metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - loss:MultipleNegativesRankingLoss
base_model: distilbert/distilroberta-base
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: She was buried in Breda .
    sentences:
      - Anna was buried in Breda .
      - Jackson Township is a township found in Will County , Illinois .
      - >-
        Saint-Genis-Pouilly is a commune in the Ain department in eastern France
        .
  - source_sentence: Have you never been mellow? No, I'm just a grumpy sumbitch
    sentences:
      - >-
        How many of you retards have ever had wooopi.? Not me... I'm saving
        myself...
      - >-
        Has anyone heard of the marketing company Vector? If so what is the
        company about and is it a good place to work?
      - >-
        I want to make hearts on the computer too?!? How do i do it!!!!i tried
        doing alt 3 but i couldn't see my heart!!!Is that normal!!
  - source_sentence: Are there UFOs?
    sentences:
      - Who has seen aliens or UFOs?
      - How do people become famous?
      - How do I learn math?
  - source_sentence: The dog runs.
    sentences:
      - A dog running.
      - A man eats a sandwich.
      - The people are sitting.
  - source_sentence: guy on a bike
    sentences:
      - Man riding a bike
      - A man cooks on a grill.
      - The woman is indoors.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 78.69029495412121
  energy_consumed: 0.2024437614268031
  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: 0.601
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: SentenceTransformer based on distilbert/distilroberta-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.8415424335219892
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.845236449663091
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8502275215819475
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.851659983857617
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8534543309306831
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8555429338051269
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6505488321872611
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6489555708500816
            name: Spearman Dot
          - type: pearson_max
            value: 0.8534543309306831
            name: Pearson Max
          - type: spearman_max
            value: 0.8555429338051269
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8105817065758533
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8144723448926713
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8225264118038157
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8131121443026537
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.825469313508584
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8164637881262432
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5910799174044387
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5760606722387962
            name: Spearman Dot
          - type: pearson_max
            value: 0.825469313508584
            name: Pearson Max
          - type: spearman_max
            value: 0.8164637881262432
            name: Spearman Max

SentenceTransformer based on distilbert/distilroberta-base

This is a sentence-transformers model finetuned from distilbert/distilroberta-base on the all-nli, sentence-compression, simple-wiki, altlex, quora-duplicates, coco-captions, flickr30k-captions, yahoo-answers and stack-exchange 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: distilbert/distilroberta-base
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Datasets:
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: RobertaModel 
  (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/distilroberta-base-paraphrases-multi")
# Run inference
sentences = [
    'guy on a bike',
    'Man riding a bike',
    'A man cooks on a grill.',
]
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

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8415
spearman_cosine	0.8452
pearson_manhattan	0.8502
spearman_manhattan	0.8517
pearson_euclidean	0.8535
spearman_euclidean	0.8555
pearson_dot	0.6505
spearman_dot	0.649
pearson_max	0.8535
spearman_max	0.8555

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8106
spearman_cosine	0.8145
pearson_manhattan	0.8225
spearman_manhattan	0.8131
pearson_euclidean	0.8255
spearman_euclidean	0.8165
pearson_dot	0.5911
spearman_dot	0.5761
pearson_max	0.8255
spearman_max	0.8165

Training Details

Training Datasets

all-nli

Dataset: all-nli at cc6c526
Size: 557,850 training 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: 10.38 tokens max: 45 tokens	min: 6 tokens mean: 12.8 tokens max: 39 tokens	min: 6 tokens mean: 13.4 tokens max: 50 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

sentence-compression

Dataset: sentence-compression at 605bc91
Size: 180,000 training samples
Columns: text and simplified
Approximate statistics based on the first 1000 samples:
text simplified
type string string
details
min: 10 tokens
mean: 33.13 tokens
max: 126 tokens

min: 5 tokens
mean: 11.13 tokens
max: 29 tokens

	text	simplified
type	string	string
details	min: 10 tokens mean: 33.13 tokens max: 126 tokens	min: 5 tokens mean: 11.13 tokens max: 29 tokens

Samples:

text	simplified
`The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.`	`USHL completes expansion draft`
`Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.`	`Bud Selig to speak at St. Norbert College`
`It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.`	`It's cherry time`

Loss: MultipleNegativesRankingLoss with these parameters:

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

simple-wiki

Dataset: simple-wiki at 60fd9b4
Size: 102,225 training samples
Columns: text and simplified
Approximate statistics based on the first 1000 samples:
text simplified
type string string
details
min: 9 tokens
mean: 35.19 tokens
max: 128 tokens

min: 8 tokens
mean: 29.1 tokens
max: 128 tokens

	text	simplified
type	string	string
details	min: 9 tokens mean: 35.19 tokens max: 128 tokens	min: 8 tokens mean: 29.1 tokens max: 128 tokens

Samples:

text	simplified
`The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular the North ( i.e. , Northern Ireland ) .`	`The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular Northern Ireland ( .`
`His reputation rose further when opposition leaders under parliamentary privilege alleged that Taoiseach Charles Haughey , who in January 1982 had been Leader of the Opposition , had not merely rung the President 's Office but threatened to end the career of the army officer who took the call and who , on Hillery 's explicit instructions , had refused to put through the call to the President .`	`President Hillery refused to speak to any opposition party politicians , but when Charles Haughey , who was Leader of the Opposition , had rang the President 's Office he threatened to end the career of the army officer answered and refused on Hillery 's explicit orders to put the call through to the President .`
`He considered returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .`	`He thought about returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .`

Loss: MultipleNegativesRankingLoss with these parameters:

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

altlex

Dataset: altlex at 97eb209
Size: 112,696 training samples
Columns: text and simplified
Approximate statistics based on the first 1000 samples:
text simplified
type string string
details
min: 9 tokens
mean: 31.8 tokens
max: 121 tokens

min: 6 tokens
mean: 26.49 tokens
max: 114 tokens

	text	simplified
type	string	string
details	min: 9 tokens mean: 31.8 tokens max: 121 tokens	min: 6 tokens mean: 26.49 tokens max: 114 tokens

Samples:

text	simplified
`A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria , who was a frequent visitor .`	`A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria who was a frequent visitor .`
`In 1929 , the building became vacant , and was given to Prince Edward , Prince of Wales , by his father , King George V . This became the Prince 's chief residence and was used extensively by him for entertaining and as a country retreat .`	`In 1929 , the building became vacant , and was given to Prince Edward , the Prince of Wales by his father , King George V . This became the Prince 's chief residence , and was used extensively by the Prince for entertaining and as a country retreat .`
`Additions included an octagon room in the north-east side , in which the King regularly had dinner .`	`Additions included an octagon room in the North-East side , where the King regularly had dinner .`

Loss: MultipleNegativesRankingLoss with these parameters:

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

quora-duplicates

Dataset: quora-duplicates at 451a485
Size: 101,762 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: 13.72 tokens max: 42 tokens	min: 6 tokens mean: 13.5 tokens max: 44 tokens	min: 4 tokens mean: 14.56 tokens max: 62 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"
}

coco-captions

Dataset: coco-captions at bd26018
Size: 414,010 training samples
Columns: caption1 and caption2
Approximate statistics based on the first 1000 samples:
caption1 caption2
type string string
details
min: 10 tokens
mean: 13.65 tokens
max: 25 tokens

min: 10 tokens
mean: 13.65 tokens
max: 25 tokens

	caption1	caption2
type	string	string
details	min: 10 tokens mean: 13.65 tokens max: 25 tokens	min: 10 tokens mean: 13.65 tokens max: 25 tokens

Samples:

caption1	caption2
`A clock that blends in with the wall hangs in a bathroom.`	`A very clean and well decorated empty bathroom`
`A very clean and well decorated empty bathroom`	`A bathroom with a border of butterflies and blue paint on the walls above it.`
`A bathroom with a border of butterflies and blue paint on the walls above it.`	`An angled view of a beautifully decorated bathroom.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

flickr30k-captions

Dataset: flickr30k-captions at 0ef0ce3
Size: 158,881 training samples
Columns: caption1 and caption2
Approximate statistics based on the first 1000 samples:
caption1 caption2
type string string
details
min: 6 tokens
mean: 16.22 tokens
max: 60 tokens

min: 6 tokens
mean: 16.22 tokens
max: 60 tokens

	caption1	caption2
type	string	string
details	min: 6 tokens mean: 16.22 tokens max: 60 tokens	min: 6 tokens mean: 16.22 tokens max: 60 tokens

Samples:

caption1	caption2
`Two men in green shirts are standing in a yard.`	`Two young, White males are outside near many bushes.`
`Two young, White males are outside near many bushes.`	`Two young guys with shaggy hair look at their hands while hanging out in the yard.`
`Two young guys with shaggy hair look at their hands while hanging out in the yard.`	`A man in a blue shirt standing in a garden.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

yahoo-answers

Dataset: yahoo-answers at 93b3605
Size: 599,417 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 12 tokens
mean: 52.48 tokens
max: 128 tokens

min: 13 tokens
mean: 83.5 tokens
max: 128 tokens

	question	answer
type	string	string
details	min: 12 tokens mean: 52.48 tokens max: 128 tokens	min: 13 tokens mean: 83.5 tokens max: 128 tokens

Samples:

question	answer
`why doesn't an optical mouse work on a glass table? or even on some surfaces?`	why doesn't an optical mouse work on a glass table? Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse. The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse. \nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion. Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.
`What is the best off-road motorcycle trail ? long-distance trail throughout CA`	`What is the best off-road motorcycle trail ? i hear that the mojave road is amazing! \nsearch for it online.`
`What is Trans Fat? How to reduce that? I heard that tras fat is bad for the body. Why is that? Where can we find it in our daily food?`	What is Trans Fat? How to reduce that? Trans fats occur in manufactured foods during the process of partial hydrogenation, when hydrogen gas is bubbled through vegetable oil to increase shelf life and stabilize the original polyunsatured oil. The resulting fat is similar to saturated fat, which raises "bad" LDL cholesterol and can lead to clogged arteries and heart disease. \nUntil very recently, food labels were not required to list trans fats, and this health risk remained hidden to consumers. In early July, FDA regulations changed, and food labels will soon begin identifying trans fat content in processed foods.

Loss: MultipleNegativesRankingLoss with these parameters:

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

stack-exchange

Dataset: stack-exchange at 1c9657a
Size: 304,525 training samples
Columns: title1 and title2
Approximate statistics based on the first 1000 samples:
title1 title2
type string string
details
min: 5 tokens
mean: 15.04 tokens
max: 63 tokens

min: 5 tokens
mean: 15.91 tokens
max: 80 tokens

	title1	title2
type	string	string
details	min: 5 tokens mean: 15.04 tokens max: 63 tokens	min: 5 tokens mean: 15.91 tokens max: 80 tokens

Samples:

title1	title2
`what is the advantage of using the GPU rendering options in Android?`	`Can anyone explain all these Developer Options?`
`Blank video when converting uncompressed AVI files with ffmpeg`	`FFmpeg lossy compression problems`
`URL Rewriting of a query string in php`	`How to create friendly URL in php?`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: False
per_device_train_batch_size: 128
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
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: round_robin

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0.0140	100	3.739	-	-
0.0279	200	1.1317	-	-
0.0419	300	0.9645	-	-
0.0558	400	0.9053	-	-
0.0698	500	0.8889	-	-
0.0838	600	0.8741	-	-
0.0977	700	0.8329	-	-
0.1117	800	0.8331	-	-
0.1256	900	0.8241	-	-
0.1396	1000	0.7829	0.8460	-
0.1535	1100	0.7871	-	-
0.1675	1200	0.7521	-	-
0.1815	1300	0.7905	-	-
0.1954	1400	0.7531	-	-
0.2094	1500	0.7677	-	-
0.2233	1600	0.7745	-	-
0.2373	1700	0.7651	-	-
0.2513	1800	0.7712	-	-
0.2652	1900	0.7476	-	-
0.2792	2000	0.7814	0.8370	-
0.2931	2100	0.7536	-	-
0.3071	2200	0.7689	-	-
0.3210	2300	0.7656	-	-
0.3350	2400	0.7672	-	-
0.3490	2500	0.6921	-	-
0.3629	2600	0.6778	-	-
0.3769	2700	0.6844	-	-
0.3908	2800	0.6907	-	-
0.4048	2900	0.6881	-	-
0.4188	3000	0.6815	0.8372	-
0.4327	3100	0.6869	-	-
0.4467	3200	0.698	-	-
0.4606	3300	0.6868	-	-
0.4746	3400	0.7174	-	-
0.4886	3500	0.6714	-	-
0.5025	3600	0.6698	-	-
0.5165	3700	0.6838	-	-
0.5304	3800	0.6927	-	-
0.5444	3900	0.6628	-	-
0.5583	4000	0.6647	0.8367	-
0.5723	4100	0.6766	-	-
0.5863	4200	0.6987	-	-
0.6002	4300	0.6895	-	-
0.6142	4400	0.6571	-	-
0.6281	4500	0.66	-	-
0.6421	4600	0.6747	-	-
0.6561	4700	0.6495	-	-
0.6700	4800	0.6746	-	-
0.6840	4900	0.6575	-	-
0.6979	5000	0.6712	0.8454	-
0.7119	5100	0.6627	-	-
0.7259	5200	0.6538	-	-
0.7398	5300	0.6659	-	-
0.7538	5400	0.6551	-	-
0.7677	5500	0.6548	-	-
0.7817	5600	0.673	-	-
0.7956	5700	0.6805	-	-
0.8096	5800	0.6537	-	-
0.8236	5900	0.6826	-	-
0.8375	6000	0.7182	0.8370	-
0.8515	6100	0.7391	-	-
0.8654	6200	0.7006	-	-
0.8794	6300	0.6774	-	-
0.8934	6400	0.7076	-	-
0.9073	6500	0.6893	-	-
0.9213	6600	0.678	-	-
0.9352	6700	0.6703	-	-
0.9492	6800	0.675	-	-
0.9631	6900	0.6842	-	-
0.9771	7000	0.6909	0.8452	-
0.9911	7100	0.681	-	-
1.0	7164	-	-	0.8145

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.202 kWh
Carbon Emitted: 0.079 kg of CO2
Hours Used: 0.601 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}
}