
# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682

## Validation Metrics

- Loss: 0.5223112106323242

## 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_24465520-26265905
```

Or Python API:

```
import torch

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265905", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265905", 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": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 103.35758036182682}
transformers pytorch xlm-roberta question-answering autonlp unk dataset:teacookies/autonlp-data-more_fine_tune_24465520 co2_eq_emissions endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682

## Validation Metrics

- Loss: 0.5223112106323242

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682 ## Validation Metrics

- Loss: 0.5223112106323242 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682 ## Validation Metrics

- Loss: 0.5223112106323242 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682## Validation Metrics

- Loss: 0.5223112106323242## Usage

You can use cURL to access this model:



Or Python API: