--- language: - en license: apache-2.0 tags: - sentence-transformers - cross-encoder - generated_from_trainer - dataset_size:578402 - loss:BinaryCrossEntropyLoss base_model: microsoft/MiniLM-L12-H384-uncased pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 co2_eq_emissions: emissions: 55.59664796149612 energy_consumed: 0.14303154591819986 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.391 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: MiniLM-L12-H384-uncased trained on GooAQ results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: gooaq dev type: gooaq-dev metrics: - type: map value: 0.6856 name: Map - type: mrr@10 value: 0.683 name: Mrr@10 - type: ndcg@10 value: 0.7314 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.432 name: Map - type: mrr@10 value: 0.4205 name: Mrr@10 - type: ndcg@10 value: 0.5022 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3503 name: Map - type: mrr@10 value: 0.5706 name: Mrr@10 - type: ndcg@10 value: 0.3846 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.5234 name: Map - type: mrr@10 value: 0.5284 name: Mrr@10 - type: ndcg@10 value: 0.5854 name: Ndcg@10 - task: type: cross-encoder-nano-beir name: Cross Encoder Nano BEIR dataset: name: NanoBEIR R100 mean type: NanoBEIR_R100_mean metrics: - type: map value: 0.4353 name: Map - type: mrr@10 value: 0.5065 name: Mrr@10 - type: ndcg@10 value: 0.4907 name: Ndcg@10 --- # MiniLM-L12-H384-uncased trained on GooAQ 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. ## Model Details ### Model Description - **Model Type:** Cross Encoder - **Base model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import CrossEncoder # Download from the 🤗 Hub model = CrossEncoder("tomaarsen/reranker-MiniLM-L12-gooaq-bce") # Get scores for pairs of texts pairs = [ ['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.'], ['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.'], ['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.']"], ['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.']"], ['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.']"], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( '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.', '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.', "['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.']", "['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.']", "['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.']", ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Reranking * Dataset: `gooaq-dev` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": false } ``` | Metric | Value | |:------------|:---------------------| | map | 0.6856 (+0.1545) | | mrr@10 | 0.6830 (+0.1591) | | **ndcg@10** | **0.7314 (+0.1402)** | #### Cross Encoder Reranking * Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": true } ``` | Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 | |:------------|:---------------------|:---------------------|:---------------------| | map | 0.4320 (-0.0576) | 0.3503 (+0.0894) | 0.5234 (+0.1038) | | mrr@10 | 0.4205 (-0.0570) | 0.5706 (+0.0708) | 0.5284 (+0.1018) | | **ndcg@10** | **0.5022 (-0.0382)** | **0.3846 (+0.0596)** | **0.5854 (+0.0847)** | #### Cross Encoder Nano BEIR * Dataset: `NanoBEIR_R100_mean` * Evaluated with [CrossEncoderNanoBEIREvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "rerank_k": 100, "at_k": 10, "always_rerank_positives": true } ``` | Metric | Value | |:------------|:---------------------| | map | 0.4353 (+0.0452) | | mrr@10 | 0.5065 (+0.0385) | | **ndcg@10** | **0.4907 (+0.0354)** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 578,402 training samples * Columns: question, answer, and label * Approximate statistics based on the first 1000 samples: | | question | answer | label | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | question | answer | label | |:-----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | 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. | 1 | | 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. | 0 | | 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.'] | 0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `bf16`: True - `dataloader_num_workers`: 4 - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 12 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | 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 | |:----------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:| | -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. ### 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 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", 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", } ```