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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-cased-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-large-cased-finetuned-ner-geocorpus
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1330
- Precision: 0.868
- Recall: 0.8872
- F1: 0.8775
- Accuracy: 0.9793
## 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 | 0.1429 | 0.7212 | 0.7318 | 0.7265 | 0.9613 |
| 0.21 | 1.9982 | 550 | 0.1111 | 0.7211 | 0.8267 | 0.7703 | 0.9654 |
| 0.21 | 2.9973 | 825 | 0.0979 | 0.8168 | 0.8168 | 0.8168 | 0.9725 |
| 0.0651 | 4.0 | 1101 | 0.1088 | 0.7574 | 0.9011 | 0.8230 | 0.9678 |
| 0.0651 | 4.9991 | 1376 | 0.1033 | 0.825 | 0.8904 | 0.8565 | 0.9744 |
| 0.0305 | 5.9982 | 1651 | 0.1132 | 0.8908 | 0.8536 | 0.8718 | 0.9785 |
| 0.0305 | 6.9973 | 1926 | 0.1127 | 0.8591 | 0.8823 | 0.8705 | 0.9786 |
| 0.0153 | 8.0 | 2202 | 0.1155 | 0.8687 | 0.8814 | 0.8750 | 0.9795 |
| 0.0153 | 8.9991 | 2477 | 0.1280 | 0.8860 | 0.8774 | 0.8817 | 0.9804 |
| 0.0089 | 9.9909 | 2750 | 0.1330 | 0.868 | 0.8872 | 0.8775 | 0.9793 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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