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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-Conll2003-ner-2024_08_05
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-finetuned-Conll2003-ner-2024_08_05
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1404
- Precision: 0.9004
- Recall: 0.9163
- F1: 0.9083
- Accuracy: 0.9780
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0838 | 0.3326 | 292 | 0.1220 | 0.8779 | 0.8841 | 0.8810 | 0.9730 |
| 0.0807 | 0.6651 | 584 | 0.1345 | 0.8695 | 0.8934 | 0.8813 | 0.9728 |
| 0.0711 | 0.9977 | 876 | 0.1336 | 0.8728 | 0.8986 | 0.8855 | 0.9733 |
| 0.0467 | 1.3303 | 1168 | 0.1443 | 0.8817 | 0.9090 | 0.8951 | 0.9748 |
| 0.0452 | 1.6629 | 1460 | 0.1311 | 0.8887 | 0.9138 | 0.9011 | 0.9759 |
| 0.0383 | 1.9954 | 1752 | 0.1324 | 0.9021 | 0.9146 | 0.9083 | 0.9776 |
| 0.026 | 2.3280 | 2044 | 0.1352 | 0.9024 | 0.9180 | 0.9101 | 0.9784 |
| 0.0245 | 2.6606 | 2336 | 0.1431 | 0.9010 | 0.9172 | 0.9090 | 0.9778 |
| 0.0235 | 2.9932 | 2628 | 0.1403 | 0.9004 | 0.9163 | 0.9083 | 0.9780 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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