metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- indian_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indian_names
type: indian_names
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9905686167304538
- name: Recall
type: recall
value: 0.910427135678392
- name: F1
type: f1
value: 0.9488085886357684
- name: Accuracy
type: accuracy
value: 0.983080223080223
my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the indian_names dataset. It achieves the following results on the evaluation set:
- Loss: 0.0679
- Precision: 0.9906
- Recall: 0.9104
- F1: 0.9488
- Accuracy: 0.9831
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 66 | 0.1499 | 0.7872 | 0.7281 | 0.7565 | 0.9557 |
| No log | 2.0 | 132 | 0.1338 | 0.8289 | 0.7524 | 0.7888 | 0.9612 |
| No log | 3.0 | 198 | 0.0884 | 0.9959 | 0.9053 | 0.9484 | 0.9820 |
| No log | 4.0 | 264 | 0.0750 | 0.9964 | 0.9070 | 0.9496 | 0.9826 |
| No log | 5.0 | 330 | 0.0679 | 0.9906 | 0.9104 | 0.9488 | 0.9831 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3