metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.002
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: caner
type: caner
config: default
split: train[5%:6%]
args: default
metrics:
- name: Precision
type: precision
value: 0.8475935828877005
- name: Recall
type: recall
value: 0.8992907801418439
- name: F1
type: f1
value: 0.8726772195457674
- name: Accuracy
type: accuracy
value: 0.9513366750208856
bert-finetuned-ner-v4.002
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: 0.2863
- Precision: 0.8476
- Recall: 0.8993
- F1: 0.8727
- Accuracy: 0.9513
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 |
---|---|---|---|---|---|---|---|
0.3262 | 1.0 | 3022 | 0.3082 | 0.8324 | 0.8667 | 0.8492 | 0.9380 |
0.2304 | 2.0 | 6044 | 0.2884 | 0.8410 | 0.8851 | 0.8625 | 0.9459 |
0.1601 | 3.0 | 9066 | 0.2863 | 0.8476 | 0.8993 | 0.8727 | 0.9513 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2