metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9860607282009942
- name: Recall
type: recall
value: 0.9693364297742606
- name: F1
type: f1
value: 0.9776270584382788
- name: Accuracy
type: accuracy
value: 0.9882459717748076
Bert-NER
This model is a fine-tuned version of distilbert-base-uncased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0372
- Precision: 0.9861
- Recall: 0.9693
- F1: 0.9776
- Accuracy: 0.9882
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0461 | 1.0 | 858 | 0.0450 | 0.9853 | 0.9602 | 0.9725 | 0.9859 |
| 0.0408 | 2.0 | 1716 | 0.0400 | 0.9836 | 0.9679 | 0.9757 | 0.9873 |
| 0.0391 | 3.0 | 2574 | 0.0372 | 0.9861 | 0.9693 | 0.9776 | 0.9882 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1