--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1893949 - loss:Contrastive base_model: nreimers/MiniLM-L6-H384-uncased widget: - source_sentence: what medicine can i give my dog for kennel cough? sentences: - Your veterinarian can prescribe a round of antibiotics to help your dog recover faster. Some of the most widely prescribed medications for Kennel Cough are Baytril, Doxycycline, and Clavamox. However, because the disease is caused by both a virus and bacteria, the dog will require a dual-purpose treatment. - Preventing Kennel Cough The kennel cough vaccination is given once a year, but should not be given at the same time as their annual booster. If you're going on holiday, it's important that your dog is vaccinated against kennel cough. Often kennels will not accept your dog if it has not had a kennel cough vaccination. - The Driver Guide A driver guide will act as your driver and tour guide in one person. He is less knowledgeable than the tour guide, but can provide you with the most important information, help you to get around at the destination and give tips about the best photo spots. - source_sentence: how to redirect http traffic to https in tomcat? sentences: - Yes, but it depends on the previous contents of the bag. FoodSaver® Bags that previously contained fruits, vegetables, breads and dry goods can be washed and reused. ... FoodSaver® Bags that contained greasy or oily foods should also be discarded, as they may be difficult to clean. FoodSaver® Bags can be washed by hand. - '[''Go to SymantecDLP\\Protect\\tomcat\\conf directory.'', ''Edit the file server.xml.'', ''Add the following above the first entry: ... '', ''Save the server. ... '', ''Edit the web.xml file in the same directory.'', ''Scroll to the bottom of the file and add the following just above the entry: ... '', ''Save the web.xml file.'']' - 'Cause: The Enforce Console''s tomcat webserver is configured to only accept HTTPS requests. Any non-secure HTTP request will not be redirected. By default the tomcat webserver is not configured to redirect HTTP requests to HTTPS.' - source_sentence: when did the last rick and morty air? sentences: - Instead of Naota, the main character of the original FLCL (pronounced “Fooly Cooly”), Progressive focuses on Hidomi, a reserved girl who closes herself off from the world by wearing headphones (that aren't actually playing music). - On May 10, 2018, Adult Swim announced a long-term deal with the creators, ordering 70 new episodes of Rick and Morty over an unknown number of seasons. As of May 31, 2020, 41 episodes of Rick and Morty have aired, concluding the fourth season. - What time is Rick and Morty season 4 episode 8 out? The latest episode of Rick and Morty airs on Sunday, May 17 on Adult Swim. US fans can stream the new episode of Rick and Morty when it airs on the channel. - source_sentence: are clothes made of plastic? sentences: - It might surprise you, but you're probably wearing plastic clothes. ... Many of our clothes contain plastics like polyester, nylon, acrylic and polyamide. In fact most new fabrics are made of plastic – up to 64% of them. The thing is, every time we wash these materials they shed millions of plastic microfibres. - General Pharmacology. Beta-blockers are drugs that bind to beta-adrenoceptors and thereby block the binding of norepinephrine and epinephrine to these receptors. ... Second generation beta-blockers are more cardioselective in that they are relatively selective for β1 adrenoceptors. - It might surprise you, but you're probably wearing plastic clothes. ... Many of our clothes contain plastics like polyester, nylon, acrylic and polyamide. In fact most new fabrics are made of plastic – up to 64% of them. The thing is, every time we wash these materials they shed millions of plastic microfibres. - source_sentence: are emg pickups any good? sentences: - EMGs are a one trick pony, and only sound good for high gain applications. Sort of, they definitely aren't as flexible as most passive options, but most metal oriented passive pickups have the same issue. A lot of guitarists forget that EMG makes more pickups than just the 81/85 set. - Among guitar and bass accessories, the company sells active humbucker pickups, such as the EMG 81, the EMG 85, the EMG 60, and the EMG 89. They also produce passive pickups such as the EMG-HZ Series, which include SRO-OC1's and SC Sets. - You can find the star next to the abandoned mansion. The Treasure Map Loading Screen is unlocked through the battle pass, and if you look at the treasure map loading screen you'll see a knife pointing in this location. pipeline_tag: sentence-similarity library_name: sentence-transformers --- # ColBERT based on nreimers/MiniLM-L6-H384-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) - **Maximum Sequence Length:** 31 tokens - **Output Dimensionality:** 128 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 31, 'do_lower_case': False}) with Transformer model: BertModel (1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## 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 SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'are emg pickups any good?', "EMGs are a one trick pony, and only sound good for high gain applications. Sort of, they definitely aren't as flexible as most passive options, but most metal oriented passive pickups have the same issue. A lot of guitarists forget that EMG makes more pickups than just the 81/85 set.", "Among guitar and bass accessories, the company sells active humbucker pickups, such as the EMG 81, the EMG 85, the EMG 60, and the EMG 89. They also produce passive pickups such as the EMG-HZ Series, which include SRO-OC1's and SC Sets.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 128] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,893,949 training samples * Columns: question, answer, and negative * Approximate statistics based on the first 1000 samples: | | question | answer | negative | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | question | answer | negative | |:-------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the relationship between humility and thankfulness? | how gratitude can influence humility and vice versa. Humility is characterized by low self-focus, secure sense of self, and increased valuation of others. Gratitude is marked by a sense that one has benefited from the actions of another. | -hum-, root. -hum- comes from Latin, where it has the meaning "ground. '' This meaning is found in such words as: exhume, humble, humiliate, humility, humus, posthumous. | | what is the difference between usb a b c? | The USB-A has a much larger physical connector than the Type C, Type C is around the same size as a micro-USB connector. Unlike, Type A, you won't need to try and insert it, flip it over and then flip it over once more just to find the right orientation when trying to make a connection. | First the transfer rates: USB 2.0 offers transfer rates of 480 Mbps and USB 3.0 offers transfer rates of 4.8 Gbps - that's 10 times faster. ... USB 2.0 provided up to 500 mA whereas USB 3.0 provides up to 900 mA, allowing power hungry devices to now be bus powered. | | how hyaluronic acid is made? | Hyaluronic acid is a substance that is naturally present in the human body. It is found in the highest concentrations in fluids in the eyes and joints. The hyaluronic acid that is used as medicine is extracted from rooster combs or made by bacteria in the laboratory. | Hyaluronic acid helps your skin hang on to the moisture. 2. ... Hyaluronic acid by itself is non-comedogenic (doesn't clog pores), but you should be careful when choosing a hyaluronic acid serum that the ingredient list doesn't contain any sneaky pore-clogging ingredients you're not expecting. | * Loss: pylate.losses.contrastive.Contrastive ### Evaluation Dataset #### Unnamed Dataset * Size: 5,000 evaluation samples * Columns: question, answer, and negative_1 * Approximate statistics based on the first 1000 samples: | | question | answer | negative_1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | question | answer | negative_1 | |:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are tefal ingenio pans suitable for induction hobs? | Tefal Ingenio is a revolutionary concept that brings a whole new take on versatility. ... The frying pans also feature Tefal's iconic Thermo-Spot which lets you know when the pan has reached optimal cooking temperature. The Ingenio Induction range is compatible with all hobs and is also dishwasher safe. | Tefal Ingenio is a revolutionary concept that brings a whole new take on versatility. ... The frying pans also feature Tefal's iconic Thermo-Spot which lets you know when the pan has reached optimal cooking temperature. The Ingenio Induction range is compatible with all hobs and is also dishwasher safe. | | how many continuing education hours is acls? | The ACLS, PALS, and NRP certification courses are approved for 8 CEUs/CMEs, and recertification courses are approved for 4 CEUs/CMEs. The BLS certification course is approved for 4 CEUs/CMEs and the recertification course is approved for 2 CEUs/CMEs. For more information, please visit our Accreditation page. | The foremost difference between the two is their advancement level. Essentially, ACLS is a sophisticated and more advanced course and builds upon the major fundamentals developed during BLS. The main purpose of BLS and ACLS certification are well explained in this article. | | what are the health benefits of drinking peppermint tea? | ['Makes you Stress Free. When it comes to relieving stress and anxiety, peppermint tea is one of the best allies. ... ', 'Sleep-Friendly. ... ', 'Aids in Weight Loss. ... ', 'Cure for an Upset Stomach. ... ', 'Improves Digestion. ... ', 'Boosts Immune System. ... ', 'Fights Bad Breath.'] | Peppermint tea is a popular herbal tea that is naturally calorie- and caffeine-free. Some research has suggested that the oils in peppermint may have a number of other health benefits, such as fresher breath, better digestion, and reduced pain from headaches. Peppermint tea also has antibacterial properties. | * Loss: pylate.losses.contrastive.Contrastive ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 3e-06 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `bf16`: True - `dataloader_num_workers`: 12 - `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`: 256 - `per_device_eval_batch_size`: 256 - `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`: 3e-06 - `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`: 12 - `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} - `tp_size`: 0 - `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 | |:------:|:----:|:-------------:| | 0.0001 | 1 | 10.8061 | | 0.0270 | 200 | 8.9391 | | 0.0541 | 400 | 5.1795 | | 0.0811 | 600 | 2.3951 | | 0.1081 | 800 | 1.6927 | | 0.1352 | 1000 | 1.404 | | 0.1622 | 1200 | 1.2496 | | 0.1892 | 1400 | 1.1613 | | 0.2162 | 1600 | 1.0843 | | 0.2433 | 1800 | 1.0427 | | 0.2703 | 2000 | 1.0005 | | 0.2973 | 2200 | 0.9695 | | 0.3244 | 2400 | 0.9325 | | 0.3514 | 2600 | 0.9122 | | 0.3784 | 2800 | 0.8832 | | 0.4055 | 3000 | 0.8689 | | 0.4325 | 3200 | 0.8626 | | 0.4595 | 3400 | 0.8452 | | 0.4866 | 3600 | 0.8329 | | 0.5136 | 3800 | 0.8132 | | 0.5406 | 4000 | 0.8111 | | 0.5676 | 4200 | 0.7952 | | 0.5947 | 4400 | 0.7892 | | 0.6217 | 4600 | 0.7772 | | 0.6487 | 4800 | 0.7793 | | 0.6758 | 5000 | 0.7705 | | 0.7028 | 5200 | 0.7692 | | 0.7298 | 5400 | 0.7625 | | 0.7569 | 5600 | 0.7595 | | 0.7839 | 5800 | 0.7405 | | 0.8109 | 6000 | 0.7513 | | 0.8380 | 6200 | 0.7396 | | 0.8650 | 6400 | 0.7312 | | 0.8920 | 6600 | 0.7325 | | 0.9190 | 6800 | 0.7371 | | 0.9461 | 7000 | 0.7422 | | 0.9731 | 7200 | 0.7296 | ### Framework Versions - Python: 3.11.0 - Sentence Transformers: 4.0.1 - Transformers: 4.50.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## 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", } ```