| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: kg_model |
| results: [] |
| datasets: |
| - vishnun/NLP-KnowledgeGraph |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # kg_model |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the custom built dataset from publicaly available sentences dataset in Kaggle 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](https://www.kaggle.com/datasets/mfekadu/sentences) |
| |
| ## 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 |
