--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:11180 - loss:CosineSimilarityLoss widget: - source_sentence: 'I really shy away from early voting narratives, which I got into in another question thats posted here (I think!) The polls could absolutely be missing a meaningful chunk of Trumps support, as was the case in 16 and 20. Though its worth noting that he is polling much better this time around than the last two times. That could be an indication that the issue has been resolved, through some combination of new polling methods and more willingness of Trump supporters to answer polls and state their support for him. (I talked with one pollster who believes he missed in 20 because theyd get Trump voters on the phone but, for whatever reason, they wouldnt say they were voting for him. Now when voters hesitate, this pollster nudges them a little to pick a candidate. He thinks its fixed the problem. Well see.) I did write about some of the polling issues here: [' sentences: - Those are ways to cope individually but to actually abolish the institution of capitalist employment relationships there has to be collective action. You can call it however you want, socialism/communism happens to be a banner that people who held these ideas have been fighting under for many many years. - everyones scared shitless of trump winning. every single one of us is voting again and then add in new voters and R voters going D and its a landslide win. - I know, that's why I said one of the reasons. The main reason is his uselessness when your team is bad - source_sentence: This movie is exactly the same as Ridley Scott's, "Someone To Watch Over Me", which is a classic. It stars Tom Berenger and Mimi Rogers and the bad guy from the Fugitive. It's a quality movie, with good direction and great acting. This movie is the polar opposite. It's the same plot, just minus any good acting and adding TV movie direction. I do have to say I like the interaction between Lowe and the woman. She's hot and their romance is believable on many levels. Unfortunately, that alone cannot save this carbon copy of the classic Ridley Scott film.Usually crappy TV movies have some redeeming quality that makes them watchable at the very least. I think for me, this film's redeeming value is that Rob Lowe and the lead female have chemistry. That and I was able to watch and compare the plot to another quality film. sentences: - The real war will never get in the books. - It's amazing to see how Nikhil Advani manages to attract people to the theater till the very day of the release. I mean..... look at the cast here , the promotion is superb, good enough songs and the trailers are fine. This makes it a house full on the first day, but it's only when people go and see the film they realize that there is no way their money is refundable. House full the first day , the movie is out the next week. This film, inspired by 'Love Actually' is what they say, didn't manage to handle the whole cast well. They tried to put in big stars but ended up by not even managing to bring out even an average performance by any one. The stories are hollow and cheesy, so the audience can't connect with any single one of them. It's a big disappointment to all those who like big stars or for that matter Nikhil Advani after his big success of 'Kal Ho Na Ho'. - I can't believe this movie has an average rating of 7.0! It is a fiendishly bad movie, and I saw it when it was fairly new, and I was in the age group that is supposed to like it! - source_sentence: Nationalism is a silly cock crowing on his own dunghill. sentences: - "I can't even go to a PC hardware sub and not see political stuff. Hell even dog\ \ subs have politics now.\n\n There was a thread with a female BLM supporter subtitling\ \ her dog's barks to saying that it hates racists and bigots and supports equality.\ \ Bloody ridiculous." - Nov. 10, 2015 - Gaily Grind - Number of gay Americans in same-sex marriages closing in on 1 million - - 'The 1970''s saw a rise and fall of what we have come to know as "Blacksploitation" Films. The term is a reference to kind of broad catch-all, rather than a true Genre of Film. In short, any comedy, drama, adventure, western or urban cops & robbers shoot-em-up, that are so constructed and so cast as to appeal to the large Urban Black population of the Mid 20th Century. That indeed could embrace the widest type of films, as long as the had a slant toward the inner-city black population.It appears that the idea of producing these films of particularly keen interest to Black Americans had its genesis with the Eastertime Release of 100 RIFLES (Marvin Schwartz Prod./20th Century-Fox, 1969). In it, former Syracuse University All-American Footballer and Several Times All-Pro Fullback for the Cleveland Browns, Jim Brown, had a Co-Starring Billing. Having appeared in a number of films already, as for example, RIO CONCHOS (1964),THE DIRTY DOZEN (1967), (ICE STTION ZEBRA (1968)* and others, it was beginning to make more sense to the Studios'' "Suits" that Jim was a hot property.Now this 100 RIFLES brings record numbers of Black patrons to the Big Cities'' central business districts on Easter Sunday to view Mr. Brown. Why not start to film more of these adventure epics and other types of film with more Black Players and Stars? Why not, indeed.** So we saw a succession of Cops & Robbers, Bad-ass Private Detective Films, Comedies, all going the route. Along the way, we eventually got to some more family oriented, wider appealing films. The movie goers were treated to SOUNDER (1972), THE TAKE (1974), CONRACK (1974)and, ultimately, CLAUDINE (1974).In CLAUDINE, we find no stigma nor easy classification as being "Blackploitation", as the story is universal, and could easily have been done as a story about people of any descent, any where, and not just in the 1970''s USA.That the story was done of a SINGLE mother, Claudine (Dianne Carroll), struggling to keep a family together after "....two marriages and two almost marriages.", is a far cry from a shoot-em-up Harlem Style. The problems that plague the everyday citizens of our nation are confronted and examined under the ol'' sociological microscope.But we also consider Claudine''s psychological and physical needs as a female. For "Woman Needs Man and Man Must Have His MATE",***and we do concede this point. (That''s S-E-X that we''re talking about, Schultz!) Claudine meets up with a very masculine, broad shouldered, athletic type in Private Scavanger Garbage Man, Ruppert B. Marshall (James Earl Jones) and they go on a date.The Great Welfare State intervenes with the Couple as Claudine''s Welfare Case Worker, Miss Tayback (Elisa Loti), comes snooping around to see just who is this unattached Male, who is suddenly paying so much attention to Claudine''s family.After a humiliating experience with the Welfare Bureau''s auditing and "deducting" binge, which would be the norm for the family, the two decide to get married with or without the blessing of Big Brother.Meanwhile, Claudine''s elder son has gotten involved with some big talking but little doing Black Activist group. But, with Ruppert''s help, he and they all come through it A.O.K.It ends on a Happy, Upbeat and Hopeful note. We know that it may not be exactly "...Happily Ever After!", but rather the''ll make it all together! If there is a single criticism that we must state it is that sometimes in a movie like this, a misconception is spread to a large portion of Urban Blacks. And that is, the apparent implied myth that all Whites are wealthy, having none of their kind ever in need of a helping hand, out of work or suffering any disabilities.Well, folks, it just ain''t true! NOTE: * At one point, Jim Brown''s career was a real hit as a rugged actioner. He was even being tauted as "...The Black John Wayne." NOTE: ** The idea of producing films with All-Black Casts, filmed for All-Black consumption was not a new idea. In the 1920''s, ''30''s and ''40''s, we saw productions from people like Noble Johnson, Spencer Williams, Jr. and Rex Ingram.NOTE: *** That''s "As Time Goes By", you know, Schultz, it''s from CASABLANCA (Warner Brothers, 1942).' - source_sentence: I think this movie has got it all. It has really cool music that I can never get out of my head. It has cool looking characters. IS REALLY funny(you know, the kind that you'll crack up on the ground and you'll keep saying the funny parts over every day for three weeks).Despite the bad acting, bad cgi, and bad story(about cops going after a robot), its really cool. Its one of those movies you and all of your family can watch, get together, eat pizza, laugh like crazy, and watch it two more times.There are so many funny parts, like when Kurt was trying to get Edison's attention and gave him the finger, and then threw a paint ball gun at him so they could play paint ball. On that part, I kept saying "Remember, Remember?"to my cousins who saw it and showed them what happened. There was also a really funny part when Edision ran into the room and Kurt was there(just before they fought) and Kurt was talking about his "Strange dream" and how he was "Superman". I LOVED that part, although it has been a while since I saw it, so I don't remember that part. Everything the actors said were funny, like how Kurt says, "I worship you, like a GOD!" to the robot.Although there was some bad things, in all it was a GREAT movie. Man, I can't stop laughing. I wish I had that movie. ); sentences: - As I looked at this movie once again, I think it belongs among Hitchcock's greatest films. The first time I saw it I was just blown away by the suspense, action and imagery. It has the gripping ending, the deranged murderer, the innocent man framed or victimized by circumstances, some great on-location shots, e.g. the Jefferson Memorial in Washington and Penn Central in New York. It also has great supporting actors with Hitch's daughter Patricia in the role of the younger sister to Ruth Roman and the stalwart Leo G. Carroll in another of his Hitchcock movies. The merry-go-round episode near the end is one of the most nerve-wracking in Hitchcock's body of work.Robert Walker as Bruno Anthony (his last full film) gives a great performance as the deranged stranger on the train, who worms himself into the life of the unsuspecting tennis star, Guy Haines (Farley Granger). Granger plays the nice guy who is caught up in a messy divorce. The movie opens with the camera showing the shoes of two separate men as they leave their taxis to board the train. Eventually, they meet and the story takes over. The stranger takes an unusual interest in the tennis star and as the movie continues,the stranger becomes a stalker. The action shifts from place to place, including Washington, the fictional small town of Metcalf, the Forest Hills tennis championship, and a passenger train taking the two leading men back and forth on separate missions. Towards the end, the pace of a tennis game is woven into the plot as they race against time. The camera cuts away to the faces of the athletes as they volley and serve in a remarkable series of shots. When the closely-fought contest is over, the climactic chase takes place. Hitchcock has a love for trains and it is great to see Penn Station, long since gone. Trains are featured in the 39 Steps, the Lady Vanishes, Shadow of a Doubt, Spellbound, North by Northwest and this movie. This classic Hitchcock thriller took place at the start of a period of great creativity for the master of suspense - the 1950's and I am convinced that one day it will be given its due in the Hitchcock hall of fame. - "My RfA (reprise) \n\nWell, it's been a week now that I've been an administrator\ \ and I'd like to take this moment to once again thank everyone who supported\ \ my RfA, and to let you all know that I don't think I've screwed anything up\ \ yet so I hope I'm living up to everyone's expectations for me. But if I ever\ \ fall short of those expectations, I'd certainly welcome folks telling me about\ \ it!" - The first few minutes of this movie don't do it justice!For me, its not funny until they board the sub and those hilarious characters begin to gel. I was born and raised in Norfolk Virginia and met my share of "different" sailors- I even married one! Most of my favorite movies are just funny, not topical, not dependent on sex or violence and funny every time I see them. Groundhog Day, Bruce Almighty and Down Periscope are still funny even after I know the dialog by heart. Kelsey Grammar with his "God I LOVE this job!"was sincere, genuine and lovable. Rob Schneider is hysterical as the crew gets back at him for being annoying. I am still amazed at the size of that fishing boat next to a sub! I can see why folks who live this life would notice the uh-oh's but its not a documentary after all its a comedy and I just love it! - source_sentence: Every American poet feels that the whole responsibility for contemporary poetry has fallen upon his shoulders, that he is a literary aristocracy of one. sentences: - 'Many thousands of youth have been deprived of the benefit of education thereby, their morals ruined, and talents irretrievably lost to society, for want of cultivation: while two parties have been idly contending who should bestow it.' - I'm not sure why Spike Lee made this train wreck of a movie and conned poor Stevie Wonder into eternally pairing his beautiful music with this theatrical mess. I also resent the way he uses profanity as a part of the normal prose of professional Blacks. The abuse of his hold on ethnic movie goers is a shame. Scenes which seem to be contrived out the blue and have nothing to do with the theme or sub themes, play as if some college kid wrote this. I especially detest the ludicrous scene where the two leads are playfully sparring for no reason at all and the cops come and rough up Snipes. The overacting of the leads makes one feel as if Spike has no respect for his viewers or he has no clue what a movie is all about. The final scene appears to be thrown in to justify the use of a sledge hammer to tack a point in. This movie also supports the myth that all people of culture use the F-word in casual conversation. I am hoping he will realize that the rest of his movies are in the same pool as this one where he is not growing as a film maker. I think his union with Scorcesee in Clockers was a wise move. He should stick to making documentaries like the Four Little Colored Girls. Shock movies do not an Oscar make. - I was a barman in the UK when it came out and loved it so much I'd pop out of the bar to feed the Jukebox to keep it playing over and over. Never knew she was only 14 or 15 at the time. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: similarity type: similarity metrics: - type: pearson_cosine value: 0.36937547630571615 name: Pearson Cosine - type: spearman_cosine value: 0.39006034741410334 name: Spearman Cosine --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. 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 - **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, 'architecture': '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}) ) ``` ## 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 = [ 'Every American poet feels that the whole responsibility for contemporary poetry has fallen upon his shoulders, that he is a literary aristocracy of one.', "I'm not sure why Spike Lee made this train wreck of a movie and conned poor Stevie Wonder into eternally pairing his beautiful music with this theatrical mess. I also resent the way he uses profanity as a part of the normal prose of professional Blacks. The abuse of his hold on ethnic movie goers is a shame. Scenes which seem to be contrived out the blue and have nothing to do with the theme or sub themes, play as if some college kid wrote this. I especially detest the ludicrous scene where the two leads are playfully sparring for no reason at all and the cops come and rough up Snipes. The overacting of the leads makes one feel as if Spike has no respect for his viewers or he has no clue what a movie is all about. The final scene appears to be thrown in to justify the use of a sledge hammer to tack a point in. This movie also supports the myth that all people of culture use the F-word in casual conversation. I am hoping he will realize that the rest of his movies are in the same pool as this one where he is not growing as a film maker. I think his union with Scorcesee in Clockers was a wise move. He should stick to making documentaries like the Four Little Colored Girls. Shock movies do not an Oscar make.", 'Many thousands of youth have been deprived of the benefit of education thereby, their morals ruined, and talents irretrievably lost to society, for want of cultivation: while two parties have been idly contending who should bestow it.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.6358, 0.5940], # [0.6358, 1.0000, 0.4347], # [0.5940, 0.4347, 1.0000]]) ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `similarity` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.3694 | | **spearman_cosine** | **0.3901** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 11,180 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | I AM ANGRY AT YOU BILLJ! YOU GOT PEOPLE BLOCKED FOR AS LONG AS YOU LIVE! I ASKED YOU TO STOP DELETING MY EDITS OR I WILL BLOCK YOU FOR ALl EONS YOU ASSHOLE! WIKIPEDIA IS NOT CENSORED SO STOP REMOVING MY FUCKING MESSAGES OR I WILL BEAT YOU UP SILLY! | The thing is i don't see any shyness from people supporting far right anymore. The life of avg Joe became signicantly shittier after covid and global conflicts. They are very vocal about their distaste. And they blame lefties and immigrants for their problems. So they are vocal and very organized.

Also most of the public already act demented and noone remembers all the moronic stuff Trump pulled during his presidency.

I don't see much reason for them to be shy about. | 0.4082482904638631 | | I understand that you may be confused, but you still shouldn't judge someone's sexual identity. Just because they haven't acted on all of their sexual inclinations, doesn't mean that they don't still have those feelings. Accept others as they present themselves. | The head of the Mormon church has married same sex couples in the temple because they were close family. It's all about $$$. | 0.5773502691896258 | | Ugh there is so many bad decisions by conservative judges that need to be undone. | you say waste a draft pick on Manziel when we have Mallet. that's why I'm telling you to delete your account. You're retarded | 0.5773502691896258 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `fp16`: True - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: True - `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 - `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`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | similarity_spearman_cosine | |:------:|:----:|:-------------:|:--------------------------:| | 0.0286 | 10 | - | -0.0664 | | 0.0571 | 20 | - | -0.0621 | | 0.0857 | 30 | - | -0.0581 | | 0.1143 | 40 | - | -0.0516 | | 0.1429 | 50 | - | -0.0444 | | 0.1714 | 60 | - | -0.0334 | | 0.2 | 70 | - | -0.0194 | | 0.2286 | 80 | - | -0.0061 | | 0.2571 | 90 | - | 0.0177 | | 0.2857 | 100 | - | 0.0317 | | 0.3143 | 110 | - | 0.0510 | | 0.3429 | 120 | - | 0.0667 | | 0.3714 | 130 | - | 0.0892 | | 0.4 | 140 | - | 0.1206 | | 0.4286 | 150 | - | 0.1584 | | 0.4571 | 160 | - | 0.1821 | | 0.4857 | 170 | - | 0.1716 | | 0.5143 | 180 | - | 0.1749 | | 0.5429 | 190 | - | 0.2192 | | 0.5714 | 200 | - | 0.2473 | | 0.6 | 210 | - | 0.2399 | | 0.6286 | 220 | - | 0.2419 | | 0.6571 | 230 | - | 0.2637 | | 0.6857 | 240 | - | 0.2672 | | 0.7143 | 250 | - | 0.2754 | | 0.7429 | 260 | - | 0.2942 | | 0.7714 | 270 | - | 0.3079 | | 0.8 | 280 | - | 0.3079 | | 0.8286 | 290 | - | 0.3077 | | 0.8571 | 300 | - | 0.3012 | | 0.8857 | 310 | - | 0.3148 | | 0.9143 | 320 | - | 0.3199 | | 0.9429 | 330 | - | 0.3306 | | 0.9714 | 340 | - | 0.3363 | | 1.0 | 350 | - | 0.3419 | | 1.0286 | 360 | - | 0.3402 | | 1.0571 | 370 | - | 0.3366 | | 1.0857 | 380 | - | 0.3402 | | 1.1143 | 390 | - | 0.3360 | | 1.1429 | 400 | - | 0.3371 | | 1.1714 | 410 | - | 0.3536 | | 1.2 | 420 | - | 0.3268 | | 1.2286 | 430 | - | 0.3443 | | 1.2571 | 440 | - | 0.3011 | | 1.2857 | 450 | - | 0.3549 | | 1.3143 | 460 | - | 0.3321 | | 1.3429 | 470 | - | 0.3505 | | 1.3714 | 480 | - | 0.3412 | | 1.4 | 490 | - | 0.3337 | | 1.4286 | 500 | 0.1211 | 0.3488 | | 1.4571 | 510 | - | 0.3486 | | 1.4857 | 520 | - | 0.3508 | | 1.5143 | 530 | - | 0.3561 | | 1.5429 | 540 | - | 0.3592 | | 1.5714 | 550 | - | 0.2950 | | 1.6 | 560 | - | 0.3287 | | 1.6286 | 570 | - | 0.3369 | | 1.6571 | 580 | - | 0.3407 | | 1.6857 | 590 | - | 0.3283 | | 1.7143 | 600 | - | 0.3547 | | 1.7429 | 610 | - | 0.3665 | | 1.7714 | 620 | - | 0.3459 | | 1.8 | 630 | - | 0.3614 | | 1.8286 | 640 | - | 0.3514 | | 1.8571 | 650 | - | 0.3714 | | 1.8857 | 660 | - | 0.3647 | | 1.9143 | 670 | - | 0.3601 | | 1.9429 | 680 | - | 0.3292 | | 1.9714 | 690 | - | 0.3321 | | 2.0 | 700 | - | 0.3624 | | 2.0286 | 710 | - | 0.3605 | | 2.0571 | 720 | - | 0.3702 | | 2.0857 | 730 | - | 0.3783 | | 2.1143 | 740 | - | 0.3788 | | 2.1429 | 750 | - | 0.3813 | | 2.1714 | 760 | - | 0.3736 | | 2.2 | 770 | - | 0.3762 | | 2.2286 | 780 | - | 0.3804 | | 2.2571 | 790 | - | 0.3805 | | 2.2857 | 800 | - | 0.3755 | | 2.3143 | 810 | - | 0.3647 | | 2.3429 | 820 | - | 0.3654 | | 2.3714 | 830 | - | 0.3767 | | 2.4 | 840 | - | 0.3727 | | 2.4286 | 850 | - | 0.3824 | | 2.4571 | 860 | - | 0.3660 | | 2.4857 | 870 | - | 0.3791 | | 2.5143 | 880 | - | 0.3723 | | 2.5429 | 890 | - | 0.3818 | | 2.5714 | 900 | - | 0.3861 | | 2.6 | 910 | - | 0.3861 | | 2.6286 | 920 | - | 0.3857 | | 2.6571 | 930 | - | 0.3825 | | 2.6857 | 940 | - | 0.3680 | | 2.7143 | 950 | - | 0.3750 | | 2.7429 | 960 | - | 0.3815 | | 2.7714 | 970 | - | 0.3851 | | 2.8 | 980 | - | 0.3879 | | 2.8286 | 990 | - | 0.3863 | | 2.8571 | 1000 | 0.1033 | 0.3818 | | 2.8857 | 1010 | - | 0.3882 | | 2.9143 | 1020 | - | 0.3896 | | 2.9429 | 1030 | - | 0.3899 | | 2.9714 | 1040 | - | 0.3901 |
### Framework Versions - Python: 3.11.9 - Sentence Transformers: 5.1.0 - Transformers: 4.53.3 - PyTorch: 2.5.1 - Accelerate: 1.10.0 - Datasets: 2.14.4 - 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", } ```