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README.md ADDED
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+ ---
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+ pipeline_tag: sentence-similarity
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+ language: en
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+ license: apache-2.0
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+ tags:
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+ - sentence-transformers
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+ - feature-extraction
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+ - sentence-similarity
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+ - transformers
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+ ---
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+
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+ # hku-nlp/instructor-large
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+ This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.)
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+
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+ The model is easy to use with `sentence-transformer` library.
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+
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+ ## Installation
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+ ```bash
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+ git clone https://github.com/HKUNLP/instructor-embedding
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+ cd sentence-transformers
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+ pip install -e .
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+ ```
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+
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+ ## Compute your customized embeddings
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+ Then you can use the model like this to calculate domain-specific and task-aware embeddings:
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
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+ instruction = "Represent the Science title; Input:"
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+ model = SentenceTransformer('hku-nlp/instructor-large')
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+ embeddings = model.encode([[instruction,sentence,0]])
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+ print(embeddings)
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+ ```
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+
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+ ## Calculate Sentence similarities
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+ You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
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+ ```python
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+ from sklearn.metrics.pairwise import cosine_similarity
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+ sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0],
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+ ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
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+ sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
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+ ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
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+ embeddings_a = model.encode(sentences_a)
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+ embeddings_b = model.encode(sentences_b)
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+ similarities = cosine_similarity(embeddings_a,embeddings_b)
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+ print(similarities)
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+ ```
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+ "_name_or_path": "/scratch/acd13578qu/huggingface_models/sentence-transformers_gtr-t5-large/",
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+ "architectures": [
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+ "T5EncoderModel"
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+ ],
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+ "d_ff": 4096,
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+ "d_kv": 64,
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+ "d_model": 1024,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "relu",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": false,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 24,
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+ "num_heads": 16,
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+ "num_layers": 24,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ "length_penalty": 2.0,
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+ "max_length": 200,
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+ "no_repeat_ngram_size": 3,
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.20.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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