metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-uncased
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 the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0626
- Precision: 0.9201
- Recall: 0.9350
- F1: 0.9275
- Accuracy: 0.9832
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0943 | 1.0 | 877 | 0.0687 | 0.9019 | 0.9149 | 0.9084 | 0.9801 |
| 0.2395 | 2.0 | 1754 | 0.0623 | 0.9221 | 0.9298 | 0.9259 | 0.9829 |
| 0.0241 | 3.0 | 2631 | 0.0626 | 0.9201 | 0.9350 | 0.9275 | 0.9832 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1