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: 0.5390279823269514
- name: Recall
type: recall
value: 0.3392029657089898
- name: F1
type: f1
value: 0.41638225255972694
- name: Accuracy
type: accuracy
value: 0.943952802359882
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: 0.2711
- Precision: 0.5390
- Recall: 0.3392
- F1: 0.4164
- Accuracy: 0.9440
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 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2856 | 0.6172 | 0.2465 | 0.3523 | 0.9386 |
| No log | 2.0 | 426 | 0.2706 | 0.5669 | 0.3142 | 0.4043 | 0.9422 |
| 0.1857 | 3.0 | 639 | 0.2711 | 0.5390 | 0.3392 | 0.4164 | 0.9440 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3