metadata

base_model: Alibaba-NLP/gte-base-en-v1.5
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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:32833
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Anonymity in online interactions can lead to a disinhibition effect, where
      individuals feel free to express hostile or aggressive opinions they might
      otherwise suppress.
    sentences:
      - What are the implications of anonymity in online interactions?
      - >-
        How does creativity function as a form of costly signalling in personal
        expressions such as invitations?
      - Why is conflict considered essential in a creative organization?
  - source_sentence: >-
      The author decides to release their novel into the world despite its
      imperfections, and finds that this allows them to move on to new projects
      and experiences, and to focus on the value of the work itself rather than
      its flaws.
    sentences:
      - >-
        How does the author's experience with their novel illustrate the concept
        of 'embracing imperfection' in creative work?
      - >-
        What does the author mean by 'ambitious programmers are better off doing
        their own thing'?
      - What is the role of 'show me' in the design process?
  - source_sentence: >-
      Tokens become more valuable as more users adopt them, creating a positive
      feedback loop that enhances their utility and encourages further adoption
      across various applications.
    sentences:
      - In what ways do tokens exhibit network effects?
      - >-
        What can sometimes be found when considering a startup with a
        lame-sounding idea?
      - >-
        How do social norms influence decision-making in the context of airport
        choices?
  - source_sentence: >-
      Philosophers are often viewed as the guardians of critical thinking;
      however, their reliance on bureaucratic structures and abstract
      discussions can become problematic. Instead of fostering open-mindedness,
      they may perpetuate dogmatic thinking and limit the exploration of diverse
      perspectives, thereby failing to fulfill their duty of promoting genuine
      critical engagement.
    sentences:
      - >-
        In what ways can the role of philosophers be seen as essential or
        problematic within the context of critical thinking?
      - >-
        How does the evolution of pair-bonding facilitate cultural exchange
        between groups?
      - What is the role of autonomy in the success of acquired startups?
  - source_sentence: >-
      Society tends to admire those who despair when others hope, viewing them
      as sages or wise figures.
    sentences:
      - >-
        What is often the societal perception of those who express pessimism
        about the future?
      - >-
        How did the realization about user engagement influence the app
        development strategy?
      - >-
        What lessons can be learned from the historical context of employee
        relations in large corporations?
model-index:
  - name: Alchemy Embedding - Anudit Nagar
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.782012613106663
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8889498217713189
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9248697559638058
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9520153550863724
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.782012613106663
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29631660725710623
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1849739511927612
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09520153550863725
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.782012613106663
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8889498217713189
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9248697559638058
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9520153550863724
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.867555587052628
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8402608580220322
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8422322227138224
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.780367425281053
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8848368522072937
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9221277762544557
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9514669591445023
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.780367425281053
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2949456174024312
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1844255552508912
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09514669591445023
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.780367425281053
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8848368522072937
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9221277762544557
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9514669591445023
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8661558392165704
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.838656038231032
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8405372438205077
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7754318618042226
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8804496846723334
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9169180148066904
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9468055936386071
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7754318618042226
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2934832282241111
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18338360296133807
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09468055936386072
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7754318618042226
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8804496846723334
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9169180148066904
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9468055936386071
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8613819477350178
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8338379881703168
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8360735900013385
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7617219632574719
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.871675349602413
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9117082533589251
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9418700301617768
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7617219632574719
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2905584498674709
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18234165067178504
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09418700301617768
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7617219632574719
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.871675349602413
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9117082533589251
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9418700301617768
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.851649908463093
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8225671458602635
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8248455884524328
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.7408829174664108
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.853852481491637
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8936111872772141
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9292569234987661
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7408829174664108
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28461749383054563
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17872223745544283
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0929256923498766
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7408829174664108
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.853852481491637
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8936111872772141
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9292569234987661
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8338956659320366
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8033378162525404
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8057702637208689
            name: Cosine Map@100

Alchemy Embedding - Anudit Nagar

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5 on the json 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: Alibaba-NLP/gte-base-en-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (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})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Society tends to admire those who despair when others hope, viewing them as sages or wise figures.',
    'What is often the societal perception of those who express pessimism about the future?',
    'How did the realization about user engagement influence the app development strategy?',
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.782
cosine_accuracy@3	0.8889
cosine_accuracy@5	0.9249
cosine_accuracy@10	0.952
cosine_precision@1	0.782
cosine_precision@3	0.2963
cosine_precision@5	0.185
cosine_precision@10	0.0952
cosine_recall@1	0.782
cosine_recall@3	0.8889
cosine_recall@5	0.9249
cosine_recall@10	0.952
cosine_ndcg@10	0.8676
cosine_mrr@10	0.8403
cosine_map@100	0.8422

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7804
cosine_accuracy@3	0.8848
cosine_accuracy@5	0.9221
cosine_accuracy@10	0.9515
cosine_precision@1	0.7804
cosine_precision@3	0.2949
cosine_precision@5	0.1844
cosine_precision@10	0.0951
cosine_recall@1	0.7804
cosine_recall@3	0.8848
cosine_recall@5	0.9221
cosine_recall@10	0.9515
cosine_ndcg@10	0.8662
cosine_mrr@10	0.8387
cosine_map@100	0.8405

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7754
cosine_accuracy@3	0.8804
cosine_accuracy@5	0.9169
cosine_accuracy@10	0.9468
cosine_precision@1	0.7754
cosine_precision@3	0.2935
cosine_precision@5	0.1834
cosine_precision@10	0.0947
cosine_recall@1	0.7754
cosine_recall@3	0.8804
cosine_recall@5	0.9169
cosine_recall@10	0.9468
cosine_ndcg@10	0.8614
cosine_mrr@10	0.8338
cosine_map@100	0.8361

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7617
cosine_accuracy@3	0.8717
cosine_accuracy@5	0.9117
cosine_accuracy@10	0.9419
cosine_precision@1	0.7617
cosine_precision@3	0.2906
cosine_precision@5	0.1823
cosine_precision@10	0.0942
cosine_recall@1	0.7617
cosine_recall@3	0.8717
cosine_recall@5	0.9117
cosine_recall@10	0.9419
cosine_ndcg@10	0.8516
cosine_mrr@10	0.8226
cosine_map@100	0.8248

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7409
cosine_accuracy@3	0.8539
cosine_accuracy@5	0.8936
cosine_accuracy@10	0.9293
cosine_precision@1	0.7409
cosine_precision@3	0.2846
cosine_precision@5	0.1787
cosine_precision@10	0.0929
cosine_recall@1	0.7409
cosine_recall@3	0.8539
cosine_recall@5	0.8936
cosine_recall@10	0.9293
cosine_ndcg@10	0.8339
cosine_mrr@10	0.8033
cosine_map@100	0.8058

Training Details

Training Dataset

json

Dataset: json
Size: 32,833 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 34.54 tokens
max: 102 tokens

min: 9 tokens
mean: 16.78 tokens
max: 77 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 34.54 tokens max: 102 tokens	min: 9 tokens mean: 16.78 tokens max: 77 tokens

Samples:

positive	anchor
`The author saw taking risks as a necessary part of the creative process, and was willing to take risks in order to explore new ideas and themes.`	`What was the author's perspective on the importance of taking risks in creative work?`
`Recognizing that older users are less likely to invite new users led to a strategic focus on younger demographics, prompting a shift in development efforts toward creating products that resonate with teens.`	`How did the realization about user engagement influence the app development strategy?`
`The phrase emphasizes the fragility of Earth and our collective responsibility to protect it and ensure sustainable resource management for future generations.`	`What is the significance of the phrase 'pale blue dot' in relation to environmental responsibility?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "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: 24
per_device_eval_batch_size: 24
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
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: 24
per_device_eval_batch_size: 24
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 4
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: 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: 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
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
eval_on_start: False
eval_use_gather_object: 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_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.0584	10	0.8567	-	-	-	-	-
0.1169	20	0.6549	-	-	-	-	-
0.1753	30	0.5407	-	-	-	-	-
0.2337	40	0.4586	-	-	-	-	-
0.2922	50	0.3914	-	-	-	-	-
0.3506	60	0.4104	-	-	-	-	-
0.4091	70	0.299	-	-	-	-	-
0.4675	80	0.2444	-	-	-	-	-
0.5259	90	0.2367	-	-	-	-	-
0.5844	100	0.2302	-	-	-	-	-
0.6428	110	0.2356	-	-	-	-	-
0.7012	120	0.1537	-	-	-	-	-
0.7597	130	0.2043	-	-	-	-	-
0.8181	140	0.1606	-	-	-	-	-
0.8766	150	0.1896	-	-	-	-	-
0.9350	160	0.1766	-	-	-	-	-
0.9934	170	0.1259	-	-	-	-	-
0.9993	171	-	0.8115	0.8233	0.8321	0.7829	0.8340
1.0519	180	0.1661	-	-	-	-	-
1.1103	190	0.1632	-	-	-	-	-
1.1687	200	0.1032	-	-	-	-	-
1.2272	210	0.1037	-	-	-	-	-
1.2856	220	0.0708	-	-	-	-	-
1.3440	230	0.0827	-	-	-	-	-
1.4025	240	0.0505	-	-	-	-	-
1.4609	250	0.0468	-	-	-	-	-
1.5194	260	0.0371	-	-	-	-	-
1.5778	270	0.049	-	-	-	-	-
1.6362	280	0.0527	-	-	-	-	-
1.6947	290	0.0316	-	-	-	-	-
1.7531	300	0.052	-	-	-	-	-
1.8115	310	0.0298	-	-	-	-	-
1.8700	320	0.0334	-	-	-	-	-
1.9284	330	0.0431	-	-	-	-	-
1.9869	340	0.0316	-	-	-	-	-
1.9985	342	-	0.8216	0.8342	0.8397	0.8006	0.8408
2.0453	350	0.0275	-	-	-	-	-
2.1037	360	0.0461	-	-	-	-	-
2.1622	370	0.0341	-	-	-	-	-
2.2206	380	0.0323	-	-	-	-	-
2.2790	390	0.0205	-	-	-	-	-
2.3375	400	0.0223	-	-	-	-	-
2.3959	410	0.0189	-	-	-	-	-
2.4543	420	0.0181	-	-	-	-	-
2.5128	430	0.0144	-	-	-	-	-
2.5712	440	0.0179	-	-	-	-	-
2.6297	450	0.0217	-	-	-	-	-
2.6881	460	0.016	-	-	-	-	-
2.7465	470	0.0143	-	-	-	-	-
2.8050	480	0.0193	-	-	-	-	-
2.8634	490	0.0183	-	-	-	-	-
2.9218	500	0.0171	-	-	-	-	-
2.9803	510	0.0195	-	-	-	-	-
2.9978	513	-	0.8242	0.8350	0.8409	0.8051	0.8413
3.0387	520	0.0127	-	-	-	-	-
3.0972	530	0.0261	-	-	-	-	-
3.1556	540	0.017	-	-	-	-	-
3.2140	550	0.0198	-	-	-	-	-
3.2725	560	0.0131	-	-	-	-	-
3.3309	570	0.0156	-	-	-	-	-
3.3893	580	0.0107	-	-	-	-	-
3.4478	590	0.0123	-	-	-	-	-
3.5062	600	0.0111	-	-	-	-	-
3.5646	610	0.0112	-	-	-	-	-
3.6231	620	0.0143	-	-	-	-	-
3.6815	630	0.013	-	-	-	-	-
3.7400	640	0.0105	-	-	-	-	-
3.7984	650	0.0126	-	-	-	-	-
3.8568	660	0.0118	-	-	-	-	-
3.9153	670	0.0163	-	-	-	-	-
3.9737	680	0.0187	-	-	-	-	-
3.9971	684	-	0.8248	0.8361	0.8405	0.8058	0.8422

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.5
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1
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",
}

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}
}