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---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-uncased-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. -->
# bert-base-multilingual-uncased-finetuned-ner-geocorpus
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210
- Precision: 0.8205
- Recall: 0.8757
- F1: 0.8472
- Accuracy: 0.9760
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 276 | 0.1670 | 0.7138 | 0.6690 | 0.6906 | 0.9555 |
| 0.247 | 2.0 | 552 | 0.1192 | 0.8109 | 0.7770 | 0.7936 | 0.9676 |
| 0.247 | 3.0 | 828 | 0.1181 | 0.8226 | 0.8188 | 0.8207 | 0.9709 |
| 0.0846 | 4.0 | 1104 | 0.1162 | 0.7656 | 0.8571 | 0.8088 | 0.9685 |
| 0.0846 | 5.0 | 1380 | 0.1248 | 0.7627 | 0.8699 | 0.8128 | 0.9687 |
| 0.0442 | 6.0 | 1656 | 0.0982 | 0.8233 | 0.8931 | 0.8568 | 0.9777 |
| 0.0442 | 7.0 | 1932 | 0.1114 | 0.8100 | 0.8862 | 0.8464 | 0.9741 |
| 0.0247 | 8.0 | 2208 | 0.1164 | 0.8342 | 0.8885 | 0.8605 | 0.9780 |
| 0.0247 | 9.0 | 2484 | 0.1208 | 0.7983 | 0.8920 | 0.8426 | 0.9746 |
| 0.0159 | 10.0 | 2760 | 0.1210 | 0.8205 | 0.8757 | 0.8472 | 0.9760 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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