---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-kyrgyzNER
  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. -->

# xlm-roberta-base-kyrgyzNER

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3229
- Precision: 0.7042
- Recall: 0.6871
- F1: 0.6956
- Accuracy: 0.9119

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 88   | 3.8135          | 0.0001    | 0.0009 | 0.0002 | 0.0002   |
| No log        | 2.0   | 176  | 1.3867          | 0.0       | 0.0    | 0.0    | 0.7162   |
| No log        | 3.0   | 264  | 0.9276          | 0.1684    | 0.0357 | 0.0590 | 0.7678   |
| No log        | 4.0   | 352  | 0.6467          | 0.4470    | 0.2771 | 0.3421 | 0.8420   |
| No log        | 5.0   | 440  | 0.5200          | 0.6394    | 0.5282 | 0.5785 | 0.8792   |
| 1.647         | 6.0   | 528  | 0.4383          | 0.6712    | 0.5918 | 0.6290 | 0.8927   |
| 1.647         | 7.0   | 616  | 0.3847          | 0.6724    | 0.6439 | 0.6578 | 0.9028   |
| 1.647         | 8.0   | 704  | 0.3586          | 0.6857    | 0.6575 | 0.6713 | 0.9061   |
| 1.647         | 9.0   | 792  | 0.3422          | 0.6786    | 0.6717 | 0.6751 | 0.9070   |
| 1.647         | 10.0  | 880  | 0.3229          | 0.7042    | 0.6871 | 0.6956 | 0.9119   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2