metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_id_nergrit_corpus_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: id_nergrit_corpus
type: id_nergrit_corpus
config: ner
split: test
args: ner
metrics:
- name: Precision
type: precision
value: 0.6222415479943472
- name: Recall
type: recall
value: 0.6438695163104612
- name: F1
type: f1
value: 0.6328708054618829
- name: Accuracy
type: accuracy
value: 0.9038083290743236
my_awesome_id_nergrit_corpus_model
This model is a fine-tuned version of distilbert-base-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.3602
- Precision: 0.6222
- Recall: 0.6439
- F1: 0.6329
- Accuracy: 0.9038
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.9117 | 1.0 | 784 | 0.4198 | 0.5691 | 0.5948 | 0.5817 | 0.8893 |
| 0.4089 | 2.0 | 1568 | 0.3602 | 0.6222 | 0.6439 | 0.6329 | 0.9038 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3