---
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_en44
  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_en44

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: 0.1507
- Precision: 0.7345
- Recall: 0.7588
- F1: 0.7464
- Accuracy: 0.9803

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869        | 1.2755  | 500   | 0.0715          | 0.6808    | 0.7308 | 0.7049 | 0.9773   |
| 0.0328        | 2.5510  | 1000  | 0.0781          | 0.7035    | 0.7319 | 0.7174 | 0.9784   |
| 0.0202        | 3.8265  | 1500  | 0.0805          | 0.7083    | 0.7340 | 0.7209 | 0.9791   |
| 0.0121        | 5.1020  | 2000  | 0.0914          | 0.7255    | 0.7495 | 0.7373 | 0.9792   |
| 0.0071        | 6.3776  | 2500  | 0.0988          | 0.7178    | 0.7557 | 0.7363 | 0.9787   |
| 0.005         | 7.6531  | 3000  | 0.1089          | 0.7178    | 0.7609 | 0.7387 | 0.9795   |
| 0.0033        | 8.9286  | 3500  | 0.1177          | 0.7353    | 0.7246 | 0.7299 | 0.9792   |
| 0.0033        | 10.2041 | 4000  | 0.1134          | 0.7219    | 0.7391 | 0.7304 | 0.9794   |
| 0.0022        | 11.4796 | 4500  | 0.1251          | 0.7243    | 0.7588 | 0.7412 | 0.9801   |
| 0.0023        | 12.7551 | 5000  | 0.1279          | 0.7070    | 0.7619 | 0.7334 | 0.9792   |
| 0.0017        | 14.0306 | 5500  | 0.1231          | 0.7165    | 0.7588 | 0.7371 | 0.9793   |
| 0.0014        | 15.3061 | 6000  | 0.1378          | 0.7289    | 0.7598 | 0.7440 | 0.9792   |
| 0.0014        | 16.5816 | 6500  | 0.1507          | 0.6986    | 0.7774 | 0.7359 | 0.9782   |
| 0.001         | 17.8571 | 7000  | 0.1421          | 0.7242    | 0.7474 | 0.7356 | 0.9793   |
| 0.0009        | 19.1327 | 7500  | 0.1396          | 0.7284    | 0.7578 | 0.7428 | 0.9796   |
| 0.0008        | 20.4082 | 8000  | 0.1394          | 0.7402    | 0.7226 | 0.7313 | 0.9788   |
| 0.0006        | 21.6837 | 8500  | 0.1425          | 0.7542    | 0.7371 | 0.7455 | 0.9797   |
| 0.0006        | 22.9592 | 9000  | 0.1446          | 0.7339    | 0.7308 | 0.7324 | 0.9793   |
| 0.0005        | 24.2347 | 9500  | 0.1459          | 0.7374    | 0.7557 | 0.7464 | 0.9804   |
| 0.0004        | 25.5102 | 10000 | 0.1443          | 0.7323    | 0.7505 | 0.7413 | 0.9800   |
| 0.0002        | 26.7857 | 10500 | 0.1485          | 0.7299    | 0.7526 | 0.7411 | 0.9801   |
| 0.0003        | 28.0612 | 11000 | 0.1507          | 0.7408    | 0.7484 | 0.7446 | 0.9803   |
| 0.0003        | 29.3367 | 11500 | 0.1507          | 0.7345    | 0.7588 | 0.7464 | 0.9803   |


### Framework versions

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