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: 0.9906929945918752
- name: Recall
type: recall
value: 0.9895728643216081
- name: F1
type: f1
value: 0.9901326126579096
- name: Accuracy
type: accuracy
value: 0.9982668382668383
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: 0.0075
- Precision: 0.9907
- Recall: 0.9896
- F1: 0.9901
- Accuracy: 0.9983
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 | 0.0313 | 0.9674 | 0.9595 | 0.9635 | 0.9926 |
| No log | 2.0 | 132 | 0.0185 | 0.9780 | 0.9732 | 0.9756 | 0.9951 |
| No log | 3.0 | 198 | 0.0120 | 0.9849 | 0.9830 | 0.9840 | 0.9970 |
| No log | 4.0 | 264 | 0.0090 | 0.9857 | 0.9849 | 0.9853 | 0.9975 |
| No log | 5.0 | 330 | 0.0075 | 0.9907 | 0.9896 | 0.9901 | 0.9983 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3