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(~20k) 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