 | 0.8167 | 0.9837 | |
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| 0.001 | 11.4796 | 4500 | 0.1376 | 0.8125 | 0.8344 | 0.8233 | 0.9843 | |
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| 0.0005 | 12.7551 | 5000 | 0.1282 | 0.8244 | 0.8313 | 0.8278 | 0.9847 | |
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| 0.0007 | 14.0306 | 5500 | 0.1247 | 0.8294 | 0.8354 | 0.8324 | 0.9848 | |
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| 0.0003 | 15.3061 | 6000 | 0.1440 | 0.8143 | 0.8354 | 0.8247 | 0.9841 | |
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| 0.0009 | 16.5816 | 6500 | 0.1456 | 0.8384 | 0.8219 | 0.8301 | 0.9840 | |
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| 0.0003 | 17.8571 | 7000 | 0.1401 | 0.8154 | 0.8416 | 0.8283 | 0.9849 | |
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| 0.0002 | 19.1327 | 7500 | 0.1478 | 0.8306 | 0.8323 | 0.8314 | 0.9850 | |
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| 0.0003 | 20.4082 | 8000 | 0.1448 | 0.8306 | 0.8271 | 0.8288 | 0.9850 | |
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| 0.0003 | 21.6837 | 8500 | 0.1416 | 0.8240 | 0.8385 | 0.8312 | 0.9847 | |
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| 0.0002 | 22.9592 | 9000 | 0.1501 | 0.8159 | 0.8395 | 0.8276 | 0.9843 | |
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| 0.0001 | 24.2347 | 9500 | 0.1457 | 0.8243 | 0.8354 | 0.8298 | 0.9845 | |
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| 0.0001 | 25.5102 | 10000 | 0.1474 | 0.8258 | 0.8344 | 0.8301 | 0.9846 | |
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| 0.0001 | 26.7857 | 10500 | 0.1493 | 0.8244 | 0.8406 | 0.8324 | 0.9847 | |
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| 0.0001 | 28.0612 | 11000 | 0.1497 | 0.8418 | 0.8209 | 0.8312 | 0.9847 | |
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| 0.0001 | 29.3367 | 11500 | 0.1490 | 0.8349 | 0.8271 | 0.8310 | 0.9846 | |
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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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