Forked from RohanAwhad/late_chunking_with_recursive_splitting.py
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September 13, 2024 23:03
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| from transformers import AutoTokenizer, AutoModel | |
| import torch | |
| # MODEL CKPT is downloaded from: "jinaai/jina-embeddings-v2-base-en" # has context len of 8192 | |
| MODEL_CKPT = "/Users/rohan/3_Resources/ai_models/jina-embeddings-v2-base-en" | |
| def recursive_splitter(text: str, separators: list[str], chunk_size: int) -> list[str]: | |
| if len(separators) == 0: | |
| words = text.strip().split(' ') | |
| return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)] | |
| ret = [] | |
| first_sep = separators[0] | |
| for chunk in text.split(first_sep): ret.extend(recursive_splitter(chunk, separators[1:], chunk_size)) | |
| return ret | |
| def embed_using_late_chunking(chunks): | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT) # this simple BERT tokenizer | |
| inp_tokens = [x[1:-1] for x in tokenizer(chunks)['input_ids']] # removing CLS and SEP token from start and end of each chunk | |
| offsets = [1] | |
| all_tokens = [tokenizer.cls_token_id] | |
| for toks in inp_tokens: | |
| offsets.append(offsets[-1] + len(toks)) | |
| all_tokens.extend(toks) | |
| all_tokens.append(tokenizer.sep_token_id) | |
| model = AutoModel.from_pretrained(MODEL_CKPT, trust_remote_code=True) | |
| model.eval() | |
| with torch.no_grad(): outputs = model(input_ids=torch.tensor(all_tokens).unsqueeze(-1)) | |
| return [outputs.last_hidden_state[0, i:j, :].mean(dim=-2).detach().numpy().tolist() for i, j in zip(offsets, offsets[1:])] |
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