File size: 1,547 Bytes
f8c7160
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
  context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-more_fine_tune_24465520
co2_eq_emissions: 108.63800043275934
---

# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265904
- CO2 Emissions (in grams): 108.63800043275934

## Validation Metrics

- Loss: 0.5807144045829773

## 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-more_fine_tune_24465520-26265904
```

Or Python API:

```
import torch

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265904", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265904", 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
```