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---
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half66
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-non-kd-po-ner-full-xlmr_data-univner_half66
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.
It achieves the following results on the evaluation set:
- Loss: 0.1641
- Precision: 0.8048
- Recall: 0.8120
- F1: 0.8084
- Accuracy: 0.9797
## 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: 66
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0757 | 0.5828 | 500 | 0.0751 | 0.7550 | 0.7817 | 0.7681 | 0.9772 |
| 0.0434 | 1.1655 | 1000 | 0.0856 | 0.7626 | 0.7987 | 0.7803 | 0.9783 |
| 0.0293 | 1.7483 | 1500 | 0.0781 | 0.7835 | 0.8022 | 0.7928 | 0.9792 |
| 0.0208 | 2.3310 | 2000 | 0.0929 | 0.7929 | 0.7868 | 0.7898 | 0.9784 |
| 0.0171 | 2.9138 | 2500 | 0.0903 | 0.7893 | 0.8176 | 0.8032 | 0.9796 |
| 0.0125 | 3.4965 | 3000 | 0.1048 | 0.7915 | 0.7876 | 0.7896 | 0.9779 |
| 0.0121 | 4.0793 | 3500 | 0.1080 | 0.7890 | 0.8033 | 0.7961 | 0.9788 |
| 0.0096 | 4.6620 | 4000 | 0.1116 | 0.7827 | 0.8083 | 0.7953 | 0.9789 |
| 0.0083 | 5.2448 | 4500 | 0.1136 | 0.7957 | 0.8002 | 0.7979 | 0.9786 |
| 0.0074 | 5.8275 | 5000 | 0.1162 | 0.7785 | 0.8139 | 0.7958 | 0.9787 |
| 0.0066 | 6.4103 | 5500 | 0.1192 | 0.7851 | 0.8058 | 0.7953 | 0.9786 |
| 0.0065 | 6.9930 | 6000 | 0.1200 | 0.8077 | 0.7791 | 0.7931 | 0.9787 |
| 0.0049 | 7.5758 | 6500 | 0.1227 | 0.8039 | 0.7974 | 0.8007 | 0.9795 |
| 0.0051 | 8.1585 | 7000 | 0.1259 | 0.7906 | 0.8058 | 0.7981 | 0.9789 |
| 0.0041 | 8.7413 | 7500 | 0.1258 | 0.7992 | 0.7905 | 0.7948 | 0.9792 |
| 0.004 | 9.3240 | 8000 | 0.1336 | 0.7865 | 0.8072 | 0.7967 | 0.9786 |
| 0.0039 | 9.9068 | 8500 | 0.1261 | 0.8145 | 0.7902 | 0.8022 | 0.9792 |
| 0.0032 | 10.4895 | 9000 | 0.1309 | 0.7940 | 0.7999 | 0.7970 | 0.9791 |
| 0.0033 | 11.0723 | 9500 | 0.1320 | 0.8054 | 0.7869 | 0.7960 | 0.9793 |
| 0.0026 | 11.6550 | 10000 | 0.1408 | 0.7915 | 0.8071 | 0.7992 | 0.9789 |
| 0.0026 | 12.2378 | 10500 | 0.1404 | 0.7942 | 0.8005 | 0.7973 | 0.9788 |
| 0.0022 | 12.8205 | 11000 | 0.1363 | 0.7897 | 0.8134 | 0.8014 | 0.9797 |
| 0.0024 | 13.4033 | 11500 | 0.1442 | 0.8065 | 0.7970 | 0.8017 | 0.9793 |
| 0.0021 | 13.9860 | 12000 | 0.1401 | 0.8092 | 0.7840 | 0.7964 | 0.9789 |
| 0.0022 | 14.5688 | 12500 | 0.1382 | 0.8100 | 0.7983 | 0.8041 | 0.9792 |
| 0.0015 | 15.1515 | 13000 | 0.1506 | 0.8066 | 0.7995 | 0.8030 | 0.9793 |
| 0.0018 | 15.7343 | 13500 | 0.1437 | 0.8047 | 0.7989 | 0.8018 | 0.9794 |
| 0.002 | 16.3170 | 14000 | 0.1448 | 0.7997 | 0.8046 | 0.8022 | 0.9794 |
| 0.0016 | 16.8998 | 14500 | 0.1466 | 0.8111 | 0.7934 | 0.8021 | 0.9792 |
| 0.0013 | 17.4825 | 15000 | 0.1506 | 0.8046 | 0.7943 | 0.7994 | 0.9791 |
| 0.0012 | 18.0653 | 15500 | 0.1508 | 0.8038 | 0.8067 | 0.8052 | 0.9796 |
| 0.0011 | 18.6480 | 16000 | 0.1493 | 0.8026 | 0.8013 | 0.8020 | 0.9796 |
| 0.0011 | 19.2308 | 16500 | 0.1570 | 0.7905 | 0.8048 | 0.7976 | 0.9787 |
| 0.0011 | 19.8135 | 17000 | 0.1510 | 0.7980 | 0.8075 | 0.8027 | 0.9793 |
| 0.001 | 20.3963 | 17500 | 0.1534 | 0.7928 | 0.8173 | 0.8049 | 0.9797 |
| 0.0009 | 20.9790 | 18000 | 0.1485 | 0.7916 | 0.8181 | 0.8046 | 0.9798 |
| 0.0008 | 21.5618 | 18500 | 0.1509 | 0.8074 | 0.8045 | 0.8060 | 0.9798 |
| 0.0009 | 22.1445 | 19000 | 0.1515 | 0.8070 | 0.8084 | 0.8077 | 0.9801 |
| 0.0006 | 22.7273 | 19500 | 0.1566 | 0.8022 | 0.8106 | 0.8064 | 0.9798 |
| 0.0007 | 23.3100 | 20000 | 0.1620 | 0.8076 | 0.7976 | 0.8026 | 0.9794 |
| 0.0008 | 23.8928 | 20500 | 0.1570 | 0.8028 | 0.8084 | 0.8056 | 0.9798 |
| 0.0005 | 24.4755 | 21000 | 0.1563 | 0.8020 | 0.8110 | 0.8065 | 0.9798 |
| 0.0004 | 25.0583 | 21500 | 0.1610 | 0.8059 | 0.8013 | 0.8036 | 0.9794 |
| 0.0004 | 25.6410 | 22000 | 0.1645 | 0.8133 | 0.7943 | 0.8036 | 0.9793 |
| 0.0004 | 26.2238 | 22500 | 0.1615 | 0.8031 | 0.8100 | 0.8066 | 0.9798 |
| 0.0004 | 26.8065 | 23000 | 0.1630 | 0.8010 | 0.8156 | 0.8083 | 0.9796 |
| 0.0003 | 27.3893 | 23500 | 0.1626 | 0.8062 | 0.8114 | 0.8088 | 0.9800 |
| 0.0003 | 27.9720 | 24000 | 0.1626 | 0.8054 | 0.8147 | 0.8101 | 0.9800 |
| 0.0002 | 28.5548 | 24500 | 0.1639 | 0.8079 | 0.8107 | 0.8093 | 0.9798 |
| 0.0003 | 29.1375 | 25000 | 0.1637 | 0.8064 | 0.8085 | 0.8075 | 0.9797 |
| 0.0002 | 29.7203 | 25500 | 0.1641 | 0.8048 | 0.8120 | 0.8084 | 0.9797 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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