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: 0.7241379310344828
- name: Recall
type: recall
value: 0.7170731707317073
- name: F1
type: f1
value: 0.7205882352941175
- name: Accuracy
type: accuracy
value: 0.9920721492851299
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: 0.0278
- Precision: 0.7241
- Recall: 0.7171
- F1: 0.7206
- Accuracy: 0.9921
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 | 63 | 0.1197 | 0.0 | 0.0 | 0.0 | 0.9745 |
| No log | 2.0 | 126 | 0.0818 | 0.8438 | 0.0263 | 0.0511 | 0.9751 |
| No log | 3.0 | 189 | 0.0470 | 0.5270 | 0.4859 | 0.5056 | 0.9857 |
| No log | 4.0 | 252 | 0.0315 | 0.7042 | 0.6341 | 0.6674 | 0.9906 |
| No log | 5.0 | 315 | 0.0278 | 0.7241 | 0.7171 | 0.7206 | 0.9921 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3