Update README.md

README.md

@@ -9,6 +9,11 @@ model-index:
 - name: bert-base-uncased-finetuned-squad2
   results: []
 pipeline_tag: question-answering
+metrics:
+- exact_match
+- f1
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -22,17 +27,34 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+BERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Masked Language Modeling and Next Sentence Prediction.
+Suitable for Question-Answering tasks, predicts answer spans within the context provided. 
+
+Training data: Train-set SQuAD2.0
+Evaluation data: Validation-set SQuAD2.0
+Hardware Accelerator used: GPU Tesla T4
 
 ## Intended uses & limitations
 
-More information needed
+For Question-Answering -
+
+question = "How many programming languages does BLOOM support?"
+context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages."
+
+from transformers import pipeline
+
+question_answerer = pipeline("question-answering", model="IProject-10/bert-base-uncased-finetuned-squad2")
+question_answerer(question=question, context=context)
+
+{{ direct_use | default("[question-answering]", true)}}
+{{ downstream_use | default("[question-answering]", true)}}
+
+## Results
+
+Evaluation on SQuAD 2.0 validation dataset:
 
-## Training and evaluation data
 
-More information needed
 
-## Training procedure
 
 ### Training hyperparameters