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: 0.5720930232558139
- name: Recall
type: recall
value: 0.34198331788693237
- name: F1
type: f1
value: 0.4280742459396752
- name: Accuracy
type: accuracy
value: 0.9453636013851482
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: 0.3526
- Precision: 0.5721
- Recall: 0.3420
- F1: 0.4281
- Accuracy: 0.9454
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 107 | 0.2863 | 0.3981 | 0.3494 | 0.3722 | 0.9328 |
| No log | 2.0 | 214 | 0.3438 | 0.5734 | 0.3151 | 0.4067 | 0.9443 |
| No log | 3.0 | 321 | 0.3482 | 0.5922 | 0.3216 | 0.4168 | 0.9445 |
| No log | 4.0 | 428 | 0.3526 | 0.5721 | 0.3420 | 0.4281 | 0.9454 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1