| | --- |
| | library_name: transformers |
| | tags: [] |
| | --- |
| | |
| | # USAGE |
| |
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| |
|
| | ```python |
| | import torch |
| | from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| | |
| | MODEL_NAME = "swarogthehater/IMAGE_INTENT" |
| | model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, load_in_8bit=True) |
| | model.eval() |
| | tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
| | |
| | text = "show me yourself" |
| | IMAGE_INTENT = "[IMG]" |
| | |
| | input_text = text+"[SEP]"+IMAGE_INTENT+"[SEP]" |
| | |
| | device = torch.device("cpu") |
| | batch = tokenizer.encode_plus(input_text, return_tensors="pt") |
| | input_ids = batch['input_ids'].to(device) |
| | attention_mask = batch['attention_mask'].to(device) |
| | token_type_ids = batch['token_type_ids'].to(device) |
| | outputs = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) |
| | |
| | #labels in common nli terms (0: entailment, 1: neutral, 2: contradiction) |
| | print(outputs.logits.argmax().item()) |
| | #labels for img intent |
| | label = 1 if outputs.logits.argmax().item() == 0 else 0 |
| | print(label) |
| | #scores |
| | print(outputs.logits.float().softmax(dim=-1).detach().numpy()) |
| | ``` |
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