--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:602010 - loss:MultipleNegativesRankingLoss base_model: thenlper/gte-large widget: - source_sentence: '''The Simpsons - Balenciaga'' is a 4.3/10 rated Movie, starring . It is about Based on the television series The Simpsons, the production staff collaborated with French luxury fashion house Balenciaga to produce this short film parodying the fashion industry and highlighting Balenciaga''s recent clothing..' sentences: - '''Apocalypse: World War I'' is a 7.68/10 rated T.V. Show, starring Mathieu Kassovitz. It is about Colorized historical footage in ascending order of World War 1. Not only the relatively known Flanders and France battles, but also the generally unknown Italian-Austrian, German-Polish-Russian, Japanese-German, Ottoman Empire- Allied and African German Colonies, and other unknown or forgotten fronts and battles..' - '''Spacy'' is a 6.3/10 rated Movie, starring . It is about "His films are like a roller-coaster. His way of throwing the act of seeing into utter confusion is an attack on the eyes in their corporeal function, and to attack the eyes is to take on tile body itself as your opponent. The film makes you break out in sweat only by shooting a safe, peaceful gymnasium in the dark." - Koharu Kisaragi.' - '''The Simpsons - Balenciaga'' is a 4.3/10 rated Movie, starring . It is about Based on the television series The Simpsons, the production staff collaborated with French luxury fashion house Balenciaga to produce this short film parodying the fashion industry and highlighting Balenciaga''s recent clothing..' - source_sentence: '''Take Off'' is a 7.0/10 rated Movie, starring Ellion Ness. It is about Ellion Ness, a thoroughly professional stripper, goes through her paces, bares her body, and then, astonishingly and literally, transcends it. While the film makes a forceful political statement on the image of woman and the true meaning of stripping, the intergalactic transcendence of its ending locates it firmly within the mainstream of joyous humanism and stubborn optimism..' sentences: - '''Paris Episodes'' is a No Rating/10 rated Movie, starring . It is about Portraits and fragmented views of Paris intertwine..' - '''Take Off'' is a 7.0/10 rated Movie, starring Ellion Ness. It is about Ellion Ness, a thoroughly professional stripper, goes through her paces, bares her body, and then, astonishingly and literally, transcends it. While the film makes a forceful political statement on the image of woman and the true meaning of stripping, the intergalactic transcendence of its ending locates it firmly within the mainstream of joyous humanism and stubborn optimism..' - '''Mordraud'' is a 5.0/10 rated Movie, starring Alex Canini, Chiara Carnevali, Federico Bartolini, William Daydan, Marco Mularoni. It is about During a bloody siege, two brothers lined up in opposing factions hunt each others, dragged by the tragic memories of their childhood..' - source_sentence: '''Acts of the Apostles'' is a No Rating/10 rated Movie, starring Sami Fekkak, Mehmet Kurtuluş, Brice Bexter, Kirk Newmann, Derek Reginald. It is about A four episode anthology following the stories in the Acts of the Apostles of the early Church using word for word narration from the Bible..' sentences: - '''Rewilding a Nation'' is a No Rating/10 rated Movie, starring Robi Watkinson, Emma Hodson, Derek Gow, George Monbiot, Paul Jepson. It is about Robi Watkinson and Emma Hodson travel across Britain and the Netherlands documenting the story of the rewilding movement from its inception, to the return of the beaver, bison and perhaps one day, the lynx to Britain..' - '''Acts of the Apostles'' is a No Rating/10 rated Movie, starring Sami Fekkak, Mehmet Kurtuluş, Brice Bexter, Kirk Newmann, Derek Reginald. It is about A four episode anthology following the stories in the Acts of the Apostles of the early Church using word for word narration from the Bible..' - '''Katie Morgan''s Sex Tips 2: Any More Questions?'' is a 9.0/10 rated Movie, starring Katie Morgan. It is about Adult-film star Katie Morgan answers a series of sex-related questions...in the buff, of course!.' - source_sentence: '''EIGHT'' is a No Rating/10 rated Movie, starring Anoaney Bissouma, Marie Bredow, Elise Dauteau, Cédric Duffand, Elodie Foussadier. It is about Eric Green, an agoraphobic, lives in seclusion with his wife Virginie and everything seems to be going well. When one morning, Eric wakes up 7 years earlier, sleeping with a woman who is not his. All the events of the day before repeat themselves and will repeat each day to come. While outside, a killer roams. more and more questions arise for Eric, and more importantly, where is his wife? But - what if everything he thought he knew wasn''t what it seems ?.' sentences: - '''So they saw it all'' is a No Rating/10 rated Movie, starring Oren Rehany, Hodaya Vaysen, Yarden Rubinfeld, Galit Sharoni, Roni Shalev. It is about During a family dinner, a young woman is forced to tell her parents about her new job—but she is not prepared for it at all....' - '''Poof Paradise'' is a No Rating/10 rated Movie, starring . It is about In poof paradise, the imagery is reminding the viewer of a pastel coloured fairy-tale. Our princess*, if you will, is a pink anus that takes on a journey to the often mystical place of the cruising area. Where colourful hands try to seduce our protagonist and communication is set to a meaningless and shallow minimum. Cruising usually happens far away from our heteronormative society but at the same time in public spaces. In poof paradise those spaces transform into a surreal world where the possibilities of queer identities are endless and the existing boundaries of our societal norms dissolve. Influenced by James Bidgood and John Waters, the artists create a lustful amalgam of the sweet and the bizarre..' - '''EIGHT'' is a No Rating/10 rated Movie, starring Anoaney Bissouma, Marie Bredow, Elise Dauteau, Cédric Duffand, Elodie Foussadier. It is about Eric Green, an agoraphobic, lives in seclusion with his wife Virginie and everything seems to be going well. When one morning, Eric wakes up 7 years earlier, sleeping with a woman who is not his. All the events of the day before repeat themselves and will repeat each day to come. While outside, a killer roams. more and more questions arise for Eric, and more importantly, where is his wife? But - what if everything he thought he knew wasn''t what it seems ?.' - source_sentence: '''American Bandstand'' is a 8.71/10 rated T.V. Show, starring Dick Clark. It is about American Bandstand was an American music-performance show that aired in various versions from 1952 to 1989 and was hosted from 1956 until its final season by Dick Clark, who also served as producer. The show featured teenagers dancing to Top 40 music introduced by Clark; at least one popular musical act—over the decades, running the gamut from Jerry Lee Lewis to Run DMC—would usually appear in person to lip-sync one of their latest singles. Freddy "Boom Boom" Cannon holds the record for most appearances at 110. The show''s popularity helped Dick Clark become an American media mogul and inspired similar long-running music programs, such as Soul Train and Top of the Pops. Clark eventually assumed ownership of the program through his Dick Clark Productions company..' sentences: - '''The Way'' is a 9.0/10 rated Movie, starring . It is about The dangerous travel makes the teenager to understand life as an adult and determines his way..' - '''White Lies'' is a No Rating/10 rated T.V. Show, starring Natalie Dormer, Brendon Daniels, Daniel Schultz, Morgan Santo, Langley Kirkwood. It is about Edie Hansen, who is set in the affluent Cape Town neighborhood of Bishopscourt, is drawn into the gritty underbelly of the city, which hides beneath its gorgeous beauty and takes her back to a stormy past..' - '''American Bandstand'' is a 8.71/10 rated T.V. Show, starring Dick Clark. It is about American Bandstand was an American music-performance show that aired in various versions from 1952 to 1989 and was hosted from 1956 until its final season by Dick Clark, who also served as producer. The show featured teenagers dancing to Top 40 music introduced by Clark; at least one popular musical act—over the decades, running the gamut from Jerry Lee Lewis to Run DMC—would usually appear in person to lip-sync one of their latest singles. Freddy "Boom Boom" Cannon holds the record for most appearances at 110. The show''s popularity helped Dick Clark become an American media mogul and inspired similar long-running music programs, such as Soul Train and Top of the Pops. Clark eventually assumed ownership of the program through his Dick Clark Productions company..' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on thenlper/gte-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-large](https://huggingface.co/thenlper/gte-large). It maps sentences & paragraphs to a 1024-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-large](https://huggingface.co/thenlper/gte-large) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 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 ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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("Dataologist/gte_large_op") # Run inference sentences = [ '\'American Bandstand\' is a 8.71/10 rated T.V. Show, starring Dick Clark. It is about American Bandstand was an American music-performance show that aired in various versions from 1952 to 1989 and was hosted from 1956 until its final season by Dick Clark, who also served as producer. The show featured teenagers dancing to Top 40 music introduced by Clark; at least one popular musical act—over the decades, running the gamut from Jerry Lee Lewis to Run DMC—would usually appear in person to lip-sync one of their latest singles. Freddy "Boom Boom" Cannon holds the record for most appearances at 110.\n\nThe show\'s popularity helped Dick Clark become an American media mogul and inspired similar long-running music programs, such as Soul Train and Top of the Pops. Clark eventually assumed ownership of the program through his Dick Clark Productions company..', '\'American Bandstand\' is a 8.71/10 rated T.V. Show, starring Dick Clark. It is about American Bandstand was an American music-performance show that aired in various versions from 1952 to 1989 and was hosted from 1956 until its final season by Dick Clark, who also served as producer. The show featured teenagers dancing to Top 40 music introduced by Clark; at least one popular musical act—over the decades, running the gamut from Jerry Lee Lewis to Run DMC—would usually appear in person to lip-sync one of their latest singles. Freddy "Boom Boom" Cannon holds the record for most appearances at 110.\n\nThe show\'s popularity helped Dick Clark become an American media mogul and inspired similar long-running music programs, such as Soul Train and Top of the Pops. Clark eventually assumed ownership of the program through his Dick Clark Productions company..', "'White Lies' is a No Rating/10 rated T.V. Show, starring Natalie Dormer, Brendon Daniels, Daniel Schultz, Morgan Santo, Langley Kirkwood. It is about Edie Hansen, who is set in the affluent Cape Town neighborhood of Bishopscourt, is drawn into the gritty underbelly of the city, which hides beneath its gorgeous beauty and takes her back to a stormy past..", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 602,010 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 'Down Stream Highway' is a No Rating/10 rated Movie, starring . It is about Narrated by Bill Slater, this short black & white educational film is about sporting and outdoor activities on the majestic Hudson River in New York State.. | 'Down Stream Highway' is a No Rating/10 rated Movie, starring . It is about Narrated by Bill Slater, this short black & white educational film is about sporting and outdoor activities on the majestic Hudson River in New York State.. | | 'La joueuse d'orgue' is a No Rating/10 rated Movie, starring Marcelle Géniat, Pierre Larquey, Jacques Varennes, Gaby Triquet, France Ellys. It is about Robert Bernier murdered his brother with the complicity of a worker. The only witness to the tragedy, Veronique was injured while rescuing her boss and remains blind. Later, cured by an operation, she denounces the criminal whose voice she recognized and who had taken over the factory.. | 'La joueuse d'orgue' is a No Rating/10 rated Movie, starring Marcelle Géniat, Pierre Larquey, Jacques Varennes, Gaby Triquet, France Ellys. It is about Robert Bernier murdered his brother with the complicity of a worker. The only witness to the tragedy, Veronique was injured while rescuing her boss and remains blind. Later, cured by an operation, she denounces the criminal whose voice she recognized and who had taken over the factory.. | | 'Disoriented' is a 8.0/10 rated Movie, starring . It is about Twenty-something West Cordova is trapped in a waking nightmare. His overbearing mother is bent on molding him into a MD. His crazy, "wannabe-a-supermodel," Japanese girlfriend craves blonde hair and round eyes. And his long lost, jock brother just returned home having traded his high tops for high heels. If young "Doctor" Cordova can pass pre-med, mend his fractured family and revive his romance, he may just discover the cure for his own unraveling identity.. | 'Disoriented' is a 8.0/10 rated Movie, starring . It is about Twenty-something West Cordova is trapped in a waking nightmare. His overbearing mother is bent on molding him into a MD. His crazy, "wannabe-a-supermodel," Japanese girlfriend craves blonde hair and round eyes. And his long lost, jock brother just returned home having traded his high tops for high heels. If young "Doctor" Cordova can pass pre-med, mend his fractured family and revive his romance, he may just discover the cure for his own unraveling identity.. | * 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 - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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_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`: round_robin
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:------:|:-------------:| | 0.0033 | 500 | 0.0483 | | 0.0066 | 1000 | 0.0 | | 0.0100 | 1500 | 0.0 | | 0.0133 | 2000 | 0.0 | | 0.0166 | 2500 | 0.0 | | 0.0199 | 3000 | 0.0 | | 0.0233 | 3500 | 0.0 | | 0.0266 | 4000 | 0.0 | | 0.0299 | 4500 | 0.0 | | 0.0332 | 5000 | 0.0 | | 0.0365 | 5500 | 0.0 | | 0.0399 | 6000 | 0.0 | | 0.0432 | 6500 | 0.0 | | 0.0465 | 7000 | 0.0 | | 0.0498 | 7500 | 0.0 | | 0.0532 | 8000 | 0.0 | | 0.0565 | 8500 | 0.0 | | 0.0598 | 9000 | 0.0 | | 0.0631 | 9500 | 0.0 | | 0.0664 | 10000 | 0.0 | | 0.0698 | 10500 | 0.0 | | 0.0731 | 11000 | 0.0 | | 0.0764 | 11500 | 0.0 | | 0.0797 | 12000 | 0.0 | | 0.0831 | 12500 | 0.0 | | 0.0864 | 13000 | 0.0 | | 0.0897 | 13500 | 0.0 | | 0.0930 | 14000 | 0.0 | | 0.0963 | 14500 | 0.0 | | 0.0997 | 15000 | 0.0 | | 0.1030 | 15500 | 0.0 | | 0.1063 | 16000 | 0.0 | | 0.1096 | 16500 | 0.0 | | 0.1130 | 17000 | 0.0 | | 0.1163 | 17500 | 0.0 | | 0.1196 | 18000 | 0.0 | | 0.1229 | 18500 | 0.0 | | 0.1262 | 19000 | 0.0 | | 0.1296 | 19500 | 0.0 | | 0.1329 | 20000 | 0.0 | | 0.1362 | 20500 | 0.0 | | 0.1395 | 21000 | 0.0 | | 0.1429 | 21500 | 0.0 | | 0.1462 | 22000 | 0.0 | | 0.1495 | 22500 | 0.0 | | 0.1528 | 23000 | 0.0 | | 0.1561 | 23500 | 0.0 | | 0.1595 | 24000 | 0.0 | | 0.1628 | 24500 | 0.0 | | 0.1661 | 25000 | 0.0 | | 0.1694 | 25500 | 0.0 | | 0.1728 | 26000 | 0.0 | | 0.1761 | 26500 | 0.0 | | 0.1794 | 27000 | 0.0 | | 0.1827 | 27500 | 0.0 | | 0.1860 | 28000 | 0.0 | | 0.1894 | 28500 | 0.0 | | 0.1927 | 29000 | 0.0 | | 0.1960 | 29500 | 0.0 | | 0.1993 | 30000 | 0.0 | | 0.2027 | 30500 | 0.0 | | 0.2060 | 31000 | 0.0 | | 0.2093 | 31500 | 0.0 | | 0.2126 | 32000 | 0.0 | | 0.2159 | 32500 | 0.0 | | 0.2193 | 33000 | 0.0 | | 0.2226 | 33500 | 0.0 | | 0.2259 | 34000 | 0.0 | | 0.2292 | 34500 | 0.0 | | 0.2326 | 35000 | 0.0 | | 0.2359 | 35500 | 0.0 | | 0.2392 | 36000 | 0.0 | | 0.2425 | 36500 | 0.0 | | 0.2458 | 37000 | 0.0 | | 0.2492 | 37500 | 0.0 | | 0.2525 | 38000 | 0.0 | | 0.2558 | 38500 | 0.0 | | 0.2591 | 39000 | 0.0 | | 0.2625 | 39500 | 0.0 | | 0.2658 | 40000 | 0.0 | | 0.2691 | 40500 | 0.0 | | 0.2724 | 41000 | 0.0 | | 0.2757 | 41500 | 0.0 | | 0.2791 | 42000 | 0.0 | | 0.2824 | 42500 | 0.0 | | 0.2857 | 43000 | 0.0 | | 0.2890 | 43500 | 0.0 | | 0.2924 | 44000 | 0.0 | | 0.2957 | 44500 | 0.0 | | 0.2990 | 45000 | 0.0 | | 0.3023 | 45500 | 0.0 | | 0.3056 | 46000 | 0.0 | | 0.3090 | 46500 | 0.0 | | 0.3123 | 47000 | 0.0 | | 0.3156 | 47500 | 0.0 | | 0.3189 | 48000 | 0.0 | | 0.3223 | 48500 | 0.0 | | 0.3256 | 49000 | 0.0 | | 0.3289 | 49500 | 0.0 | | 0.3322 | 50000 | 0.0 | | 0.3355 | 50500 | 0.0 | | 0.3389 | 51000 | 0.0 | | 0.3422 | 51500 | 0.0 | | 0.3455 | 52000 | 0.0 | | 0.3488 | 52500 | 0.0 | | 0.3522 | 53000 | 0.0 | | 0.3555 | 53500 | 0.0 | | 0.3588 | 54000 | 0.0 | | 0.3621 | 54500 | 0.0 | | 0.3654 | 55000 | 0.0 | | 0.3688 | 55500 | 0.0 | | 0.3721 | 56000 | 0.0 | | 0.3754 | 56500 | 0.0 | | 0.3787 | 57000 | 0.0 | | 0.3821 | 57500 | 0.0 | | 0.3854 | 58000 | 0.0 | | 0.3887 | 58500 | 0.0 | | 0.3920 | 59000 | 0.0 | | 0.3953 | 59500 | 0.0 | | 0.3987 | 60000 | 0.0 | | 0.4020 | 60500 | 0.0 | | 0.4053 | 61000 | 0.0 | | 0.4086 | 61500 | 0.0 | | 0.4120 | 62000 | 0.0 | | 0.4153 | 62500 | 0.0 | | 0.4186 | 63000 | 0.0 | | 0.4219 | 63500 | 0.0 | | 0.4252 | 64000 | 0.0 | | 0.4286 | 64500 | 0.0 | | 0.4319 | 65000 | 0.0 | | 0.4352 | 65500 | 0.0 | | 0.4385 | 66000 | 0.0 | | 0.4419 | 66500 | 0.0 | | 0.4452 | 67000 | 0.0 | | 0.4485 | 67500 | 0.0 | | 0.4518 | 68000 | 0.0 | | 0.4551 | 68500 | 0.0 | | 0.4585 | 69000 | 0.0 | | 0.4618 | 69500 | 0.0 | | 0.4651 | 70000 | 0.0 | | 0.4684 | 70500 | 0.0 | | 0.4718 | 71000 | 0.0 | | 0.4751 | 71500 | 0.0 | | 0.4784 | 72000 | 0.0 | | 0.4817 | 72500 | 0.0 | | 0.4850 | 73000 | 0.0 | | 0.4884 | 73500 | 0.0 | | 0.4917 | 74000 | 0.0 | | 0.4950 | 74500 | 0.0 | | 0.4983 | 75000 | 0.0 | | 0.5017 | 75500 | 0.0 | | 0.5050 | 76000 | 0.0 | | 0.5083 | 76500 | 0.0 | | 0.5116 | 77000 | 0.0 | | 0.5149 | 77500 | 0.0 | | 0.5183 | 78000 | 0.0 | | 0.5216 | 78500 | 0.0 | | 0.5249 | 79000 | 0.0 | | 0.5282 | 79500 | 0.0 | | 0.5316 | 80000 | 0.0 | | 0.5349 | 80500 | 0.0 | | 0.5382 | 81000 | 0.0 | | 0.5415 | 81500 | 0.0 | | 0.5448 | 82000 | 0.0 | | 0.5482 | 82500 | 0.0 | | 0.5515 | 83000 | 0.0 | | 0.5548 | 83500 | 0.0 | | 0.5581 | 84000 | 0.0 | | 0.5615 | 84500 | 0.0 | | 0.5648 | 85000 | 0.0 | | 0.5681 | 85500 | 0.0 | | 0.5714 | 86000 | 0.0 | | 0.5747 | 86500 | 0.0 | | 0.5781 | 87000 | 0.0 | | 0.5814 | 87500 | 0.0 | | 0.5847 | 88000 | 0.0 | | 0.5880 | 88500 | 0.0 | | 0.5914 | 89000 | 0.0 | | 0.5947 | 89500 | 0.0 | | 0.5980 | 90000 | 0.0 | | 0.6013 | 90500 | 0.0 | | 0.6046 | 91000 | 0.0 | | 0.6080 | 91500 | 0.0 | | 0.6113 | 92000 | 0.0 | | 0.6146 | 92500 | 0.0 | | 0.6179 | 93000 | 0.0 | | 0.6213 | 93500 | 0.0 | | 0.6246 | 94000 | 0.0 | | 0.6279 | 94500 | 0.0 | | 0.6312 | 95000 | 0.0 | | 0.6345 | 95500 | 0.0 | | 0.6379 | 96000 | 0.0 | | 0.6412 | 96500 | 0.0 | | 0.6445 | 97000 | 0.0 | | 0.6478 | 97500 | 0.0 | | 0.6511 | 98000 | 0.0 | | 0.6545 | 98500 | 0.0 | | 0.6578 | 99000 | 0.0 | | 0.6611 | 99500 | 0.0 | | 0.6644 | 100000 | 0.0 | | 0.6678 | 100500 | 0.0 | | 0.6711 | 101000 | 0.0 | | 0.6744 | 101500 | 0.0 | | 0.6777 | 102000 | 0.0 | | 0.6810 | 102500 | 0.0 | | 0.6844 | 103000 | 0.0 | | 0.6877 | 103500 | 0.0 | | 0.6910 | 104000 | 0.0 | | 0.6943 | 104500 | 0.0 | | 0.6977 | 105000 | 0.0 | | 0.7010 | 105500 | 0.0 | | 0.7043 | 106000 | 0.0 | | 0.7076 | 106500 | 0.0 | | 0.7109 | 107000 | 0.0 | | 0.7143 | 107500 | 0.0 | | 0.7176 | 108000 | 0.0 | | 0.7209 | 108500 | 0.0 | | 0.7242 | 109000 | 0.0 | | 0.7276 | 109500 | 0.0 | | 0.7309 | 110000 | 0.0 | | 0.7342 | 110500 | 0.0 | | 0.7375 | 111000 | 0.0 | | 0.7408 | 111500 | 0.0 | | 0.7442 | 112000 | 0.0 | | 0.7475 | 112500 | 0.0 | | 0.7508 | 113000 | 0.0 | | 0.7541 | 113500 | 0.0 | | 0.7575 | 114000 | 0.0 | | 0.7608 | 114500 | 0.0 | | 0.7641 | 115000 | 0.0 | | 0.7674 | 115500 | 0.0 | | 0.7707 | 116000 | 0.0 | | 0.7741 | 116500 | 0.0 | | 0.7774 | 117000 | 0.0 | | 0.7807 | 117500 | 0.0 | | 0.7840 | 118000 | 0.0 | | 0.7874 | 118500 | 0.0 | | 0.7907 | 119000 | 0.0 | | 0.7940 | 119500 | 0.0 | | 0.7973 | 120000 | 0.0 | | 0.8006 | 120500 | 0.0 | | 0.8040 | 121000 | 0.0 | | 0.8073 | 121500 | 0.0 | | 0.8106 | 122000 | 0.0 | | 0.8139 | 122500 | 0.0 | | 0.8173 | 123000 | 0.0 | | 0.8206 | 123500 | 0.0 | | 0.8239 | 124000 | 0.0 | | 0.8272 | 124500 | 0.0 | | 0.8305 | 125000 | 0.0 | | 0.8339 | 125500 | 0.0 | | 0.8372 | 126000 | 0.0 | | 0.8405 | 126500 | 0.0 | | 0.8438 | 127000 | 0.0 | | 0.8472 | 127500 | 0.0 | | 0.8505 | 128000 | 0.0 | | 0.8538 | 128500 | 0.0 | | 0.8571 | 129000 | 0.0 | | 0.8604 | 129500 | 0.0 | | 0.8638 | 130000 | 0.0 | | 0.8671 | 130500 | 0.0 | | 0.8704 | 131000 | 0.0 | | 0.8737 | 131500 | 0.0 | | 0.8771 | 132000 | 0.0 | | 0.8804 | 132500 | 0.0 | | 0.8837 | 133000 | 0.0 | | 0.8870 | 133500 | 0.0 | | 0.8903 | 134000 | 0.0 | | 0.8937 | 134500 | 0.0 | | 0.8970 | 135000 | 0.0 | | 0.9003 | 135500 | 0.0 | | 0.9036 | 136000 | 0.0 | | 0.9070 | 136500 | 0.0 | | 0.9103 | 137000 | 0.0 | | 0.9136 | 137500 | 0.0 | | 0.9169 | 138000 | 0.0 | | 0.9202 | 138500 | 0.0 | | 0.9236 | 139000 | 0.0 | | 0.9269 | 139500 | 0.0 | | 0.9302 | 140000 | 0.0 | | 0.9335 | 140500 | 0.0 | | 0.9369 | 141000 | 0.0 | | 0.9402 | 141500 | 0.0 | | 0.9435 | 142000 | 0.0 | | 0.9468 | 142500 | 0.0 | | 0.9501 | 143000 | 0.0 | | 0.9535 | 143500 | 0.0 | | 0.9568 | 144000 | 0.0 | | 0.9601 | 144500 | 0.0 | | 0.9634 | 145000 | 0.0 | | 0.9668 | 145500 | 0.0 | | 0.9701 | 146000 | 0.0 | | 0.9734 | 146500 | 0.0 | | 0.9767 | 147000 | 0.0 | | 0.9800 | 147500 | 0.0 | | 0.9834 | 148000 | 0.0 | | 0.9867 | 148500 | 0.0 | | 0.9900 | 149000 | 0.0 | | 0.9933 | 149500 | 0.0 | | 0.9967 | 150000 | 0.0 | | 1.0000 | 150500 | 0.0 | | 1.0033 | 151000 | 0.0 | | 1.0066 | 151500 | 0.0 | | 1.0099 | 152000 | 0.0 | | 1.0133 | 152500 | 0.0 | | 1.0166 | 153000 | 0.0 | | 1.0199 | 153500 | 0.0 | | 1.0232 | 154000 | 0.0 | | 1.0266 | 154500 | 0.0 | | 1.0299 | 155000 | 0.0 | | 1.0332 | 155500 | 0.0 | | 1.0365 | 156000 | 0.0 | | 1.0398 | 156500 | 0.0 | | 1.0432 | 157000 | 0.0 | | 1.0465 | 157500 | 0.0 | | 1.0498 | 158000 | 0.0 | | 1.0531 | 158500 | 0.0 | | 1.0565 | 159000 | 0.0 | | 1.0598 | 159500 | 0.0 | | 1.0631 | 160000 | 0.0 | | 1.0664 | 160500 | 0.0 | | 1.0697 | 161000 | 0.0 | | 1.0731 | 161500 | 0.0 | | 1.0764 | 162000 | 0.0 | | 1.0797 | 162500 | 0.0 | | 1.0830 | 163000 | 0.0 | | 1.0864 | 163500 | 0.0 | | 1.0897 | 164000 | 0.0 | | 1.0930 | 164500 | 0.0 | | 1.0963 | 165000 | 0.0 | | 1.0996 | 165500 | 0.0 | | 1.1030 | 166000 | 0.0 | | 1.1063 | 166500 | 0.0 | | 1.1096 | 167000 | 0.0 | | 1.1129 | 167500 | 0.0 | | 1.1163 | 168000 | 0.0 | | 1.1196 | 168500 | 0.0 | | 1.1229 | 169000 | 0.0 | | 1.1262 | 169500 | 0.0 | | 1.1295 | 170000 | 0.0 | | 1.1329 | 170500 | 0.0 | | 1.1362 | 171000 | 0.0 | | 1.1395 | 171500 | 0.0 | | 1.1428 | 172000 | 0.0 | | 1.1462 | 172500 | 0.0 | | 1.1495 | 173000 | 0.0 | | 1.1528 | 173500 | 0.0 | | 1.1561 | 174000 | 0.0 | | 1.1594 | 174500 | 0.0 | | 1.1628 | 175000 | 0.0 | | 1.1661 | 175500 | 0.0 | | 1.1694 | 176000 | 0.0 | | 1.1727 | 176500 | 0.0 | | 1.1761 | 177000 | 0.0 | | 1.1794 | 177500 | 0.0 | | 1.1827 | 178000 | 0.0 | | 1.1860 | 178500 | 0.0 | | 1.1893 | 179000 | 0.0 | | 1.1927 | 179500 | 0.0 | | 1.1960 | 180000 | 0.0 | | 1.1993 | 180500 | 0.0 | | 1.2026 | 181000 | 0.0 | | 1.2060 | 181500 | 0.0 | | 1.2093 | 182000 | 0.0 | | 1.2126 | 182500 | 0.0 | | 1.2159 | 183000 | 0.0 | | 1.2192 | 183500 | 0.0 | | 1.2226 | 184000 | 0.0 | | 1.2259 | 184500 | 0.0 | | 1.2292 | 185000 | 0.0 | | 1.2325 | 185500 | 0.0 | | 1.2359 | 186000 | 0.0 | | 1.2392 | 186500 | 0.0 | | 1.2425 | 187000 | 0.0 | | 1.2458 | 187500 | 0.0 | | 1.2491 | 188000 | 0.0 | | 1.2525 | 188500 | 0.0 | | 1.2558 | 189000 | 0.0 | | 1.2591 | 189500 | 0.0 | | 1.2624 | 190000 | 0.0 | | 1.2658 | 190500 | 0.0 | | 1.2691 | 191000 | 0.0 | | 1.2724 | 191500 | 0.0 | | 1.2757 | 192000 | 0.0 | | 1.2790 | 192500 | 0.0 | | 1.2824 | 193000 | 0.0 | | 1.2857 | 193500 | 0.0 | | 1.2890 | 194000 | 0.0 | | 1.2923 | 194500 | 0.0 | | 1.2957 | 195000 | 0.0 | | 1.2990 | 195500 | 0.0 | | 1.3023 | 196000 | 0.0 | | 1.3056 | 196500 | 0.0 | | 1.3089 | 197000 | 0.0 | | 1.3123 | 197500 | 0.0 | | 1.3156 | 198000 | 0.0 | | 1.3189 | 198500 | 0.0 | | 1.3222 | 199000 | 0.0 | | 1.3256 | 199500 | 0.0 | | 1.3289 | 200000 | 0.0 | | 1.3322 | 200500 | 0.0 | | 1.3355 | 201000 | 0.0 | | 1.3388 | 201500 | 0.0 | | 1.3422 | 202000 | 0.0 | | 1.3455 | 202500 | 0.0 | | 1.3488 | 203000 | 0.0 | | 1.3521 | 203500 | 0.0 | | 1.3555 | 204000 | 0.0 | | 1.3588 | 204500 | 0.0 | | 1.3621 | 205000 | 0.0 | | 1.3654 | 205500 | 0.0 | | 1.3687 | 206000 | 0.0 | | 1.3721 | 206500 | 0.0 | | 1.3754 | 207000 | 0.0 | | 1.3787 | 207500 | 0.0 | | 1.3820 | 208000 | 0.0 | | 1.3854 | 208500 | 0.0 | | 1.3887 | 209000 | 0.0 | | 1.3920 | 209500 | 0.0 | | 1.3953 | 210000 | 0.0 | | 1.3986 | 210500 | 0.0 | | 1.4020 | 211000 | 0.0 | | 1.4053 | 211500 | 0.0 | | 1.4086 | 212000 | 0.0 | | 1.4119 | 212500 | 0.0 | | 1.4153 | 213000 | 0.0 | | 1.4186 | 213500 | 0.0 | | 1.4219 | 214000 | 0.0 | | 1.4252 | 214500 | 0.0 | | 1.4285 | 215000 | 0.0 | | 1.4319 | 215500 | 0.0 | | 1.4352 | 216000 | 0.0 | | 1.4385 | 216500 | 0.0 | | 1.4418 | 217000 | 0.0 | | 1.4452 | 217500 | 0.0 | | 1.4485 | 218000 | 0.0 | | 1.4518 | 218500 | 0.0 | | 1.4551 | 219000 | 0.0 | | 1.4584 | 219500 | 0.0 | | 1.4618 | 220000 | 0.0 | | 1.4651 | 220500 | 0.0 | | 1.4684 | 221000 | 0.0 | | 1.4717 | 221500 | 0.0 | | 1.4751 | 222000 | 0.0 | | 1.4784 | 222500 | 0.0 | | 1.4817 | 223000 | 0.0 | | 1.4850 | 223500 | 0.0 | | 1.4883 | 224000 | 0.0 | | 1.4917 | 224500 | 0.0 | | 1.4950 | 225000 | 0.0 | | 1.4983 | 225500 | 0.0 | | 1.5016 | 226000 | 0.0 | | 1.5050 | 226500 | 0.0 | | 1.5083 | 227000 | 0.0 | | 1.5116 | 227500 | 0.0 | | 1.5149 | 228000 | 0.0 | | 1.5182 | 228500 | 0.0 | | 1.5216 | 229000 | 0.0 | | 1.5249 | 229500 | 0.0 | | 1.5282 | 230000 | 0.0 | | 1.5315 | 230500 | 0.0 | | 1.5349 | 231000 | 0.0 | | 1.5382 | 231500 | 0.0 | | 1.5415 | 232000 | 0.0 | | 1.5448 | 232500 | 0.0 | | 1.5481 | 233000 | 0.0 | | 1.5515 | 233500 | 0.0 | | 1.5548 | 234000 | 0.0 | | 1.5581 | 234500 | 0.0 | | 1.5614 | 235000 | 0.0 | | 1.5648 | 235500 | 0.0 | | 1.5681 | 236000 | 0.0 | | 1.5714 | 236500 | 0.0 | | 1.5747 | 237000 | 0.0 | | 1.5780 | 237500 | 0.0 | | 1.5814 | 238000 | 0.0 | | 1.5847 | 238500 | 0.0 | | 1.5880 | 239000 | 0.0 | | 1.5913 | 239500 | 0.0 | | 1.5947 | 240000 | 0.0 | | 1.5980 | 240500 | 0.0 | | 1.6013 | 241000 | 0.0 | | 1.6046 | 241500 | 0.0 | | 1.6079 | 242000 | 0.0 | | 1.6113 | 242500 | 0.0 | | 1.6146 | 243000 | 0.0 | | 1.6179 | 243500 | 0.0 | | 1.6212 | 244000 | 0.0 | | 1.6246 | 244500 | 0.0 | | 1.6279 | 245000 | 0.0 | | 1.6312 | 245500 | 0.0 | | 1.6345 | 246000 | 0.0 | | 1.6378 | 246500 | 0.0 | | 1.6412 | 247000 | 0.0 | | 1.6445 | 247500 | 0.0 | | 1.6478 | 248000 | 0.0 | | 1.6511 | 248500 | 0.0 | | 1.6545 | 249000 | 0.0 | | 1.6578 | 249500 | 0.0 | | 1.6611 | 250000 | 0.0 | | 1.6644 | 250500 | 0.0 | | 1.6677 | 251000 | 0.0 | | 1.6711 | 251500 | 0.0 | | 1.6744 | 252000 | 0.0 | | 1.6777 | 252500 | 0.0 | | 1.6810 | 253000 | 0.0 | | 1.6844 | 253500 | 0.0 | | 1.6877 | 254000 | 0.0 | | 1.6910 | 254500 | 0.0 | | 1.6943 | 255000 | 0.0 | | 1.6976 | 255500 | 0.0 | | 1.7010 | 256000 | 0.0 | | 1.7043 | 256500 | 0.0 | | 1.7076 | 257000 | 0.0 | | 1.7109 | 257500 | 0.0 | | 1.7143 | 258000 | 0.0 | | 1.7176 | 258500 | 0.0 | | 1.7209 | 259000 | 0.0 | | 1.7242 | 259500 | 0.0 | | 1.7275 | 260000 | 0.0 | | 1.7309 | 260500 | 0.0 | | 1.7342 | 261000 | 0.0 | | 1.7375 | 261500 | 0.0 | | 1.7408 | 262000 | 0.0 | | 1.7442 | 262500 | 0.0 | | 1.7475 | 263000 | 0.0 | | 1.7508 | 263500 | 0.0 | | 1.7541 | 264000 | 0.0 | | 1.7574 | 264500 | 0.0 | | 1.7608 | 265000 | 0.0 | | 1.7641 | 265500 | 0.0 | | 1.7674 | 266000 | 0.0 | | 1.7707 | 266500 | 0.0 | | 1.7741 | 267000 | 0.0 | | 1.7774 | 267500 | 0.0 | | 1.7807 | 268000 | 0.0 | | 1.7840 | 268500 | 0.0 | | 1.7873 | 269000 | 0.0 | | 1.7907 | 269500 | 0.0 | | 1.7940 | 270000 | 0.0 | | 1.7973 | 270500 | 0.0 | | 1.8006 | 271000 | 0.0 | | 1.8040 | 271500 | 0.0 | | 1.8073 | 272000 | 0.0 | | 1.8106 | 272500 | 0.0 | | 1.8139 | 273000 | 0.0 | | 1.8172 | 273500 | 0.0 | | 1.8206 | 274000 | 0.0 | | 1.8239 | 274500 | 0.0 | | 1.8272 | 275000 | 0.0 | | 1.8305 | 275500 | 0.0 | | 1.8339 | 276000 | 0.0 | | 1.8372 | 276500 | 0.0 | | 1.8405 | 277000 | 0.0 | | 1.8438 | 277500 | 0.0 | | 1.8471 | 278000 | 0.0 | | 1.8505 | 278500 | 0.0 | | 1.8538 | 279000 | 0.0 | | 1.8571 | 279500 | 0.0 | | 1.8604 | 280000 | 0.0 | | 1.8638 | 280500 | 0.0 | | 1.8671 | 281000 | 0.0 | | 1.8704 | 281500 | 0.0 | | 1.8737 | 282000 | 0.0 | | 1.8770 | 282500 | 0.0 | | 1.8804 | 283000 | 0.0 | | 1.8837 | 283500 | 0.0 | | 1.8870 | 284000 | 0.0 | | 1.8903 | 284500 | 0.0 | | 1.8936 | 285000 | 0.0 | | 1.8970 | 285500 | 0.0 | | 1.9003 | 286000 | 0.0 | | 1.9036 | 286500 | 0.0 | | 1.9069 | 287000 | 0.0 | | 1.9103 | 287500 | 0.0 | | 1.9136 | 288000 | 0.0 | | 1.9169 | 288500 | 0.0 | | 1.9202 | 289000 | 0.0 | | 1.9235 | 289500 | 0.0 | | 1.9269 | 290000 | 0.0 | | 1.9302 | 290500 | 0.0 | | 1.9335 | 291000 | 0.0 | | 1.9368 | 291500 | 0.0 | | 1.9402 | 292000 | 0.0 | | 1.9435 | 292500 | 0.0 | | 1.9468 | 293000 | 0.0 | | 1.9501 | 293500 | 0.0 | | 1.9534 | 294000 | 0.0 | | 1.9568 | 294500 | 0.0 | | 1.9601 | 295000 | 0.0 | | 1.9634 | 295500 | 0.0 | | 1.9667 | 296000 | 0.0 | | 1.9701 | 296500 | 0.0 | | 1.9734 | 297000 | 0.0 | | 1.9767 | 297500 | 0.0 | | 1.9800 | 298000 | 0.0 | | 1.9833 | 298500 | 0.0 | | 1.9867 | 299000 | 0.0 | | 1.9900 | 299500 | 0.0 | | 1.9933 | 300000 | 0.0 | | 1.9966 | 300500 | 0.0 | | 2.0000 | 301000 | 0.0 | | 2.0033 | 301500 | 0.0 | | 2.0066 | 302000 | 0.0 | | 2.0099 | 302500 | 0.0 | | 2.0132 | 303000 | 0.0 | | 2.0166 | 303500 | 0.0 | | 2.0199 | 304000 | 0.0 | | 2.0232 | 304500 | 0.0 | | 2.0265 | 305000 | 0.0 | | 2.0299 | 305500 | 0.0 | | 2.0332 | 306000 | 0.0 | | 2.0365 | 306500 | 0.0 | | 2.0398 | 307000 | 0.0 | | 2.0431 | 307500 | 0.0 | | 2.0465 | 308000 | 0.0 | | 2.0498 | 308500 | 0.0 | | 2.0531 | 309000 | 0.0 | | 2.0564 | 309500 | 0.0 | | 2.0598 | 310000 | 0.0 | | 2.0631 | 310500 | 0.0 | | 2.0664 | 311000 | 0.0 | | 2.0697 | 311500 | 0.0 | | 2.0730 | 312000 | 0.0 | | 2.0764 | 312500 | 0.0 | | 2.0797 | 313000 | 0.0 | | 2.0830 | 313500 | 0.0 | | 2.0863 | 314000 | 0.0 | | 2.0897 | 314500 | 0.0 | | 2.0930 | 315000 | 0.0 | | 2.0963 | 315500 | 0.0 | | 2.0996 | 316000 | 0.0 | | 2.1029 | 316500 | 0.0 | | 2.1063 | 317000 | 0.0 | | 2.1096 | 317500 | 0.0 | | 2.1129 | 318000 | 0.0 | | 2.1162 | 318500 | 0.0 | | 2.1196 | 319000 | 0.0 | | 2.1229 | 319500 | 0.0 | | 2.1262 | 320000 | 0.0 | | 2.1295 | 320500 | 0.0 | | 2.1328 | 321000 | 0.0 | | 2.1362 | 321500 | 0.0 | | 2.1395 | 322000 | 0.0 | | 2.1428 | 322500 | 0.0 | | 2.1461 | 323000 | 0.0 | | 2.1495 | 323500 | 0.0 | | 2.1528 | 324000 | 0.0 | | 2.1561 | 324500 | 0.0 | | 2.1594 | 325000 | 0.0 | | 2.1627 | 325500 | 0.0 | | 2.1661 | 326000 | 0.0 | | 2.1694 | 326500 | 0.0 | | 2.1727 | 327000 | 0.0 | | 2.1760 | 327500 | 0.0 | | 2.1794 | 328000 | 0.0 | | 2.1827 | 328500 | 0.0 | | 2.1860 | 329000 | 0.0 | | 2.1893 | 329500 | 0.0 | | 2.1926 | 330000 | 0.0 | | 2.1960 | 330500 | 0.0 | | 2.1993 | 331000 | 0.0 | | 2.2026 | 331500 | 0.0 | | 2.2059 | 332000 | 0.0 | | 2.2093 | 332500 | 0.0 | | 2.2126 | 333000 | 0.0 | | 2.2159 | 333500 | 0.0 | | 2.2192 | 334000 | 0.0 | | 2.2225 | 334500 | 0.0 | | 2.2259 | 335000 | 0.0 | | 2.2292 | 335500 | 0.0 | | 2.2325 | 336000 | 0.0 | | 2.2358 | 336500 | 0.0 | | 2.2392 | 337000 | 0.0 | | 2.2425 | 337500 | 0.0 | | 2.2458 | 338000 | 0.0 | | 2.2491 | 338500 | 0.0 | | 2.2524 | 339000 | 0.0 | | 2.2558 | 339500 | 0.0 | | 2.2591 | 340000 | 0.0 | | 2.2624 | 340500 | 0.0 | | 2.2657 | 341000 | 0.0 | | 2.2691 | 341500 | 0.0 | | 2.2724 | 342000 | 0.0 | | 2.2757 | 342500 | 0.0 | | 2.2790 | 343000 | 0.0 | | 2.2823 | 343500 | 0.0 | | 2.2857 | 344000 | 0.0 | | 2.2890 | 344500 | 0.0 | | 2.2923 | 345000 | 0.0 | | 2.2956 | 345500 | 0.0 | | 2.2990 | 346000 | 0.0 | | 2.3023 | 346500 | 0.0 | | 2.3056 | 347000 | 0.0 | | 2.3089 | 347500 | 0.0 | | 2.3122 | 348000 | 0.0 | | 2.3156 | 348500 | 0.0 | | 2.3189 | 349000 | 0.0 | | 2.3222 | 349500 | 0.0 | | 2.3255 | 350000 | 0.0 | | 2.3289 | 350500 | 0.0 | | 2.3322 | 351000 | 0.0 | | 2.3355 | 351500 | 0.0 | | 2.3388 | 352000 | 0.0 | | 2.3421 | 352500 | 0.0 | | 2.3455 | 353000 | 0.0 | | 2.3488 | 353500 | 0.0 | | 2.3521 | 354000 | 0.0 | | 2.3554 | 354500 | 0.0 | | 2.3588 | 355000 | 0.0 | | 2.3621 | 355500 | 0.0 | | 2.3654 | 356000 | 0.0 | | 2.3687 | 356500 | 0.0 | | 2.3720 | 357000 | 0.0 | | 2.3754 | 357500 | 0.0 | | 2.3787 | 358000 | 0.0 | | 2.3820 | 358500 | 0.0 | | 2.3853 | 359000 | 0.0 | | 2.3887 | 359500 | 0.0 | | 2.3920 | 360000 | 0.0 | | 2.3953 | 360500 | 0.0 | | 2.3986 | 361000 | 0.0 | | 2.4019 | 361500 | 0.0 | | 2.4053 | 362000 | 0.0 | | 2.4086 | 362500 | 0.0 | | 2.4119 | 363000 | 0.0 | | 2.4152 | 363500 | 0.0 | | 2.4186 | 364000 | 0.0 | | 2.4219 | 364500 | 0.0 | | 2.4252 | 365000 | 0.0 | | 2.4285 | 365500 | 0.0 | | 2.4318 | 366000 | 0.0 | | 2.4352 | 366500 | 0.0 | | 2.4385 | 367000 | 0.0 | | 2.4418 | 367500 | 0.0 | | 2.4451 | 368000 | 0.0 | | 2.4485 | 368500 | 0.0 | | 2.4518 | 369000 | 0.0 | | 2.4551 | 369500 | 0.0 | | 2.4584 | 370000 | 0.0 | | 2.4617 | 370500 | 0.0 | | 2.4651 | 371000 | 0.0 | | 2.4684 | 371500 | 0.0 | | 2.4717 | 372000 | 0.0 | | 2.4750 | 372500 | 0.0 | | 2.4784 | 373000 | 0.0 | | 2.4817 | 373500 | 0.0 | | 2.4850 | 374000 | 0.0 | | 2.4883 | 374500 | 0.0 | | 2.4916 | 375000 | 0.0 | | 2.4950 | 375500 | 0.0 | | 2.4983 | 376000 | 0.0 | | 2.5016 | 376500 | 0.0 | | 2.5049 | 377000 | 0.0 | | 2.5083 | 377500 | 0.0 | | 2.5116 | 378000 | 0.0 | | 2.5149 | 378500 | 0.0 | | 2.5182 | 379000 | 0.0 | | 2.5215 | 379500 | 0.0 | | 2.5249 | 380000 | 0.0 | | 2.5282 | 380500 | 0.0 | | 2.5315 | 381000 | 0.0 | | 2.5348 | 381500 | 0.0 | | 2.5382 | 382000 | 0.0 | | 2.5415 | 382500 | 0.0 | | 2.5448 | 383000 | 0.0 | | 2.5481 | 383500 | 0.0 | | 2.5514 | 384000 | 0.0 | | 2.5548 | 384500 | 0.0 | | 2.5581 | 385000 | 0.0 | | 2.5614 | 385500 | 0.0 | | 2.5647 | 386000 | 0.0 | | 2.5681 | 386500 | 0.0 | | 2.5714 | 387000 | 0.0 | | 2.5747 | 387500 | 0.0 | | 2.5780 | 388000 | 0.0 | | 2.5813 | 388500 | 0.0 | | 2.5847 | 389000 | 0.0 | | 2.5880 | 389500 | 0.0 | | 2.5913 | 390000 | 0.0 | | 2.5946 | 390500 | 0.0 | | 2.5980 | 391000 | 0.0 | | 2.6013 | 391500 | 0.0 | | 2.6046 | 392000 | 0.0 | | 2.6079 | 392500 | 0.0 | | 2.6112 | 393000 | 0.0 | | 2.6146 | 393500 | 0.0 | | 2.6179 | 394000 | 0.0 | | 2.6212 | 394500 | 0.0 | | 2.6245 | 395000 | 0.0 | | 2.6279 | 395500 | 0.0 | | 2.6312 | 396000 | 0.0 | | 2.6345 | 396500 | 0.0 | | 2.6378 | 397000 | 0.0 | | 2.6411 | 397500 | 0.0 | | 2.6445 | 398000 | 0.0 | | 2.6478 | 398500 | 0.0 | | 2.6511 | 399000 | 0.0 | | 2.6544 | 399500 | 0.0 | | 2.6578 | 400000 | 0.0 | | 2.6611 | 400500 | 0.0 | | 2.6644 | 401000 | 0.0 | | 2.6677 | 401500 | 0.0 | | 2.6710 | 402000 | 0.0 | | 2.6744 | 402500 | 0.0 | | 2.6777 | 403000 | 0.0 | | 2.6810 | 403500 | 0.0 | | 2.6843 | 404000 | 0.0 | | 2.6877 | 404500 | 0.0 | | 2.6910 | 405000 | 0.0 | | 2.6943 | 405500 | 0.0 | | 2.6976 | 406000 | 0.0 | | 2.7009 | 406500 | 0.0 | | 2.7043 | 407000 | 0.0 | | 2.7076 | 407500 | 0.0 | | 2.7109 | 408000 | 0.0 | | 2.7142 | 408500 | 0.0 | | 2.7176 | 409000 | 0.0 | | 2.7209 | 409500 | 0.0 | | 2.7242 | 410000 | 0.0 | | 2.7275 | 410500 | 0.0 | | 2.7308 | 411000 | 0.0 | | 2.7342 | 411500 | 0.0 | | 2.7375 | 412000 | 0.0 | | 2.7408 | 412500 | 0.0 | | 2.7441 | 413000 | 0.0 | | 2.7475 | 413500 | 0.0 | | 2.7508 | 414000 | 0.0 | | 2.7541 | 414500 | 0.0 | | 2.7574 | 415000 | 0.0 | | 2.7607 | 415500 | 0.0 | | 2.7641 | 416000 | 0.0 | | 2.7674 | 416500 | 0.0 | | 2.7707 | 417000 | 0.0 | | 2.7740 | 417500 | 0.0 | | 2.7774 | 418000 | 0.0 | | 2.7807 | 418500 | 0.0 | | 2.7840 | 419000 | 0.0 | | 2.7873 | 419500 | 0.0 | | 2.7906 | 420000 | 0.0 | | 2.7940 | 420500 | 0.0 | | 2.7973 | 421000 | 0.0 | | 2.8006 | 421500 | 0.0 | | 2.8039 | 422000 | 0.0 | | 2.8073 | 422500 | 0.0 | | 2.8106 | 423000 | 0.0 | | 2.8139 | 423500 | 0.0 | | 2.8172 | 424000 | 0.0 | | 2.8205 | 424500 | 0.0 | | 2.8239 | 425000 | 0.0 | | 2.8272 | 425500 | 0.0 | | 2.8305 | 426000 | 0.0 | | 2.8338 | 426500 | 0.0 | | 2.8372 | 427000 | 0.0 | | 2.8405 | 427500 | 0.0 | | 2.8438 | 428000 | 0.0 | | 2.8471 | 428500 | 0.0 | | 2.8504 | 429000 | 0.0 | | 2.8538 | 429500 | 0.0 | | 2.8571 | 430000 | 0.0 | | 2.8604 | 430500 | 0.0 | | 2.8637 | 431000 | 0.0 | | 2.8671 | 431500 | 0.0 | | 2.8704 | 432000 | 0.0 | | 2.8737 | 432500 | 0.0 | | 2.8770 | 433000 | 0.0 | | 2.8803 | 433500 | 0.0 | | 2.8837 | 434000 | 0.0 | | 2.8870 | 434500 | 0.0 | | 2.8903 | 435000 | 0.0 | | 2.8936 | 435500 | 0.0 | | 2.8970 | 436000 | 0.0 | | 2.9003 | 436500 | 0.0 | | 2.9036 | 437000 | 0.0 | | 2.9069 | 437500 | 0.0 | | 2.9102 | 438000 | 0.0 | | 2.9136 | 438500 | 0.0 | | 2.9169 | 439000 | 0.0 | | 2.9202 | 439500 | 0.0 | | 2.9235 | 440000 | 0.0 | | 2.9269 | 440500 | 0.0 | | 2.9302 | 441000 | 0.0 | | 2.9335 | 441500 | 0.0 | | 2.9368 | 442000 | 0.0 | | 2.9401 | 442500 | 0.0 | | 2.9435 | 443000 | 0.0 | | 2.9468 | 443500 | 0.0 | | 2.9501 | 444000 | 0.0 | | 2.9534 | 444500 | 0.0 | | 2.9568 | 445000 | 0.0 | | 2.9601 | 445500 | 0.0 | | 2.9634 | 446000 | 0.0 | | 2.9667 | 446500 | 0.0 | | 2.9700 | 447000 | 0.0 | | 2.9734 | 447500 | 0.0 | | 2.9767 | 448000 | 0.0 | | 2.9800 | 448500 | 0.0 | | 2.9833 | 449000 | 0.0 | | 2.9867 | 449500 | 0.0 | | 2.9900 | 450000 | 0.0 | | 2.9933 | 450500 | 0.0 | | 2.9966 | 451000 | 0.0 | | 2.9999 | 451500 | 0.0 |
### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - 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", } ``` #### 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} } ```