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: 0.9961035696329814
- name: Recall
type: recall
value: 0.9956030150753769
- name: F1
type: f1
value: 0.9958532294546368
- name: Accuracy
type: accuracy
value: 0.9992964392964393
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: 0.0042
- Precision: 0.9961
- Recall: 0.9956
- F1: 0.9959
- Accuracy: 0.9993
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 | 0.0152 | 0.9810 | 0.9820 | 0.9815 | 0.9963 |
| No log | 2.0 | 132 | 0.0108 | 0.9850 | 0.9849 | 0.9850 | 0.9971 |
| No log | 3.0 | 198 | 0.0067 | 0.9913 | 0.9920 | 0.9916 | 0.9986 |
| No log | 4.0 | 264 | 0.0056 | 0.9927 | 0.9928 | 0.9928 | 0.9988 |
| No log | 5.0 | 330 | 0.0042 | 0.9961 | 0.9956 | 0.9959 | 0.9993 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3