Update README.md

README.md

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-# AVISHKAARAM/avishkaarak-ekta-hindi for QA 
-
-This is the [avishkaarak-ekta-hindi](https://huggingface.co/AVISHKAARAM/avishkaarak-ekta-hindi) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 
-
-
-## Overview
-**Language model:** avishkaarak-ekta-hindi  
-**Language:** English, Hindi(Upcoming)  
-**Downstream-task:** Extractive QA  
-**Training data:** SQuAD 2.0  
-**Eval data:** SQuAD 2.0  
-**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
-**Infrastructure**: 4x Tesla v100
-
-## Hyperparameters
-
-```
-batch_size = 4
-n_epochs = 50
-base_LM_model = "roberta-base"
-max_seq_len = 512
-learning_rate = 9e-5
-lr_schedule = LinearWarmup
-warmup_proportion = 0.2
-doc_stride=128
-max_query_length=64
-``` 
-
-## Usage
-
-### In Haystack
-Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
-```python
-reader = FARMReader(model_name_or_path="AVISHKAARAM/avishkaarak-ekta-hindi")
-# or 
-reader = TransformersReader(model_name_or_path="AVISHKAARAM/avishkaarak-ekta-hindi",tokenizer="deepset/roberta-base-squad2")
-```
-For a complete example of ``AVISHKAARAM/avishkaarak-ekta-hindi`` being used for  Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
-
-### In Transformers
-```python
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-
-model_name = "AVISHKAARAM/avishkaarak-ekta-hindi"
-
-# a) Get predictions
-nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
-QA_input = {
-    'question': 'Why is model conversion important?',
-    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
-}
-res = nlp(QA_input)
-
-# b) Load model & tokenizer
-model = AutoModelForQuestionAnswering.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-
-## Performance
-Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
-
-```
-"exact": 79.87029394424324,
-"f1": 82.91251169582613,
-
-"total": 11873,
-"HasAns_exact": 77.93522267206478,
-"HasAns_f1": 84.02838248389763,
-"HasAns_total": 5928,
-"NoAns_exact": 81.79983179142137,
-"NoAns_f1": 81.79983179142137,
-"NoAns_total": 5945
-```
-
-## Authors
-**Shashwat Bindal:** optimus.coders.@ai
-
-**Sanoj:** optimus.coders.@ai
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