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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- biencoder |
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- sentence-transformers |
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- text-classification |
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- sentence-pair-classification |
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- semantic-similarity |
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- semantic-search |
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- retrieval |
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- reranking |
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- generated_from_trainer |
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- dataset_size:76349300 |
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- loss:ArcFaceInBatchLoss |
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base_model: Alibaba-NLP/gte-modernbert-base |
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widget: |
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- source_sentence: '"How much would I need to narrate a ""Let''s Play"" video in order |
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to make money from it on YouTube?"' |
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sentences: |
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- How much money do people make from YouTube videos with 1 million views? |
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- '"How much would I need to narrate a ""Let''s Play"" video in order to make money |
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from it on YouTube?"' |
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- '"Does the sentence, ""I expect to be disappointed,"" make sense?"' |
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- source_sentence: '"I appreciate that.' |
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sentences: |
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- '"How is the Mariner rewarded in ""The Rime of the Ancient Mariner"" by Samuel |
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Taylor Coleridge?"' |
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- '"I appreciate that.' |
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- I can appreciate that. |
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- source_sentence: '"""It is very easy to defeat someone, but too hard to win some |
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one"". What does the previous sentence mean?"' |
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sentences: |
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- '"How can you use the word ""visceral"" in a sentence?"' |
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- '"""It is very easy to defeat someone, but too hard to win some one"". What does |
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the previous sentence mean?"' |
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- '"What does ""The loudest one in the room is the weakest one in the room."" Mean?"' |
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- source_sentence: '" We condemn this raid which is in our view illegal and morally |
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and politically unjustifiable , " London-based NCRI official Ali Safavi told Reuters |
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by telephone .' |
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sentences: |
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- 'London-based NCRI official Ali Safavi told Reuters : " We condemn this raid , |
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which is in our view illegal and morally and politically unjustifiable . "' |
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- The social awkwardness is complicated by the fact that Marianne is a white girl |
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living with a black family . |
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- art's cause, this in my opinion |
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- source_sentence: '"If you click ""like"" on an old post that someone made on your |
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wall yet you''re no longer Facebook friends, will they still receive a notification?"' |
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sentences: |
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- '"Is there is any two wheeler having a gear box which has the feature ""automatic |
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neutral"" when the engine is off while it is in gear?"' |
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- '"If you click ""like"" on an old post that someone made on your wall yet you''re |
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no longer Facebook friends, will they still receive a notification?"' |
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- '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would |
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you be concerned?"' |
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datasets: |
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- redis/langcache-sentencepairs-v2 |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_precision@1 |
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- cosine_recall@1 |
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- cosine_ndcg@10 |
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- cosine_mrr@1 |
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- cosine_map@100 |
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- cosine_auc_precision_cache_hit_ratio |
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- cosine_auc_similarity_distribution |
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model-index: |
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- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache |
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results: |
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- task: |
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type: custom-information-retrieval |
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name: Custom Information Retrieval |
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dataset: |
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name: test |
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type: test |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5955802603036876 |
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name: Cosine Accuracy@1 |
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- type: cosine_precision@1 |
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value: 0.5955802603036876 |
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name: Cosine Precision@1 |
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- type: cosine_recall@1 |
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value: 0.5780913232288468 |
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name: Cosine Recall@1 |
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- type: cosine_ndcg@10 |
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value: 0.777639866271746 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@1 |
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value: 0.5955802603036876 |
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name: Cosine Mrr@1 |
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- type: cosine_map@100 |
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value: 0.7275779687157514 |
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name: Cosine Map@100 |
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- type: cosine_auc_precision_cache_hit_ratio |
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value: 0.3639683124583609 |
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name: Cosine Auc Precision Cache Hit Ratio |
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- type: cosine_auc_similarity_distribution |
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value: 0.15401896350374616 |
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name: Cosine Auc Similarity Distribution |
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--- |
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# Redis fine-tuned BiEncoder model for semantic caching on LangCache |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> |
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- **Maximum Sequence Length:** 100 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("redis/langcache-embed-v3") |
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# Run inference |
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sentences = [ |
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'"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"', |
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'"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"', |
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'"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would you be concerned?"', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 1.0000, 0.6758], |
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# [1.0000, 1.0000, 0.6758], |
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# [0.6758, 0.6758, 1.0078]], dtype=torch.bfloat16) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Evaluation |
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### Metrics |
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#### Custom Information Retrieval |
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* Dataset: `test` |
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* Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code> |
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| Metric | Value | |
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|:-------------------------------------|:-----------| |
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| cosine_accuracy@1 | 0.5956 | |
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| cosine_precision@1 | 0.5956 | |
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| cosine_recall@1 | 0.5781 | |
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| **cosine_ndcg@10** | **0.7776** | |
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| cosine_mrr@1 | 0.5956 | |
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| cosine_map@100 | 0.7276 | |
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| cosine_auc_precision_cache_hit_ratio | 0.364 | |
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| cosine_auc_similarity_distribution | 0.154 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### LangCache Sentence Pairs (all) |
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* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
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* Size: 132,354 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
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| <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> | |
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| <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> | |
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| <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Childlessness is low in Eastern European countries.</code> | |
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* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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### Evaluation Dataset |
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#### LangCache Sentence Pairs (all) |
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* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
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* Size: 132,354 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
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| <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> | |
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| <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> | |
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| <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Childlessness is low in Eastern European countries.</code> | |
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* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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### Training Logs |
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| Epoch | Step | test_cosine_ndcg@10 | |
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|:-----:|:----:|:-------------------:| |
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| -1 | -1 | 0.7776 | |
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### Framework Versions |
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- Python: 3.12.3 |
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- Sentence Transformers: 5.1.0 |
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- Transformers: 4.56.0 |
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- PyTorch: 2.8.0+cu128 |
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- Accelerate: 1.10.1 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.22.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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