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from transformers import AutoTokenizer, AutoModel
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import torch
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import torch.nn.functional as F
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# Helper: Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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def model_fn(model_dir):
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  # Load model from HuggingFace Hub
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  tokenizer = AutoTokenizer.from_pretrained(model_dir)
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  model = AutoModel.from_pretrained(model_dir)
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  return model, tokenizer
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def predict_fn(data, model_and_tokenizer):
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    # destruct model and tokenizer
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    model, tokenizer = model_and_tokenizer
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    # Tokenize sentences
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    sentences = data.pop("inputs", data)
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    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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    # Compute token embeddings
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    with torch.no_grad():
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        model_output = model(**encoded_input)
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    # Perform pooling
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    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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    # Normalize embeddings
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    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
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    # return dictonary, which will be json serializable
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    return {"vectors": sentence_embeddings}
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