metadata
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_half
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half44
results: []
scenario-non-kd-po-ner-full-xlmr_data-univner_half44
This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_half on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1624
- Precision: 0.8067
- Recall: 0.8152
- F1: 0.8109
- Accuracy: 0.9801
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.075 | 0.5828 | 500 | 0.0769 | 0.7627 | 0.7648 | 0.7638 | 0.9772 |
0.0416 | 1.1655 | 1000 | 0.0813 | 0.7656 | 0.8016 | 0.7832 | 0.9786 |
0.0287 | 1.7483 | 1500 | 0.0833 | 0.7729 | 0.7989 | 0.7857 | 0.9785 |
0.022 | 2.3310 | 2000 | 0.1011 | 0.7465 | 0.7987 | 0.7717 | 0.9767 |
0.0171 | 2.9138 | 2500 | 0.1033 | 0.7570 | 0.8166 | 0.7857 | 0.9774 |
0.0133 | 3.4965 | 3000 | 0.0985 | 0.7734 | 0.8160 | 0.7942 | 0.9786 |
0.0121 | 4.0793 | 3500 | 0.1039 | 0.7721 | 0.8120 | 0.7916 | 0.9787 |
0.0093 | 4.6620 | 4000 | 0.1150 | 0.7766 | 0.8031 | 0.7896 | 0.9781 |
0.0083 | 5.2448 | 4500 | 0.1197 | 0.7771 | 0.8194 | 0.7977 | 0.9788 |
0.0077 | 5.8275 | 5000 | 0.1180 | 0.7721 | 0.8094 | 0.7903 | 0.9784 |
0.0073 | 6.4103 | 5500 | 0.1169 | 0.7924 | 0.8061 | 0.7992 | 0.9793 |
0.0065 | 6.9930 | 6000 | 0.1201 | 0.8011 | 0.8018 | 0.8014 | 0.9791 |
0.0055 | 7.5758 | 6500 | 0.1152 | 0.7950 | 0.7958 | 0.7954 | 0.9792 |
0.0045 | 8.1585 | 7000 | 0.1259 | 0.7825 | 0.8133 | 0.7976 | 0.9790 |
0.0039 | 8.7413 | 7500 | 0.1300 | 0.7878 | 0.8101 | 0.7988 | 0.9793 |
0.0043 | 9.3240 | 8000 | 0.1279 | 0.8026 | 0.7960 | 0.7993 | 0.9792 |
0.0041 | 9.9068 | 8500 | 0.1249 | 0.8033 | 0.7997 | 0.8015 | 0.9790 |
0.0033 | 10.4895 | 9000 | 0.1315 | 0.7890 | 0.8110 | 0.7999 | 0.9793 |
0.0034 | 11.0723 | 9500 | 0.1268 | 0.7914 | 0.8005 | 0.7959 | 0.9789 |
0.0024 | 11.6550 | 10000 | 0.1354 | 0.7943 | 0.8088 | 0.8015 | 0.9798 |
0.0023 | 12.2378 | 10500 | 0.1397 | 0.8003 | 0.8130 | 0.8066 | 0.9798 |
0.0024 | 12.8205 | 11000 | 0.1428 | 0.7779 | 0.8175 | 0.7972 | 0.9788 |
0.0023 | 13.4033 | 11500 | 0.1389 | 0.7896 | 0.8070 | 0.7982 | 0.9792 |
0.0022 | 13.9860 | 12000 | 0.1429 | 0.8 | 0.8051 | 0.8025 | 0.9794 |
0.0017 | 14.5688 | 12500 | 0.1458 | 0.7922 | 0.8113 | 0.8016 | 0.9791 |
0.002 | 15.1515 | 13000 | 0.1430 | 0.7955 | 0.8121 | 0.8037 | 0.9794 |
0.0016 | 15.7343 | 13500 | 0.1487 | 0.7795 | 0.8185 | 0.7985 | 0.9789 |
0.0019 | 16.3170 | 14000 | 0.1401 | 0.7994 | 0.8044 | 0.8019 | 0.9796 |
0.0016 | 16.8998 | 14500 | 0.1468 | 0.7964 | 0.8133 | 0.8048 | 0.9797 |
0.0016 | 17.4825 | 15000 | 0.1440 | 0.7955 | 0.8100 | 0.8027 | 0.9794 |
0.0012 | 18.0653 | 15500 | 0.1430 | 0.8002 | 0.8119 | 0.8060 | 0.9798 |
0.0009 | 18.6480 | 16000 | 0.1558 | 0.7855 | 0.8166 | 0.8007 | 0.9788 |
0.0014 | 19.2308 | 16500 | 0.1478 | 0.8012 | 0.8119 | 0.8065 | 0.9797 |
0.001 | 19.8135 | 17000 | 0.1498 | 0.8101 | 0.8087 | 0.8094 | 0.9798 |
0.0009 | 20.3963 | 17500 | 0.1515 | 0.7946 | 0.8116 | 0.8030 | 0.9795 |
0.0008 | 20.9790 | 18000 | 0.1546 | 0.7967 | 0.8165 | 0.8065 | 0.9796 |
0.0009 | 21.5618 | 18500 | 0.1553 | 0.7953 | 0.8162 | 0.8056 | 0.9796 |
0.0008 | 22.1445 | 19000 | 0.1540 | 0.8001 | 0.8103 | 0.8052 | 0.9798 |
0.0006 | 22.7273 | 19500 | 0.1620 | 0.7877 | 0.8208 | 0.8039 | 0.9793 |
0.0007 | 23.3100 | 20000 | 0.1605 | 0.8127 | 0.7999 | 0.8062 | 0.9795 |
0.0005 | 23.8928 | 20500 | 0.1589 | 0.7974 | 0.8132 | 0.8052 | 0.9799 |
0.0005 | 24.4755 | 21000 | 0.1593 | 0.8016 | 0.8093 | 0.8054 | 0.9797 |
0.0006 | 25.0583 | 21500 | 0.1561 | 0.8060 | 0.8114 | 0.8087 | 0.9800 |
0.0005 | 25.6410 | 22000 | 0.1587 | 0.8028 | 0.8152 | 0.8089 | 0.9798 |
0.0004 | 26.2238 | 22500 | 0.1582 | 0.8058 | 0.8101 | 0.8080 | 0.9802 |
0.0004 | 26.8065 | 23000 | 0.1612 | 0.8003 | 0.8162 | 0.8081 | 0.9799 |
0.0003 | 27.3893 | 23500 | 0.1609 | 0.8066 | 0.8142 | 0.8104 | 0.9801 |
0.0003 | 27.9720 | 24000 | 0.1627 | 0.8032 | 0.8178 | 0.8104 | 0.9801 |
0.0003 | 28.5548 | 24500 | 0.1616 | 0.8081 | 0.8127 | 0.8104 | 0.9801 |
0.0003 | 29.1375 | 25000 | 0.1627 | 0.8065 | 0.8153 | 0.8109 | 0.9801 |
0.0002 | 29.7203 | 25500 | 0.1624 | 0.8067 | 0.8152 | 0.8109 | 0.9801 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1