
# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-hindi-question-answering-23865268
```

Or Python API:

```
import torch

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model = AutoModelForQuestionAnswering.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)

from transformers import BertTokenizer, BertForQuestionAnswering

question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"

inputs = tokenizer(question, text, return_tensors='pt')

start_positions = torch.tensor([1])

end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)

loss = outputs.loss

start_scores = outputs.start_logits

end_scores = outputs.end_logits
```
{"language": "hi", "tags": ["autonlp", "question-answering"], "datasets": ["abhishek/autonlp-data-hindi-question-answering"], "widget": [{"text": "\u00b4\u0938\u0924\u0940\u0936 \u0927\u0935\u0928 \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0915\u0947\u0902\u0926\u094d\u0930\u00b4 \u0915\u093f\u0938 \u0930\u093e\u091c\u094d\u092f \u092e\u0947\u0902 \u0938\u094d\u0925\u093f\u0924 \u0939\u0948?", "context": "\u0938\u0924\u0940\u0936 \u0927\u0935\u0928 \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0915\u0947\u0902\u0926\u094d\u0930, \u092d\u093e\u0930\u0924\u0940\u092f \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0905\u0928\u0941\u0938\u0902\u0927\u093e\u0928 \u0938\u0902\u0917\u0920\u0928 (\u0907\u0938\u0930\u094b) \u0915\u093e \u092a\u094d\u0930\u0915\u094d\u0937\u0947\u092a\u0923 \u0915\u0947\u0902\u0926\u094d\u0930 \u0939\u0948\u0964 \u092f\u0939 \u0906\u0902\u0927\u094d\u0930 \u092a\u094d\u0930\u0926\u0947\u0936 \u0915\u0947 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transformers pytorch xlm-roberta question-answering autonlp hi dataset:abhishek/autonlp-data-hindi-question-answering co2_eq_emissions endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446## Usage

You can use cURL to access this model:



Or Python API: