metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.012
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: caner
type: caner
config: default
split: train[67%:68%]
args: default
metrics:
- name: Precision
type: precision
value: 0.7985739750445633
- name: Recall
type: recall
value: 0.8373831775700935
- name: F1
type: f1
value: 0.8175182481751825
- name: Accuracy
type: accuracy
value: 0.9538148524923703
bert-finetuned-ner-v4.012
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: 0.2304
- Precision: 0.7986
- Recall: 0.8374
- F1: 0.8175
- Accuracy: 0.9538
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 |
|---|---|---|---|---|---|---|---|
| 0.2776 | 1.0 | 3228 | 0.3328 | 0.7988 | 0.7720 | 0.7852 | 0.9406 |
| 0.1617 | 2.0 | 6456 | 0.2514 | 0.8240 | 0.8224 | 0.8232 | 0.9591 |
| 0.1266 | 3.0 | 9684 | 0.2304 | 0.7986 | 0.8374 | 0.8175 | 0.9538 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2