metadata

datasets: []
language: []
library_name: sentence-transformers
metrics:
  - 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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:14593
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Macro ingredients needed to cook Poha: Orange Carrot, French Bean, Fresh
      Green Pea, Medium Poha, Red Onion, Curry Leaf, Green Chili Pepper
    sentences:
      - Can you list recipes that contain canned chickpea and canned black bean?
      - What are the leading macro ingredients in Pigeon Pea Curry (Toor Dal)?
      - What macro ingredients form the base of Poha?
  - source_sentence: >-
      I do have some good recommendations for you! Here are few good
      alternatives to kashmiri pulao:

      Kashmiri Dum Aloo, Shivani's Kashmiri Dum Aloo, Chicken Pulao, Chicken
      Rezala, Chicken Kheema Masala, Hyderabadi Chicken Masala, Masala Khichdi,
      Lentils and Rice (Dal Chawal), Homestyle Vegetable Pulao
    sentences:
      - What recipes are comparable to kashmiri pulao in flavor profile?
      - >-
        Can you give me step-by-step instructions to cook Hariyali Chicken
        Curry?
      - >-
        What are some recipes that utilize baking soda and olive oil
        effectively?
  - source_sentence: 'Garnishing tip for Yellow Rice: Sprinkle with chopped cilantro.'
    sentences:
      - How can I make Yellow Rice look appealing with garnishes?
      - Describe General Tso's Tofu for me.
      - What are the best garnishing tips for Paneer Tikka Masala?
  - source_sentence: >-
      Recipes that can be made using green chili pepper and grated coconut:
      Kerala Mix Vegetables (Aviyal), Carrot Poriyal, Cauliflower Poriyal,
      Beetroot Poriyal, Maithilee's Fish Curry, Mix Vegetable Poriyal, Ivy Gourd
      Curry (Tindora Masala), Spiced Indian Moth Beans (Matki Usal), Fish Curry,
      Andhra Garlic Chicken
    sentences:
      - What are the culinary uses of ground pork and chayote?
      - What are the dishes prepared using green cardamom and clove?
      - >-
        Can you suggest recipes that include green chili pepper and grated
        coconut?
  - source_sentence: >-
      Recipes that can be made using red onion and paprika: Breakfast Potatoes
      with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac &
      Cheese, Tomato Chicken Curry
    sentences:
      - >-
        Are there dishes that closely resemble spiced potatoes & fenugreek (aloo
        methi)?
      - >-
        What recipes incorporate black pepper and habanero chili in their
        ingredients?
      - What are some ways to use red onion and paprika in recipes?
model-index:
  - name: SentenceTransformer
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.9704069050554871
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9926017262638718
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.998766954377312
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9993834771886559
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9704069050554871
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33086724208795726
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1997533908754624
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09993834771886559
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9704069050554871
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9926017262638718
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.998766954377312
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9993834771886559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9865445143406266
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9822089131583582
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9822089131583582
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.9728729963008631
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9932182490752158
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.998766954377312
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9993834771886559
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9728729963008631
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3310727496917386
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1997533908754624
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09993834771886559
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9728729963008631
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9932182490752158
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.998766954377312
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9993834771886559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9875922381599775
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9836107685984382
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9836107685984381
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.9722564734895192
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9944512946979038
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9993834771886559
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9993834771886559
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9722564734895192
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33148376489930126
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19987669543773118
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09993834771886559
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9722564734895192
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9944512946979038
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9993834771886559
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9993834771886559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9873346466071089
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9832511302918208
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9832511302918209
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.9704069050554871
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9944512946979038
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9993834771886559
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9993834771886559
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9704069050554871
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33148376489930126
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19987669543773118
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09993834771886559
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9704069050554871
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9944512946979038
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9993834771886559
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9993834771886559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9867057287670639
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9823982737361283
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9823982737361281
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 32
          type: dim_32
        metrics:
          - type: cosine_accuracy@1
            value: 0.971023427866831
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9950678175092479
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9993834771886559
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9993834771886559
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.971023427866831
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3316892725030826
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19987669543773118
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09993834771886559
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.971023427866831
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9950678175092479
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9993834771886559
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9993834771886559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9872988931953259
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9831689272503082
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9831689272503081
            name: Cosine Map@100

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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()
)

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("Adi-0-0-Gupta/Embedding-v1")
# Run inference
sentences = [
    'Recipes that can be made using red onion and paprika: Breakfast Potatoes with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac & Cheese, Tomato Chicken Curry',
    'What are some ways to use red onion and paprika in recipes?',
    'Are there dishes that closely resemble spiced potatoes & fenugreek (aloo methi)?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Information Retrieval

Dataset: dim_384
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9704
cosine_accuracy@3	0.9926
cosine_accuracy@5	0.9988
cosine_accuracy@10	0.9994
cosine_precision@1	0.9704
cosine_precision@3	0.3309
cosine_precision@5	0.1998
cosine_precision@10	0.0999
cosine_recall@1	0.9704
cosine_recall@3	0.9926
cosine_recall@5	0.9988
cosine_recall@10	0.9994
cosine_ndcg@10	0.9865
cosine_mrr@10	0.9822
cosine_map@100	0.9822

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9729
cosine_accuracy@3	0.9932
cosine_accuracy@5	0.9988
cosine_accuracy@10	0.9994
cosine_precision@1	0.9729
cosine_precision@3	0.3311
cosine_precision@5	0.1998
cosine_precision@10	0.0999
cosine_recall@1	0.9729
cosine_recall@3	0.9932
cosine_recall@5	0.9988
cosine_recall@10	0.9994
cosine_ndcg@10	0.9876
cosine_mrr@10	0.9836
cosine_map@100	0.9836

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9723
cosine_accuracy@3	0.9945
cosine_accuracy@5	0.9994
cosine_accuracy@10	0.9994
cosine_precision@1	0.9723
cosine_precision@3	0.3315
cosine_precision@5	0.1999
cosine_precision@10	0.0999
cosine_recall@1	0.9723
cosine_recall@3	0.9945
cosine_recall@5	0.9994
cosine_recall@10	0.9994
cosine_ndcg@10	0.9873
cosine_mrr@10	0.9833
cosine_map@100	0.9833

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9704
cosine_accuracy@3	0.9945
cosine_accuracy@5	0.9994
cosine_accuracy@10	0.9994
cosine_precision@1	0.9704
cosine_precision@3	0.3315
cosine_precision@5	0.1999
cosine_precision@10	0.0999
cosine_recall@1	0.9704
cosine_recall@3	0.9945
cosine_recall@5	0.9994
cosine_recall@10	0.9994
cosine_ndcg@10	0.9867
cosine_mrr@10	0.9824
cosine_map@100	0.9824

Information Retrieval

Dataset: dim_32
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.971
cosine_accuracy@3	0.9951
cosine_accuracy@5	0.9994
cosine_accuracy@10	0.9994
cosine_precision@1	0.971
cosine_precision@3	0.3317
cosine_precision@5	0.1999
cosine_precision@10	0.0999
cosine_recall@1	0.971
cosine_recall@3	0.9951
cosine_recall@5	0.9994
cosine_recall@10	0.9994
cosine_ndcg@10	0.9873
cosine_mrr@10	0.9832
cosine_map@100	0.9832

Training Details

Training Dataset

Unnamed Dataset

Size: 14,593 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 11 tokens
mean: 53.46 tokens
max: 512 tokens

min: 7 tokens
mean: 15.83 tokens
max: 32 tokens

	positive	anchor
type	string	string
details	min: 11 tokens mean: 53.46 tokens max: 512 tokens	min: 7 tokens mean: 15.83 tokens max: 32 tokens

Samples:

positive	anchor
`Calories information of Hyderabadi Chicken Masala, based on different serving sizes: Serving 1 - 345 calories, Serving 2 - 580 calories, Serving 3 - 1220 calories, Serving 4 - 1450 calories`	`What’s the calorie content of Hyderabadi Chicken Masala?`
`Recipes that can be made using dried herb mix and onion powder: Chorizo Queso Soup, Cheesy Chicken & Broccoli`	`What are some food items made using dried herb mix and onion powder?`
`Recipes that can be made using roasted semolina/bombay rava and saffron: Rashmi's Kesari Bath, Pineapple Kesari Bath`	`What recipes have roasted semolina/bombay rava and saffron in them?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64,
        32
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 16
learning_rate: 1e-05
num_train_epochs: 20
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 1e-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: 20
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
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: True
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_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_32_cosine_map@100	dim_384_cosine_map@100	dim_64_cosine_map@100
0.3501	10	0.0066	-	-	-	-	-
0.7002	20	0.0056	-	-	-	-	-
0.9803	28	-	0.9746	0.9771	0.9776	0.9758	0.9763
1.0503	30	0.0057	-	-	-	-	-
1.4004	40	0.0048	-	-	-	-	-
1.7505	50	0.0039	-	-	-	-	-
1.9956	57	-	0.9783	0.9787	0.9815	0.9788	0.9793
2.1007	60	0.0046	-	-	-	-	-
2.4508	70	0.0035	-	-	-	-	-
2.8009	80	0.0028	-	-	-	-	-
2.9759	85	-	0.9818	0.9811	0.9836	0.9803	0.9823
3.1510	90	0.0036	-	-	-	-	-
3.5011	100	0.0033	-	-	-	-	-
3.8512	110	0.0026	-	-	-	-	-
3.9912	114	-	0.9814	0.9818	0.9844	0.9814	0.9821
4.2013	120	0.0025	-	-	-	-	-
4.5514	130	0.003	-	-	-	-	-
4.9015	140	0.0027	-	-	-	-	-
4.9716	142	-	0.9825	0.9819	0.9844	0.9823	0.9825
5.2516	150	0.0024	-	-	-	-	-
5.6018	160	0.0023	-	-	-	-	-
5.9519	170	0.0024	-	-	-	-	-
5.9869	171	-	0.9831	0.9826	0.9846	0.9818	0.9831
6.3020	180	0.0025	-	-	-	-	-
6.6521	190	0.0025	-	-	-	-	-
6.9672	199	-	0.9830	0.9825	0.9844	0.9823	0.9831
7.0022	200	0.0019	-	-	-	-	-
7.3523	210	0.0022	-	-	-	-	-
7.7024	220	0.0026	-	-	-	-	-
7.9825	228	-	0.9828	0.9825	0.9836	0.9821	0.9821
8.0525	230	0.0022	-	-	-	-	-
8.4026	240	0.0021	-	-	-	-	-
8.7527	250	0.0021	-	-	-	-	-
8.9978	257	-	0.9827	0.9826	0.9848	0.9827	0.9827
9.1028	260	0.0025	-	-	-	-	-
9.4530	270	0.0022	-	-	-	-	-
9.8031	280	0.0019	-	-	-	-	-
9.9781	285	-	0.9832	0.9833	0.9858	0.9825	0.9834
10.1532	290	0.0021	-	-	-	-	-
10.5033	300	0.0019	-	-	-	-	-
10.8534	310	0.0024	-	-	-	-	-
10.9934	314	-	0.9830	0.9827	0.9850	0.9825	0.9829
11.2035	320	0.0017	-	-	-	-	-
11.5536	330	0.0017	-	-	-	-	-
11.9037	340	0.0018	-	-	-	-	-
11.9737	342	-	0.9827	0.9835	0.9841	0.9826	0.9827
12.2538	350	0.0018	-	-	-	-	-
12.6039	360	0.0018	-	-	-	-	-
12.9540	370	0.0023	-	-	-	-	-
12.9891	371	-	0.9828	0.9834	0.9832	0.9826	0.9823
13.3042	380	0.0017	-	-	-	-	-
13.6543	390	0.0018	-	-	-	-	-
13.9694	399	-	0.9830	0.9831	0.9838	0.9820	0.9826
14.0044	400	0.0016	-	-	-	-	-
14.3545	410	0.0018	-	-	-	-	-
14.7046	420	0.0018	-	-	-	-	-
14.9847	428	-	0.9827	0.9825	0.9832	0.9816	0.9826
15.0547	430	0.0018	-	-	-	-	-
15.4048	440	0.0015	-	-	-	-	-
15.7549	450	0.0017	-	-	-	-	-
16.0	457	-	0.9833	0.9836	0.9832	0.9822	0.9824

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.31.0
Datasets: 2.19.1
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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}