---
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
---

<!-- 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. -->

# PSYCHIC  ![alt text](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRKarOKfR67Qmi9Z4qNQmHZHyvBQBBxcra9qoV-8gSu&s)

PSYCHIC (**P**re-trained **SY**mbolic **CH**ecker **I**n **C**ontext) is a model that is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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