---
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