metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- szeged_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: szeged_ner
type: szeged_ner
config: business
split: test
args: business
metrics:
- name: Precision
type: precision
value: 0.8253343823760818
- name: Recall
type: recall
value: 0.856326530612245
- name: F1
type: f1
value: 0.8405448717948719
- name: Accuracy
type: accuracy
value: 0.9829550592277783
my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the szeged_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0590
- Precision: 0.8253
- Recall: 0.8563
- F1: 0.8405
- Accuracy: 0.9830
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2068 | 1.0 | 511 | 0.0724 | 0.8008 | 0.8237 | 0.8121 | 0.9797 |
| 0.0835 | 2.0 | 1022 | 0.0590 | 0.8253 | 0.8563 | 0.8405 | 0.9830 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3