metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: kg_model
results: []
kg_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2587
- Precision: 0.8356
- Recall: 0.8057
- F1: 0.8204
- Accuracy: 0.9170
Model description
Finetuned model for knowledge graph creation in NLP. The dataset was created by creating KG using the spaCy library. The original dataset is available in kaggle
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4931 | 1.0 | 957 | 0.3031 | 0.7872 | 0.7592 | 0.7729 | 0.8935 |
| 0.2693 | 2.0 | 1914 | 0.2645 | 0.8345 | 0.7868 | 0.8100 | 0.9110 |
| 0.2142 | 3.0 | 2871 | 0.2602 | 0.8330 | 0.7980 | 0.8152 | 0.9152 |
| 0.1894 | 4.0 | 3828 | 0.2587 | 0.8356 | 0.8057 | 0.8204 | 0.9170 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2