metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
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: 0.9994683935820607
- name: Recall
type: recall
value: 0.999371798588963
- name: F1
type: f1
value: 0.9994200937515101
- name: Accuracy
type: accuracy
value: 0.9998144414067816
my_awesome_wnut_model
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: 0.0018
- Precision: 0.9995
- Recall: 0.9994
- F1: 0.9994
- Accuracy: 0.9998
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 |
|---|---|---|---|---|---|---|---|
| 0.1459 | 1.0 | 533 | 0.0584 | 0.9602 | 0.9620 | 0.9611 | 0.9876 |
| 0.0546 | 2.0 | 1066 | 0.0237 | 0.9866 | 0.9866 | 0.9866 | 0.9957 |
| 0.025 | 3.0 | 1599 | 0.0080 | 0.9967 | 0.9945 | 0.9956 | 0.9985 |
| 0.0116 | 4.0 | 2132 | 0.0040 | 0.9980 | 0.9979 | 0.9980 | 0.9994 |
| 0.0058 | 5.0 | 2665 | 0.0018 | 0.9995 | 0.9994 | 0.9994 | 0.9998 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3