---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-pre-ner-full-xlmr-halfen_data-univner_en66
  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-halfen_data-univner_en66

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: 51.6812
- Precision: 0.7610
- Recall: 0.7712
- F1: 0.7661
- Accuracy: 0.9817

## 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: 66
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 107.359       | 1.28  | 500  | 73.7771         | 0.7295    | 0.7008 | 0.7149 | 0.9786   |
| 64.0888       | 2.55  | 1000 | 62.1069         | 0.7445    | 0.7298 | 0.7371 | 0.9808   |
| 56.4531       | 3.83  | 1500 | 57.7062         | 0.7495    | 0.7340 | 0.7416 | 0.9805   |
| 52.7856       | 5.1   | 2000 | 54.8858         | 0.7482    | 0.7598 | 0.7540 | 0.9814   |
| 50.5437       | 6.38  | 2500 | 53.2901         | 0.7482    | 0.7505 | 0.7494 | 0.9811   |
| 49.0825       | 7.65  | 3000 | 52.2441         | 0.7503    | 0.7557 | 0.7530 | 0.9815   |
| 48.2494       | 8.93  | 3500 | 51.6812         | 0.7610    | 0.7712 | 0.7661 | 0.9817   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3