Update README.md

README.md

@@ -4,30 +4,36 @@ base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
 model-index:
-- name: qa_model_2
+- 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. -->
 
-# qa_model_2
+# PSYCHIC
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
-It achieves the following results on the evaluation set:
+PSYCHIC (Pre-trained SYmbolic CHecker In Context) 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
 
-More information needed
+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
 
-More information needed
+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
 
-More information needed
+The DBLP-QuAD dataset is used for training and evaluation.
 
 ## Training procedure
 
@@ -56,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.33.1
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.5
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3