Tom Aarsen
commited on
Commit
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c28c400
1
Parent(s):
026a71c
Add Sentence Transformers integration + README
Browse files- 1_Pooling/config.json +10 -0
- README.md +34 -1
- config_sentence_transformers.json +11 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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@@ -2936,6 +2936,39 @@ Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5
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## Usage
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### Using Huggingface transformers
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-long')
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', add_pooling_layer=False)
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model.eval()
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query_prefix = 'Represent this sentence for searching relevant passages: '
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## Usage
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### Using Sentence Transformers
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You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-long", trust_remote_code=True)
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queries = ['what is snowflake?', 'Where can I get the best tacos?']
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documents = ['The Data Cloud!', 'Mexico City of Course!']
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query_embeddings = model.encode(queries, prompt_name="query")
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document_embeddings = model.encode(documents)
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scores = query_embeddings @ document_embeddings.T
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for query, query_scores in zip(queries, scores):
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doc_score_pairs = list(zip(documents, query_scores))
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
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# Output passages & scores
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print("Query:", query)
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for document, score in doc_score_pairs:
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print(score, document)
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```
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```
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Query: what is snowflake?
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0.46484852 The Data Cloud!
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0.3758855 Mexico City of Course!
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Query: Where can I get the best tacos?
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0.42407742 Mexico City of Course!
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0.36740506 The Data Cloud!
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```
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### Using Huggingface transformers
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-long')
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False)
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model.eval()
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query_prefix = 'Represent this sentence for searching relevant passages: '
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.7.0.dev0",
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"transformers": "4.39.3",
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"pytorch": "2.1.0+cu121"
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},
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"prompts": {
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"query": "Represent this sentence for searching relevant passages: "
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},
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"default_prompt_name": null
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}
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 8192,
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"do_lower_case": false
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}
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