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--- |
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tags: |
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- autonlp |
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- question-answering |
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language: unk |
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widget: |
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- text: "Who loves AutoNLP?" |
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context: "Everyone loves AutoNLP" |
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datasets: |
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- teacookies/autonlp-data-roberta-base-squad2 |
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co2_eq_emissions: 54.44076291568145 |
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--- |
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# Model Trained Using AutoNLP |
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- Problem type: Extractive Question Answering |
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- Model ID: 24465514 |
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- CO2 Emissions (in grams): 54.44076291568145 |
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## Validation Metrics |
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- Loss: 0.5786784887313843 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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-squad2-24465514 |
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``` |
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Or Python API: |
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``` |
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import torch |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465514", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465514", use_auth_token=True) |
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from transformers import BertTokenizer, BertForQuestionAnswering |
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question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP" |
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inputs = tokenizer(question, text, return_tensors='pt') |
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start_positions = torch.tensor([1]) |
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end_positions = torch.tensor([3]) |
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outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions) |
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loss = outputs.loss |
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start_scores = outputs.start_logits |
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end_scores = outputs.end_logits |
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``` |
