---
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-scr-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-scr-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: 0.5850
- Precision: 0.3090
- Recall: 0.4141
- F1: 0.3539
- Accuracy: 0.9219

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3726        | 0.5828  | 500   | 0.3180          | 0.3185    | 0.1437 | 0.1981 | 0.9252   |
| 0.2842        | 1.1655  | 1000  | 0.3582          | 0.1651    | 0.2194 | 0.1884 | 0.9023   |
| 0.2484        | 1.7483  | 1500  | 0.3015          | 0.2876    | 0.1958 | 0.2330 | 0.9274   |
| 0.2164        | 2.3310  | 2000  | 0.3746          | 0.1573    | 0.2725 | 0.1995 | 0.8904   |
| 0.2037        | 2.9138  | 2500  | 0.3387          | 0.1816    | 0.2516 | 0.2110 | 0.9029   |
| 0.1786        | 3.4965  | 3000  | 0.3330          | 0.2181    | 0.2813 | 0.2457 | 0.9115   |
| 0.1563        | 4.0793  | 3500  | 0.3990          | 0.1694    | 0.3402 | 0.2262 | 0.8842   |
| 0.1285        | 4.6620  | 4000  | 0.3549          | 0.2149    | 0.3561 | 0.2680 | 0.9025   |
| 0.1123        | 5.2448  | 4500  | 0.3587          | 0.2343    | 0.3646 | 0.2852 | 0.9051   |
| 0.0914        | 5.8275  | 5000  | 0.3688          | 0.2347    | 0.3747 | 0.2886 | 0.9041   |
| 0.0742        | 6.4103  | 5500  | 0.3943          | 0.2424    | 0.3805 | 0.2961 | 0.9068   |
| 0.0692        | 6.9930  | 6000  | 0.3877          | 0.2513    | 0.3896 | 0.3055 | 0.9102   |
| 0.0538        | 7.5758  | 6500  | 0.3864          | 0.2837    | 0.3865 | 0.3272 | 0.9199   |
| 0.0463        | 8.1585  | 7000  | 0.4363          | 0.2537    | 0.4067 | 0.3125 | 0.9082   |
| 0.0395        | 8.7413  | 7500  | 0.4260          | 0.2618    | 0.4046 | 0.3179 | 0.9118   |
| 0.0342        | 9.3240  | 8000  | 0.4661          | 0.2441    | 0.4050 | 0.3046 | 0.9081   |
| 0.0309        | 9.9068  | 8500  | 0.4307          | 0.2850    | 0.3963 | 0.3315 | 0.9195   |
| 0.0246        | 10.4895 | 9000  | 0.4781          | 0.2541    | 0.4157 | 0.3154 | 0.9080   |
| 0.0223        | 11.0723 | 9500  | 0.4891          | 0.2721    | 0.4035 | 0.3250 | 0.9129   |
| 0.0182        | 11.6550 | 10000 | 0.4741          | 0.2897    | 0.3969 | 0.3349 | 0.9190   |
| 0.0172        | 12.2378 | 10500 | 0.4542          | 0.3522    | 0.3968 | 0.3732 | 0.9320   |
| 0.0155        | 12.8205 | 11000 | 0.4927          | 0.2961    | 0.3885 | 0.3361 | 0.9220   |
| 0.0127        | 13.4033 | 11500 | 0.4922          | 0.2991    | 0.4096 | 0.3458 | 0.9196   |
| 0.0118        | 13.9860 | 12000 | 0.5092          | 0.2809    | 0.4193 | 0.3364 | 0.9161   |
| 0.0096        | 14.5688 | 12500 | 0.4991          | 0.3093    | 0.4059 | 0.3511 | 0.9246   |
| 0.0095        | 15.1515 | 13000 | 0.5892          | 0.2645    | 0.4292 | 0.3273 | 0.9095   |
| 0.0082        | 15.7343 | 13500 | 0.5076          | 0.3108    | 0.4054 | 0.3519 | 0.9230   |
| 0.007         | 16.3170 | 14000 | 0.5248          | 0.3094    | 0.4147 | 0.3544 | 0.9228   |
| 0.0069        | 16.8998 | 14500 | 0.5352          | 0.2932    | 0.4210 | 0.3457 | 0.9183   |
| 0.006         | 17.4825 | 15000 | 0.5458          | 0.3034    | 0.4092 | 0.3485 | 0.9205   |
| 0.0056        | 18.0653 | 15500 | 0.5786          | 0.2857    | 0.4366 | 0.3454 | 0.9150   |
| 0.004         | 18.6480 | 16000 | 0.5608          | 0.3048    | 0.4168 | 0.3521 | 0.9214   |
| 0.0047        | 19.2308 | 16500 | 0.5394          | 0.3339    | 0.4007 | 0.3643 | 0.9263   |
| 0.0041        | 19.8135 | 17000 | 0.5308          | 0.3333    | 0.4085 | 0.3671 | 0.9279   |
| 0.0037        | 20.3963 | 17500 | 0.5628          | 0.3081    | 0.4184 | 0.3548 | 0.9207   |
| 0.0035        | 20.9790 | 18000 | 0.5735          | 0.3115    | 0.4144 | 0.3556 | 0.9219   |
| 0.0026        | 21.5618 | 18500 | 0.5682          | 0.3123    | 0.4109 | 0.3549 | 0.9225   |
| 0.0027        | 22.1445 | 19000 | 0.5672          | 0.3264    | 0.4108 | 0.3637 | 0.9255   |
| 0.0022        | 22.7273 | 19500 | 0.6179          | 0.2866    | 0.4356 | 0.3457 | 0.9147   |
| 0.0025        | 23.3100 | 20000 | 0.5594          | 0.3419    | 0.4004 | 0.3688 | 0.9288   |
| 0.0021        | 23.8928 | 20500 | 0.5850          | 0.3090    | 0.4141 | 0.3539 | 0.9219   |


### Framework versions

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