metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- fursov/gec_ner_val3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-gec-v2
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fursov/gec_ner_val3
type: fursov/gec_ner_val3
metrics:
- name: Precision
type: precision
value: 0.36697832554186144
- name: Recall
type: recall
value: 0.23284346770931644
- name: F1
type: f1
value: 0.2849129753361379
- name: Accuracy
type: accuracy
value: 0.941991634627572
ner-gec-v2
This model is a fine-tuned version of bert-base-uncased on the fursov/gec_ner_val3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2067
- Precision: 0.3670
- Recall: 0.2328
- F1: 0.2849
- Accuracy: 0.9420
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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2324 | 1.15 | 500 | 0.2359 | 0.2070 | 0.0883 | 0.1238 | 0.9353 |
| 0.1901 | 2.3 | 1000 | 0.2137 | 0.3467 | 0.2212 | 0.2701 | 0.9399 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0