---
license: cc-by-4.0
datasets:
- squad
- squad_v2
language:
- en
- hi
metrics:
- accuracy
pipeline_tag: question-answering
---


# avishkaarak-ekta-hindi

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