license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: psychic
results: []
datasets:
- awalesushil/DBLP-QuAD
language:
- en
library_name: transformers
pipeline_tag: question-answering
PSYCHIC 
PSYCHIC (Pre-trained SYmbolic CHecker In Context) is a model that is a fine-tuned version of distilbert-base-uncased on the DBLP-QuAD dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
Model description
The model is trained to learn specific tokens from a question and its context to better determine the answer from the context. It is fine-tuned on the Extractive QA task from which it should return the answer to a knowledge graph question in the form of a SPARQL query. The advantage of PSYCHIC is that it leverages neuro-symbolic capabilities to validate query structures as well as LLM capacities to learn from context tokens.
Intended uses & limitations
This model is intended to be used with a question-context pair to determine the answer in the form of a SPARQL query.
Training and evaluation data
The DBLP-QuAD dataset is used for training and evaluation.
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.001 | 1.0 | 1000 | 0.0001 |
| 0.0005 | 2.0 | 2000 | 0.0000 |
| 0.0002 | 3.0 | 3000 | 0.0000 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3