metadata
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9223001949317738
- name: Recall
type: recall
value: 0.9191824999028636
- name: F1
type: f1
value: 0.9207387082335999
- name: Accuracy
type: accuracy
value: 0.9606758109142285
bert-finetuned-ner
This model is a fine-tuned version of distilbert/distilbert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1640
- Precision: 0.9223
- Recall: 0.9192
- F1: 0.9207
- Accuracy: 0.9607
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 |
|---|---|---|---|---|---|---|---|
| 0.1926 | 1.0 | 1756 | 0.1809 | 0.9104 | 0.9056 | 0.9080 | 0.9543 |
| 0.1318 | 2.0 | 3512 | 0.1622 | 0.9200 | 0.9156 | 0.9178 | 0.9592 |
| 0.0933 | 3.0 | 5268 | 0.1640 | 0.9223 | 0.9192 | 0.9207 | 0.9607 |
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.2.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1