--- language: - en license: apache-2.0 tags: - biencoder - sentence-transformers - text-classification - sentence-pair-classification - semantic-similarity - semantic-search - retrieval - reranking - generated_from_trainer - dataset_size:483820 - loss:MultipleNegativesSymmetricRankingLoss base_model: Alibaba-NLP/gte-modernbert-base widget: - source_sentence: 'See Precambrian time scale # Proposed Geologic timeline for another set of periods 4600 -- 541 MYA .' sentences: - In 2014 election , Biju Janata Dal candidate Tathagat Satapathy Bharatiya Janata party candidate Rudra Narayan Pany defeated with a margin of 1.37,340 votes . - In Scotland , the Strathclyde Partnership for Transport , formerly known as Strathclyde Passenger Transport Executive , comprises the former Strathclyde region , which includes the urban area around Glasgow . - 'See Precambrian Time Scale # Proposed Geological Timeline for another set of periods of 4600 -- 541 MYA .' - source_sentence: It is also 5 kilometers northeast of Tamaqua , 27 miles south of Allentown and 9 miles northwest of Hazleton . sentences: - In 1948 he moved to Massachusetts , and eventually settled in Vermont . - Suddenly I remembered that I was a New Zealander , I caught the first plane home and came back . - It is also 5 miles northeast of Tamaqua , 27 miles south of Allentown , and 9 miles northwest of Hazleton . - source_sentence: The party has a Member of Parliament , a member of the House of Lords , three members of the London Assembly and two Members of the European Parliament . sentences: - The party has one Member of Parliament , one member of the House of Lords , three Members of the London Assembly and two Members of the European Parliament . - Grapsid crabs dominate in Australia , Malaysia and Panama , while gastropods Cerithidea scalariformis and Melampus coeffeus are important seed predators in Florida mangroves . - Music Story is a music service website and international music data provider that curates , aggregates and analyses metadata for digital music services . - source_sentence: 'The play received two 1969 Tony Award nominations : Best Actress in a Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .' sentences: - Ravishanker is a fellow of the International Statistical Institute and an elected member of the American Statistical Association . - 'In 1969 , the play received two Tony - Award nominations : Best Actress in a Theatre Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .' - AMD and Nvidia both have proprietary methods of scaling , CrossFireX for AMD , and SLI for Nvidia . - source_sentence: He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto . sentences: - He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto . - Eugenijus Bartulis ( born December 7 , 1949 in Kaunas ) is a Lithuanian Roman Catholic priest , and Bishop of Šiauliai . - UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News . datasets: - redis/langcache-sentencepairs-v1 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_precision@1 - cosine_recall@1 - cosine_ndcg@10 - cosine_mrr@1 - cosine_map@100 model-index: - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache results: - task: type: information-retrieval name: Information Retrieval dataset: name: train type: train metrics: - type: cosine_accuracy@1 value: 0.5978783286425633 name: Cosine Accuracy@1 - type: cosine_precision@1 value: 0.5978783286425633 name: Cosine Precision@1 - type: cosine_recall@1 value: 0.5765917883925028 name: Cosine Recall@1 - type: cosine_ndcg@10 value: 0.7905393533594786 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.5978783286425633 name: Cosine Mrr@1 - type: cosine_map@100 value: 0.7375956597574003 name: Cosine Map@100 --- # Redis fine-tuned BiEncoder model for semantic caching on LangCache This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) - **Maximum Sequence Length:** 100 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) - **Language:** en - **License:** apache-2.0 ### 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': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("redis/langcache-embed-v3") # Run inference sentences = [ 'He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto .', 'He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto .', 'UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.9922, 0.0547], # [0.9922, 1.0000, 0.0449], # [0.0547, 0.0449, 1.0000]], dtype=torch.bfloat16) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `train` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy@1 | 0.5979 | | cosine_precision@1 | 0.5979 | | cosine_recall@1 | 0.5766 | | **cosine_ndcg@10** | **0.7905** | | cosine_mrr@1 | 0.5979 | | cosine_map@100 | 0.7376 | ## Training Details ### Training Dataset #### LangCache Sentence Pairs (all) * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) * Size: 26,850 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------| | The newer Punts are still very much in existence today and race in the same fleets as the older boats . | The newer punts are still very much in existence today and run in the same fleets as the older boats . | 1 | | After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall . | Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall . | 1 | | The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 . | The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon . | 1 | * Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Evaluation Dataset #### LangCache Sentence Pairs (all) * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) * Size: 26,850 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------| | The newer Punts are still very much in existence today and race in the same fleets as the older boats . | The newer punts are still very much in existence today and run in the same fleets as the older boats . | 1 | | After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall . | Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall . | 1 | | The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 . | The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon . | 1 | * Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 0.0003 - `adam_beta2`: 0.98 - `adam_epsilon`: 1e-06 - `max_steps`: 200000 - `warmup_steps`: 1000 - `load_best_model_at_end`: True - `optim`: adamw_torch - `ddp_find_unused_parameters`: False - `push_to_hub`: True - `hub_model_id`: redis/langcache-embed-v3 - `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`: 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`: 0.0003 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.98 - `adam_epsilon`: 1e-06 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3.0 - `max_steps`: 200000 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 1000 - `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`: 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} - `parallelism_config`: 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`: False - `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`: True - `resume_from_checkpoint`: None - `hub_model_id`: redis/langcache-embed-v3 - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | train_cosine_ndcg@10 | |:-----------:|:---------:|:-------------:|:---------------:|:--------------------:| | -1 | -1 | - | - | 0.7522 | | 0.5291 | 1000 | 0.0231 | 0.1710 | 0.7518 | | 1.0582 | 2000 | 0.0147 | 0.1552 | 0.7593 | | 1.5873 | 3000 | 0.0126 | 0.1616 | 0.7603 | | 2.1164 | 4000 | 0.0113 | 0.1301 | 0.7644 | | 2.6455 | 5000 | 0.0119 | 0.1276 | 0.7659 | | 3.1746 | 6000 | 0.0099 | 0.1270 | 0.7648 | | 3.7037 | 7000 | 0.0101 | 0.1239 | 0.7676 | | 4.2328 | 8000 | 0.0093 | 0.1267 | 0.7709 | | 4.7619 | 9000 | 0.0092 | 0.1190 | 0.7711 | | 5.2910 | 10000 | 0.0088 | 0.1145 | 0.7735 | | 5.8201 | 11000 | 0.009 | 0.1172 | 0.7735 | | 6.3492 | 12000 | 0.0083 | 0.1144 | 0.7749 | | 6.8783 | 13000 | 0.0088 | 0.1140 | 0.7736 | | 7.4074 | 14000 | 0.0083 | 0.1134 | 0.7751 | | 7.9365 | 15000 | 0.0087 | 0.1108 | 0.7742 | | 8.4656 | 16000 | 0.0084 | 0.1119 | 0.7759 | | 8.9947 | 17000 | 0.0081 | 0.1125 | 0.7762 | | 9.5238 | 18000 | 0.0081 | 0.1134 | 0.7768 | | 10.0529 | 19000 | 0.008 | 0.1126 | 0.7766 | | 10.5820 | 20000 | 0.0079 | 0.1119 | 0.7755 | | 11.1111 | 21000 | 0.0078 | 0.1112 | 0.7781 | | 11.6402 | 22000 | 0.008 | 0.1113 | 0.7778 | | 12.1693 | 23000 | 0.0082 | 0.1066 | 0.7796 | | 12.6984 | 24000 | 0.0078 | 0.1098 | 0.7775 | | 13.2275 | 25000 | 0.0078 | 0.1089 | 0.7800 | | 13.7566 | 26000 | 0.0074 | 0.1091 | 0.7779 | | 14.2857 | 27000 | 0.0078 | 0.1061 | 0.7782 | | 14.8148 | 28000 | 0.0074 | 0.1073 | 0.7769 | | 15.3439 | 29000 | 0.0078 | 0.1022 | 0.7804 | | 15.8730 | 30000 | 0.0078 | 0.1035 | 0.7799 | | 16.4021 | 31000 | 0.0074 | 0.1046 | 0.7793 | | 16.9312 | 32000 | 0.0074 | 0.1043 | 0.7817 | | 17.4603 | 33000 | 0.0071 | 0.1056 | 0.7831 | | 17.9894 | 34000 | 0.0074 | 0.1022 | 0.7820 | | 18.5185 | 35000 | 0.0073 | 0.1035 | 0.7820 | | 19.0476 | 36000 | 0.0074 | 0.1020 | 0.7836 | | 19.5767 | 37000 | 0.0071 | 0.1036 | 0.7828 | | 20.1058 | 38000 | 0.007 | 0.1029 | 0.7845 | | 20.6349 | 39000 | 0.0071 | 0.1019 | 0.7835 | | 21.1640 | 40000 | 0.007 | 0.0991 | 0.7849 | | 21.6931 | 41000 | 0.0071 | 0.1013 | 0.7828 | | 22.2222 | 42000 | 0.0073 | 0.1033 | 0.7833 | | 22.7513 | 43000 | 0.0068 | 0.0996 | 0.7835 | | 23.2804 | 44000 | 0.007 | 0.0976 | 0.7850 | | 23.8095 | 45000 | 0.0069 | 0.0986 | 0.7840 | | 24.3386 | 46000 | 0.0068 | 0.0992 | 0.7856 | | 24.8677 | 47000 | 0.0068 | 0.0988 | 0.7838 | | 25.3968 | 48000 | 0.0068 | 0.0980 | 0.7857 | | 25.9259 | 49000 | 0.007 | 0.0976 | 0.7860 | | 26.4550 | 50000 | 0.0071 | 0.0994 | 0.7850 | | 26.9841 | 51000 | 0.0067 | 0.0984 | 0.7862 | | 27.5132 | 52000 | 0.0064 | 0.0992 | 0.7845 | | 28.0423 | 53000 | 0.0068 | 0.1021 | 0.7840 | | 28.5714 | 54000 | 0.0066 | 0.0974 | 0.7863 | | 29.1005 | 55000 | 0.0066 | 0.1001 | 0.7848 | | 29.6296 | 56000 | 0.0067 | 0.0997 | 0.7848 | | 30.1587 | 57000 | 0.0067 | 0.0965 | 0.7868 | | 30.6878 | 58000 | 0.0067 | 0.0968 | 0.7858 | | 31.2169 | 59000 | 0.0066 | 0.0973 | 0.7861 | | 31.7460 | 60000 | 0.0067 | 0.0972 | 0.7865 | | 32.2751 | 61000 | 0.0065 | 0.0991 | 0.7855 | | 32.8042 | 62000 | 0.0062 | 0.0960 | 0.7871 | | 33.3333 | 63000 | 0.0068 | 0.1006 | 0.7863 | | 33.8624 | 64000 | 0.0063 | 0.0980 | 0.7872 | | 34.3915 | 65000 | 0.0066 | 0.0957 | 0.7871 | | 34.9206 | 66000 | 0.0066 | 0.0971 | 0.7870 | | 35.4497 | 67000 | 0.0063 | 0.0982 | 0.7857 | | 35.9788 | 68000 | 0.0067 | 0.0944 | 0.7871 | | 36.5079 | 69000 | 0.0062 | 0.0961 | 0.7870 | | 37.0370 | 70000 | 0.0061 | 0.0924 | 0.7880 | | 37.5661 | 71000 | 0.0064 | 0.0928 | 0.7878 | | 38.0952 | 72000 | 0.0065 | 0.0934 | 0.7888 | | 38.6243 | 73000 | 0.0069 | 0.0948 | 0.7873 | | **39.1534** | **74000** | **0.0064** | **0.0922** | **0.7885** | | 39.6825 | 75000 | 0.0064 | 0.0937 | 0.7888 | | 40.2116 | 76000 | 0.0059 | 0.0941 | 0.7882 | | 40.7407 | 77000 | 0.0067 | 0.0934 | 0.7900 | | 41.2698 | 78000 | 0.0064 | 0.0926 | 0.7888 | | 41.7989 | 79000 | 0.006 | 0.0948 | 0.7880 | | 42.3280 | 80000 | 0.006 | 0.0953 | 0.7876 | | 42.8571 | 81000 | 0.0058 | 0.0955 | 0.7887 | | 43.3862 | 82000 | 0.0065 | 0.0945 | 0.7875 | | 43.9153 | 83000 | 0.0063 | 0.0928 | 0.7888 | | 44.4444 | 84000 | 0.0065 | 0.0959 | 0.7883 | | 44.9735 | 85000 | 0.0063 | 0.0956 | 0.7876 | | 45.5026 | 86000 | 0.006 | 0.0946 | 0.7893 | | 46.0317 | 87000 | 0.0062 | 0.0954 | 0.7908 | | 46.5608 | 88000 | 0.0061 | 0.0960 | 0.7896 | | 47.0899 | 89000 | 0.006 | 0.0953 | 0.7893 | | 47.6190 | 90000 | 0.0058 | 0.0941 | 0.7899 | | 48.1481 | 91000 | 0.0059 | 0.0950 | 0.7892 | | 48.6772 | 92000 | 0.0066 | 0.0948 | 0.7890 | | 49.2063 | 93000 | 0.0058 | 0.0947 | 0.7886 | | 49.7354 | 94000 | 0.006 | 0.0952 | 0.7891 | | 50.2646 | 95000 | 0.0058 | 0.0948 | 0.7885 | | 50.7937 | 96000 | 0.0058 | 0.0945 | 0.7894 | | 51.3228 | 97000 | 0.0059 | 0.0936 | 0.7901 | | 51.8519 | 98000 | 0.0059 | 0.0950 | 0.7900 | | 52.3810 | 99000 | 0.0058 | 0.0954 | 0.7893 | | 52.9101 | 100000 | 0.0058 | 0.0946 | 0.7900 | | 53.4392 | 101000 | 0.0056 | 0.0943 | 0.7900 | | 53.9683 | 102000 | 0.006 | 0.0950 | 0.7895 | | 54.4974 | 103000 | 0.0059 | 0.0937 | 0.7899 | | 55.0265 | 104000 | 0.0061 | 0.0941 | 0.7897 | | 55.5556 | 105000 | 0.0059 | 0.0941 | 0.7903 | | 56.0847 | 106000 | 0.0057 | 0.0924 | 0.7904 | | 56.6138 | 107000 | 0.006 | 0.0933 | 0.7901 | | 57.1429 | 108000 | 0.0059 | 0.0948 | 0.7888 | | 57.6720 | 109000 | 0.0061 | 0.0938 | 0.7899 | | 58.2011 | 110000 | 0.0058 | 0.0942 | 0.7904 | | 58.7302 | 111000 | 0.0056 | 0.0943 | 0.7913 | | 59.2593 | 112000 | 0.0056 | 0.0949 | 0.7915 | | 59.7884 | 113000 | 0.0058 | 0.0947 | 0.7907 | | 60.3175 | 114000 | 0.0058 | 0.0939 | 0.7910 | | 60.8466 | 115000 | 0.0058 | 0.0942 | 0.7906 | | 61.3757 | 116000 | 0.0055 | 0.0933 | 0.7910 | | 61.9048 | 117000 | 0.0055 | 0.0936 | 0.7913 | | 62.4339 | 118000 | 0.0059 | 0.0937 | 0.7904 | | 62.9630 | 119000 | 0.0057 | 0.0943 | 0.7908 | | 63.4921 | 120000 | 0.0056 | 0.0934 | 0.7912 | | 64.0212 | 121000 | 0.0058 | 0.0936 | 0.7909 | | 64.5503 | 122000 | 0.0055 | 0.0942 | 0.7896 | | 65.0794 | 123000 | 0.0058 | 0.0939 | 0.7901 | | 65.6085 | 124000 | 0.0057 | 0.0936 | 0.7907 | | 66.1376 | 125000 | 0.0054 | 0.0951 | 0.7901 | | 66.6667 | 126000 | 0.0055 | 0.0942 | 0.7912 | | 67.1958 | 127000 | 0.0057 | 0.0943 | 0.7914 | | 67.7249 | 128000 | 0.0057 | 0.0937 | 0.7910 | | 68.2540 | 129000 | 0.0057 | 0.0933 | 0.7918 | | 68.7831 | 130000 | 0.0055 | 0.0935 | 0.7913 | | 69.3122 | 131000 | 0.0053 | 0.0935 | 0.7908 | | 69.8413 | 132000 | 0.0057 | 0.0937 | 0.7905 | | 70.3704 | 133000 | 0.0055 | 0.0940 | 0.7912 | | 70.8995 | 134000 | 0.0052 | 0.0937 | 0.7913 | | 71.4286 | 135000 | 0.005 | 0.0940 | 0.7917 | | 71.9577 | 136000 | 0.0053 | 0.0933 | 0.7914 | | 72.4868 | 137000 | 0.0056 | 0.0940 | 0.7915 | | 73.0159 | 138000 | 0.0054 | 0.0937 | 0.7909 | | 73.5450 | 139000 | 0.0051 | 0.0940 | 0.7909 | | 74.0741 | 140000 | 0.0058 | 0.0938 | 0.7911 | | 74.6032 | 141000 | 0.0056 | 0.0938 | 0.7912 | | 75.1323 | 142000 | 0.0052 | 0.0931 | 0.7908 | | 75.6614 | 143000 | 0.0052 | 0.0937 | 0.7905 | | 76.1905 | 144000 | 0.0054 | 0.0940 | 0.7905 | | 76.7196 | 145000 | 0.0055 | 0.0940 | 0.7907 | | 77.2487 | 146000 | 0.0053 | 0.0941 | 0.7909 | | 77.7778 | 147000 | 0.0057 | 0.0944 | 0.7907 | | 78.3069 | 148000 | 0.0054 | 0.0947 | 0.7909 | | 78.8360 | 149000 | 0.0054 | 0.0949 | 0.7907 | | 79.3651 | 150000 | 0.0055 | 0.0948 | 0.7907 | | 79.8942 | 151000 | 0.0058 | 0.0950 | 0.7907 | | 80.4233 | 152000 | 0.0054 | 0.0946 | 0.7907 | | 80.9524 | 153000 | 0.0053 | 0.0949 | 0.7909 | | 81.4815 | 154000 | 0.0055 | 0.0947 | 0.7908 | | 82.0106 | 155000 | 0.0053 | 0.0946 | 0.7906 | | 82.5397 | 156000 | 0.0053 | 0.0949 | 0.7906 | | 83.0688 | 157000 | 0.0051 | 0.0948 | 0.7912 | | 83.5979 | 158000 | 0.0052 | 0.0954 | 0.7906 | | 84.1270 | 159000 | 0.0054 | 0.0953 | 0.7908 | | 84.6561 | 160000 | 0.005 | 0.0951 | 0.7911 | | 85.1852 | 161000 | 0.0054 | 0.0953 | 0.7910 | | 85.7143 | 162000 | 0.0056 | 0.0957 | 0.7907 | | 86.2434 | 163000 | 0.0054 | 0.0953 | 0.7909 | | 86.7725 | 164000 | 0.0051 | 0.0955 | 0.7912 | | 87.3016 | 165000 | 0.0055 | 0.0956 | 0.7911 | | 87.8307 | 166000 | 0.0056 | 0.0954 | 0.7909 | | 88.3598 | 167000 | 0.0052 | 0.0955 | 0.7911 | | 88.8889 | 168000 | 0.0052 | 0.0953 | 0.7910 | | 89.4180 | 169000 | 0.0052 | 0.0952 | 0.7906 | | 89.9471 | 170000 | 0.0053 | 0.0952 | 0.7908 | | 90.4762 | 171000 | 0.0052 | 0.0954 | 0.7908 | | 91.0053 | 172000 | 0.0054 | 0.0954 | 0.7907 | | 91.5344 | 173000 | 0.0052 | 0.0951 | 0.7909 | | 92.0635 | 174000 | 0.0053 | 0.0951 | 0.7907 | | 92.5926 | 175000 | 0.0051 | 0.0950 | 0.7906 | | 93.1217 | 176000 | 0.0054 | 0.0953 | 0.7907 | | 93.6508 | 177000 | 0.0052 | 0.0953 | 0.7907 | | 94.1799 | 178000 | 0.0051 | 0.0951 | 0.7908 | | 94.7090 | 179000 | 0.0052 | 0.0952 | 0.7906 | | 95.2381 | 180000 | 0.0053 | 0.0953 | 0.7909 | | 95.7672 | 181000 | 0.0052 | 0.0953 | 0.7908 | | 96.2963 | 182000 | 0.0051 | 0.0952 | 0.7906 | | 96.8254 | 183000 | 0.0053 | 0.0953 | 0.7907 | | 97.3545 | 184000 | 0.0051 | 0.0953 | 0.7907 | | 97.8836 | 185000 | 0.0049 | 0.0953 | 0.7906 | | 98.4127 | 186000 | 0.0051 | 0.0953 | 0.7907 | | 98.9418 | 187000 | 0.0051 | 0.0954 | 0.7906 | | 99.4709 | 188000 | 0.0053 | 0.0954 | 0.7906 | | 100.0 | 189000 | 0.0051 | 0.0954 | 0.7904 | | 100.5291 | 190000 | 0.0054 | 0.0953 | 0.7907 | | 101.0582 | 191000 | 0.0052 | 0.0954 | 0.7905 | | 101.5873 | 192000 | 0.0051 | 0.0954 | 0.7907 | | 102.1164 | 193000 | 0.0052 | 0.0953 | 0.7907 | | 102.6455 | 194000 | 0.0051 | 0.0955 | 0.7908 | | 103.1746 | 195000 | 0.0054 | 0.0954 | 0.7906 | | 103.7037 | 196000 | 0.0052 | 0.0954 | 0.7905 | | 104.2328 | 197000 | 0.0053 | 0.0954 | 0.7906 | | 104.7619 | 198000 | 0.0052 | 0.0954 | 0.7907 | | 105.2910 | 199000 | 0.0055 | 0.0954 | 0.7904 | | 105.8201 | 200000 | 0.0054 | 0.0955 | 0.7905 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.12.3 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## 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", } ```