metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.001
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: caner
type: caner
config: default
split: train[-1%:]
args: default
metrics:
- name: Precision
type: precision
value: 0.8814432989690721
- name: Recall
type: recall
value: 0.8208
- name: F1
type: f1
value: 0.8500414250207124
- name: Accuracy
type: accuracy
value: 0.9327371695178849
bert-finetuned-ner-v4.001
This model is a fine-tuned version of bert-base-multilingual-cased on the caner dataset. It achieves the following results on the evaluation set:
- Loss: 0.4995
- Precision: 0.8814
- Recall: 0.8208
- F1: 0.8500
- Accuracy: 0.9327
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 |
|---|---|---|---|---|---|---|---|
| 0.2518 | 1.0 | 4842 | 0.5403 | 0.8354 | 0.7712 | 0.8020 | 0.9178 |
| 0.1364 | 2.0 | 9684 | 0.4746 | 0.8728 | 0.8016 | 0.8357 | 0.9287 |
| 0.0915 | 3.0 | 14526 | 0.4995 | 0.8814 | 0.8208 | 0.8500 | 0.9327 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2