metadata
library_name: transformers
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: 0.5479274611398963
- name: Recall
type: recall
value: 0.39202965708989806
- name: F1
type: f1
value: 0.45705024311183146
- name: Accuracy
type: accuracy
value: 0.9464323885255013
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: 0.3127
- Precision: 0.5479
- Recall: 0.3920
- F1: 0.4571
- Accuracy: 0.9464
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2736 | 0.5923 | 0.3123 | 0.4090 | 0.9435 |
| No log | 2.0 | 426 | 0.2811 | 0.5439 | 0.3614 | 0.4343 | 0.9456 |
| 0.0767 | 3.0 | 639 | 0.3117 | 0.5765 | 0.3596 | 0.4429 | 0.9463 |
| 0.0767 | 4.0 | 852 | 0.3040 | 0.5443 | 0.3874 | 0.4526 | 0.9463 |
| 0.0315 | 5.0 | 1065 | 0.3127 | 0.5479 | 0.3920 | 0.4571 | 0.9464 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1