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: 0.9985643067910454
- name: Recall
type: recall
value: 0.998333826213025
- name: F1
type: f1
value: 0.9984490532010821
- name: Accuracy
type: accuracy
value: 0.9986316806181584
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: 0.0014
- Precision: 0.9986
- Recall: 0.9983
- F1: 0.9984
- Accuracy: 0.9986
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0316 | 1.0 | 7904 | 0.0056 | 0.9940 | 0.9931 | 0.9935 | 0.9945 |
| 0.0102 | 2.0 | 15808 | 0.0014 | 0.9986 | 0.9983 | 0.9984 | 0.9986 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3