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# flag-text-embedding-chinese |
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Map any text to a 1024-dimensional dense vector space and can be used for tasks like retrieval, classification, clustering, or semantic search. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["样例数据-1", "样例数据-2"] |
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model = SentenceTransformer('Shitao/flag-text-embedding-chinese') |
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embeddings = model.encode(sentences, normalize_embeddings=True) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Sentences we want sentence embeddings for |
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sentences = ["样例数据-1", "样例数据-2"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('Shitao/flag-text-embedding-chinese') |
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model = AutoModel.from_pretrained('Shitao/flag-text-embedding-chinese') |
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# Tokenize sentences |
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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. In this case, cls pooling. |
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sentence_embeddings = model_output[0][:, 0] |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Evaluation Results |
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For an automated evaluation of this model, see the *Chinese Embedding Benchmark*: [link]() |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |