metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - loss:MultipleNegativesRankingLoss
  - loss:ContrastiveLoss
base_model: sentence-transformers/stsb-distilbert-base
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
  - average_precision
  - f1
  - precision
  - recall
  - threshold
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
widget:
  - source_sentence: What is Mindset?
    sentences:
      - What is a mindset?
      - Can you eat only once a day?
      - Is law a good career choice?
  - source_sentence: Is a queef real?
    sentences:
      - Is "G" based on real events?
      - What is the entire court process?
      - How do I reduce my weight?
  - source_sentence: Is Cicret a scam?
    sentences:
      - Is the Cicret Bracelet a scam?
      - Was World War II Inevitable?
      - What are some of the best photos?
  - source_sentence: What is Planet X?
    sentences:
      - Do planet X exist?
      - What are the best C++ books?
      - How can I lose my weight fast?
  - source_sentence: How fast is fast?
    sentences:
      - How does light travel so fast?
      - How do I copyright my books?
      - What is a black hole made of?
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 32.724475965905576
  energy_consumed: 0.08418911136527617
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.399
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: quora duplicates
          type: quora-duplicates
        metrics:
          - type: cosine_accuracy
            value: 0.846
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7969297170639038
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7791495198902607
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7139598727226257
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.6977886977886978
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8819875776397516
            name: Cosine Recall
          - type: cosine_ap
            value: 0.8230449963294564
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.843
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 151.2908477783203
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.7660818713450294
            name: Dot F1
          - type: dot_f1_threshold
            value: 143.77838134765625
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.7237569060773481
            name: Dot Precision
          - type: dot_recall
            value: 0.8136645962732919
            name: Dot Recall
          - type: dot_ap
            value: 0.7946044629726107
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.838
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 194.99119567871094
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.7704081632653061
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 247.49777221679688
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.6536796536796536
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.937888198757764
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.8149715271935773
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.841
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 9.02225112915039
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.7703889585947302
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 11.385245323181152
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.6463157894736842
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.953416149068323
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.8152967320117391
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.846
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 194.99119567871094
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.7791495198902607
            name: Max F1
          - type: max_f1_threshold
            value: 247.49777221679688
            name: Max F1 Threshold
          - type: max_precision
            value: 0.7237569060773481
            name: Max Precision
          - type: max_recall
            value: 0.953416149068323
            name: Max Recall
          - type: max_ap
            value: 0.8230449963294564
            name: Max Ap
      - task:
          type: paraphrase-mining
          name: Paraphrase Mining
        dataset:
          name: quora duplicates dev
          type: quora-duplicates-dev
        metrics:
          - type: average_precision
            value: 0.5888649029434471
            name: Average Precision
          - type: f1
            value: 0.5761652140962487
            name: F1
          - type: precision
            value: 0.5477552123204396
            name: Precision
          - type: recall
            value: 0.6076834690513064
            name: Recall
          - type: threshold
            value: 0.7728720009326935
            name: Threshold
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.963
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9906
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9944
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9982
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.963
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4285333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.27568000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.14494
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8299562338609103
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9590366552956846
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9806221849555673
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9925738410935468
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9784033087450696
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9771579365079368
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9709189650394419
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9514
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9852
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.991
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9968
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9514
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.4247333333333334
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.27364
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14458000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.8194380520427287
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.9520212390452685
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9755502441186265
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9910547306614953
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9715023430522326
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9692583333333334
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.961739772177385
            name: Dot Map@100

SentenceTransformer based on sentence-transformers/stsb-distilbert-base

This is a sentence-transformers model finetuned from sentence-transformers/stsb-distilbert-base on the mnrl and cl datasets. 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: sentence-transformers/stsb-distilbert-base
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Datasets:
- mnrl
- cl
Language: en

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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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("tomaarsen/stsb-distilbert-base-mnrl-cl-multi")
# Run inference
sentences = [
    'How fast is fast?',
    'How does light travel so fast?',
    'How do I copyright my books?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Binary Classification

Dataset: quora-duplicates
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.846
cosine_accuracy_threshold	0.7969
cosine_f1	0.7791
cosine_f1_threshold	0.714
cosine_precision	0.6978
cosine_recall	0.882
cosine_ap	0.823
dot_accuracy	0.843
dot_accuracy_threshold	151.2908
dot_f1	0.7661
dot_f1_threshold	143.7784
dot_precision	0.7238
dot_recall	0.8137
dot_ap	0.7946
manhattan_accuracy	0.838
manhattan_accuracy_threshold	194.9912
manhattan_f1	0.7704
manhattan_f1_threshold	247.4978
manhattan_precision	0.6537
manhattan_recall	0.9379
manhattan_ap	0.815
euclidean_accuracy	0.841
euclidean_accuracy_threshold	9.0223
euclidean_f1	0.7704
euclidean_f1_threshold	11.3852
euclidean_precision	0.6463
euclidean_recall	0.9534
euclidean_ap	0.8153
max_accuracy	0.846
max_accuracy_threshold	194.9912
max_f1	0.7791
max_f1_threshold	247.4978
max_precision	0.7238
max_recall	0.9534
max_ap	0.823

Paraphrase Mining

Dataset: quora-duplicates-dev
Evaluated with ParaphraseMiningEvaluator

Metric	Value
average_precision	0.5889
f1	0.5762
precision	0.5478
recall	0.6077
threshold	0.7729

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.963
cosine_accuracy@3	0.9906
cosine_accuracy@5	0.9944
cosine_accuracy@10	0.9982
cosine_precision@1	0.963
cosine_precision@3	0.4285
cosine_precision@5	0.2757
cosine_precision@10	0.1449
cosine_recall@1	0.83
cosine_recall@3	0.959
cosine_recall@5	0.9806
cosine_recall@10	0.9926
cosine_ndcg@10	0.9784
cosine_mrr@10	0.9772
cosine_map@100	0.9709
dot_accuracy@1	0.9514
dot_accuracy@3	0.9852
dot_accuracy@5	0.991
dot_accuracy@10	0.9968
dot_precision@1	0.9514
dot_precision@3	0.4247
dot_precision@5	0.2736
dot_precision@10	0.1446
dot_recall@1	0.8194
dot_recall@3	0.952
dot_recall@5	0.9756
dot_recall@10	0.9911
dot_ndcg@10	0.9715
dot_mrr@10	0.9693
dot_map@100	0.9617

Training Details

Training Datasets

mnrl

Dataset: mnrl at 451a485
Size: 100,000 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 13.85 tokens max: 42 tokens	min: 6 tokens mean: 13.65 tokens max: 44 tokens	min: 4 tokens mean: 14.76 tokens max: 64 tokens

Samples:

anchor	positive	negative
`Why in India do we not have one on one political debate as in USA?`	`Why cant we have a public debate between politicians in India like the one in US?`	`Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?`
`What is OnePlus One?`	`How is oneplus one?`	`Why is OnePlus One so good?`
`Does our mind control our emotions?`	`How do smart and successful people control their emotions?`	`How can I control my positive emotions for the people whom I love but they don't care about me?`

Loss: MultipleNegativesRankingLoss with these parameters:

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

cl

Dataset: cl at 451a485
Size: 100,000 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 6 tokens
mean: 15.3 tokens
max: 57 tokens

min: 6 tokens
mean: 15.66 tokens
max: 56 tokens

0: ~62.00%
1: ~38.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 6 tokens mean: 15.3 tokens max: 57 tokens	min: 6 tokens mean: 15.66 tokens max: 56 tokens	0: ~62.00% 1: ~38.00%

Samples:

sentence1	sentence2	label
`What is the step by step guide to invest in share market in india?`	`What is the step by step guide to invest in share market?`	`0`
`What is the story of Kohinoor (Koh-i-Noor) Diamond?`	`What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?`	`0`
`How can I increase the speed of my internet connection while using a VPN?`	`How can Internet speed be increased by hacking through DNS?`	`0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Evaluation Datasets

mnrl

Dataset: mnrl at 451a485
Size: 1,000 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 13.84 tokens max: 43 tokens	min: 6 tokens mean: 13.8 tokens max: 38 tokens	min: 6 tokens mean: 14.71 tokens max: 56 tokens

Samples:

anchor	positive	negative
`Which programming language is best for developing low-end games?`	`What coding language should I learn first for making games?`	`I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?`
`Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?`	`Should Meryl Streep be using her position to attack the president?`	`Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?`
`Where can I found excellent commercial fridges in Sydney?`	`Where can I found impressive range of commercial fridges in Sydney?`	`What is the best grocery delivery service in Sydney?`

Loss: MultipleNegativesRankingLoss with these parameters:

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

cl

Dataset: cl at 451a485
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 5 tokens
mean: 15.59 tokens
max: 59 tokens

min: 6 tokens
mean: 15.65 tokens
max: 76 tokens

0: ~63.40%
1: ~36.60%

	sentence1	sentence2	label
type	string	string	int
details	min: 5 tokens mean: 15.59 tokens max: 59 tokens	min: 6 tokens mean: 15.65 tokens max: 76 tokens	0: ~63.40% 1: ~36.60%

Samples:

sentence1	sentence2	label
`What should I ask my friend to get from UK to India?`	`What is the process of getting a surgical residency in UK after completing MBBS from India?`	`0`
`How can I learn hacking for free?`	`How can I learn to hack seriously?`	`1`
`Which is the best website to learn programming language C++?`	`Which is the best website to learn C++ Programming language for free?`	`0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: False
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1.0
num_train_epochs: 1
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
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: None
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: 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_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	cl loss	mnrl loss	cosine_map@100	quora-duplicates-dev_average_precision	quora-duplicates_max_ap
0	0	-	-	-	0.9245	0.4200	0.6890
0.0320	100	0.1634	-	-	-	-	-
0.0640	200	0.1206	-	-	-	-	-
0.0800	250	-	0.0190	0.1469	0.9530	0.5068	0.7354
0.0960	300	0.1036	-	-	-	-	-
0.1280	400	0.0836	-	-	-	-	-
0.1599	500	0.0918	0.0180	0.1008	0.9553	0.5259	0.7643
0.1919	600	0.0784	-	-	-	-	-
0.2239	700	0.0656	-	-	-	-	-
0.2399	750	-	0.0177	0.0905	0.9593	0.5305	0.7686
0.2559	800	0.0593	-	-	-	-	-
0.2879	900	0.0534	-	-	-	-	-
0.3199	1000	0.0612	0.0161	0.0736	0.9642	0.5512	0.7881
0.3519	1100	0.0572	-	-	-	-	-
0.3839	1200	0.06	-	-	-	-	-
0.3999	1250	-	0.0158	0.0641	0.9649	0.5567	0.7983
0.4159	1300	0.0565	-	-	-	-	-
0.4479	1400	0.0565	-	-	-	-	-
0.4798	1500	0.0475	0.0154	0.0578	0.9645	0.5614	0.8062
0.5118	1600	0.0596	-	-	-	-	-
0.5438	1700	0.0509	-	-	-	-	-
0.5598	1750	-	0.0150	0.0525	0.9674	0.5762	0.8092
0.5758	1800	0.0403	-	-	-	-	-
0.6078	1900	0.0431	-	-	-	-	-
0.6398	2000	0.0481	0.0150	0.0531	0.9689	0.5824	0.8128
0.6718	2100	0.05	-	-	-	-	-
0.7038	2200	0.0468	-	-	-	-	-
0.7198	2250	-	0.0146	0.0486	0.9684	0.5756	0.8195
0.7358	2300	0.0436	-	-	-	-	-
0.7678	2400	0.0409	-	-	-	-	-
0.7997	2500	0.0391	0.0145	0.0454	0.9705	0.5822	0.8190
0.8317	2600	0.0412	-	-	-	-	-
0.8637	2700	0.0373	-	-	-	-	-
0.8797	2750	-	0.0143	0.0451	0.9705	0.5889	0.8229
0.8957	2800	0.0428	-	-	-	-	-
0.9277	2900	0.0419	-	-	-	-	-
0.9597	3000	0.0376	0.0143	0.0435	0.9709	0.5889	0.8230
0.9917	3100	0.0366	-	-	-	-	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.084 kWh
Carbon Emitted: 0.033 kg of CO2
Hours Used: 0.399 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 3.0.0.dev0
Transformers: 4.41.0.dev0
PyTorch: 2.3.0+cu121
Accelerate: 0.26.1
Datasets: 2.18.0
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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}