metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5485122897800776
- name: Recall
type: recall
value: 0.39295644114921224
- name: F1
type: f1
value: 0.45788336933045354
- name: Accuracy
type: accuracy
value: 0.9461331281262024
my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2926
- Precision: 0.5485
- Recall: 0.3930
- F1: 0.4579
- Accuracy: 0.9461
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: 2e-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 | 213 | 0.2744 | 0.6308 | 0.2660 | 0.3742 | 0.9396 |
No log | 2.0 | 426 | 0.2644 | 0.6006 | 0.3457 | 0.4388 | 0.9438 |
0.1817 | 3.0 | 639 | 0.2953 | 0.6426 | 0.3466 | 0.4503 | 0.9456 |
0.1817 | 4.0 | 852 | 0.3107 | 0.5796 | 0.3577 | 0.4424 | 0.9455 |
0.0532 | 5.0 | 1065 | 0.2926 | 0.5485 | 0.3930 | 0.4579 | 0.9461 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1