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---
base_model: FacebookAI/xlm-roberta-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-pre-ner-full-xlmr_data-univner_half44
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-pre-ner-full-xlmr_data-univner_half44
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1667
- Precision: 0.8
- Recall: 0.8085
- F1: 0.8042
- Accuracy: 0.9794
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1407 | 0.5828 | 500 | 0.0805 | 0.7146 | 0.7383 | 0.7262 | 0.9736 |
| 0.0685 | 1.1655 | 1000 | 0.0771 | 0.7453 | 0.7904 | 0.7672 | 0.9766 |
| 0.0504 | 1.7483 | 1500 | 0.0763 | 0.7569 | 0.7948 | 0.7754 | 0.9778 |
| 0.0384 | 2.3310 | 2000 | 0.0867 | 0.7371 | 0.7931 | 0.7641 | 0.9760 |
| 0.0306 | 2.9138 | 2500 | 0.0880 | 0.7501 | 0.8074 | 0.7777 | 0.9768 |
| 0.0223 | 3.4965 | 3000 | 0.0928 | 0.7585 | 0.8097 | 0.7833 | 0.9775 |
| 0.0202 | 4.0793 | 3500 | 0.0958 | 0.7641 | 0.7971 | 0.7803 | 0.9777 |
| 0.0151 | 4.6620 | 4000 | 0.0985 | 0.7690 | 0.8044 | 0.7863 | 0.9778 |
| 0.0134 | 5.2448 | 4500 | 0.1051 | 0.7857 | 0.7963 | 0.7910 | 0.9787 |
| 0.0112 | 5.8275 | 5000 | 0.1080 | 0.7677 | 0.8016 | 0.7843 | 0.9786 |
| 0.0096 | 6.4103 | 5500 | 0.1158 | 0.7698 | 0.8083 | 0.7886 | 0.9781 |
| 0.0092 | 6.9930 | 6000 | 0.1130 | 0.7857 | 0.8009 | 0.7932 | 0.9783 |
| 0.0074 | 7.5758 | 6500 | 0.1161 | 0.7749 | 0.8068 | 0.7906 | 0.9785 |
| 0.0067 | 8.1585 | 7000 | 0.1194 | 0.7887 | 0.7922 | 0.7905 | 0.9783 |
| 0.0058 | 8.7413 | 7500 | 0.1179 | 0.7796 | 0.8129 | 0.7959 | 0.9786 |
| 0.0053 | 9.3240 | 8000 | 0.1266 | 0.7824 | 0.8088 | 0.7954 | 0.9782 |
| 0.0049 | 9.9068 | 8500 | 0.1273 | 0.7858 | 0.7960 | 0.7909 | 0.9786 |
| 0.0043 | 10.4895 | 9000 | 0.1301 | 0.7965 | 0.7977 | 0.7971 | 0.9789 |
| 0.0041 | 11.0723 | 9500 | 0.1289 | 0.7992 | 0.7941 | 0.7966 | 0.9784 |
| 0.0035 | 11.6550 | 10000 | 0.1344 | 0.7904 | 0.8088 | 0.7995 | 0.9786 |
| 0.0033 | 12.2378 | 10500 | 0.1391 | 0.7889 | 0.8032 | 0.7960 | 0.9786 |
| 0.0032 | 12.8205 | 11000 | 0.1431 | 0.7642 | 0.8096 | 0.7862 | 0.9777 |
| 0.0029 | 13.4033 | 11500 | 0.1359 | 0.8006 | 0.7969 | 0.7987 | 0.9787 |
| 0.0028 | 13.9860 | 12000 | 0.1393 | 0.7874 | 0.8137 | 0.8003 | 0.9790 |
| 0.0023 | 14.5688 | 12500 | 0.1426 | 0.7907 | 0.8012 | 0.7959 | 0.9787 |
| 0.0022 | 15.1515 | 13000 | 0.1441 | 0.7945 | 0.8096 | 0.8020 | 0.9791 |
| 0.002 | 15.7343 | 13500 | 0.1498 | 0.7860 | 0.8041 | 0.7950 | 0.9782 |
| 0.0023 | 16.3170 | 14000 | 0.1442 | 0.7844 | 0.8090 | 0.7965 | 0.9787 |
| 0.0016 | 16.8998 | 14500 | 0.1534 | 0.7943 | 0.8080 | 0.8011 | 0.9789 |
| 0.0017 | 17.4825 | 15000 | 0.1483 | 0.7915 | 0.8019 | 0.7967 | 0.9789 |
| 0.0017 | 18.0653 | 15500 | 0.1521 | 0.8066 | 0.7932 | 0.7999 | 0.9791 |
| 0.0014 | 18.6480 | 16000 | 0.1517 | 0.7984 | 0.8022 | 0.8003 | 0.9792 |
| 0.0014 | 19.2308 | 16500 | 0.1549 | 0.7820 | 0.8136 | 0.7975 | 0.9789 |
| 0.0011 | 19.8135 | 17000 | 0.1546 | 0.7980 | 0.8035 | 0.8007 | 0.9792 |
| 0.0012 | 20.3963 | 17500 | 0.1601 | 0.7842 | 0.8062 | 0.7950 | 0.9785 |
| 0.0011 | 20.9790 | 18000 | 0.1596 | 0.7830 | 0.8012 | 0.7920 | 0.9785 |
| 0.0009 | 21.5618 | 18500 | 0.1616 | 0.7911 | 0.8116 | 0.8012 | 0.9788 |
| 0.0012 | 22.1445 | 19000 | 0.1617 | 0.7834 | 0.8087 | 0.7958 | 0.9782 |
| 0.0008 | 22.7273 | 19500 | 0.1621 | 0.7917 | 0.8104 | 0.8009 | 0.9792 |
| 0.0009 | 23.3100 | 20000 | 0.1637 | 0.7927 | 0.8010 | 0.7968 | 0.9786 |
| 0.0007 | 23.8928 | 20500 | 0.1637 | 0.7720 | 0.8126 | 0.7918 | 0.9784 |
| 0.0006 | 24.4755 | 21000 | 0.1628 | 0.8003 | 0.7995 | 0.7999 | 0.9791 |
| 0.0007 | 25.0583 | 21500 | 0.1635 | 0.7904 | 0.8094 | 0.7998 | 0.9789 |
| 0.0005 | 25.6410 | 22000 | 0.1651 | 0.7942 | 0.8121 | 0.8031 | 0.9793 |
| 0.0005 | 26.2238 | 22500 | 0.1652 | 0.7958 | 0.8114 | 0.8035 | 0.9792 |
| 0.0005 | 26.8065 | 23000 | 0.1653 | 0.7936 | 0.8087 | 0.8011 | 0.9792 |
| 0.0005 | 27.3893 | 23500 | 0.1661 | 0.8005 | 0.8097 | 0.8050 | 0.9794 |
| 0.0004 | 27.9720 | 24000 | 0.1661 | 0.7953 | 0.8100 | 0.8026 | 0.9792 |
| 0.0004 | 28.5548 | 24500 | 0.1668 | 0.7940 | 0.8108 | 0.8023 | 0.9793 |
| 0.0004 | 29.1375 | 25000 | 0.1666 | 0.7944 | 0.8081 | 0.8012 | 0.9791 |
| 0.0003 | 29.7203 | 25500 | 0.1667 | 0.8 | 0.8085 | 0.8042 | 0.9794 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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