--- base_model: thenlper/gte-base library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10932 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Medicinal And Botanical Chemicals, Drugs, And Other Products  sentences: - Alkyl benzene for surfactants - Botanical extracts for supplements - Industrial chemicals - source_sentence: Ball And Roller Bearings sentences: - Bearing races - Dishwashing liquid - Bearing walls - source_sentence: Scientific Time Keeping Device sentences: - Digital wristwatches - Quartz crystals - Natural rubber for tires - source_sentence: Miscellaneous Electrical Industrial Apparatus sentences: - Consumer electronics - Stainless steel hollow sections - Industrial circuit breakers - source_sentence: Mineral Fuels, Lubricants Etc. sentences: - Coal - Logistics costs for machinery distribution - Crude oil --- # SentenceTransformer based on thenlper/gte-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). It maps sentences & paragraphs to a 768-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:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **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 ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("neel2306/gte-cp-base") # Run inference sentences = [ 'Mineral Fuels, Lubricants Etc.', 'Crude oil', 'Coal', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,932 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------|:-----------------------------------| | Clay Floor And Wall Tile, Glazed And Unglazed (Including Quarry Tile And Ceramic Mosaic Tile) | Ceramic mosaic tiles | Natural stone tiles | | Electrical Relay/Conductor | Relay switches | Electrical insulators | | Plasterer (Kelowna, British Columbia 5 13) (Union Rate) | Labor costs for plasterers | Painting supplies | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 2,733 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:------------------------------------------------------------|:---------------------------------------------|:------------------------------| | Asphalt Paving Mixture and Block Manufacturing | Recycled asphalt pavement (RAP) | Asphalt shingles | | Air Conditioning Plant | Refrigerant gases | Heating elements | | Oak Lumber | Oak plywood | Pine lumber | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 6e-05 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `optim`: adamw_hf - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 6e-05 - `weight_decay`: 0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `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`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `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`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `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_hf - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | loss | |:------:|:----:|:-------------:|:------:| | 0.0731 | 50 | 1.9026 | 1.5169 | | 0.1462 | 100 | 1.5479 | 1.0813 | | 0.2193 | 150 | 1.0239 | 0.7291 | | 0.2924 | 200 | 0.6914 | 0.6372 | | 0.3655 | 250 | 0.653 | 0.5887 | | 0.4386 | 300 | 0.5469 | 0.5605 | | 0.5117 | 350 | 0.5312 | 0.5408 | | 0.5848 | 400 | 0.4996 | 0.5100 | | 0.6579 | 450 | 0.4445 | 0.4830 | | 0.7310 | 500 | 0.5092 | 0.4734 | | 0.8041 | 550 | 0.532 | 0.4476 | | 0.8772 | 600 | 0.4147 | 0.4714 | | 0.9503 | 650 | 0.477 | 0.4400 | | 1.0234 | 700 | 0.4243 | 0.4466 | | 1.0965 | 750 | 0.485 | 0.4172 | | 1.1696 | 800 | 0.3717 | 0.4271 | | 1.2427 | 850 | 0.3716 | 0.4369 | | 1.3158 | 900 | 0.3742 | 0.4104 | | 1.3889 | 950 | 0.3157 | 0.4436 | | 1.4620 | 1000 | 0.3035 | 0.4444 | | 1.5351 | 1050 | 0.2797 | 0.4558 | | 1.6082 | 1100 | 0.2639 | 0.4248 | | 1.6813 | 1150 | 0.2286 | 0.4308 | | 1.7544 | 1200 | 0.2753 | 0.4098 | | 1.8275 | 1250 | 0.1904 | 0.4415 | | 1.9006 | 1300 | 0.2175 | 0.4503 | | 1.9737 | 1350 | 0.1806 | 0.4245 | | 2.0468 | 1400 | 0.1826 | 0.4418 | | 2.1199 | 1450 | 0.1952 | 0.4138 | | 2.1930 | 1500 | 0.1612 | 0.4061 | | 2.2661 | 1550 | 0.1604 | 0.3910 | | 2.3392 | 1600 | 0.1199 | 0.3852 | | 2.4123 | 1650 | 0.1439 | 0.4082 | | 2.4854 | 1700 | 0.1402 | 0.4352 | | 2.5585 | 1750 | 0.1116 | 0.4338 | | 2.6316 | 1800 | 0.1113 | 0.4189 | | 2.7047 | 1850 | 0.1159 | 0.4013 | | 2.7778 | 1900 | 0.1241 | 0.3853 | | 2.8509 | 1950 | 0.0977 | 0.3919 | | 2.9240 | 2000 | 0.0953 | 0.4022 | | 2.9971 | 2050 | 0.1159 | 0.4073 | | 3.0702 | 2100 | 0.0923 | 0.3903 | | 3.1433 | 2150 | 0.0958 | 0.3833 | | 3.2164 | 2200 | 0.0787 | 0.3875 | | 3.2895 | 2250 | 0.083 | 0.3807 | | 3.3626 | 2300 | 0.0714 | 0.3806 | | 3.4357 | 2350 | 0.0748 | 0.3997 | | 3.5088 | 2400 | 0.0779 | 0.4027 | | 3.5819 | 2450 | 0.0709 | 0.3921 | | 3.6550 | 2500 | 0.0482 | 0.3905 | | 3.7281 | 2550 | 0.0784 | 0.3760 | | 3.8012 | 2600 | 0.0694 | 0.3809 | | 3.8743 | 2650 | 0.0725 | 0.3957 | | 3.9474 | 2700 | 0.0718 | 0.3897 | | 4.0205 | 2750 | 0.05 | 0.3894 | | 4.0936 | 2800 | 0.0597 | 0.4014 | | 4.1667 | 2850 | 0.0445 | 0.3929 | | 4.2398 | 2900 | 0.039 | 0.3856 | | 4.3129 | 2950 | 0.0405 | 0.3723 | | 4.3860 | 3000 | 0.0456 | 0.3764 | | 4.4591 | 3050 | 0.0493 | 0.3876 | | 4.5322 | 3100 | 0.036 | 0.3866 | | 4.6053 | 3150 | 0.0517 | 0.3791 | | 4.6784 | 3200 | 0.0383 | 0.3724 | | 4.7515 | 3250 | 0.0453 | 0.3886 | | 4.8246 | 3300 | 0.0469 | 0.3897 | | 4.8977 | 3350 | 0.0385 | 0.3940 | | 4.9708 | 3400 | 0.0427 | 0.3877 | | 5.0439 | 3450 | 0.0212 | 0.3914 | | 5.1170 | 3500 | 0.0452 | 0.3899 | | 5.1901 | 3550 | 0.0252 | 0.3925 | | 5.2632 | 3600 | 0.0228 | 0.3895 | | 5.3363 | 3650 | 0.0219 | 0.3792 | | 5.4094 | 3700 | 0.0275 | 0.3882 | | 5.4825 | 3750 | 0.0246 | 0.3892 | | 5.5556 | 3800 | 0.0226 | 0.3895 | | 5.6287 | 3850 | 0.0219 | 0.3912 | | 5.7018 | 3900 | 0.027 | 0.3800 | | 5.7749 | 3950 | 0.0268 | 0.3667 | | 5.8480 | 4000 | 0.0313 | 0.3687 | | 5.9211 | 4050 | 0.0233 | 0.3675 | | 5.9942 | 4100 | 0.0201 | 0.3649 | | 6.0673 | 4150 | 0.0207 | 0.3727 | | 6.1404 | 4200 | 0.0175 | 0.3802 | | 6.2135 | 4250 | 0.0117 | 0.3760 | | 6.2865 | 4300 | 0.0124 | 0.3731 | | 6.3596 | 4350 | 0.0164 | 0.3713 | | 6.4327 | 4400 | 0.0149 | 0.3782 | | 6.5058 | 4450 | 0.0127 | 0.3747 | | 6.5789 | 4500 | 0.013 | 0.3746 | | 6.6520 | 4550 | 0.0078 | 0.3756 | | 6.7251 | 4600 | 0.0171 | 0.3741 | | 6.7982 | 4650 | 0.0211 | 0.3680 | | 6.8713 | 4700 | 0.0186 | 0.3686 | | 6.9444 | 4750 | 0.0213 | 0.3688 | | 7.0175 | 4800 | 0.0107 | 0.3647 | | 7.0906 | 4850 | 0.011 | 0.3677 | | 7.1637 | 4900 | 0.0098 | 0.3671 | | 7.2368 | 4950 | 0.0091 | 0.3708 | | 7.3099 | 5000 | 0.0074 | 0.3673 | | 7.3830 | 5050 | 0.0101 | 0.3672 | | 7.4561 | 5100 | 0.0115 | 0.3676 | | 7.5292 | 5150 | 0.0054 | 0.3656 | | 7.6023 | 5200 | 0.0076 | 0.3657 | | 7.6754 | 5250 | 0.0054 | 0.3639 | | 7.7485 | 5300 | 0.0115 | 0.3600 | | 7.8216 | 5350 | 0.0105 | 0.3657 | | 7.8947 | 5400 | 0.0175 | 0.3649 | | 7.9678 | 5450 | 0.0091 | 0.3634 | | 8.0409 | 5500 | 0.0043 | 0.3646 | | 8.1140 | 5550 | 0.0078 | 0.3650 | | 8.1871 | 5600 | 0.004 | 0.3683 | | 8.2602 | 5650 | 0.0045 | 0.3669 | | 8.3333 | 5700 | 0.005 | 0.3661 | | 8.4064 | 5750 | 0.0074 | 0.3652 | | 8.4795 | 5800 | 0.0042 | 0.3662 | | 8.5526 | 5850 | 0.0039 | 0.3696 | | 8.6257 | 5900 | 0.004 | 0.3724 | | 8.6988 | 5950 | 0.008 | 0.3714 | | 8.7719 | 6000 | 0.0057 | 0.3711 | | 8.8450 | 6050 | 0.0045 | 0.3702 | | 8.9181 | 6100 | 0.0122 | 0.3715 | | 8.9912 | 6150 | 0.0064 | 0.3703 | | 9.0643 | 6200 | 0.0039 | 0.3689 | | 9.1374 | 6250 | 0.0034 | 0.3680 | | 9.2105 | 6300 | 0.0022 | 0.3680 | | 9.2836 | 6350 | 0.0021 | 0.3684 | | 9.3567 | 6400 | 0.0025 | 0.3685 | | 9.4298 | 6450 | 0.0041 | 0.3679 | | 9.5029 | 6500 | 0.0018 | 0.3679 | | 9.5760 | 6550 | 0.0039 | 0.3686 | | 9.6491 | 6600 | 0.0021 | 0.3691 | | 9.7222 | 6650 | 0.0056 | 0.3689 | | 9.7953 | 6700 | 0.0025 | 0.3691 | | 9.8684 | 6750 | 0.0063 | 0.3692 | | 9.9415 | 6800 | 0.0074 | 0.3692 |
### Framework Versions - Python: 3.12.6 - Sentence Transformers: 3.1.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cpu - Accelerate: 0.34.2 - Datasets: 3.0.0 - Tokenizers: 0.19.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```