metadata
tags:
- generated_from_trainer
datasets:
- fdner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fdner
type: fdner
args: fdner
metrics:
- name: Precision
type: precision
value: 0
- name: Recall
type: recall
value: 0
- name: F1
type: f1
value: 0
- name: Accuracy
type: accuracy
value: 0.6147582697201018
bert-base-chinese-finetuned-ner
This model is a fine-tuned version of bert-base-chinese on the fdner dataset. It achieves the following results on the evaluation set:
- Loss: 1.7493
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.6148
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 3 | 2.1820 | 0.0 | 0.0 | 0.0 | 0.6125 |
| No log | 2.0 | 6 | 1.8292 | 0.0 | 0.0 | 0.0 | 0.6148 |
| No log | 3.0 | 9 | 1.7493 | 0.0 | 0.0 | 0.0 | 0.6148 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0