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README.md

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+---
+pipeline_tag: sentence-similarity
+language: en
+license: apache-2.0
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# hkunlp/instructor-base
+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.)
+
+The model is easy to use with `sentence-transformer` library.
+
+## Installation
+```bash
+git clone https://github.com/HKUNLP/instructor-embedding
+cd sentence-transformers
+pip install -e .
+```
+
+## Compute your customized embeddings
+Then you can use the model like this to calculate domain-specific and task-aware embeddings:
+```python
+from sentence_transformers import SentenceTransformer
+sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+instruction = "Represent the Science title; Input:"
+model = SentenceTransformer('hkunlp/instructor-base')
+embeddings = model.encode([[instruction,sentence,0]])
+print(embeddings)
+```
+
+## Calculate Sentence similarities
+You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
+```python
+from sklearn.metrics.pairwise import cosine_similarity
+sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0], 
+               ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
+sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
+               ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
+embeddings_a = model.encode(sentences_a)
+embeddings_b = model.encode(sentences_b)
+similarities = cosine_similarity(embeddings_a,embeddings_b)
+print(similarities)
+```

config.json

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+{
+  "_name_or_path": "/scratch/acd13578qu/huggingface_models/sentence-transformers_gtr-t5-base/",
+  "architectures": [
+    "T5EncoderModel"
+  ],
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+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+  "do_lower_case": false
+}

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