Add new SentenceTransformer model

97d910b verified 29 days ago

46.9 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:1340
  - loss:TripletLoss
base_model: unsloth/embeddinggemma-300m
widget:
  - source_sentence: >-
      How many 'Laboratory' sub-type admissions from the last year have not yet
      generated an invoice, and what is the average duration (in days) from
      their admission date to today?
    sentences:
      - >-
        Primary reference table listing the most granular level of
        administrative geography, typically representing villages or
        'kelurahan'. It provides standardized names and codes for these local
        areas, linking each sub-district to its parent `DistrictId` to complete
        the geographic hierarchy. **Use this table to** perform highly localized
        demographic analysis, standardize patient addresses to the village
        level, or define precise service areas. **Note: This is a static lookup
        list of geographic areas and does not contain any patient-specific
        addresses, postal codes, or household information.**
      - >-
        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 (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: >-
      Calculate the proportion of invoices issued to patients with 'Inactive'
      medical records out of the total invoices issued in the last fiscal year.
    sentences:
      - >-
        Standard reference table for patient biological sex and gender
        classification. Contains standard values 'Male' (Pria) and 'Female'
        (Wanita). Use this table to filter patient demographics by gender,
        support clinical reference range checks, or generate gender-based
        population statistics. Note: Used for biological sex classification.
      - >-
        Primary reference table storing the comprehensive master profiles of all
        patients registered in the hospital system. It contains critical
        demographic data including Name, Medical Record Number (MrNo), Birth
        Date, Gender (SexId), Address, Contact Information (Phone, Email), and
        Identification details (National ID, Passport). It also tracks patient
        status (Active/Merged/Deceased) and links to external Payer/Insurance
        information. Use this table to identify unique patients, retrieve
        contact details for notifications, segment patient populations by
        demographics, or link clinical transactions to specific individuals.
        Note: This table represents the 'Golden Record' of a patient's identity;
        historical changes might be tracked in audit logs, but this table holds
        the current state.
      - >-
        Primary reference table storing the comprehensive master profiles of all
        patients registered in the hospital system. It contains critical
        demographic data including Name, Medical Record Number (MrNo), Birth
        Date, Gender (SexId), Address, Contact Information (Phone, Email), and
        Identification details (National ID, Passport). It also tracks patient
        status (Active/Merged/Deceased) and links to external Payer/Insurance
        information. Use this table to identify unique patients, retrieve
        contact details for notifications, segment patient populations by
        demographics, or link clinical transactions to specific individuals.
        Note: This table represents the 'Golden Record' of a patient's identity;
        historical changes might be tracked in audit logs, but this table holds
        the current state.
  - source_sentence: >-
      What is the total number of invoices and the total value of these invoices
      issued in Q1 2024 for female patients whose current status is 'Active'?
    sentences:
      - >-
        Operational snapshot table that holds the **single, current financial
        state** of a patient at a specific hospital unit. Unlike a transaction
        log, this table does not track history; it contains only the final
        calculated totals: `BalanceAmount` (Total AR/Debt outstanding) and
        `DepositBalanceAmount` (Total Advance Payment available). **Use this
        table for** quick validation of whether a patient can be discharged,
        checking total debt before billing, or viewing available deposit funds.
        **Concept:** Think of this as the 'ATM Screen Balance' showing only the
        final amount available right now.
      - >-
        Primary reference table defining the validity and lifecycle status of a
        patient's master record. Contains status codes such as 'Active',
        'Inactive', and 'Deceased' to classify patient accounts. **Use this
        table to** look up status meanings or filter patient queries based on
        their account state (e.g., excluding deceased patients from active
        lists). **Note: This is a lookup dictionary for status codes, NOT a
        transaction log of status changes for specific patients.**
      - >-
        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.**
  - source_sentence: >-
      How many unique male patients were referred by a 'Doctor Referral' and had
      a 'General' patient type for any admission in the last 6 months?
    sentences:
      - >-
        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.
      - >-
        Standard reference table for patient biological sex and gender
        classification. Contains standard values 'Male' (Pria) and 'Female'
        (Wanita). Use this table to filter patient demographics by gender,
        support clinical reference range checks, or generate gender-based
        population statistics. Note: Used for biological sex classification.
      - >-
        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.
  - source_sentence: >-
      Retrieve the names of all patients who have a 'MedicalRecordStatusId'
      indicating 'Active' in their PatientOrganization record and have received
      an invoice from any organization in the current month.
    sentences:
      - >-
        Primary reference table storing the comprehensive master profiles of all
        patients registered in the hospital system. It contains critical
        demographic data including Name, Medical Record Number (MrNo), Birth
        Date, Gender (SexId), Address, Contact Information (Phone, Email), and
        Identification details (National ID, Passport). It also tracks patient
        status (Active/Merged/Deceased) and links to external Payer/Insurance
        information. Use this table to identify unique patients, retrieve
        contact details for notifications, segment patient populations by
        demographics, or link clinical transactions to specific individuals.
        Note: This table represents the 'Golden Record' of a patient's identity;
        historical changes might be tracked in audit logs, but this table holds
        the current state.
      - >-
        Primary reference table defining the standard set of religions and
        spiritual beliefs for patient demographic data. It contains codes and
        names for major religions such as Islam, Christian, Catholic, Hindu, and
        Buddhist. **Use this table to** interpret the `ReligionId` from patient
        master data to standardize demographic reporting or analyze patient
        spiritual care needs. **Note: This table lists the CATEGORIES of
        religion only, not the religious affiliation of individual patients.**
      - >-
        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.
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: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.03283582089552239
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.0626865671641791
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.08358208955223881
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.13134328358208955
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.03283582089552239
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.0208955223880597
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.01671641791044776
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.013134328358208956
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.03283582089552239
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.0626865671641791
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.08358208955223881
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.13134328358208955
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.07347950410091954
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.05603056147832267
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.06301005721428388
            name: Cosine Map@100

SentenceTransformer based on unsloth/embeddinggemma-300m

This is a sentence-transformers model finetuned from unsloth/embeddinggemma-300m. 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

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("afalaudn/gemma-embedding-ft3")
# Run inference
sentences = [
    "Retrieve the names of all patients who have a 'MedicalRecordStatusId' indicating 'Active' in their PatientOrganization record and have received an invoice from any organization in the current month.",
    "Primary reference table storing the comprehensive master profiles of all patients registered in the hospital system. It contains critical demographic data including Name, Medical Record Number (MrNo), Birth Date, Gender (SexId), Address, Contact Information (Phone, Email), and Identification details (National ID, Passport). It also tracks patient status (Active/Merged/Deceased) and links to external Payer/Insurance information. Use this table to identify unique patients, retrieve contact details for notifications, segment patient populations by demographics, or link clinical transactions to specific individuals. Note: This table represents the 'Golden Record' of a patient's identity; historical changes might be tracked in audit logs, but this table holds the current state.",
    'Primary reference table defining the standard set of religions and spiritual beliefs for patient demographic data. It contains codes and names for major religions such as Islam, Christian, Catholic, Hindu, and Buddhist. **Use this table to** interpret the `ReligionId` from patient master data to standardize demographic reporting or analyze patient spiritual care needs. **Note: This table lists the CATEGORIES of religion only, not the religious affiliation of individual patients.**',
]
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,  0.6662, -0.1269],
#         [ 0.6662,  1.0000,  0.1456],
#         [-0.1269,  0.1456,  1.0000]])

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0328
cosine_accuracy@3	0.0627
cosine_accuracy@5	0.0836
cosine_accuracy@10	0.1313
cosine_precision@1	0.0328
cosine_precision@3	0.0209
cosine_precision@5	0.0167
cosine_precision@10	0.0131
cosine_recall@1	0.0328
cosine_recall@3	0.0627
cosine_recall@5	0.0836
cosine_recall@10	0.1313
cosine_ndcg@10	0.0735
cosine_mrr@10	0.056
cosine_map@100	0.063

Training Details

Training Dataset

Unnamed Dataset

Size: 1,340 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 10 tokens mean: 35.94 tokens max: 82 tokens	min: 63 tokens mean: 129.79 tokens max: 171 tokens	min: 63 tokens mean: 112.02 tokens max: 175 tokens

Samples:

anchor	positive	negative
`What is the total number of patient admissions that resulted in a 'Recovered' discharge condition for each organization in 2023?`	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.	This reference table defines the high-level administrative outcomes of a patient's discharge process. It includes standard codes, English names, and Indonesian translations (LocalName) such as 'Discharged' (Pulang) and 'Cancelled' (Batal). Use this table to categorize the final resolution of a hospital visit, determining if a patient successfully left care or if the discharge process was voided. Note: This table tracks the administrative status of the discharge event itself, not the clinical medical condition (e.g., improved, cured) or the specific reason for discharge.
`Retrieve the AdmissionNo and InvoiceNo for all admissions where the patient was not previously registered (Admission.IsPatientRegistered = 0) and the invoice was issued by an organization with an IsActive status of 'true'.`	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 formal requests to retrieve physical medical record folders from the Medical Records Department (MRD). It tracks the details of the request, including the specific patient file (PatientOrganizationId), the requesting doctor (DoctorUserId), the intended destination (FolderDestinationTypeId), and the urgency or type of request (FolderRequestTypeId). Use this table to measure MRD service levels, track the volume of physical file retrievals, or audit the reasons for accessing physical records. Note: This tracks the request for a physical object, NOT the digital access to the EMR or the actual content of the medical record.
`What is the average age of patients with 'Active' status who had an 'Inpatient' admission in 2024, compared to those with 'Inactive' status?`	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 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.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 1
}

Evaluation Dataset

Unnamed Dataset

Size: 335 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 335 samples:

	anchor	positive	negative
type	string	string	string
details	min: 10 tokens mean: 35.25 tokens max: 77 tokens	min: 63 tokens mean: 128.23 tokens max: 171 tokens	min: 63 tokens mean: 111.21 tokens max: 171 tokens

Samples:

anchor	positive	negative
`Retrieve the admission number, invoice number, and admission type name for all admissions that occurred in January 2023 and had an invoice issued within 7 days of the 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 that records the detailed line items of an inventory stock take event. It captures the specific ItemId being counted, the quantity recorded in the system (SystemStockQuantity), the actual PhysicalStockQuantity counted, and the resulting VarianceStockQuantity. For batch-tracked items, it also includes BatchNo and ExpiryDate. Use this table to analyze inventory discrepancies, identify expired or expiring stock during a count, or audit the accuracy of stock records for a specific item. Note: This table contains the detailed results of a specific stock count event; it does NOT provide the real-time inventory balance of an item.
`What is the total number of invoices and the total value of these invoices issued in Q1 2024 for female patients whose current status is 'Active'?`	`Primary reference table defining the validity and lifecycle status of a patient's master record. Contains status codes such as 'Active', 'Inactive', and 'Deceased' to classify patient accounts. Use this table to look up status meanings or filter patient queries based on their account state (e.g., excluding deceased patients from active lists). Note: This is a lookup dictionary for status codes, NOT a transaction log of status changes for specific patients.`	Operational snapshot table that holds the single, current financial state of a patient at a specific hospital unit. Unlike a transaction log, this table does not track history; it contains only the final calculated totals: BalanceAmount (Total AR/Debt outstanding) and DepositBalanceAmount (Total Advance Payment available). Use this table for quick validation of whether a patient can be discharged, checking total debt before billing, or viewing available deposit funds. Concept: Think of this as the 'ATM Screen Balance' showing only the final amount available right now.
`Tampilkan data Master Data Hospital Rooms (Room)`	Primary reference table defining individual patient rooms within a hospital ward. It contains specific room codes and names, linking each room to its parent Ward (WardId) and hospital branch (OrganizationId), representing the most granular level of inpatient location master data. Use this table to map RoomId from bed management or admission transactions to a specific room number, analyze room availability, or manage hospital accommodation resources. Note: This table lists the physical room definitions only, not the individual beds within a room or the real-time patient occupancy status.	Operational transaction table recording the historical log of bed movements and transfers within the hospital organization. This table captures the exact timestamp of transfer events and identifies the specific Bed asset involved. Use this table to track the history of bed locations, audit bed usage timelines, or monitor the frequency of bed transfers. Note: This table focuses on the physical Bed entity's movement timeline; based on the provided columns, it does not explicitly contain Patient or Admission IDs.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 4
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: 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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
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: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
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
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: 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}
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
project: huggingface
trackio_space_id: trackio
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: no
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: True
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	cosine_ndcg@10
-1	-1	-	-	0.0698
0.2381	5	0.7215	0.3602	0.0706
0.4762	10	0.3287	0.2451	0.0765
0.7143	15	0.2145	0.1896	0.0652
0.9524	20	0.2181	0.2286	0.0535
1.1905	25	0.2774	0.1610	0.0694
1.4286	30	0.1948	0.1818	0.0733
-1	-1	-	-	0.0733
0.2381	5	0.1696	0.1475	0.0705
0.4762	10	0.1657	0.2067	0.0661
0.7143	15	0.1346	0.1991	0.0635
0.9524	20	0.1197	0.1466	0.0473
1.1905	25	0.2657	0.1380	0.0544
1.4286	30	0.1706	0.1906	0.0719
1.6667	35	0.1686	0.1746	0.0741
1.9048	40	0.1217	0.1577	0.0669
2.1429	45	0.1191	0.1355	0.0606
2.3810	50	0.108	0.1362	0.0615
2.6190	55	0.1065	0.1595	0.0647
2.8571	60	0.1314	0.1126	0.0667
3.0952	65	0.072	0.0934	0.0589
3.3333	70	0.0868	0.0977	0.0655
3.5714	75	0.0719	0.1310	0.0496
3.8095	80	0.1184	0.1388	0.0681
4.0476	85	0.0997	0.1132	0.0656
4.2857	90	0.0659	0.1029	0.0724
4.5238	95	0.0554	0.1018	0.0707
4.7619	100	0.0729	0.0989	0.0659
5.0	105	0.0422	0.0971	0.0735
-1	-1	-	-	0.0735

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.2
Transformers: 4.57.6
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}
}