---
base_model: FacebookAI/xlm-roberta-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-pre-ner-full-xlmr_data-univner_full44
  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-kd-pre-ner-full-xlmr_data-univner_full44

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: 48.3210
- Precision: 0.8129
- Recall: 0.8285
- F1: 0.8206
- Accuracy: 0.9813

## 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: 8
- eval_batch_size: 32
- seed: 44
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 126.6202      | 0.2911 | 500   | 87.2751         | 0.7296    | 0.7159 | 0.7227 | 0.9734   |
| 79.5244       | 0.5822 | 1000  | 74.0801         | 0.7585    | 0.7846 | 0.7713 | 0.9774   |
| 71.0482       | 0.8732 | 1500  | 69.1703         | 0.7824    | 0.7813 | 0.7818 | 0.9785   |
| 66.2795       | 1.1643 | 2000  | 65.4836         | 0.8008    | 0.7984 | 0.7996 | 0.9800   |
| 62.9041       | 1.4554 | 2500  | 63.7689         | 0.8089    | 0.7806 | 0.7945 | 0.9794   |
| 60.9832       | 1.7465 | 3000  | 61.2403         | 0.7992    | 0.8150 | 0.8071 | 0.9804   |
| 59.0595       | 2.0375 | 3500  | 59.8787         | 0.7874    | 0.8251 | 0.8058 | 0.9800   |
| 56.95         | 2.3286 | 4000  | 58.4894         | 0.8164    | 0.8054 | 0.8109 | 0.9807   |
| 55.6946       | 2.6197 | 4500  | 57.2299         | 0.8001    | 0.8189 | 0.8094 | 0.9809   |
| 54.6743       | 2.9108 | 5000  | 56.1128         | 0.8083    | 0.8231 | 0.8156 | 0.9809   |
| 53.4043       | 3.2019 | 5500  | 55.4426         | 0.8135    | 0.8080 | 0.8107 | 0.9806   |
| 52.4045       | 3.4929 | 6000  | 54.4679         | 0.7964    | 0.8325 | 0.8141 | 0.9809   |
| 51.6726       | 3.7840 | 6500  | 53.7073         | 0.8099    | 0.8306 | 0.8201 | 0.9813   |
| 50.959        | 4.0751 | 7000  | 53.1030         | 0.8108    | 0.8272 | 0.8189 | 0.9812   |
| 50.2364       | 4.3662 | 7500  | 52.5818         | 0.8055    | 0.8342 | 0.8196 | 0.9815   |
| 49.7482       | 4.6573 | 8000  | 52.1174         | 0.8119    | 0.8306 | 0.8211 | 0.9816   |
| 49.2511       | 4.9483 | 8500  | 51.6036         | 0.8157    | 0.8214 | 0.8185 | 0.9812   |
| 48.5192       | 5.2394 | 9000  | 51.1405         | 0.8103    | 0.8285 | 0.8193 | 0.9810   |
| 48.1685       | 5.5305 | 9500  | 50.7789         | 0.8189    | 0.8300 | 0.8244 | 0.9818   |
| 47.9619       | 5.8216 | 10000 | 50.5119         | 0.8044    | 0.8344 | 0.8191 | 0.9813   |
| 47.4978       | 6.1126 | 10500 | 50.1726         | 0.817     | 0.8251 | 0.8210 | 0.9818   |
| 47.1816       | 6.4037 | 11000 | 49.9418         | 0.8162    | 0.8260 | 0.8211 | 0.9819   |
| 46.9279       | 6.6948 | 11500 | 49.6736         | 0.8223    | 0.8290 | 0.8256 | 0.9818   |
| 46.5873       | 6.9859 | 12000 | 49.4774         | 0.8228    | 0.8303 | 0.8266 | 0.9821   |
| 46.3493       | 7.2770 | 12500 | 49.1917         | 0.8194    | 0.8270 | 0.8232 | 0.9817   |
| 46.133        | 7.5680 | 13000 | 48.9379         | 0.8227    | 0.8370 | 0.8298 | 0.9821   |
| 46.0883       | 7.8591 | 13500 | 48.9742         | 0.8248    | 0.8254 | 0.8251 | 0.9819   |
| 45.812        | 8.1502 | 14000 | 48.6892         | 0.8200    | 0.8332 | 0.8265 | 0.9818   |
| 45.6582       | 8.4413 | 14500 | 48.5991         | 0.8153    | 0.8339 | 0.8245 | 0.9820   |
| 45.6454       | 8.7324 | 15000 | 48.5257         | 0.8204    | 0.8290 | 0.8247 | 0.9819   |
| 45.4808       | 9.0234 | 15500 | 48.4097         | 0.8107    | 0.8270 | 0.8188 | 0.9815   |
| 45.3486       | 9.3145 | 16000 | 48.4108         | 0.8191    | 0.8290 | 0.8240 | 0.9819   |
| 45.2876       | 9.6056 | 16500 | 48.3039         | 0.8148    | 0.8326 | 0.8236 | 0.9819   |
| 45.3167       | 9.8967 | 17000 | 48.3210         | 0.8129    | 0.8285 | 0.8206 | 0.9813   |


### Framework versions

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