metadata

base_model: BAAI/bge-base-en-v1.5
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:160
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Priya Softweb emphasizes the importance of maintaining a clean and
      organized workspace. The company's HR policies clearly state that
      employees are responsible for keeping their assigned workspaces clean,
      orderly, and free from unnecessary items. Spitting tobacco, gum, or other
      substances in the washrooms is strictly prohibited. The company believes
      that a clean and organized work environment contributes to a more
      efficient and professional work experience for everyone. This emphasis on
      cleanliness reflects the company's commitment to creating a pleasant and
      hygienic workspace for its employees.
    sentences:
      - >-
        What is Priya Softweb's policy on the use of mobile phones during work
        hours?
      - >-
        What steps does Priya Softweb take to ensure that the workspace is clean
        and organized?
      - >-
        What are the repercussions for employees who violate the Non-Disclosure
        Agreement at Priya Softweb?
  - source_sentence: >-
      Priya Softweb provides allocated basement parking facilities for employees
      to park their two-wheelers and four-wheelers. However, parking on the
      ground floor, around the lawn or main premises, is strictly prohibited as
      this space is reserved for Directors. Employees should use the parking
      under wings 5 and 6, while other parking spaces are allocated to different
      wings. Parking two-wheelers in the car parking zone is not permitted, even
      if space is available. Two-wheelers should be parked in the designated
      basement space on the main stand, not on the side stand. Employees are
      encouraged to park in common spaces on a first-come, first-served basis.
      The company clarifies that it is not responsible for providing parking and
      that employees park their vehicles at their own risk. This comprehensive
      parking policy ensures organized parking arrangements and clarifies the
      company's liability regarding vehicle safety.
    sentences:
      - What is the application process for planned leaves at Priya Softweb?
      - What are the parking arrangements at Priya Softweb?
      - What is the process for reporting a security breach at Priya Softweb?
  - source_sentence: >-
      The Diwali bonus at Priya Softweb is a discretionary benefit linked to the
      company's business performance. Distributed during the festive season of
      Diwali, it serves as a gesture of appreciation for employees'
      contributions throughout the year. However, it's important to note that
      employees currently under the notice period are not eligible for this
      bonus. This distinction highlights that the bonus is intended to reward
      ongoing commitment and contribution to the company's success.
    sentences:
      - >-
        What steps does Priya Softweb take to promote responsible use of company
        resources?
      - >-
        How does Priya Softweb demonstrate its commitment to Diversity, Equity,
        and Inclusion (DEI)?
      - What is the significance of the company's Diwali bonus at Priya Softweb?
  - source_sentence: >-
      Priya Softweb's HR Manual paints a picture of a company that values its
      employees while upholding a strong sense of professionalism and ethical
      conduct. The company emphasizes a structured and transparent approach to
      its HR processes, ensuring clarity and fairness in areas like recruitment,
      performance appraisals, compensation, leave management, work-from-home
      arrangements, and incident reporting. The manual highlights the importance
      of compliance with company policies, promotes diversity and inclusion, and
      encourages a culture of continuous learning and development. Overall, the
      message conveyed is one of creating a supportive, respectful, and
      growth-oriented work environment for all employees.
    sentences:
      - What is the overall message conveyed by Priya Softweb's HR Manual?
      - What is the process for reporting employee misconduct at Priya Softweb?
      - What is Priya Softweb's policy on salary disbursement and payslips?
  - source_sentence: >-
      No, work-from-home arrangements do not affect an employee's employment
      terms, compensation, and benefits at Priya Softweb. This clarifies that
      work-from-home is a flexible work arrangement and does not impact the
      employee's overall employment status or benefits.
    sentences:
      - >-
        Do work-from-home arrangements affect compensation and benefits at Priya
        Softweb?
      - What is the objective of the Work From Home Policy at Priya Softweb?
      - What is the procedure for a new employee joining Priya Softweb?
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.8333333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8333333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8333333333333334
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.923940541865081
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.898148148148148
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.898148148148148
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.8333333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8333333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8333333333333334
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.923940541865081
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.898148148148148
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.898148148148148
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.8333333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8333333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8333333333333334
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9312144170634953
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9074074074074076
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9074074074074073
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7777777777777778
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7777777777777778
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7777777777777778
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9107105144841319
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8796296296296297
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8796296296296295
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6111111111111112
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9444444444444444
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9444444444444444
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6111111111111112
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31481481481481477
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1888888888888889
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6111111111111112
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9444444444444444
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9444444444444444
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.826662566744103
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7685185185185186
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7685185185185185
            name: Cosine Map@100

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("kr-manish/fine-tune-embedding-bge-base-HrPolicy")
# Run inference
sentences = [
    "No, work-from-home arrangements do not affect an employee's employment terms, compensation, and benefits at Priya Softweb. This clarifies that work-from-home is a flexible work arrangement and does not impact the employee's overall employment status or benefits.",
    'Do work-from-home arrangements affect compensation and benefits at Priya Softweb?',
    'What is the objective of the Work From Home Policy at Priya Softweb?',
]
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	0.8333
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9239
cosine_mrr@10	0.8981
cosine_map@100	0.8981

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9239
cosine_mrr@10	0.8981
cosine_map@100	0.8981

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9312
cosine_mrr@10	0.9074
cosine_map@100	0.9074

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7778
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.7778
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.7778
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9107
cosine_mrr@10	0.8796
cosine_map@100	0.8796

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6111
cosine_accuracy@3	0.9444
cosine_accuracy@5	0.9444
cosine_accuracy@10	1.0
cosine_precision@1	0.6111
cosine_precision@3	0.3148
cosine_precision@5	0.1889
cosine_precision@10	0.1
cosine_recall@1	0.6111
cosine_recall@3	0.9444
cosine_recall@5	0.9444
cosine_recall@10	1.0
cosine_ndcg@10	0.8267
cosine_mrr@10	0.7685
cosine_map@100	0.7685

Training Details

Training Dataset

Unnamed Dataset

Size: 160 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 18 tokens
mean: 93.95 tokens
max: 381 tokens

min: 13 tokens
mean: 20.32 tokens
max: 34 tokens

	positive	anchor
type	string	string
details	min: 18 tokens mean: 93.95 tokens max: 381 tokens	min: 13 tokens mean: 20.32 tokens max: 34 tokens

Samples:

positive	anchor
Priya Softweb's HR Manual provides valuable insights into the company's culture and values. Key takeaways include: * Structure and Transparency: The company emphasizes a structured and transparent approach to its HR processes. This is evident in its clear policies for recruitment, performance appraisals, compensation, leave management, work-from-home arrangements, and incident reporting. * Professionalism and Ethics: Priya Softweb places a high value on professionalism and ethical conduct. Its dress code, guidelines for mobile phone usage, and strict policies against tobacco use within the office all point toward a commitment to maintaining a professional and respectful work environment. * Employee Well-being: The company demonstrates a genuine concern for the well-being of its employees. This is reflected in its comprehensive leave policies, flexible work-from-home arrangements, and efforts to promote a healthy and clean workspace. * Diversity and Inclusion: Priya Softweb is committed to fostering a diverse and inclusive workplace, where employees from all backgrounds feel valued and respected. Its DEI policy outlines the company's commitment to equal opportunities, diverse hiring practices, and inclusive benefits and policies. * Continuous Learning and Development: The company encourages a culture of continuous learning and development, providing opportunities for employees to expand their skillsets and stay current with industry advancements. This is evident in its policies for Ethics & Compliance training and its encouragement of utilizing idle time for self-learning and exploring new technologies. Overall, Priya Softweb's HR Manual reveals a company culture that prioritizes structure, transparency, professionalism, employee well-being, diversity, and a commitment to continuous improvement. The company strives to create a supportive and growth-oriented work environment where employees feel valued and empowered to succeed.	`What are the key takeaways from Priya Softweb's HR Manual regarding the company's culture and values?`
Priya Softweb provides allocated basement parking facilities for employees to park their two-wheelers and four-wheelers. However, parking on the ground floor, around the lawn or main premises, is strictly prohibited as this space is reserved for Directors. Employees should use the parking under wings 5 and 6, while other parking spaces are allocated to different wings. Parking two-wheelers in the car parking zone is not permitted, even if space is available. Two-wheelers should be parked in the designated basement space on the main stand, not on the side stand. Employees are encouraged to park in common spaces on a first-come, first-served basis. The company clarifies that it is not responsible for providing parking and that employees park their vehicles at their own risk. This comprehensive parking policy ensures organized parking arrangements and clarifies the company's liability regarding vehicle safety.	`What are the parking arrangements at Priya Softweb?`
`Investments and declarations must be submitted on or before the 25th of each month through OMS at Priya Softweb.`	`What is the deadline for submitting investments and declarations at Priya Softweb?`

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: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 10
lr_scheduler_type: cosine
warmup_ratio: 0.1
load_best_model_at_end: True
optim: adamw_torch_fused

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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_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: 10
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: False
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_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: batch_sampler
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	0	-	0.5729	0.5863	0.6595	0.5079	0.6896
1.0	1	-	0.6636	0.6914	0.8213	0.6036	0.8472
2.0	2	-	0.7833	0.8148	0.9352	0.7171	0.8796
3.0	3	-	0.8213	0.8519	0.8981	0.7333	0.8981
4.0	5	-	0.8426	0.9074	0.8981	0.75	0.8981
5.0	6	-	0.8426	0.9074	0.8981	0.7685	0.8981
6.0	7	-	0.8796	0.9074	0.8981	0.7685	0.8981
7.0	9	-	0.8796	0.9074	0.8981	0.7685	0.8981
8.0	10	0.5275	0.8796	0.9074	0.8981	0.7685	0.8981

The bold row denotes the saved checkpoint.

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}
}