Update README.md

README.md

@@ -8,30 +8,71 @@ datasets:
 model-index:
 - name: distilbert-base-uncased-finetuned-squad2
   results: []
+language:
+- en
+metrics:
+- exact_match
+- f1
+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. -->
 
-# distilbert-base-uncased-finetuned-squad2
-
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.4648
-
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
+DistilBERT fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model. DistilBERT is a compact and efficient version of BERT (Bidirectional Encoder Representations from Transformers).<br>
+It employs a distillation process that transfers knowledge from a larger pretrained model (like BERT) to a smaller one. <br>
+Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>
 
-More information needed
+**Language model:** distilbert-base-uncased  
+**Language:** English  
+**Downstream-task:** Question-Answering  
+**Training data:** Train-set SQuAD 2.0  
+**Evaluation data:** Evaluation-set SQuAD 2.0   
+**Hardware Accelerator used**: GPU Tesla T4
 
-## Training and evaluation data
-
-More information needed
+## Intended uses & limitations
 
-## Training procedure
+For Question-Answering - 
+
+```python
+!pip install transformers
+from transformers import pipeline
+model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
+question_answerer = pipeline("question-answering", model=model_checkpoint)
+
+context = """
+🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
+between them. It's straightforward to train your models with one before loading them for inference with the other.
+"""
+
+question = "Which deep learning libraries back 🤗 Transformers?"
+question_answerer(question=question, context=context)
+```
+
+## Results
+
+Evaluation on SQuAD 2.0 validation dataset:
+
+```
+ exact: 65.88056935904994,
+ f1: 68.9782873196397,
+ total': 11873,
+ HasAns_exact': 68.15114709851552,
+ HasAns_f1': 74.35546648888003,
+ HasAns_total': 5928,
+ NoAns_exact': 63.61648444070648,
+ NoAns_f1': 63.61648444070648,
+ NoAns_total': 5945,
+ best_exact': 65.88056935904994,
+ best_exact_thresh': 0.9993563294410706,
+ best_f1': 68.97828731963992,
+ best_f1_thresh': 0.9993563294410706,
+ total_time_in_seconds': 122.51037029999998,
+ samples_per_second': 96.91424465476456,
+ latency_in_seconds': 0.01031840059799545}
+```
 
 ### Training hyperparameters
 
@@ -52,10 +93,13 @@ The following hyperparameters were used during training:
 | 0.8749        | 2.0   | 16470 | 1.3015          |
 | 0.6708        | 3.0   | 24705 | 1.4648          |
 
-
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4648
+  
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.2
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3