Update README.md

README.md

@@ -8,31 +8,70 @@ datasets:
 model-index:
 - name: albert-base-v2-finetuned-squad2
   results: []
+language:
+- en
+metrics:
+- exact_match
+- f1
+pipeline_tag: zero-shot-classification
 ---
 
 <!-- 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. -->
 
-# albert-base-v2-finetuned-squad2
-
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.9900
-
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
+ALBERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Parameter Reduction techniques and Sentence Order Prediction<br>
+Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>
 
-More information needed
+**Language model:** albert-base-v2  
+**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 procedure
+## Intended uses & limitations
 
+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: 78.12684241556472,
+ f1: 81.54753481344116,
+ total: 11873,
+ HasAns_exact: 73.80229419703105,
+ HasAns_f1: 80.65348867071317,
+ HasAns_total: 5928,
+ NoAns_exact: 82.4390243902439,
+ NoAns_f1: 82.4390243902439,
+ NoAns_total: 5945,
+ best_exact: 78.12684241556472,
+ best_exact_thresh: 0.9990358352661133,
+ best_f1: 81.54753481344157,
+ best_f1_thresh: 0.9990358352661133,
+ total_time_in_seconds: 248.44505145400035,
+ samples_per_second: 47.78923923223437,
+ latency_in_seconds: 0.020925212789859374
+```
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -52,10 +91,13 @@ The following hyperparameters were used during training:
 | 0.6593        | 2.0   | 16496 | 0.8548          |
 | 0.4314        | 3.0   | 24744 | 0.9900          |
 
-
+This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9900
+  
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.3
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3