YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
ehdwns1516/bert-base-uncased_SWAG
This model has been trained as a SWAG dataset.
Sentence Inference Multiple Choice DEMO: Ainize DEMO
Sentence Inference Multiple Choice API: Ainize API
Overview
Language model: bert-base-uncased
Language: English
Training data: SWAG dataset
Code: See Ainize Workspace
Usage
In Transformers
from transformers import AutoTokenizer, AutoModelForMultipleChoice
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
model = AutoModelForMultipleChoice.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
def run_model(candicates_count, context: str, candicates: list[str]):
assert len(candicates) == candicates_count, "you need " + candicates_count + " candidates"
choices_inputs = []
for c in candicates:
text_a = "" # empty context
text_b = context + " " + c
inputs = tokenizer(
text_a,
text_b,
add_special_tokens=True,
max_length=128,
padding="max_length",
truncation=True,
return_overflowing_tokens=True,
)
choices_inputs.append(inputs)
input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
output = model(input_ids=input_ids)
return {"result": candicates[torch.argmax(output.logits).item()]}
items = list()
count = 4 # candicates count
context = "your context"
for i in range(int(count)):
items.append("sentence")
result = run_model(count, context, items)
- Downloads last month
- 3
Inference API (serverless) does not yet support transformers models for this pipeline type.