metadata

base_model: bobox/DeBERTa-small-ST-v1-test-step3
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:120849
  - loss:CachedGISTEmbedLoss
widget:
  - source_sentence: >-
      Brian Cummins, who was in his early 60s, was refereeing an Under-16s final
      in Elburton, Devon, on Sunday when he fell to the ground.

      He was taken to Derriford Hospital in Plymouth but died shortly after, the
      Devon Junior Minor League said.

      A spokesman said: "Our thoughts and condolences at this time are with his
      family. The league has lost a very loyal referee."

      The game between Woolwell Juniors and Tavistock was abandoned after Mr
      Cummins collapsed.

      His daughter Sarah said: "He was a loving father, father in law, granddad,
      husband and friend to all who knew him. He loved his football and
      refereeing.

      "He will be greatly missed by all and will forever be in our hearts."

      Brian Rimes, general secretary of the league, said former Devonport
      dockyard worker Mr Cummins had been a referee for the league for about 20
      years.

      "He was a very good referee, a man well respected by youngsters and the
      referee fraternity," he said.

      Mark Davies wrote on the league's Facebook page: "Very sad news indeed and
      our thoughts go out to Brian's family and friends. It's a shame that it
      takes such awful circumstances to unite the local footballing community
      but in Brian we know we have lost a true gent. RIP Brian."

      Mark Evans wrote: "RIP Brian. Grassroots football has lost an amazing guy
      and great referee. Deepest sympathies from all Devon FA referees."

      Michael Davies tweeted: "#RIP Brian Cummins, such sad news! Top Ref, top
      neighbour but most of all a top bloke! Will be sadly missed."
    sentences:
      - >-
        Ptosis (Sagging Eyelids): Check Your Symptoms and Signs Watery Eye A
        drooping or sagging of the eyelid is medically known as ptosis or
        blepharoptosis . Drooping eyelids may occur on both sides (bilateral) or
        on one side only (unilateral), in which case it is more easily noticed.
        Congenital ptosis is eyelid drooping that is present at birth; when it
        develops later, it is referred to as acquired ptosis. Depending upon the
        severity of the condition, drooping eyelids may be barely noticeable or
        quite prominent. Some sagging of the skin and connective tissues occurs
        during the normal aging process, potentially leading to drooping of the
        eyelids. Other causes include conditions that affect the muscles and
        nerves of the eyelid as well as conditions that affect the skin and
        connective tissues of the eyelid. Rarely, tumors of the brain or eye
        area are the cause of drooping eyelids. Medically Reviewed by a Doctor
        on 3/6/2012 Health concern on your mind? Visit the Symptom Checker.
        REFERENCE: Fauci, Anthony S., et al. Harrison's Principles of Internal
        Medicine. 17th ed. United States: McGraw-Hill Professional, 2008. Causes
        of Ptosis Allergy (Allergies) An allergy refers to a misguided reaction
        by our immune system in response to bodily contact with certain foreign
        substances. ... learn more » Botulism Botulism is an illness caused by a
        neurotoxin produced by the bacterium Clostridium botulinum. There are
        three types of botulism:... learn more » In This Article
      - A football referee has died after collapsing during a boys' cup final.
      - The car is at the intersection while the sun is setting.
  - source_sentence: sparge water temperature
    sentences:
      - >-
        This is easy to translate to gallons and degrees F. (for example,
        suggested sparge water temperature is 167Â° F., which is 75Â° C.). It
        also features a stay warm feature - after the target water temperature
        is hit, it will keep it at the desired temperature as long as it is on.
      - >-
        Arsenal playmaker Mesut Ozil says he will put talks over his future at
        the club on hold until the summer.
      - a greenhouse is used to protect plants by keeping them warm
  - source_sentence: What does sunlight create for plants?
    sentences:
      - >-
        a plant requires sunlight for photosynthesis. Photosynthesis occurs
        using the suns energy to create the plants own energy. 
         sunlight creates energy for plants
      - >-
        His references in electronic music are Todd Terry , Armand Van Helden ,
        Roger Sanchez , Tiesto and the Epic Sax Guy.
      - >-
        if a neutral atom loses an electron then an atom with a negative charge
        will be formed. Ions are neutral atoms. 
         ions can have a negative charge if they lose an electron
  - source_sentence: Metals, metalloids, and nonmetals are the different classes of what?
    sentences:
      - when an animal sheds its fur , its fur becomes less dense
      - >-
        Though there's no limit to how much you can keep in a savings account,
        you should know the rules surrounding large deposits to savings
        accounts. When it comes to making deposits to a bank account, $10,000 is
        the magic number.
      - >-
        The classes of elements are metals, metalloids, and nonmetals. They are
        color-coded in the table. Blue stands for metals, orange for metalloids,
        and green for nonmetals. You can read about each of these three classes
        of elements later in the chapter, in the lesson "Classes of Elements. ".
  - source_sentence: >-
      More than 336,000 COVID-19 cases have been reported in over 190 countries
      .
    sentences:
      - >-
        Birds are four-limbed, endothermic vertebrates with wings and feathers.
        They produce amniotic eggs and are the most numerous class of
        vertebrates.
      - >-
        As of 23 March , more than 337,000 cases of COVID-19 have been reported
        in over 190 countries and territories , resulting in more than 14,600
        deaths and 97,000 recoveries .
      - >-
        Apocalypticism Apocalypticism is the religious belief that there will be
        an apocalypse, a term which originally referred to a revelation of God's
        will, but now usually refers to the belief that the end of the world is
        imminent, even within one's own lifetime. This belief is usually
        accompanied by the idea that civilization will soon come to a tumultuous
        end due to some sort of catastrophic global event.
model-index:
  - name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8845727889510453
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9102842891809226
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.912934056355235
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.9097905485139913
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9131377744832304
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9097165746789112
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8665715810539829
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8672268182819262
            name: Spearman Dot
          - type: pearson_max
            value: 0.9131377744832304
            name: Pearson Max
          - type: spearman_max
            value: 0.9102842891809226
            name: Spearman Max

SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3

This is a sentence-transformers model finetuned from bobox/DeBERTa-small-ST-v1-test-step3 on the bobox/enhanced_nli-50_k dataset. It maps sentences & paragraphs to a 768-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: bobox/DeBERTa-small-ST-v1-test-step3
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- bobox/enhanced_nli-50_k

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}) with Transformer model: DebertaV2Model 
  (1): Pooling({'word_embedding_dimension': 768, '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("bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-bis-checkpoints-tmp")
# Run inference
sentences = [
    'More than 336,000 COVID-19 cases have been reported in over 190 countries .',
    'As of 23 March , more than 337,000 cases of COVID-19 have been reported in over 190 countries and territories , resulting in more than 14,600 deaths and 97,000 recoveries .',
    "Apocalypticism Apocalypticism is the religious belief that there will be an apocalypse, a term which originally referred to a revelation of God's will, but now usually refers to the belief that the end of the world is imminent, even within one's own lifetime. This belief is usually accompanied by the idea that civilization will soon come to a tumultuous end due to some sort of catastrophic global event.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8846
spearman_cosine	0.9103
pearson_manhattan	0.9129
spearman_manhattan	0.9098
pearson_euclidean	0.9131
spearman_euclidean	0.9097
pearson_dot	0.8666
spearman_dot	0.8672
pearson_max	0.9131
spearman_max	0.9103

Training Details

Training Dataset

bobox/enhanced_nli-50_k

Dataset: bobox/enhanced_nli-50_k
Size: 120,849 training samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 4 tokens
mean: 33.98 tokens
max: 358 tokens

min: 2 tokens
mean: 63.13 tokens
max: 414 tokens

	sentence1	sentence2
type	string	string
details	min: 4 tokens mean: 33.98 tokens max: 358 tokens	min: 2 tokens mean: 63.13 tokens max: 414 tokens

Samples:

sentence1	sentence2
`A lady working in a kitchen with several different types of dishes.`	`A woman is cooking and cleaning in her kitchen.`
`is it possible to get pregnant after delivery?`	`How soon can you get pregnant after giving birth? It's possible to get pregnant before you even have your first postpartum period, which can occur as early as four weeks after giving birth or as late as 24 weeks after baby arrives (or later), depending on whether you're breastfeeding exclusively or not.`
`how long does corn take to grill in foil`	`Place each corn on top of one piece the heavy-duty foil. Brush each ear generously with soft butter. Season lightly with seasoned salt or white salt and black pepper. Wrap the corn then seal the foil loosely but leave room for expansion, then cut a very small hole in the foil to allow steam to escape. Grill over medium coals for about 15-20 minutes (the larger ears may take a little longer).`

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
), 'temperature': 0.025}

Evaluation Dataset

bobox/enhanced_nli-50_k

Dataset: bobox/enhanced_nli-50_k
Size: 3,052 evaluation samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 4 tokens
mean: 33.63 tokens
max: 328 tokens

min: 2 tokens
mean: 60.36 tokens
max: 501 tokens

	sentence1	sentence2
type	string	string
details	min: 4 tokens mean: 33.63 tokens max: 328 tokens	min: 2 tokens mean: 60.36 tokens max: 501 tokens

Samples:

sentence1	sentence2
The 17-year-old asked not to be named but said he lost control of his silver hatchback when he swerved to avoid a cat in Parkway, Chellaston, Derbyshire. He estimated he was travelling at about 30mph when he smashed into the garage of a residential home on Friday night. The owners were away at the time and the crash was reported to police. The driver told BBC News: "I swerved to the right and the back of the car went out. "I then swerved to the left and lost control - I couldn't bring it back. "I just missed two parked cars and a tree and ended up in the wall - it was a big impact. "I smelt burning, I thought it was the car, so I got out and laid down - I was in shock." Neighbours reported hearing a "loud screeching" followed by a "massive bang". The driver said: "It was a natural reaction to swerve to miss the cat, but I went into a state of shock and panic." Derbyshire Police said the driver was due to appear before magistrates at a later date charged with driving without due care or attention.	`A new driver who ploughed into a house, having swerved around two parked cars and a tree to avoid hitting a cat in the road, faces court.`
`what requirements are needed to be a psychologist?`	`To become a clinician, you must apply for registration with the College of Psychologists of Ontario, a process which requires 4 years of work experience and one year of supervised practice. In some provinces, you can be registered to practice as a psychologist with either a masters or doctoral degree.`
`What does sunlight create for plants?`	`a plant requires sunlight for photosynthesis. Photosynthesis occurs using the suns energy to create the plants own energy. sunlight creates energy for plants`

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
), 'temperature': 0.025}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 640
per_device_eval_batch_size: 128
learning_rate: 3.5e-05
weight_decay: 0.0001
num_train_epochs: 2
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 5.833333333333333e-06}
warmup_ratio: 0.25
save_safetensors: False
fp16: True
push_to_hub: True
hub_model_id: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-bis-checkpoints-tmp
hub_strategy: all_checkpoints
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: 640
per_device_eval_batch_size: 128
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: 3.5e-05
weight_decay: 0.0001
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 5.833333333333333e-06}
warmup_ratio: 0.25
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: False
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: True
resume_from_checkpoint: None
hub_model_id: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-bis-checkpoints-tmp
hub_strategy: all_checkpoints
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss	sts-test_spearman_cosine
0.0053	1	0.3768	-	-
0.0106	2	0.3162	-	-
0.0159	3	0.275	-	-
0.0212	4	0.293	-	-
0.0265	5	0.2437	0.2190	0.9079
0.0317	6	0.3681	-	-
0.0370	7	0.2314	-	-
0.0423	8	0.2481	-	-
0.0476	9	0.2403	-	-
0.0529	10	0.2966	0.2125	0.9079
0.0582	11	0.2867	-	-
0.0635	12	0.3413	-	-
0.0688	13	0.4119	-	-
0.0741	14	0.3118	-	-
0.0794	15	0.327	0.2031	0.9082
0.0847	16	0.3389	-	-
0.0899	17	0.2018	-	-
0.0952	18	0.2861	-	-
0.1005	19	0.2848	-	-
0.1058	20	0.2563	0.1943	0.9082
0.1111	21	0.3058	-	-
0.1164	22	0.285	-	-
0.1217	23	0.3151	-	-
0.1270	24	0.2716	-	-
0.1323	25	0.2422	0.1794	0.9082
0.1376	26	0.2858	-	-
0.1429	27	0.3211	-	-
0.1481	28	0.2158	-	-
0.1534	29	0.2811	-	-
0.1587	30	0.2063	0.1636	0.9077
0.1640	31	0.2492	-	-
0.1693	32	0.3096	-	-
0.1746	33	0.2914	-	-
0.1799	34	0.2888	-	-
0.1852	35	0.223	0.1532	0.9072
0.1905	36	0.2595	-	-
0.1958	37	0.3122	-	-
0.2011	38	0.2327	-	-
0.2063	39	0.1718	-	-
0.2116	40	0.3162	0.1443	0.9067
0.2169	41	0.296	-	-
0.2222	42	0.2821	-	-
0.2275	43	0.2069	-	-
0.2328	44	0.2573	-	-
0.2381	45	0.3119	0.1343	0.9064
0.2434	46	0.2743	-	-
0.2487	47	0.2666	-	-
0.2540	48	0.2414	-	-
0.2593	49	0.2793	-	-
0.2646	50	0.2212	0.1251	0.9068
0.2698	51	0.2071	-	-
0.2751	52	0.296	-	-
0.2804	53	0.2061	-	-
0.2857	54	0.2164	-	-
0.2910	55	0.188	0.1197	0.9072
0.2963	56	0.2411	-	-
0.3016	57	0.2031	-	-
0.3069	58	0.2438	-	-
0.3122	59	0.2417	-	-
0.3175	60	0.1515	0.1233	0.9066
0.3228	61	0.21	-	-
0.3280	62	0.21	-	-
0.3333	63	0.2157	-	-
0.3386	64	0.2138	-	-
0.3439	65	0.2403	0.1273	0.9058
0.3492	66	0.2808	-	-
0.3545	67	0.1891	-	-
0.3598	68	0.1991	-	-
0.3651	69	0.2121	-	-
0.3704	70	0.2039	0.1311	0.9066
0.3757	71	0.1986	-	-
0.3810	72	0.2925	-	-
0.3862	73	0.2527	-	-
0.3915	74	0.279	-	-
0.3968	75	0.2419	0.1315	0.9066
0.4021	76	0.2228	-	-
0.4074	77	0.2242	-	-
0.4127	78	0.2737	-	-
0.4180	79	0.2328	-	-
0.4233	80	0.2802	0.1262	0.9058
0.4286	81	0.2044	-	-
0.4339	82	0.1828	-	-
0.4392	83	0.2372	-	-
0.4444	84	0.2241	-	-
0.4497	85	0.2782	0.1207	0.9063
0.4550	86	0.3244	-	-
0.4603	87	0.2102	-	-
0.4656	88	0.2265	-	-
0.4709	89	0.2666	-	-
0.4762	90	0.23	0.1186	0.9078
0.4815	91	0.2358	-	-
0.4868	92	0.2896	-	-
0.4921	93	0.2126	-	-
0.4974	94	0.2669	-	-
0.5026	95	0.2375	0.1128	0.9087
0.5079	96	0.1903	-	-
0.5132	97	0.2507	-	-
0.5185	98	0.1897	-	-
0.5238	99	0.2775	-	-
0.5291	100	0.2098	0.1168	0.9064
0.5344	101	0.1628	-	-
0.5397	102	0.2158	-	-
0.5450	103	0.1552	-	-
0.5503	104	0.2364	-	-
0.5556	105	0.272	0.1178	0.9056
0.5608	106	0.2271	-	-
0.5661	107	0.2132	-	-
0.5714	108	0.1782	-	-
0.5767	109	0.1598	-	-
0.5820	110	0.2472	0.1269	0.9050
0.5873	111	0.2041	-	-
0.5926	112	0.2426	-	-
0.5979	113	0.2105	-	-
0.6032	114	0.1923	-	-
0.6085	115	0.2271	0.1233	0.9061
0.6138	116	0.3029	-	-
0.6190	117	0.2554	-	-
0.6243	118	0.2182	-	-
0.6296	119	0.2852	-	-
0.6349	120	0.2285	0.1280	0.9053
0.6402	121	0.218	-	-
0.6455	122	0.1841	-	-
0.6508	123	0.2629	-	-
0.6561	124	0.1749	-	-
0.6614	125	0.2417	0.1415	0.9057
0.6667	126	0.2305	-	-
0.6720	127	0.2841	-	-
0.6772	128	0.1785	-	-
0.6825	129	0.2153	-	-
0.6878	130	0.2548	0.1413	0.9079
0.6931	131	0.2059	-	-
0.6984	132	0.2073	-	-
0.7037	133	0.191	-	-
0.7090	134	0.1633	-	-
0.7143	135	0.2627	0.1333	0.9077
0.7196	136	0.2451	-	-
0.7249	137	0.1441	-	-
0.7302	138	0.2138	-	-
0.7354	139	0.2564	-	-
0.7407	140	0.1524	0.1323	0.9049
0.7460	141	0.1786	-	-
0.7513	142	0.2104	-	-
0.7566	143	0.2512	-	-
0.7619	144	0.1889	-	-
0.7672	145	0.2127	0.1291	0.9015
0.7725	146	0.2115	-	-
0.7778	147	0.179	-	-
0.7831	148	0.2188	-	-
0.7884	149	0.1687	-	-
0.7937	150	0.2265	0.1145	0.9014
0.7989	151	0.182	-	-
0.8042	152	0.1789	-	-
0.8095	153	0.225	-	-
0.8148	154	0.3015	-	-
0.8201	155	0.1656	0.1009	0.9045
0.8254	156	0.2321	-	-
0.8307	157	0.2514	-	-
0.8360	158	0.2348	-	-
0.8413	159	0.2003	-	-
0.8466	160	0.2487	0.0983	0.9057
0.8519	161	0.1674	-	-
0.8571	162	0.1968	-	-
0.8624	163	0.2237	-	-
0.8677	164	0.2636	-	-
0.8730	165	0.1858	0.1015	0.9043
0.8783	166	0.198	-	-
0.8836	167	0.2011	-	-
0.8889	168	0.2887	-	-
0.8942	169	0.2014	-	-
0.8995	170	0.2122	0.1002	0.9025
0.9048	171	0.2537	-	-
0.9101	172	0.2104	-	-
0.9153	173	0.1291	-	-
0.9206	174	0.1794	-	-
0.9259	175	0.2569	0.0948	0.9027
0.9312	176	0.26	-	-
0.9365	177	0.1665	-	-
0.9418	178	0.2012	-	-
0.9471	179	0.2171	-	-
0.9524	180	0.1554	0.0874	0.9055
0.9577	181	0.2177	-	-
0.9630	182	0.1919	-	-
0.9683	183	0.1686	-	-
0.9735	184	0.2277	-	-
0.9788	185	0.2169	0.0847	0.9089
0.9841	186	0.1991	-	-
0.9894	187	0.2373	-	-
0.9947	188	0.1636	-	-
1.0	189	0.0	-	-
1.0053	190	0.0	0.0846	0.9103

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.44.0
PyTorch: 2.4.0
Accelerate: 0.33.0
Datasets: 2.21.0
Tokenizers: 0.19.1

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",
}