---
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-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-large-uncased-finetuned-ner-geocorpus

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1293
- Precision: 0.8171
- Recall: 0.8806
- F1: 0.8476
- Accuracy: 0.9721

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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        | 0.9955 | 137  | 0.2292          | 0.4527    | 0.3450 | 0.3916 | 0.9378   |
| No log        | 1.9982 | 275  | 0.1339          | 0.6814    | 0.7175 | 0.6990 | 0.9606   |
| No log        | 2.9936 | 412  | 0.1147          | 0.7385    | 0.8057 | 0.7706 | 0.9647   |
| 0.2052        | 3.9964 | 550  | 0.1217          | 0.7099    | 0.8607 | 0.7781 | 0.9611   |
| 0.2052        | 4.9991 | 688  | 0.1076          | 0.7705    | 0.8531 | 0.8097 | 0.9674   |
| 0.2052        | 5.9946 | 825  | 0.1130          | 0.7970    | 0.8483 | 0.8219 | 0.9701   |
| 0.2052        | 6.9973 | 963  | 0.1332          | 0.7357    | 0.8758 | 0.7997 | 0.9637   |
| 0.0384        | 8.0    | 1101 | 0.1241          | 0.7798    | 0.8929 | 0.8325 | 0.9690   |
| 0.0384        | 8.9955 | 1238 | 0.1241          | 0.8303    | 0.8720 | 0.8507 | 0.9728   |
| 0.0384        | 9.9546 | 1370 | 0.1293          | 0.8171    | 0.8806 | 0.8476 | 0.9721   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1