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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-ner-geocorpus
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-large-finetuned-ner-geocorpus
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1447
- Precision: 0.8446
- Recall: 0.8970
- F1: 0.8700
- Accuracy: 0.9783
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.9991 | 275 | 0.1397 | 0.7230 | 0.7245 | 0.7237 | 0.9581 |
| 0.2086 | 1.9982 | 550 | 0.1079 | 0.7256 | 0.8507 | 0.7832 | 0.9655 |
| 0.2086 | 2.9973 | 825 | 0.0866 | 0.8121 | 0.8496 | 0.8304 | 0.9733 |
| 0.0636 | 4.0 | 1101 | 0.0830 | 0.8030 | 0.9001 | 0.8488 | 0.9759 |
| 0.0636 | 4.9991 | 1376 | 0.1024 | 0.8433 | 0.8770 | 0.8598 | 0.9756 |
| 0.032 | 5.9982 | 1651 | 0.1036 | 0.84 | 0.8833 | 0.8611 | 0.9784 |
| 0.032 | 6.9973 | 1926 | 0.1132 | 0.8278 | 0.8948 | 0.8600 | 0.9752 |
| 0.0165 | 8.0 | 2202 | 0.1211 | 0.8473 | 0.8927 | 0.8694 | 0.9782 |
| 0.0165 | 8.9991 | 2477 | 0.1406 | 0.8402 | 0.8959 | 0.8672 | 0.9775 |
| 0.0089 | 9.9909 | 2750 | 0.1447 | 0.8446 | 0.8970 | 0.8700 | 0.9783 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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