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: >-
"Today I lost those who for 24 years I called...my family," said Enes
Kanter of the Oklahoma City Thunder.
Turkish President Recep Tayyip Erdogan blames Mr Gulen for inciting a
failed coup last month and is seeking the cleric's extradition to Turkey.
Mr Gulen, who has a large following, denies being involved in the coup.
Kanter's father, Mehmet, disowned his son in a letter published on Monday
by Sabah, a pro-government newspaper.
Mehmet Kanter wrote his son had been "hypnotised" by the Gulen movement.
"With a feeling of shame I apologise to our president and the Turkish
people for having such a son," the letter said.
Q&A on the Gulen movement
Mr Gulen is regarded by followers as a spiritual leader and sometimes
described as Turkey's second most powerful man.
Enes Kanter has been a vocal supporter of Mr Gulen on Twitter.
The movement - known in Turkey as Hizmet, or service - runs schools all
over Turkey and around the world, including in Turkic former Soviet
republics, Muslim countries such as Pakistan and Western nations including
Romania and the US, where it runs more than 100 schools.
In May 2016, the Turkish government formally declared the Gulen movement a
terrorist organisation.
After the failed coup, suspected Gulen supporters in Turkey were purged in
a wave of arrests.
Western nations have been critical of the government's response to the
coup. US officials have said they will extradite Mr Gulen only if Turkey
provides evidence.
sentences:
- "The Thinker | Rodin Museum H. 189 cm ; W. 98 cm ; D. 140 cm S.2838 When conceived in 1880 in its original size (approx. 70 cm) as the crowning element of\_The Gates of Hell\_, seated on the tympanum ,\_The Thinker\_was entitled\_The Poet. He represented Dante, author of the\_Divine Comedy\_which had inspired\_The Gates, leaning forward to observe the circles of Hell, while meditating on his work.\_The Thinker\_was therefore initially both a being with a tortured body, almost a damned soul, and a free-thinking man, determined to transcend his suffering through poetry. The pose of this figure owes much to Carpeaux’s\_Ugolino\_(1861) and to the seated portrait of Lorenzo de’ Medici carved by Michelangelo (1526-31). \_ While remaining in place on the monumental\_Gates of Hell,\_The Thinker\_was exhibited individually in 1888 and thus became an independent work. Enlarged in 1904, its colossal version proved even more popular: this image of a man lost in thought, but whose powerful body suggests a great capacity for action, has became one of the most celebrated sculptures ever known. Numerous casts exist worldwide, including the one now in the gardens of the Musée Rodin, a gift to the City of Paris installed outside the Panthéon in 1906, and another in the gardens of Rodin’s house in Meudon, on the tomb of the sculptor and his wife. George Bernard Shaw in the Pose of \"The Thinker\" Rodin, the Monument to Victor Hugo and The Thinker Rodin's \"Thinker\" in Dr Linde's Garden in Lübeck"
- >-
An American basketball player has cut ties with his Turkish family over
his support for Pennsylvania-based preacher Fethullah Gulen.
- Police are investigating a death at a bus stop in Fife.
- source_sentence: Two adorable birds perched on a piece of bamboo.
sentences:
- Two birds are sitting perched on a tree limb
- A young boy with a spoon looking at a birthday cupcake.
- >-
As part of his attempt to turn the Austrian right , Dessaix ordered a
battalion to move along the Aire stream near Tairier and Crache .
- source_sentence: how do venom snake keepers make money?
sentences:
- "The USDA regulates who can buy and sell snake venom. It is very important to learn about these regulations so that you can operate properly. On average, snake milkers make around $2,500 per month, but snake venom is an expensive market. One gram of certain types of snake venom can sell for $2,000.If you are crazy enough to capture, milk, and breed snakes, please take the precaution to wear protective clothing and always have antivenom close at hand.nake milkers have an insane job. They â\x80\x9Cmilkâ\x80\x9D snakes for their venom. This means that every single day, a snake milker handles deadly, venomous snakes. Itâ\x80\x99s a hands on job where you put your fingers millimeters away from the sharp, fangs of asps, vipers, cobras, corals, mambas, kraits, and rattlesnakes."
- a greenhouse is used to protect plants by keeping them warm
- >-
Nashville Mayor Megan Barry has said her 22-year-old son died of what
appeared to be a drug overdose, according to a family statement.
- source_sentence: Adult bees include workers, a queen and what other type?
sentences:
- |-
matter vibrating can cause sound. Thus, sound is a wave in air .
matter vibrating can cause a wave in air
- >-
His references in electronic music are Todd Terry , Armand Van Helden ,
Roger Sanchez , Tiesto and the Epic Sax Guy.
- >-
Look at the honeybees in Figure below . Honeybees live in colonies that
may consist of thousands of individual bees. Generally, there are three
types of adult bees in a colony: workers, a queen, and drones.
- source_sentence: can an object have constant non zero velocity and changing acceleration?
sentences:
- when an animal sheds its fur , its fur becomes less dense
- >-
Acceleration is defined as the time derivative of the velocity; if the
velocity is unchanging the acceleration is zero. Velocity is a vector,
speed is a scalar magnitude of the vector. If the velocity vector
changes direction you can have constant speed (not velocity) with a
non-zero acceleration.
- >-
Acne treatment is individual and customized to the type of acne you
have. On average, mild acne responds in 1-2 months, moderate acne
responds in 2-4 months and severe acne can take 4-6 months to clear,
granted that the most effective measures can be used.
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: 0.874702030760496
name: Pearson Cosine
- type: spearman_cosine
value: 0.9021036690960521
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9071871020121037
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9048875555646884
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9065531106539271
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9043656663543417
name: Spearman Euclidean
- type: pearson_dot
value: 0.8555439931828537
name: Pearson Dot
- type: spearman_dot
value: 0.8598106392441436
name: Spearman Dot
- type: pearson_max
value: 0.9071871020121037
name: Pearson Max
- type: spearman_max
value: 0.9048875555646884
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-checkpoints-tmp")
# Run inference
sentences = [
'can an object have constant non zero velocity and changing acceleration?',
'Acceleration is defined as the time derivative of the velocity; if the velocity is unchanging the acceleration is zero. Velocity is a vector, speed is a scalar magnitude of the vector. If the velocity vector changes direction you can have constant speed (not velocity) with a non-zero acceleration.',
'Acne treatment is individual and customized to the type of acne you have. On average, mild acne responds in 1-2 months, moderate acne responds in 2-4 months and severe acne can take 4-6 months to clear, granted that the most effective measures can be used.',
]
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 | 0.8747 |
spearman_cosine | 0.9021 |
pearson_manhattan | 0.9072 |
spearman_manhattan | 0.9049 |
pearson_euclidean | 0.9066 |
spearman_euclidean | 0.9044 |
pearson_dot | 0.8555 |
spearman_dot | 0.8598 |
pearson_max | 0.9072 |
spearman_max | 0.9049 |
Training Details
Training Dataset
bobox/enhanced_nli-50_k
- Dataset: bobox/enhanced_nli-50_k
- Size: 120,849 training samples
- Columns:
sentence1
andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 type string string details - min: 4 tokens
- mean: 32.01 tokens
- max: 336 tokens
- min: 2 tokens
- mean: 60.45 tokens
- max: 512 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.
can you renew your licence online sa?
You can renew your licence online for as long as your photo is valid. Renew your driver's licence online with a mySA GOV account. With a mySA GOV account, you can access a legally compliant digital licence through the mySA GOV app.
how can coconut oil lower cholesterol
It has been shown that lauric acid increases the good HDL cholesterol in the blood to help improve cholesterol ratio levels. Coconut oil lowers cholesterol by promoting its conversion to pregnenolone, a molecule that is a precursor to many of the hormones our bodies need. Coconut can help restore normal thyroid function. When the thyroid does not function optimally, it can contribute to higher levels of bad cholesterol.
- 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
andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 type string string details - min: 4 tokens
- mean: 32.91 tokens
- max: 342 tokens
- min: 2 tokens
- mean: 60.3 tokens
- max: 408 tokens
- Samples:
sentence1 sentence2 The body was found in the River Avon in Bath, Avon and Somerset Police said.
Officers said although formal identification had not yet taken place, Henry Burke's family had been told.
Earlier officers said they were looking for Mr Burke, who was last seen leaving a nightclub in George Street late on Thursday.
A force spokesman said the death was being treated as unexplained and inquiries were continuing.
Mr Burke's girlfriend, Em Comley, earlier said he had been texting her "throughout the night" but then the messages suddenly stopped just after midnight.A man's body has been found in a river after search and rescue teams were called in to try and find a missing 19-year-old student.
what happens when the president of united states is impeached?
Parliament votes on the proposal by secret ballot, and if two thirds of all representatives agree, the president is impeached. Once impeached, the president's powers are suspended, and the Constitutional Court decides whether or not the President should be removed from office.
What can feed at more than one trophic level?
Many consumers feed at more than one trophic level.. Nuts are also consumed by deer, turkey, foxes, wood ducks and squirrels.
wood ducks can feed at more than one trophic level - 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
: stepsper_device_train_batch_size
: 960per_device_eval_batch_size
: 128learning_rate
: 3.5e-05weight_decay
: 0.0001num_train_epochs
: 2lr_scheduler_type
: cosine_with_min_lrlr_scheduler_kwargs
: {'num_cycles': 0.5, 'min_lr': 5.833333333333333e-06}warmup_ratio
: 0.25save_safetensors
: Falsefp16
: Truepush_to_hub
: Truehub_model_id
: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-checkpoints-tmphub_strategy
: all_checkpointsbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 960per_device_eval_batch_size
: 128per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 3.5e-05weight_decay
: 0.0001adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: cosine_with_min_lrlr_scheduler_kwargs
: {'num_cycles': 0.5, 'min_lr': 5.833333333333333e-06}warmup_ratio
: 0.25warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Falsesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Trueresume_from_checkpoint
: Nonehub_model_id
: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-checkpoints-tmphub_strategy
: all_checkpointshub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | loss | sts-test_spearman_cosine |
---|---|---|---|---|
0.0079 | 1 | 0.404 | - | - |
0.0159 | 2 | 0.3185 | - | - |
0.0238 | 3 | 0.2821 | - | - |
0.0317 | 4 | 0.4036 | - | - |
0.0397 | 5 | 0.3442 | 0.1253 | 0.9078 |
0.0476 | 6 | 0.4145 | - | - |
0.0556 | 7 | 0.4224 | - | - |
0.0635 | 8 | 0.4048 | - | - |
0.0714 | 9 | 0.3899 | - | - |
0.0794 | 10 | 0.4127 | 0.1237 | 0.9079 |
0.0873 | 11 | 0.3496 | - | - |
0.0952 | 12 | 0.3731 | - | - |
0.1032 | 13 | 0.3929 | - | - |
0.1111 | 14 | 0.2957 | - | - |
0.1190 | 15 | 0.3324 | 0.1206 | 0.9083 |
0.1270 | 16 | 0.3341 | - | - |
0.1349 | 17 | 0.3466 | - | - |
0.1429 | 18 | 0.3558 | - | - |
0.1508 | 19 | 0.2634 | - | - |
0.1587 | 20 | 0.3095 | 0.1156 | 0.9088 |
0.1667 | 21 | 0.2973 | - | - |
0.1746 | 22 | 0.2884 | - | - |
0.1825 | 23 | 0.3697 | - | - |
0.1905 | 24 | 0.2683 | - | - |
0.1984 | 25 | 0.3026 | 0.1096 | 0.9088 |
0.2063 | 26 | 0.2441 | - | - |
0.2143 | 27 | 0.3145 | - | - |
0.2222 | 28 | 0.3119 | - | - |
0.2302 | 29 | 0.2766 | - | - |
0.2381 | 30 | 0.3343 | 0.1054 | 0.9084 |
0.2460 | 31 | 0.344 | - | - |
0.2540 | 32 | 0.3005 | - | - |
0.2619 | 33 | 0.2526 | - | - |
0.2698 | 34 | 0.2422 | - | - |
0.2778 | 35 | 0.3447 | 0.1022 | 0.9072 |
0.2857 | 36 | 0.2809 | - | - |
0.2937 | 37 | 0.2836 | - | - |
0.3016 | 38 | 0.2878 | - | - |
0.3095 | 39 | 0.2738 | - | - |
0.3175 | 40 | 0.2806 | 0.1003 | 0.9065 |
0.3254 | 41 | 0.2797 | - | - |
0.3333 | 42 | 0.3217 | - | - |
0.3413 | 43 | 0.2544 | - | - |
0.3492 | 44 | 0.3203 | - | - |
0.3571 | 45 | 0.2987 | 0.0990 | 0.9064 |
0.3651 | 46 | 0.2765 | - | - |
0.3730 | 47 | 0.2716 | - | - |
0.3810 | 48 | 0.3726 | - | - |
0.3889 | 49 | 0.2963 | - | - |
0.3968 | 50 | 0.2784 | 0.0952 | 0.9072 |
0.4048 | 51 | 0.2437 | - | - |
0.4127 | 52 | 0.2258 | - | - |
0.4206 | 53 | 0.2821 | - | - |
0.4286 | 54 | 0.249 | - | - |
0.4365 | 55 | 0.2813 | 0.0928 | 0.9080 |
0.4444 | 56 | 0.3003 | - | - |
0.4524 | 57 | 0.2812 | - | - |
0.4603 | 58 | 0.2619 | - | - |
0.4683 | 59 | 0.299 | - | - |
0.4762 | 60 | 0.2706 | 0.0927 | 0.9088 |
0.4841 | 61 | 0.297 | - | - |
0.4921 | 62 | 0.2906 | - | - |
0.5 | 63 | 0.2914 | - | - |
0.5079 | 64 | 0.2669 | - | - |
0.5159 | 65 | 0.2723 | 0.0946 | 0.9093 |
0.5238 | 66 | 0.3194 | - | - |
0.5317 | 67 | 0.3585 | - | - |
0.5397 | 68 | 0.2843 | - | - |
0.5476 | 69 | 0.1916 | - | - |
0.5556 | 70 | 0.351 | 0.0971 | 0.9104 |
0.5635 | 71 | 0.3105 | - | - |
0.5714 | 72 | 0.2847 | - | - |
0.5794 | 73 | 0.2641 | - | - |
0.5873 | 74 | 0.3305 | - | - |
0.5952 | 75 | 0.2461 | 0.0965 | 0.9096 |
0.6032 | 76 | 0.259 | - | - |
0.6111 | 77 | 0.2506 | - | - |
0.6190 | 78 | 0.2832 | - | - |
0.6270 | 79 | 0.3322 | - | - |
0.6349 | 80 | 0.2533 | 0.1001 | 0.9089 |
0.6429 | 81 | 0.2349 | - | - |
0.6508 | 82 | 0.2748 | - | - |
0.6587 | 83 | 0.223 | - | - |
0.6667 | 84 | 0.2416 | - | - |
0.6746 | 85 | 0.2637 | 0.1034 | 0.9082 |
0.6825 | 86 | 0.2856 | - | - |
0.6905 | 87 | 0.2476 | - | - |
0.6984 | 88 | 0.2427 | - | - |
0.7063 | 89 | 0.2614 | - | - |
0.7143 | 90 | 0.26 | 0.1032 | 0.9088 |
0.7222 | 91 | 0.1862 | - | - |
0.7302 | 92 | 0.267 | - | - |
0.7381 | 93 | 0.2175 | - | - |
0.7460 | 94 | 0.2079 | - | - |
0.7540 | 95 | 0.2562 | 0.0999 | 0.9086 |
0.7619 | 96 | 0.2516 | - | - |
0.7698 | 97 | 0.2956 | - | - |
0.7778 | 98 | 0.2733 | - | - |
0.7857 | 99 | 0.2919 | - | - |
0.7937 | 100 | 0.2997 | 0.1032 | 0.9069 |
0.8016 | 101 | 0.2276 | - | - |
0.8095 | 102 | 0.2582 | - | - |
0.8175 | 103 | 0.2559 | - | - |
0.8254 | 104 | 0.2864 | - | - |
0.8333 | 105 | 0.2839 | 0.1074 | 0.9076 |
0.8413 | 106 | 0.2549 | - | - |
0.8492 | 107 | 0.2826 | - | - |
0.8571 | 108 | 0.2334 | - | - |
0.8651 | 109 | 0.2632 | - | - |
0.8730 | 110 | 0.2255 | 0.1090 | 0.9056 |
0.8810 | 111 | 0.2589 | - | - |
0.8889 | 112 | 0.2569 | - | - |
0.8968 | 113 | 0.2797 | - | - |
0.9048 | 114 | 0.2742 | - | - |
0.9127 | 115 | 0.2295 | 0.1070 | 0.9014 |
0.9206 | 116 | 0.2047 | - | - |
0.9286 | 117 | 0.2577 | - | - |
0.9365 | 118 | 0.2614 | - | - |
0.9444 | 119 | 0.2722 | - | - |
0.9524 | 120 | 0.1927 | 0.1024 | 0.9008 |
0.9603 | 121 | 0.2649 | - | - |
0.9683 | 122 | 0.2386 | - | - |
0.9762 | 123 | 0.2801 | - | - |
0.9841 | 124 | 0.2583 | - | - |
0.9921 | 125 | 0.3076 | 0.0949 | 0.9016 |
1.0 | 126 | 0.5477 | - | - |
1.0079 | 127 | 0.0031 | - | - |
1.0159 | 128 | 0.0 | - | - |
1.0238 | 129 | 0.0 | - | - |
1.0317 | 130 | 0.0 | 0.0955 | 0.9021 |
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",
}