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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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- sentence-transformers
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- cross-encoder
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- generated_from_trainer
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- dataset_size:578402
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- loss:BinaryCrossEntropyLoss
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base_model: microsoft/MiniLM-L12-H384-uncased
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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metrics:
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- map
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- mrr@10
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- ndcg@10
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co2_eq_emissions:
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emissions: 55.59664796149612
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energy_consumed: 0.14303154591819986
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source: codecarbon
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training_type: fine-tuning
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on_cloud: false
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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ram_total_size: 31.777088165283203
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hours_used: 0.391
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hardware_used: 1 x NVIDIA GeForce RTX 3090
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model-index:
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- name: MiniLM-L12-H384-uncased trained on GooAQ
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results:
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- task:
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type: cross-encoder-reranking
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name: Cross Encoder Reranking
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dataset:
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name: gooaq dev
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type: gooaq-dev
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metrics:
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- type: map
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value: 0.6856
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name: Map
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- type: mrr@10
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value: 0.683
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name: Mrr@10
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- type: ndcg@10
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value: 0.7314
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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name: Cross Encoder Reranking
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dataset:
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name: NanoMSMARCO R100
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type: NanoMSMARCO_R100
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metrics:
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- type: map
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value: 0.432
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name: Map
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- type: mrr@10
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value: 0.4205
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name: Mrr@10
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- type: ndcg@10
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value: 0.5022
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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name: Cross Encoder Reranking
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dataset:
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name: NanoNFCorpus R100
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type: NanoNFCorpus_R100
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metrics:
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- type: map
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value: 0.3503
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name: Map
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- type: mrr@10
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value: 0.5706
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name: Mrr@10
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- type: ndcg@10
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value: 0.3846
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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name: Cross Encoder Reranking
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dataset:
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name: NanoNQ R100
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type: NanoNQ_R100
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metrics:
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- type: map
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value: 0.5234
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name: Map
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- type: mrr@10
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value: 0.5284
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name: Mrr@10
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- type: ndcg@10
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value: 0.5854
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name: Ndcg@10
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- task:
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type: cross-encoder-nano-beir
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name: Cross Encoder Nano BEIR
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dataset:
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name: NanoBEIR R100 mean
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type: NanoBEIR_R100_mean
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metrics:
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- type: map
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value: 0.4353
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name: Map
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- type: mrr@10
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value: 0.5065
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name: Mrr@10
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- type: ndcg@10
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value: 0.4907
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name: Ndcg@10
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---
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# MiniLM-L12-H384-uncased trained on GooAQ
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Output Labels:** 1 label
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<!-- - **Training Dataset:** Unknown -->
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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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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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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 CrossEncoder
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# Download from the 🤗 Hub
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model = CrossEncoder("tomaarsen/reranker-MiniLM-L12-gooaq-bce")
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# Get scores for pairs of texts
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pairs = [
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['what is the remote desktop connection broker?', 'A remote desktop connection broker is software that allows clients to access various types of server-hosted desktops and applications. ... Load balancing the servers that host the desktops. Managing desktop images. Redirecting multimedia processing to the client.'],
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['what is the remote desktop connection broker?', 'Remote Desktop Connection (RDC, also called Remote Desktop, formerly Microsoft Terminal Services Client, mstsc or tsclient) is the client application for RDS. It allows a user to remotely log into a networked computer running the terminal services server.'],
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['what is the remote desktop connection broker?', "['Click the Start menu on your PC and search for Remote Desktop Connection.', 'Launch Remote Desktop Connection and click on Show Options.', 'Select the Local Resources tab and click More.', 'Under Drives, check the box for your C: drive or the drives that contain the files you will transfer and click OK.']"],
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['what is the remote desktop connection broker?', "['Press the MENU button on your remote.', 'Select Parental Favs & Setup > System Setup > Remote or Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you want to program. ... ', 'Follow the on-screen instructions to finish programming your remote.']"],
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['what is the remote desktop connection broker?', "['Press MENU on your remote.', 'Select Parental Favs & Setup > System Setup > Remote or Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete the programming.']"],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# (5,)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'what is the remote desktop connection broker?',
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[
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'A remote desktop connection broker is software that allows clients to access various types of server-hosted desktops and applications. ... Load balancing the servers that host the desktops. Managing desktop images. Redirecting multimedia processing to the client.',
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'Remote Desktop Connection (RDC, also called Remote Desktop, formerly Microsoft Terminal Services Client, mstsc or tsclient) is the client application for RDS. It allows a user to remotely log into a networked computer running the terminal services server.',
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"['Click the Start menu on your PC and search for Remote Desktop Connection.', 'Launch Remote Desktop Connection and click on Show Options.', 'Select the Local Resources tab and click More.', 'Under Drives, check the box for your C: drive or the drives that contain the files you will transfer and click OK.']",
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"['Press the MENU button on your remote.', 'Select Parental Favs & Setup > System Setup > Remote or Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you want to program. ... ', 'Follow the on-screen instructions to finish programming your remote.']",
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"['Press MENU on your remote.', 'Select Parental Favs & Setup > System Setup > Remote or Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete the programming.']",
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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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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</details>
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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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<!--
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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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-->
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## Evaluation
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### Metrics
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#### Cross Encoder Reranking
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* Dataset: `gooaq-dev`
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* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
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```json
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{
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"at_k": 10,
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"always_rerank_positives": false
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}
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```
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| Metric | Value |
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|:------------|:---------------------|
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| map | 0.6856 (+0.1545) |
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| mrr@10 | 0.6830 (+0.1591) |
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| **ndcg@10** | **0.7314 (+0.1402)** |
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#### Cross Encoder Reranking
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* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
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* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
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```json
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{
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"at_k": 10,
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"always_rerank_positives": true
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}
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```
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| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
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|:------------|:---------------------|:---------------------|:---------------------|
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| map | 0.4320 (-0.0576) | 0.3503 (+0.0894) | 0.5234 (+0.1038) |
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| mrr@10 | 0.4205 (-0.0570) | 0.5706 (+0.0708) | 0.5284 (+0.1018) |
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| **ndcg@10** | **0.5022 (-0.0382)** | **0.3846 (+0.0596)** | **0.5854 (+0.0847)** |
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#### Cross Encoder Nano BEIR
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* Dataset: `NanoBEIR_R100_mean`
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* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
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```json
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{
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"dataset_names": [
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"msmarco",
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"nfcorpus",
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"nq"
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],
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"rerank_k": 100,
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"at_k": 10,
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"always_rerank_positives": true
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}
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```
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| Metric | Value |
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|:------------|:---------------------|
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| map | 0.4353 (+0.0452) |
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| mrr@10 | 0.5065 (+0.0385) |
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| **ndcg@10** | **0.4907 (+0.0354)** |
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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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#### Unnamed Dataset
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* Size: 578,402 training samples
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* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | question | answer | label |
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|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 18 characters</li><li>mean: 42.66 characters</li><li>max: 73 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 252.61 characters</li><li>max: 368 characters</li></ul> | <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> |
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* Samples:
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| question | answer | label |
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|:-----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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| <code>what is the remote desktop connection broker?</code> | <code>A remote desktop connection broker is software that allows clients to access various types of server-hosted desktops and applications. ... Load balancing the servers that host the desktops. Managing desktop images. Redirecting multimedia processing to the client.</code> | <code>1</code> |
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| <code>what is the remote desktop connection broker?</code> | <code>Remote Desktop Connection (RDC, also called Remote Desktop, formerly Microsoft Terminal Services Client, mstsc or tsclient) is the client application for RDS. It allows a user to remotely log into a networked computer running the terminal services server.</code> | <code>0</code> |
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| <code>what is the remote desktop connection broker?</code> | <code>['Click the Start menu on your PC and search for Remote Desktop Connection.', 'Launch Remote Desktop Connection and click on Show Options.', 'Select the Local Resources tab and click More.', 'Under Drives, check the box for your C: drive or the drives that contain the files you will transfer and click OK.']</code> | <code>0</code> |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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"activation_fn": "torch.nn.modules.linear.Identity",
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"pos_weight": 5
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `seed`: 12
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- `bf16`: True
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- `dataloader_num_workers`: 4
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- `load_best_model_at_end`: True
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 2e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 12
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: True
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 4
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: True
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: None
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `include_for_metrics`: []
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `use_liger_kernel`: False
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: None
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- `batch_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
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|:----------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
|
|
| -1 | -1 | - | 0.1548 (-0.4365) | 0.0475 (-0.4929) | 0.2762 (-0.0489) | 0.0485 (-0.4521) | 0.1241 (-0.3313) |
|
|
| 0.0001 | 1 | 1.0439 | - | - | - | - | - |
|
|
| 0.0221 | 200 | 1.1645 | - | - | - | - | - |
|
|
| 0.0443 | 400 | 1.0837 | - | - | - | - | - |
|
|
| 0.0664 | 600 | 0.8732 | - | - | - | - | - |
|
|
| 0.0885 | 800 | 0.7901 | - | - | - | - | - |
|
|
| 0.1106 | 1000 | 0.755 | 0.6710 (+0.0798) | 0.5150 (-0.0254) | 0.3164 (-0.0086) | 0.6085 (+0.1079) | 0.4800 (+0.0246) |
|
|
| 0.1328 | 1200 | 0.7095 | - | - | - | - | - |
|
|
| 0.1549 | 1400 | 0.7094 | - | - | - | - | - |
|
|
| 0.1770 | 1600 | 0.6715 | - | - | - | - | - |
|
|
| 0.1992 | 1800 | 0.6583 | - | - | - | - | - |
|
|
| 0.2213 | 2000 | 0.6865 | 0.6994 (+0.1082) | 0.5033 (-0.0372) | 0.3608 (+0.0357) | 0.6058 (+0.1052) | 0.4900 (+0.0346) |
|
|
| 0.2434 | 2200 | 0.6392 | - | - | - | - | - |
|
|
| 0.2655 | 2400 | 0.6403 | - | - | - | - | - |
|
|
| 0.2877 | 2600 | 0.6538 | - | - | - | - | - |
|
|
| 0.3098 | 2800 | 0.6273 | - | - | - | - | - |
|
|
| 0.3319 | 3000 | 0.6091 | 0.7033 (+0.1121) | 0.4779 (-0.0625) | 0.3369 (+0.0119) | 0.5859 (+0.0852) | 0.4669 (+0.0115) |
|
|
| 0.3541 | 3200 | 0.6244 | - | - | - | - | - |
|
|
| 0.3762 | 3400 | 0.6246 | - | - | - | - | - |
|
|
| 0.3983 | 3600 | 0.6222 | - | - | - | - | - |
|
|
| 0.4204 | 3800 | 0.5986 | - | - | - | - | - |
|
|
| 0.4426 | 4000 | 0.622 | 0.7252 (+0.1339) | 0.5538 (+0.0133) | 0.3718 (+0.0468) | 0.5965 (+0.0959) | 0.5074 (+0.0520) |
|
|
| 0.4647 | 4200 | 0.5742 | - | - | - | - | - |
|
|
| 0.4868 | 4400 | 0.6171 | - | - | - | - | - |
|
|
| 0.5090 | 4600 | 0.6023 | - | - | - | - | - |
|
|
| 0.5311 | 4800 | 0.5988 | - | - | - | - | - |
|
|
| 0.5532 | 5000 | 0.5693 | 0.7248 (+0.1336) | 0.5174 (-0.0231) | 0.3631 (+0.0381) | 0.5575 (+0.0569) | 0.4793 (+0.0240) |
|
|
| 0.5753 | 5200 | 0.5783 | - | - | - | - | - |
|
|
| 0.5975 | 5400 | 0.5866 | - | - | - | - | - |
|
|
| 0.6196 | 5600 | 0.543 | - | - | - | - | - |
|
|
| 0.6417 | 5800 | 0.57 | - | - | - | - | - |
|
|
| 0.6639 | 6000 | 0.5662 | 0.7273 (+0.1361) | 0.5148 (-0.0256) | 0.3644 (+0.0393) | 0.5754 (+0.0748) | 0.4849 (+0.0295) |
|
|
| 0.6860 | 6200 | 0.5605 | - | - | - | - | - |
|
|
| 0.7081 | 6400 | 0.5836 | - | - | - | - | - |
|
|
| 0.7303 | 6600 | 0.5703 | - | - | - | - | - |
|
|
| 0.7524 | 6800 | 0.5732 | - | - | - | - | - |
|
|
| 0.7745 | 7000 | 0.5679 | 0.7306 (+0.1394) | 0.5185 (-0.0219) | 0.3767 (+0.0517) | 0.5826 (+0.0820) | 0.4926 (+0.0372) |
|
|
| 0.7966 | 7200 | 0.5454 | - | - | - | - | - |
|
|
| 0.8188 | 7400 | 0.5471 | - | - | - | - | - |
|
|
| 0.8409 | 7600 | 0.5592 | - | - | - | - | - |
|
|
| 0.8630 | 7800 | 0.5545 | - | - | - | - | - |
|
|
| **0.8852** | **8000** | **0.5477** | **0.7314 (+0.1402)** | **0.5022 (-0.0382)** | **0.3846 (+0.0596)** | **0.5854 (+0.0847)** | **0.4907 (+0.0354)** |
|
|
| 0.9073 | 8200 | 0.5411 | - | - | - | - | - |
|
|
| 0.9294 | 8400 | 0.5299 | - | - | - | - | - |
|
|
| 0.9515 | 8600 | 0.5677 | - | - | - | - | - |
|
|
| 0.9737 | 8800 | 0.5202 | - | - | - | - | - |
|
|
| 0.9958 | 9000 | 0.5211 | 0.7311 (+0.1399) | 0.5090 (-0.0315) | 0.3735 (+0.0484) | 0.5923 (+0.0916) | 0.4916 (+0.0362) |
|
|
| -1 | -1 | - | 0.7314 (+0.1402) | 0.5022 (-0.0382) | 0.3846 (+0.0596) | 0.5854 (+0.0847) | 0.4907 (+0.0354) |
|
|
|
|
* The bold row denotes the saved checkpoint.
|
|
|
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### Environmental Impact
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
|
- **Energy Consumed**: 0.143 kWh
|
|
- **Carbon Emitted**: 0.056 kg of CO2
|
|
- **Hours Used**: 0.391 hours
|
|
|
|
### Training Hardware
|
|
- **On Cloud**: No
|
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
- **RAM Size**: 31.78 GB
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.6
|
|
- Sentence Transformers: 3.5.0.dev0
|
|
- Transformers: 4.49.0
|
|
- PyTorch: 2.6.0+cu124
|
|
- Accelerate: 1.5.1
|
|
- Datasets: 3.3.2
|
|
- Tokenizers: 0.21.0
|
|
|
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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",
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
|
month = "11",
|
|
year = "2019",
|
|
publisher = "Association for Computational Linguistics",
|
|
url = "https://arxiv.org/abs/1908.10084",
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}
|
|
```
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