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--- |
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license: apache-2.0 |
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language: |
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- vi |
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- en |
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base_model: |
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- intfloat/multilingual-e5-large |
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--- |
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## Support for Sentence Transformers |
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Below is an example for usage with sentence_transformers. |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer('theblackhacker/cono_embedding') |
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input_texts = [ |
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'query: how much protein should a female eat', |
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'query: 南瓜的家常做法', |
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"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
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"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" |
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] |
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embeddings = model.encode(input_texts, normalize_embeddings=True) |
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``` |
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Package requirements |
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`pip install sentence_transformers~=2.2.2` |
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## FAQ |
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**1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
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Yes, this is how the model is trained, otherwise you will see a performance degradation. |
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Here are some rules of thumb: |
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- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
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- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. |
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- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
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**2. Why are my reproduced results slightly different from reported in the model card?** |
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
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**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
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This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
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For text embedding tasks like text retrieval or semantic similarity, |
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what matters is the relative order of the scores instead of the absolute values, |
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so this should not be an issue. |
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## Limitations |
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Long texts will be truncated to at most 512 tokens. |
