metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:584355
  - loss:CachedInfonce
widget:
  - source_sentence: >-
      What were the criticisms made by Joe Joseph and Thomas Sutcliffe about the
      film
    sentences:
      - Heirloom quality! Shop Now For Chanukah!
      - >-
        Charlie Rymer (born December 18, 1967) is an American professional
        golfer who played on the PGA Tour and the Nike Tour. He is currently an
        analyst for the Golf Channel. Amateur career

        Rymer was born in Cleveland, Tennessee and grew up in Fort Mill, South
        Carolina. Rymer played college golf at Georgia Tech, where he was a
        third-team All-American in 1988 and an honorable mention All-American in
        1989. He won five tournaments during his time at Georgia Tech.
      - >-
        Joe Joseph of The Times agreed that the film was insubstantial, calling
        it a "speedy, cost-efficient way to interleave stock library footage
        with quotes from DJs and showbiz journalists in order to fill gaps in
        the late summer schedules." The Independents Thomas Sutcliffe felt the
        airing of the film on the same week as the first anniversary of the
        September 11 attacks was ill-timed, and described the film as "a scrappy
        collage of warmed-over gossip and underpowered revelation."
  - source_sentence: >-
      What is the expected impact on AT&T's networks if Apple releases a
      WiFi-only model of the iPad
    sentences:
      - >-
        You don't have to take yourself too seriously, try to fit a mold, or
        fall into the imitation trap. Your personal brand should look and feel
        like the best representation of you.
      - >-
        Add to that the fact that techies everywhere are either frothing or
        scoffing (with a willingness to buy) at the iPad, and you're looking at
        yet another surge in subscribers come March. Unless Apple releases the
        iPad without 3G support, as may well be the case with the starting model
        priced at $499 and rumored to be available only with WiFi, there will
        likely be hundreds of thousands of new 3G devices added to AT&T's
        networks, and these devices are pretty data-heavy.
      - >-
        He lives on Vashon Island with his wife, who is a teacher, and his
        teenage son and daughter. They enjoy their gardens, walking, and
        spending vacations near water.
  - source_sentence: When did Donald last see an office from the inside
    sentences:
      - >-
        Casso was endorsed by the Denver Post, but not the Rocky Mountain News.
        2007 legislative session

        In the 2007 session of the Colorado General Assembly, Casso sat on the
        House Education Committee and the House State, Veterans, & Military
        Affairs Committee. During the 2007 session, Casso sponsored two bills to
        revise the ways in which schools' CSAP test scores were reported. One,
        which would have exempted scores from special education students, was
        killed in a Senate committee; the other, which would have exempted
        scores for students whose parents opt the students out of the test, was
        killed in a House committee at Casso's request because of concerns that
        it would jeopardize federal school funding. Following the legislative
        session, Casso was present at the Colorado State Capitol during an
        incident in which state troopers shot and killed a mentally ill
        individual gunman targeting Gov. Bill Ritter. Casso observed the dead
        body and afterwards supported increased security, including metal
        detectors, for the state capitol building.
      - >-
        Donald saw the last time an office from the inside in 2006. Ever since
        did he work online for himself in all different kind of coffee shops
        from Cambodia to Tuvalu Islands. He started to get into serious trouble
        when AdWords Banned his account of the blue. Oliver: Nice to meet you
        Donald, how are you
      - >-
        - His Immortal Logness was magnificent

        So bummed that I missed The Orb while touring the area. Thanks for the
        tribute mix!
  - source_sentence: >-
      What percentage of tax revenues does corporate income tax revenue
      currently account for in the U.S.
    sentences:
      - >-
        The fourth quarter unraveled at both ends, offensive stagnation leading
        to Houston scoring chances that allowed the Rockets to set their defense
        and make the Pistons attack in the half-court, where poor shooting from
        their backcourt - Rodney Stuckey (1 of 10) and Brandon Knight combined
        to go 6 of 25 - made it difficult to spread out Houston's defense. "I
        just think we lost our pace," Frank said.
      - >-
        WELCOME TO KANEN INC.

        AEROSPACE TOOL DESIGN

        Kanen Inc. provides its services based on quality, customer
        satisfaction, and a dedication to see your project succeed.
      - >-
        In his May 31 column (Abolish the Corporate Income Tax! ), he points out
        that corporate income tax revenue has declined to 10% of tax revenues,
        despite the U.S. having, by far, the highest corporate income tax rate
        in the world.
  - source_sentence: >-
      What challenges does Mayor Hundred face in leading New York as depicted in
      March to War
    sentences:
      - >-
        He retired in 2004. Honours

        Morgan was appointed an Officer of the Order of the British Empire (OBE)
        in 2005.
      - >-
        Bring your sewing machine - scissors - material - pattern (if you
        already have one that you want to work on) - have a project that you
        need assistance with - just want to spend the day with your fellow
        Caerthen's in a day of sewing and socializing? Come on out!
      - >-
        His heroics convinced the citizens of New York to elect him mayor, and
        March to War opens with Mayor Hundred dealing with unrest in the city.
        Political cartoons display him as a caped superhero unable to handle the
        daily needs of the city, and a protest against the war in Iraq has it
        divided.
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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: 8192 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(
  (transformer): Transformer(
    (auto_model): XLMRobertaLoRA(
      (roberta): XLMRobertaModel(
        (embeddings): XLMRobertaEmbeddings(
          (word_embeddings): ParametrizedEmbedding(
            250002, 1024, padding_idx=1
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (token_type_embeddings): ParametrizedEmbedding(
            1, 1024
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
        )
        (emb_drop): Dropout(p=0.1, inplace=False)
        (emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder): XLMRobertaEncoder(
          (layers): ModuleList(
            (0-23): 24 x Block(
              (mixer): MHA(
                (rotary_emb): RotaryEmbedding()
                (Wqkv): ParametrizedLinearResidual(
                  in_features=1024, out_features=3072, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (inner_attn): FlashSelfAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (inner_cross_attn): FlashCrossAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (out_proj): ParametrizedLinear(
                  in_features=1024, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout1): Dropout(p=0.1, inplace=False)
              (drop_path1): StochasticDepth(p=0.0, mode=row)
              (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (mlp): Mlp(
                (fc1): ParametrizedLinear(
                  in_features=1024, out_features=4096, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (fc2): ParametrizedLinear(
                  in_features=4096, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout2): Dropout(p=0.1, inplace=False)
              (drop_path2): StochasticDepth(p=0.0, mode=row)
              (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            )
          )
        )
        (pooler): XLMRobertaPooler(
          (dense): ParametrizedLinear(
            in_features=1024, out_features=1024, bias=True
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (activation): Tanh()
        )
      )
    )
  )
  (pooler): 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})
  (normalizer): Normalize()
)

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("Jrinky/jina3")
# Run inference
sentences = [
    'What challenges does Mayor Hundred face in leading New York as depicted in March to War',
    'His heroics convinced the citizens of New York to elect him mayor, and March to War opens with Mayor Hundred dealing with unrest in the city. Political cartoons display him as a caped superhero unable to handle the daily needs of the city, and a protest against the war in Iraq has it divided.',
    "Bring your sewing machine - scissors - material - pattern (if you already have one that you want to work on) - have a project that you need assistance with - just want to spend the day with your fellow Caerthen's in a day of sewing and socializing? Come on out!",
]
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: 584,355 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 6 tokens
mean: 17.41 tokens
max: 42 tokens

min: 5 tokens
mean: 119.19 tokens
max: 1979 tokens

	anchor	positive
type	string	string
details	min: 6 tokens mean: 17.41 tokens max: 42 tokens	min: 5 tokens mean: 119.19 tokens max: 1979 tokens

Samples:

anchor	positive
`What resources and tools are recommended for busy brides planning their weddings`	`If you are planning on spending a little bit of time on your wedding planning, here is part 2 of my series of great resources and tools for wedding planning that every busy bride should know about. The previous instalment can be viewed here.`
`How many girls were raised in the house described`	`This house is where my parents proudly hung up our diplomas. This house is where 3 girls were raised.`
`Where did the narrator's dad always barbecue for Easter`	`This house is where my dad always barbequed for Easter, rain or shine. This house is where we welcomed family and friends on their first visit to the United States.`

Loss: cachedselfloss2.CachedInfonce with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 18,073 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 6 tokens
mean: 17.52 tokens
max: 40 tokens

min: 5 tokens
mean: 107.58 tokens
max: 1832 tokens

	anchor	positive
type	string	string
details	min: 6 tokens mean: 17.52 tokens max: 40 tokens	min: 5 tokens mean: 107.58 tokens max: 1832 tokens

Samples:

anchor	positive
`What significant role did the character Raven portray in early cinema`	He played cynical tough guys in modern films, but then branched into westerns where for the most part he was the gallant hero. In fact the ultimate gallant white knight hero in Shane. His part as Raven is a difficult one, yet he pulls it off. He's a cold blooded contract killer, one of the earliest ever portrayed as a film protagonist. Yet he's human and you see flashes of it, his concern for cats. As a cat lover, I can sure identify with that. Raven is also one of the earliest characters in cinema who talks about child abuse making him what he is. Groundbreaking when you think about it. Next to Ladd, the biggest kudos have to go to Laird Cregar, borrowed from 20th Century Fox to play Willard Gates. Gates is a top company executive with Marshall's firm which is a defense contractor which is why the Senate is interested in him. He's basically a jerk who thinks he's so clever. Veronica Lake gets to him real easy because of his weakness for the nightclub scene.
`What are the key features and characteristics of the Majestic Pure Dead Sea Mud Mask`	At the same time, it can be used all over your body, not just face. This way, you can clear any part of your skin from its impurities. An additional feature that caught our eyes immediately was the beautifully designed packaging that makes this affordable product look high-end. The combination of grey, black, and blue colors will easily make it stand out in any beauty shop. - Good for sensitive and dry skin - Affordable price - Treats many different skin conditions - Not good for oily skin - Can feel a bit oily Majestic Pure Dead Sea Mud Mask Review Majestic Pure is a well-known brand among the beauty and skincare community. They make affordable, natural products and are oftentimes among the celebrity favorites.
`What benefits does this product provide for the skin`	`This will provide you with softer skin that glows. At the same time, it will help you deal with your clogged pores and provide you the necessary daily acne treatment.`

Loss: cachedselfloss2.CachedInfonce with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 800
per_device_eval_batch_size: 800
learning_rate: 2e-05
num_train_epochs: 10
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 800
per_device_eval_batch_size: 800
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.2052	150	13.7695	17.4625
0.4104	300	14.2067	17.4452
0.6156	450	14.344	17.4289
0.8208	600	13.705	17.3620
1.0260	750	13.0304	17.1246
1.2312	900	13.28	16.7495
1.4364	1050	13.0314	16.5068
1.6416	1200	13.0861	16.3113
1.8468	1350	13.2752	16.1406
2.0520	1500	12.2868	16.0122
2.2572	1650	12.9551	15.9320
2.4624	1800	12.8339	15.8444
2.6676	1950	12.0719	15.8108
2.8728	2100	12.7803	15.7694
3.0780	2250	11.9023	15.7460
3.2832	2400	12.6882	15.7291
3.4884	2550	12.3062	15.7165
3.6936	2700	12.402	15.7071
3.8988	2850	12.0136	15.7014
4.1040	3000	12.821	15.6873
4.3092	3150	12.4667	15.6835
4.5144	3300	12.6469	15.6740
4.7196	3450	12.1751	15.6519
4.9248	3600	12.3627	15.6637

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.3.1+cu121
Accelerate: 1.5.2
Datasets: 3.4.1
Tokenizers: 0.21.1

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",
}

CachedInfonce

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}