---
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: 0.4609
- Precision: 0.8151
- Recall: 0.8199
- F1: 0.8175
- Accuracy: 0.9812

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.4889        | 0.2911 | 500   | 0.8586          | 0.6595    | 0.7179 | 0.6875 | 0.9708   |
| 0.756         | 0.5822 | 1000  | 0.7200          | 0.7071    | 0.7746 | 0.7393 | 0.9739   |
| 0.6626        | 0.8732 | 1500  | 0.6671          | 0.7417    | 0.7530 | 0.7473 | 0.9760   |
| 0.5626        | 1.1643 | 2000  | 0.6205          | 0.7676    | 0.7785 | 0.7730 | 0.9772   |
| 0.514         | 1.4554 | 2500  | 0.6545          | 0.8094    | 0.7420 | 0.7743 | 0.9774   |
| 0.4905        | 1.7465 | 3000  | 0.5754          | 0.7770    | 0.7869 | 0.7819 | 0.9782   |
| 0.461         | 2.0375 | 3500  | 0.5559          | 0.7697    | 0.8101 | 0.7894 | 0.9790   |
| 0.4097        | 2.3286 | 4000  | 0.5613          | 0.7862    | 0.7836 | 0.7849 | 0.9785   |
| 0.3973        | 2.6197 | 4500  | 0.5514          | 0.7850    | 0.8003 | 0.7926 | 0.9795   |
| 0.3878        | 2.9108 | 5000  | 0.5299          | 0.7913    | 0.8039 | 0.7975 | 0.9791   |
| 0.3579        | 3.2019 | 5500  | 0.5424          | 0.8023    | 0.7852 | 0.7936 | 0.9790   |
| 0.3434        | 3.4929 | 6000  | 0.5077          | 0.7881    | 0.8085 | 0.7982 | 0.9795   |
| 0.3362        | 3.7840 | 6500  | 0.5244          | 0.8012    | 0.7943 | 0.7977 | 0.9793   |
| 0.3243        | 4.0751 | 7000  | 0.5158          | 0.8068    | 0.8108 | 0.8088 | 0.9801   |
| 0.3134        | 4.3662 | 7500  | 0.5081          | 0.8001    | 0.8137 | 0.8069 | 0.9799   |
| 0.3027        | 4.6573 | 8000  | 0.4989          | 0.8003    | 0.8169 | 0.8085 | 0.9803   |
| 0.2977        | 4.9483 | 8500  | 0.4926          | 0.8013    | 0.8121 | 0.8067 | 0.9804   |
| 0.2822        | 5.2394 | 9000  | 0.4905          | 0.8052    | 0.8081 | 0.8067 | 0.9801   |
| 0.2773        | 5.5305 | 9500  | 0.4864          | 0.8012    | 0.8049 | 0.8031 | 0.9798   |
| 0.2803        | 5.8216 | 10000 | 0.4883          | 0.7963    | 0.8090 | 0.8026 | 0.9798   |
| 0.2717        | 6.1126 | 10500 | 0.4941          | 0.8169    | 0.7909 | 0.8037 | 0.9798   |
| 0.258         | 6.4037 | 11000 | 0.4842          | 0.8008    | 0.8078 | 0.8043 | 0.9802   |
| 0.2572        | 6.6948 | 11500 | 0.4760          | 0.8129    | 0.8097 | 0.8113 | 0.9805   |
| 0.2553        | 6.9859 | 12000 | 0.4742          | 0.8119    | 0.8116 | 0.8117 | 0.9809   |
| 0.2462        | 7.2770 | 12500 | 0.4791          | 0.8116    | 0.8054 | 0.8085 | 0.9806   |
| 0.2447        | 7.5680 | 13000 | 0.4750          | 0.8017    | 0.8171 | 0.8093 | 0.9804   |
| 0.2463        | 7.8591 | 13500 | 0.4657          | 0.8179    | 0.8113 | 0.8146 | 0.9811   |
| 0.2381        | 8.1502 | 14000 | 0.4677          | 0.8025    | 0.8153 | 0.8088 | 0.9805   |
| 0.2357        | 8.4413 | 14500 | 0.4658          | 0.8135    | 0.8184 | 0.8159 | 0.9810   |
| 0.2333        | 8.7324 | 15000 | 0.4638          | 0.8144    | 0.8116 | 0.8130 | 0.9807   |
| 0.234         | 9.0234 | 15500 | 0.4605          | 0.8126    | 0.8165 | 0.8145 | 0.9810   |
| 0.2297        | 9.3145 | 16000 | 0.4670          | 0.8116    | 0.8080 | 0.8098 | 0.9808   |
| 0.2258        | 9.6056 | 16500 | 0.4651          | 0.8095    | 0.8142 | 0.8118 | 0.9808   |
| 0.2272        | 9.8967 | 17000 | 0.4609          | 0.8151    | 0.8199 | 0.8175 | 0.9812   |


### Framework versions

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