metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
results: []
bert-base-uncased-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2222
- Precision: 0.9410
- Recall: 0.9416
- F1: 0.9413
- Accuracy: 0.9374
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 313 | 0.3063 | 0.9106 | 0.9115 | 0.9110 | 0.9073 |
0.5047 | 2.0 | 626 | 0.2395 | 0.9281 | 0.9357 | 0.9319 | 0.9284 |
0.5047 | 3.0 | 939 | 0.2174 | 0.9388 | 0.9426 | 0.9407 | 0.9372 |
0.2056 | 4.0 | 1252 | 0.2222 | 0.9410 | 0.9416 | 0.9413 | 0.9374 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1