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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.

         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) 


        Well, 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 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
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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

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
min: 6 tokens
mean: 107.53 tokens
max: 512 tokens

min: 5 tokens
mean: 111.53 tokens
max: 512 tokens

min: 0.0
mean: 0.53
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 6 tokens mean: 107.53 tokens max: 512 tokens	min: 5 tokens mean: 111.53 tokens max: 512 tokens	min: 0.0 mean: 0.53 max: 1.0

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 with these parameters:

{
    "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

@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",
}