transformers pytorch xlm-roberta question-answering autonlp unk dataset:teacookies/autonlp-data-roberta-base-squad2 co2_eq_emissions endpoints_compatible region:us
---
datasets:
- teacookies/autonlp-data-roberta-base-squad2
language: unk
tags:
- autonlp
- question-answering
widget:
- text: Who loves AutoNLP?
  context: Everyone loves AutoNLP
co2_eq_emissions: 65.5797497320557
---

# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 24465516
- CO2 Emissions (in grams): 65.5797497320557

## Validation Metrics

- Loss: 0.6545609831809998

## 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/teacookies/autonlp-roberta-base-squad2-24465516
```

Or Python API:

```
import torch

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465516", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465516", 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
```