|
--- |
|
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 |
|
|
