metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikigold_splits
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: temp
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikigold_splits
type: wikigold_splits
args: default
metrics:
- name: Precision
type: precision
value: 0.8517110266159695
- name: Recall
type: recall
value: 0.875
- name: F1
type: f1
value: 0.8631984585741811
- name: Accuracy
type: accuracy
value: 0.9607367910809501
temp
This model is a fine-tuned version of bert-base-cased on the wikigold_splits dataset. It achieves the following results on the evaluation set:
- Loss: 0.1322
- Precision: 0.8517
- Recall: 0.875
- F1: 0.8632
- Accuracy: 0.9607
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 167 | 0.1490 | 0.7583 | 0.7760 | 0.7671 | 0.9472 |
| No log | 2.0 | 334 | 0.1337 | 0.8519 | 0.8464 | 0.8491 | 0.9572 |
| 0.1569 | 3.0 | 501 | 0.1322 | 0.8517 | 0.875 | 0.8632 | 0.9607 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6