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: 0.9987202862934734
- name: Recall
type: recall
value: 0.9989804934411745
- name: F1
type: f1
value: 0.9988503729209022
- name: Accuracy
type: accuracy
value: 0.9993990151023617
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: 0.0019
- Precision: 0.9987
- Recall: 0.9990
- F1: 0.9989
- Accuracy: 0.9994
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 486 | 0.0038 | 0.9961 | 0.9983 | 0.9972 | 0.9985 |
| 0.0034 | 2.0 | 972 | 0.0024 | 0.9980 | 0.9990 | 0.9985 | 0.9992 |
| 0.0041 | 3.0 | 1458 | 0.0019 | 0.9987 | 0.9990 | 0.9989 | 0.9994 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1