---
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_half55
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-non-kd-pre-ner-full-xlmr_data-univner_half55

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: 0.1716
- Precision: 0.7969
- Recall: 0.7989
- F1: 0.7979
- Accuracy: 0.9786

## 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: 55
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.144         | 0.5828  | 500   | 0.0813          | 0.6995    | 0.7625 | 0.7297 | 0.9732   |
| 0.0678        | 1.1655  | 1000  | 0.0775          | 0.7546    | 0.7816 | 0.7678 | 0.9772   |
| 0.0531        | 1.7483  | 1500  | 0.0810          | 0.7244    | 0.8137 | 0.7665 | 0.9760   |
| 0.0368        | 2.3310  | 2000  | 0.0831          | 0.7742    | 0.7919 | 0.7830 | 0.9782   |
| 0.0311        | 2.9138  | 2500  | 0.0871          | 0.7695    | 0.7917 | 0.7804 | 0.9773   |
| 0.0222        | 3.4965  | 3000  | 0.0916          | 0.7856    | 0.7860 | 0.7858 | 0.9785   |
| 0.0196        | 4.0793  | 3500  | 0.0982          | 0.7778    | 0.7990 | 0.7883 | 0.9781   |
| 0.0145        | 4.6620  | 4000  | 0.1004          | 0.7769    | 0.7927 | 0.7847 | 0.9778   |
| 0.0131        | 5.2448  | 4500  | 0.1058          | 0.7719    | 0.7878 | 0.7798 | 0.9769   |
| 0.0108        | 5.8275  | 5000  | 0.1116          | 0.7830    | 0.7919 | 0.7875 | 0.9775   |
| 0.0086        | 6.4103  | 5500  | 0.1137          | 0.7743    | 0.8018 | 0.7878 | 0.9778   |
| 0.009         | 6.9930  | 6000  | 0.1180          | 0.7739    | 0.8078 | 0.7905 | 0.9778   |
| 0.0066        | 7.5758  | 6500  | 0.1189          | 0.7761    | 0.8090 | 0.7922 | 0.9782   |
| 0.007         | 8.1585  | 7000  | 0.1281          | 0.7813    | 0.7869 | 0.7841 | 0.9777   |
| 0.0059        | 8.7413  | 7500  | 0.1222          | 0.7781    | 0.8070 | 0.7923 | 0.9785   |
| 0.0057        | 9.3240  | 8000  | 0.1298          | 0.7694    | 0.8124 | 0.7903 | 0.9781   |
| 0.0053        | 9.9068  | 8500  | 0.1260          | 0.7919    | 0.7951 | 0.7935 | 0.9787   |
| 0.0043        | 10.4895 | 9000  | 0.1356          | 0.7719    | 0.8062 | 0.7887 | 0.9778   |
| 0.0042        | 11.0723 | 9500  | 0.1309          | 0.7850    | 0.7982 | 0.7915 | 0.9786   |
| 0.0042        | 11.6550 | 10000 | 0.1356          | 0.7789    | 0.7922 | 0.7855 | 0.9779   |
| 0.0034        | 12.2378 | 10500 | 0.1367          | 0.7781    | 0.8013 | 0.7895 | 0.9782   |
| 0.0032        | 12.8205 | 11000 | 0.1409          | 0.7732    | 0.8123 | 0.7923 | 0.9781   |
| 0.0022        | 13.4033 | 11500 | 0.1498          | 0.7707    | 0.8068 | 0.7883 | 0.9778   |
| 0.0031        | 13.9860 | 12000 | 0.1454          | 0.7704    | 0.8133 | 0.7913 | 0.9781   |
| 0.0022        | 14.5688 | 12500 | 0.1436          | 0.7922    | 0.7934 | 0.7928 | 0.9784   |
| 0.0024        | 15.1515 | 13000 | 0.1461          | 0.7734    | 0.8077 | 0.7902 | 0.9778   |
| 0.0023        | 15.7343 | 13500 | 0.1465          | 0.7918    | 0.7996 | 0.7957 | 0.9786   |
| 0.0017        | 16.3170 | 14000 | 0.1506          | 0.7838    | 0.8022 | 0.7929 | 0.9783   |
| 0.0018        | 16.8998 | 14500 | 0.1466          | 0.7953    | 0.7973 | 0.7963 | 0.9787   |
| 0.0018        | 17.4825 | 15000 | 0.1502          | 0.7941    | 0.8012 | 0.7976 | 0.9789   |
| 0.0019        | 18.0653 | 15500 | 0.1515          | 0.7871    | 0.8052 | 0.7960 | 0.9786   |
| 0.0018        | 18.6480 | 16000 | 0.1501          | 0.8062    | 0.7780 | 0.7918 | 0.9782   |
| 0.0016        | 19.2308 | 16500 | 0.1547          | 0.7887    | 0.7963 | 0.7924 | 0.9780   |
| 0.001         | 19.8135 | 17000 | 0.1650          | 0.7819    | 0.8070 | 0.7942 | 0.9778   |
| 0.0009        | 20.3963 | 17500 | 0.1612          | 0.7971    | 0.7833 | 0.7901 | 0.9780   |
| 0.0012        | 20.9790 | 18000 | 0.1569          | 0.7903    | 0.8023 | 0.7962 | 0.9785   |
| 0.0008        | 21.5618 | 18500 | 0.1640          | 0.7787    | 0.8081 | 0.7931 | 0.9779   |
| 0.0009        | 22.1445 | 19000 | 0.1640          | 0.7950    | 0.7924 | 0.7937 | 0.9781   |
| 0.0008        | 22.7273 | 19500 | 0.1650          | 0.7982    | 0.8023 | 0.8003 | 0.9789   |
| 0.0007        | 23.3100 | 20000 | 0.1635          | 0.7962    | 0.8022 | 0.7992 | 0.9787   |
| 0.0008        | 23.8928 | 20500 | 0.1678          | 0.7852    | 0.8005 | 0.7927 | 0.9784   |
| 0.0006        | 24.4755 | 21000 | 0.1686          | 0.7970    | 0.8025 | 0.7997 | 0.9788   |
| 0.0006        | 25.0583 | 21500 | 0.1686          | 0.7963    | 0.7970 | 0.7967 | 0.9785   |
| 0.0006        | 25.6410 | 22000 | 0.1706          | 0.7941    | 0.7948 | 0.7945 | 0.9784   |
| 0.0005        | 26.2238 | 22500 | 0.1681          | 0.7935    | 0.7963 | 0.7949 | 0.9785   |
| 0.0005        | 26.8065 | 23000 | 0.1688          | 0.8008    | 0.7938 | 0.7973 | 0.9788   |
| 0.0004        | 27.3893 | 23500 | 0.1700          | 0.7898    | 0.7996 | 0.7947 | 0.9784   |
| 0.0005        | 27.9720 | 24000 | 0.1708          | 0.7914    | 0.8096 | 0.8004 | 0.9786   |
| 0.0003        | 28.5548 | 24500 | 0.1713          | 0.7965    | 0.7979 | 0.7972 | 0.9785   |
| 0.0004        | 29.1375 | 25000 | 0.1710          | 0.7951    | 0.8010 | 0.7980 | 0.9786   |
| 0.0005        | 29.7203 | 25500 | 0.1716          | 0.7969    | 0.7989 | 0.7979 | 0.9786   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1