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tags:
  - unsloth
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:4927
  - loss:TripletLoss
base_model: unsloth/embeddinggemma-300m
widget:
  - source_sentence: organization id
    sentences:
      - >-
        Primary reference table for classifying Payers into broader financial or
        business categories. This table groups Payers into segments such as
        'Insurance Private', 'Insurance Government (BPJS)', 'Corporate', and
        'Related Parties'. Use this table to aggregate revenue reporting by
        payer channel, analyze market segmentation (e.g., Private Insurance vs.
        Government Scheme), or apply high-level billing policies to groups of
        payers. Note: This serves as a categorization layer above the individual
        'Payer' table.
      - >-
        Operational transaction table recording unstructured free-text medical
        notes and preliminary clinical remarks associated with a patient
        admission. It captures initial diagnosis impressions, symptoms, or
        observation notes (e.g., 'Asthma', 'Observation Febris') entered during
        the admission process. Use this table to retrieve qualitative clinical
        context for a visit or search for specific medical conditions mentioned
        in preliminary notes. Note: This table contains raw free-text
        descriptions, NOT structured ICD-10 diagnosis codes used for billing.
      - >-
        Operational transaction table recording every patient registration and
        visit event at the hospital. This table consolidates patient
        demographics, visit types (Inpatient, Outpatient, Emergency), primary
        and referral doctors, payer/insurance eligibility, and critical
        timelines (Admission and Discharge dates). Use this table to calculate
        patient census, Average Length of Stay (ALOS), track patient flow, or
        analyze admission volume by doctor or department. Note: This table
        focuses on administrative registration and billing initiation; it does
        not contain detailed clinical notes, specific lab results, or medication
        prescriptions. When analyzing patient administrative inflow and outflow
        data, this table is the primary and essential source for all patient
        visit metrics.
  - source_sentence: >-
      What is the total count of admissions for each patient payment category
      (e.g., 'Private', 'Payer') as defined in the PatientType master, grouped
      by the AdmissionType from the Admission table, for the year 2024?
    sentences:
      - >-
        Operational transaction table recording unstructured free-text medical
        notes and preliminary clinical remarks associated with a patient
        admission. It captures initial diagnosis impressions, symptoms, or
        observation notes (e.g., 'Asthma', 'Observation Febris') entered during
        the admission process. Use this table to retrieve qualitative clinical
        context for a visit or search for specific medical conditions mentioned
        in preliminary notes. Note: This table contains raw free-text
        descriptions, NOT structured ICD-10 diagnosis codes used for billing.
      - >-
        Operational transaction table recording every patient registration and
        visit event at the hospital. This table consolidates patient
        demographics, visit types (Inpatient, Outpatient, Emergency), primary
        and referral doctors, payer/insurance eligibility, and critical
        timelines (Admission and Discharge dates). Use this table to calculate
        patient census, Average Length of Stay (ALOS), track patient flow, or
        analyze admission volume by doctor or department. Note: This table
        focuses on administrative registration and billing initiation; it does
        not contain detailed clinical notes, specific lab results, or medication
        prescriptions. When analyzing patient administrative inflow and outflow
        data, this table is the primary and essential source for all patient
        visit metrics.
      - >-
        Core reference table that links a central patient profile to their local
        record at a specific hospital branch. It connects the central
        `PatientId` to a local Medical Record Number (`MrNo`) at a specific
        hospital (`OrganizationId`). The table also includes the patient's
        registration date at that particular location and the status of their
        medical record file (e.g., Active, Merged). **Use this table to** find a
        patient's local MR Number for a specific hospital, determine when a
        patient first registered at a site, or check the administrative status
        of a patient's file at a given location. **Note: This table defines the
        relationship and local record number, not the patient's demographic
        details (found in the `Patient` table) or their visit history (found in
        `Admission` or `Encounter` tables).**
  - source_sentence: >-
      List the patient names, their primary payer's name, and the invoice
      numbers for all invoices issued in the last 90 days to male patients whose
      payer is a 'Corporate' type.
    sentences:
      - >-
        Operational transaction table that records the movement of inventory
        items from one storage location (store) to another within the hospital
        network. It captures the header-level details of each transfer,
        including the transaction number, date, the originating store, and the
        receiving store. **Use this table to** track the flow of goods, monitor
        stock levels across different warehouses or departments, and audit
        inventory movements for logistics and supply chain management. **Note:**
        This table contains only the header information for the transfer event;
        it does NOT list the specific items or quantities transferred. Join with
        the Transfer Detail table for item-level information.
      - >-
        Operational transaction table recording the official event of a patient
        leaving the hospital (Discharge). It captures the precise discharge
        timestamp, the patient's condition upon exit (e.g., Recovered,
        Improved), and the type of discharge (e.g., Medical Consent, Transfer)
        linked to their Admission. **Use this table to** calculate Length of
        Stay (LOS), analyze clinical outcomes, or track bed turnover rates.
        **Note: This table signifies the physical or administrative end of a
        visit; it does NOT contain the final invoice amount, though it triggers
        the billing closure process.**
      - >-
        Primary reference table containing the master list of all external
        organizations responsible for patient payment guarantees. This includes
        Insurance Companies, Corporate Clients/Employers, and Government Health
        Schemes (e.g., BPJS, Jamkesda). The table stores Payer details such as
        Legal Name, Address, Contact Information, and specific Payer Group
        classifications. Use this table to link patient visits to their
        financial guarantors, generate invoices for corporate clients, or
        analyze revenue contribution by payer. Note: This table defines the 'Who
        Pays' entity; specific policy terms or benefit limits are typically
        stored in separate configuration tables.
  - source_sentence: >-
      Identify `OrganizationId`s that have more than 100 `Admission` records
      currently in 'Active' `AdmissionStatus` where an `ArInvoice` exists, and
      the `InvoiceDate` is more than 7 days after the `AdmissionDate`.
    sentences:
      - >-
        Primary reference table listing the specific bank accounts owned or
        utilized by various Siloam Hospital units (Organizations). It stores
        detailed Account Numbers, Account Names, and operational notes (e.g.,
        Receipt or Payment accounts), linking them to the parent Bank entity.
        **Use this table to** identify the destination account for financial
        settlements, reconcile deposits, or manage treasury master data. **Note:
        This defines static master data for the hospital's bank accounts, NOT a
        transaction log of transfers or balances.**
      - >-
        Operational transaction table recording every patient registration and
        visit event at the hospital. This table consolidates patient
        demographics, visit types (Inpatient, Outpatient, Emergency), primary
        and referral doctors, payer/insurance eligibility, and critical
        timelines (Admission and Discharge dates). Use this table to calculate
        patient census, Average Length of Stay (ALOS), track patient flow, or
        analyze admission volume by doctor or department. Note: This table
        focuses on administrative registration and billing initiation; it does
        not contain detailed clinical notes, specific lab results, or medication
        prescriptions. When analyzing patient administrative inflow and outflow
        data, this table is the primary and essential source for all patient
        visit metrics.
      - >-
        Operational transaction table (Financial Log) recording the header-level
        details of patient invoices and billing events. This table captures the
        financial breakdown of a visit, distinguishing between Patient
        responsibility (Out-of-pocket) and Payer responsibility
        (Insurance/Corporate Coverage), including Gross Amounts, Discounts,
        Taxes, and Net Payable values. Use this table to analyze hospital
        revenue streams, track Accounts Receivable (AR), monitor billing
        cancellations, or calculate the financial yield per admission. Note:
        This is the Invoice HEADER table containing total values; it does not
        typically list the specific individual line items (drugs, labs,
        services) charged within the bill. For any financial analysis related to
        hospital revenue, Payments, Accounts Receivable (AR), billing
        breakdowns, or insurance claims, this invoice header table is the
        definitive starting point.
  - source_sentence: master data payer group
    sentences:
      - >-
        Strategic reference table linking Payers to specific Hospital
        Organizations (Units/Branches). This table manages the contractual
        relationships between insurance providers/corporate clients and
        individual hospital sites. It stores Contract Numbers, Validity Periods
        (Start/End Dates), Contract Status, and site-specific contact details.
        Use this table to validate insurance acceptance at a specific hospital
        branch, track contract expiration dates, or manage site-specific payer
        agreements. Note: This table enables the many-to-many relationship
        between Payers (Global) and Organizations (Local Sites).
      - >-
        Primary reference table containing the master list of all external
        organizations responsible for patient payment guarantees. This includes
        Insurance Companies, Corporate Clients/Employers, and Government Health
        Schemes (e.g., BPJS, Jamkesda). The table stores Payer details such as
        Legal Name, Address, Contact Information, and specific Payer Group
        classifications. Use this table to link patient visits to their
        financial guarantors, generate invoices for corporate clients, or
        analyze revenue contribution by payer. Note: This table defines the 'Who
        Pays' entity; specific policy terms or benefit limits are typically
        stored in separate configuration tables.
      - >-
        Operational transaction table recording the official event of a patient
        leaving the hospital (Discharge). It captures the precise discharge
        timestamp, the patient's condition upon exit (e.g., Recovered,
        Improved), and the type of discharge (e.g., Medical Consent, Transfer)
        linked to their Admission. **Use this table to** calculate Length of
        Stay (LOS), analyze clinical outcomes, or track bed turnover rates.
        **Note: This table signifies the physical or administrative end of a
        visit; it does NOT contain the final invoice amount, though it triggers
        the billing closure process.**
pipeline_tag: sentence-similarity
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
model-index:
  - name: SentenceTransformer based on unsloth/embeddinggemma-300m
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: his retrieval eval
          type: his-retrieval-eval
        metrics:
          - type: cosine_accuracy@1
            value: 0.0016233766233766235
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.00487012987012987
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.007305194805194805
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.012175324675324676
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.0016233766233766235
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.0016233766233766235
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.001461038961038961
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0012175324675324677
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0016233766233766235
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.00487012987012987
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.007305194805194805
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.012175324675324676
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.005969101397650474
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.004109977324263038
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.005835580133437121
            name: Cosine Map@100

SentenceTransformer

This model was finetuned with Unsloth.

based on unsloth/embeddinggemma-300m

This is a sentence-transformers model finetuned from unsloth/embeddinggemma-300m 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: unsloth/embeddinggemma-300m
Maximum Sequence Length: 768 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': 768, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'master data payer group',
    "Primary reference table containing the master list of all external organizations responsible for patient payment guarantees. This includes Insurance Companies, Corporate Clients/Employers, and Government Health Schemes (e.g., BPJS, Jamkesda). The table stores Payer details such as Legal Name, Address, Contact Information, and specific Payer Group classifications. Use this table to link patient visits to their financial guarantors, generate invoices for corporate clients, or analyze revenue contribution by payer. Note: This table defines the 'Who Pays' entity; specific policy terms or benefit limits are typically stored in separate configuration tables.",
    'Strategic reference table linking Payers to specific Hospital Organizations (Units/Branches). This table manages the contractual relationships between insurance providers/corporate clients and individual hospital sites. It stores Contract Numbers, Validity Periods (Start/End Dates), Contract Status, and site-specific contact details. Use this table to validate insurance acceptance at a specific hospital branch, track contract expiration dates, or manage site-specific payer agreements. Note: This table enables the many-to-many relationship between Payers (Global) and Organizations (Local Sites).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.9426, -0.8527],
#         [ 0.9426,  1.0000, -0.8639],
#         [-0.8527, -0.8639,  1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: his-retrieval-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0016
cosine_accuracy@3	0.0049
cosine_accuracy@5	0.0073
cosine_accuracy@10	0.0122
cosine_precision@1	0.0016
cosine_precision@3	0.0016
cosine_precision@5	0.0015
cosine_precision@10	0.0012
cosine_recall@1	0.0016
cosine_recall@3	0.0049
cosine_recall@5	0.0073
cosine_recall@10	0.0122
cosine_ndcg@10	0.006
cosine_mrr@10	0.0041
cosine_map@100	0.0058

Training Details

Training Dataset

json

Dataset: json
Size: 4,927 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 12.32 tokens max: 76 tokens	min: 63 tokens mean: 128.13 tokens max: 171 tokens	min: 63 tokens mean: 108.19 tokens max: 171 tokens

Samples:

anchor	positive	negative
`master patient`	Primary reference table defining the lifecycle stages of a patient admission event. It categorizes visits into states such as 'Active' (currently in hospital), 'Discharged' (left hospital), 'Invoiced' (bill generated), or 'Cancelled'. Use this table to interpret AdmissionStatusId in transaction tables to filter visits by their current operational state (e.g., calculating current census vs. historical discharges). Note: This is a static lookup table for status definitions, NOT a transaction log of patient movements.	Operational transaction table recording unstructured free-text medical notes and preliminary clinical remarks associated with a patient admission. It captures initial diagnosis impressions, symptoms, or observation notes (e.g., 'Asthma', 'Observation Febris') entered during the admission process. Use this table to retrieve qualitative clinical context for a visit or search for specific medical conditions mentioned in preliminary notes. Note: This table contains raw free-text descriptions, NOT structured ICD-10 diagnosis codes used for billing.
`transaction ar invoice`	Primary reference table containing the master list of all external organizations responsible for patient payment guarantees. This includes Insurance Companies, Corporate Clients/Employers, and Government Health Schemes (e.g., BPJS, Jamkesda). The table stores Payer details such as Legal Name, Address, Contact Information, and specific Payer Group classifications. Use this table to link patient visits to their financial guarantors, generate invoices for corporate clients, or analyze revenue contribution by payer. Note: This table defines the 'Who Pays' entity; specific policy terms or benefit limits are typically stored in separate configuration tables.	Operational transaction table recording unstructured free-text medical notes and preliminary clinical remarks associated with a patient admission. It captures initial diagnosis impressions, symptoms, or observation notes (e.g., 'Asthma', 'Observation Febris') entered during the admission process. Use this table to retrieve qualitative clinical context for a visit or search for specific medical conditions mentioned in preliminary notes. Note: This table contains raw free-text descriptions, NOT structured ICD-10 diagnosis codes used for billing.
`admission date`	Primary reference table defining the high-level classification of patient visits and hospital service lines. Contains standard categories including Inpatient (Hospitalization), Outpatient (Clinical visits), Emergency (ER), and Health Checkups (MCU). Use this table to group patient volume by service type, filter admission logs, or analyze revenue streams by visit category. Note: This is a static lookup list defining the 'Types' of visits; it does not contain actual patient visit transaction records.	Operational transaction table recording unstructured free-text medical notes and preliminary clinical remarks associated with a patient admission. It captures initial diagnosis impressions, symptoms, or observation notes (e.g., 'Asthma', 'Observation Febris') entered during the admission process. Use this table to retrieve qualitative clinical context for a visit or search for specific medical conditions mentioned in preliminary notes. Note: This table contains raw free-text descriptions, NOT structured ICD-10 diagnosis codes used for billing.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 0.5
}

Evaluation Dataset

json

Dataset: json
Size: 1,232 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 13.51 tokens max: 76 tokens	min: 63 tokens mean: 127.79 tokens max: 171 tokens	min: 63 tokens mean: 107.3 tokens max: 171 tokens

Samples:

anchor	positive	negative
`transaction ar item`	Operational transaction table recording every patient registration and visit event at the hospital. This table consolidates patient demographics, visit types (Inpatient, Outpatient, Emergency), primary and referral doctors, payer/insurance eligibility, and critical timelines (Admission and Discharge dates). Use this table to calculate patient census, Average Length of Stay (ALOS), track patient flow, or analyze admission volume by doctor or department. Note: This table focuses on administrative registration and billing initiation; it does not contain detailed clinical notes, specific lab results, or medication prescriptions. When analyzing patient administrative inflow and outflow data, this table is the primary and essential source for all patient visit metrics.	Operational transaction table recording individual line items within patient invoices (Accounts Receivable). It captures granular billing details including specific items sold (drugs, services), quantities, unit prices, discounts, tax calculations, and the financial split between Patient and Payer (Insurance/Guarantor). It also tracks revenue allocation (Hospital vs. Doctor portion). Use this table to generate detailed patient bills, audit revenue streams per item, calculate doctor performance fees, or analyze discount utilization. Note: This table contains financial billing data per item, NOT the clinical medical results or the master list of available services.
`patient demographic country`	Operational transaction table (Financial Log) recording the header-level details of patient invoices and billing events. This table captures the financial breakdown of a visit, distinguishing between Patient responsibility (Out-of-pocket) and Payer responsibility (Insurance/Corporate Coverage), including Gross Amounts, Discounts, Taxes, and Net Payable values. Use this table to analyze hospital revenue streams, track Accounts Receivable (AR), monitor billing cancellations, or calculate the financial yield per admission. Note: This is the Invoice HEADER table containing total values; it does not typically list the specific individual line items (drugs, labs, services) charged within the bill. For any financial analysis related to hospital revenue, Payments, Accounts Receivable (AR), billing breakdowns, or insurance claims, this invoice header table is the definitive starting point.	Operational transaction table recording individual line items within patient invoices (Accounts Receivable). It captures granular billing details including specific items sold (drugs, services), quantities, unit prices, discounts, tax calculations, and the financial split between Patient and Payer (Insurance/Guarantor). It also tracks revenue allocation (Hospital vs. Doctor portion). Use this table to generate detailed patient bills, audit revenue streams per item, calculate doctor performance fees, or analyze discount utilization. Note: This table contains financial billing data per item, NOT the clinical medical results or the master list of available services.
`BPJS Kesehatan`	Primary reference table that classifies financial guarantors into high-level categories such as 'Government' programs, 'Corporate' accounts, 'Insurance' companies, and Third Party Administrators ('TPA'). Use this table to group and analyze patient revenue streams by the type of financial coverage or to interpret the PayerTypeId in the main Payer master data table. Note: This table defines the broad categories of payers only, not the specific insurance companies or corporate entities themselves (which are listed in the Payer table).	Operational transaction table that records the movement of inventory items from one storage location (store) to another within the hospital network. It captures the header-level details of each transfer, including the transaction number, date, the originating store, and the receiving store. Use this table to track the flow of goods, monitor stock levels across different warehouses or departments, and audit inventory movements for logistics and supply chain management. Note: This table contains only the header information for the transfer event; it does NOT list the specific items or quantities transferred. Join with the Transfer Detail table for item-level information.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 0.5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 2e-05
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
prompts: {'anchor': ' ', 'positive': '', 'negative': ''}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
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: 3
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: 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}
parallelism_config: 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: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: {'anchor': ' ', 'positive': '', 'negative': ''}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	his-retrieval-eval_cosine_ndcg@10
-1	-1	-	-	0.0034
0.1299	5	0.4772	-	-
0.2597	10	0.2436	0.0812	0.0021
0.3896	15	0.1075	-	-
0.5195	20	0.0794	0.0613	0.0036
0.6494	25	0.089	-	-
0.7792	30	0.1045	0.0248	0.0030
0.9091	35	0.0629	-	-
1.0260	40	0.0552	0.0298	0.0025
1.1558	45	0.0597	-	-
1.2857	50	0.0684	0.0236	0.0017
1.4156	55	0.0629	-	-
1.5455	60	0.0438	0.0213	0.0040
1.6753	65	0.0504	-	-
1.8052	70	0.0501	0.0237	0.0010
1.9351	75	0.0443	-	-
2.0519	80	0.0202	0.0232	0.0052
2.1818	85	0.0414	-	-
2.3117	90	0.0497	0.0233	0.0033
2.4416	95	0.0367	-	-
2.5714	100	0.0491	0.0232	0.0054
2.7013	105	0.0262	-	-
2.8312	110	0.0222	0.0232	0.0045
2.9610	115	0.0225	-	-
-1	-1	-	-	0.0060

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.2.2
Transformers: 4.56.2
PyTorch: 2.10.0+cu128
Accelerate: 1.12.0
Datasets: 4.3.0
Tokenizers: 0.22.2

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}