sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:56041 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:56041
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What is the significance of the tables 6.1.6.2.5-1 and 6.1.6.2.6-1
    in the context of the Namf_Communication Service API?
  sentences:
  - The 'notifId' attribute in the PolicyDataSubscription type serves as a Notification
    Correlation ID assigned by the NF service consumer. It is included when the 'ConditionalSubscriptionwithPartialNotification'
    or the 'ConditionalSubscriptionWithExcludeNotification' feature is supported.
    This ID is used to correlate notifications with the specific subscription request,
    ensuring that the NF service consumer can track and manage notifications effectively.
  - The 'sessRuleReports' attribute in the 'ErrorReport' type is specifically used
    to report failures related to session rules, whereas the 'ruleReports' attribute
    reports failures related to PCC rules. 'sessRuleReports' contains an array of
    'SessionRuleReport' objects, which provide details about the session rule failures.
    Like 'ruleReports', it is optional and can have one or more entries (cardinality
    1..N).
  - Tables 6.1.6.2.5-1 and 6.1.6.2.6-1 are significant in the Namf_Communication Service
    API as they provide the definitions for the types 'AssignEbiData' and 'AssignedEbiData',
    respectively. These tables outline the structure, attributes, and possibly the
    constraints or rules associated with these data types, which are essential for
    understanding and implementing the API's functionality related to EBI assignment
    and management.
- source_sentence: What document defines the basic principles for online charging,
    and where is this information referenced?
  sentences:
  - The UDM (Unified Data Management) returns the Ranging and Sidelink Positioning
    Subscription Data for the UE (User Equipment) identified by the supi (Subscription
    Permanent Identifier). This data is retrieved using the GET method, which supports
    the URI query parameters outlined in table 6.1.3.37.3.1-1.
  - The Nsmf_PDUSession_SMContextStatusNotify service operation is used by the SMF
    (Session Management Function) to notify its consumers about the status of an SM
    (Session Management) context related to a PDU (Packet Data Unit) Session. In the
    context of I-SMF (Intermediate SMF) context transfer, this service operation is
    used to indicate the transfer of the SM context to a new I-SMF or SMF set. It
    also allows the SMF to update the SMF-derived CN (Core Network) assisted RAN (Radio
    Access Network) parameters tuning in the AMF (Access and Mobility Management Function).
    Additionally, it can report DDN (Downlink Data Notification) failures and provide
    target DNAI (Data Network Access Identifier) information for the current or next
    PDU session.
  - The basic principles for online charging are defined in TS 32.240 [1]. This information
    is referenced in section 5.2.1 of the document, which is part of the '5.2 Online
    charging scenario' chapter.
- source_sentence: What are the possible values for the 'ReportingLevel' enumeration,
    and what do they indicate?
  sentences:
  - If protected User Plane (UP) messages reach the SN before the SN has received
    the SN Counter value in the SN Reconfiguration Complete message, the SN chooses
    the first unused KSN key of the UE to establish the security association. This
    ensures that communication can proceed securely even if the SN Counter value has
    not yet been received. Once the SN Counter value is received, the SN verifies
    it to ensure there is no KSN mismatch.
  - 'The ''ReportingLevel'' enumeration has three possible values: ''SER_ID_LEVEL'',
    ''RAT_GR_LEVEL'', and ''SPON_CON_LEVEL''. ''SER_ID_LEVEL'' indicates that usage
    should be reported at the service ID and rating group combination level. ''RAT_GR_LEVEL''
    indicates that usage should be reported at the rating group level. ''SPON_CON_LEVEL''
    indicates that usage should be reported at the sponsor identity and rating group
    combination level. These levels help in categorizing and reporting usage data
    based on different granularities.'
  - Structured data types in the Nudr_GroupIDmap Service API are more complex than
    simple data types. While simple data types represent single values like integers
    or strings, structured data types are composed of multiple simple data types or
    other structured data types, forming a more complex data structure. For example,
    a structured data type might represent a user profile containing fields for name,
    age, and address, each of which could be a simple data type. This allows for the
    representation of more intricate and hierarchical data within the API.
- source_sentence: What is the purpose of the Intermediate Spending Limit Report Request
    procedure described in the document?
  sentences:
  - The Resource URI variables defined in table 6.1.3.8.2-1 for the 'sm-data' resource
    serve to dynamically construct the URI based on specific parameters. These variables
    include {apiRoot}, <apiVersion>, and {supi}. The {apiRoot} variable specifies
    the base URL of the API, <apiVersion> indicates the version of the API to be used,
    and {supi} represents the Subscription Permanent Identifier, which is used to
    uniquely identify the subscriber. These variables ensure that the URI is correctly
    formatted and points to the appropriate resource for the given subscriber and
    API version.
  - The purpose of the Intermediate Spending Limit Report Request procedure is to
    allow the PCF (Policy Control Function) to request the status of additional policy
    counters available at the CHF (Charging Function) or to remove the request for
    the status of policy counters. The PCF can modify the list of subscribed policy
    counters based on its policy decisions, and the CHF responds by providing the
    policy counter status, optionally including pending statuses and their activation
    times, for the requested policy counters.
  - When ABC online charging is employed, the TDF uses Debit / Reserve Units Request[Initial],
    update, or termination to convey charging information related to the detected
    application traffic. The OCS responds with Debit / Reserve Units Response, which
    includes quotas for rating groups or instructions on handling the application
    traffic (e.g., terminate, continue, reroute). The TDF must request a quota before
    service delivery. If only certain quotas are authorized by the OCS (e.g., due
    to insufficient credit), the rating groups without authorized quotas are handled
    according to the received Result Code value. The quota supervision mechanism is
    further described in TS 32.299 [50].
- source_sentence: What types of data structures are supported by the GET request
    body on the resource described in table 5.2.11.3.4-2, and how do they influence
    the request?
  sentences:
  - In Direct Communication mode, the NF Service consumer can subscribe to status
    change notifications of NF instances from the NRF. If the NF Service consumer
    is notified by the NRF or detects by itself (e.g., through a lack of response
    to a request) that the NF producer instance is no longer available, it selects
    another available NF producer instance within the same NF Set. In Indirect Communication
    mode, the SCP or NF Service consumer may also subscribe to status change notifications
    from the NRF and select another NF producer instance within the same NF Set if
    the original instance serving the UE becomes unavailable. The specific implementation
    details of how the SCP detects the unavailability of an NF producer instance are
    left to the implementation.
  - The data structures supported by the GET request body on the resource are detailed
    in table 5.2.11.3.4-2. These structures define the format and content of the data
    that can be sent in the request body. They might include fields such as 'filterCriteria',
    'sortOrder', or 'pagination', which influence how the server processes the request
    and returns the appropriate data.
  - 'The specific triggers on the Ro interface that can lead to the termination of
    the IMS service include: 1) Reception of an unsuccessful Operation Result different
    from DIAMETER_CREDIT_CONTROL_NOT_APPLICABLE in the Debit/Reserve Units Response
    message. 2) Reception of an unsuccessful Result Code different from DIAMETER_CREDIT_CONTROL_NOT_APPLICABLE
    within the multiple units operation in the Debit/Reserve Units Response message
    when only one instance of the multiple units operation field is used. 3) Execution
    of the termination action procedure as defined in TS 32.299 when only one instance
    of the Multiple Unit Operation field is used. 4) Execution of the failure handling
    procedures when the Failure Action is set to ''Terminate'' or ''Retry & Terminate''.
    5) Reception in the IMS-GWF of an Abort-Session-Request message from OCS.'
model-index:
- name: BGE_base_3gpp-qa-v2_Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8347103013864849
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9628129405256866
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9806391748898128
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9927196159954319
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8347103013864849
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32093764684189546
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1961278349779626
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09927196159954321
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8347103013864849
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9628129405256866
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9806391748898128
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9927196159954319
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9235193716202091
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9002603606826465
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9006611894428589
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8341214467978801
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9630270694669973
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.980835459752681
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9925947074463339
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8341214467978801
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32100902315566576
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19616709195053625
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09925947074463341
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8341214467978801
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9630270694669973
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.980835459752681
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9925947074463339
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9232781516394674
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8999735171216805
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9003855301087177
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8326047001302618
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9624382148783927
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9801930729287486
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9922913581128102
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8326047001302618
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3208127382927975
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19603861458574973
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09922913581128105
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8326047001302618
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9624382148783927
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9801930729287486
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9922913581128102
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9223721780180253
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.898869719250338
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8993021227310489
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.8294462982459271
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9610642208383148
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9796399064970289
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.991720347602648
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8294462982459271
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3203547402794382
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19592798129940583
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09917203476026483
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8294462982459271
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9610642208383148
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9796399064970289
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.991720347602648
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9204835891487085
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8965493659262566
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.897020544909686
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.8210595813779198
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9574775610713585
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9771595795935119
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9906497028960939
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8210595813779198
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3191591870237861
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19543191591870243
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09906497028960942
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8210595813779198
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9574775610713585
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9771595795935119
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9906497028960939
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9158816707476002
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8908051588080549
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8913320555914594
      name: Cosine Map@100
---

# BGE_base_3gpp-qa-v2_Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("iris49/3gpp-embedding-model-v0")
# Run inference
sentences = [
    'What types of data structures are supported by the GET request body on the resource described in table 5.2.11.3.4-2, and how do they influence the request?',
    "The data structures supported by the GET request body on the resource are detailed in table 5.2.11.3.4-2. These structures define the format and content of the data that can be sent in the request body. They might include fields such as 'filterCriteria', 'sortOrder', or 'pagination', which influence how the server processes the request and returns the appropriate data.",
    "The specific triggers on the Ro interface that can lead to the termination of the IMS service include: 1) Reception of an unsuccessful Operation Result different from DIAMETER_CREDIT_CONTROL_NOT_APPLICABLE in the Debit/Reserve Units Response message. 2) Reception of an unsuccessful Result Code different from DIAMETER_CREDIT_CONTROL_NOT_APPLICABLE within the multiple units operation in the Debit/Reserve Units Response message when only one instance of the multiple units operation field is used. 3) Execution of the termination action procedure as defined in TS 32.299 when only one instance of the Multiple Unit Operation field is used. 4) Execution of the failure handling procedures when the Failure Action is set to 'Terminate' or 'Retry & Terminate'. 5) Reception in the IMS-GWF of an Abort-Session-Request message from OCS.",
]
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]
```

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</details>
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### Downstream Usage (Sentence Transformers)

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<details><summary>Click to expand</summary>

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.8347     | 0.8341     | 0.8326     | 0.8294     | 0.8211     |
| cosine_accuracy@3   | 0.9628     | 0.963      | 0.9624     | 0.9611     | 0.9575     |
| cosine_accuracy@5   | 0.9806     | 0.9808     | 0.9802     | 0.9796     | 0.9772     |
| cosine_accuracy@10  | 0.9927     | 0.9926     | 0.9923     | 0.9917     | 0.9906     |
| cosine_precision@1  | 0.8347     | 0.8341     | 0.8326     | 0.8294     | 0.8211     |
| cosine_precision@3  | 0.3209     | 0.321      | 0.3208     | 0.3204     | 0.3192     |
| cosine_precision@5  | 0.1961     | 0.1962     | 0.196      | 0.1959     | 0.1954     |
| cosine_precision@10 | 0.0993     | 0.0993     | 0.0992     | 0.0992     | 0.0991     |
| cosine_recall@1     | 0.8347     | 0.8341     | 0.8326     | 0.8294     | 0.8211     |
| cosine_recall@3     | 0.9628     | 0.963      | 0.9624     | 0.9611     | 0.9575     |
| cosine_recall@5     | 0.9806     | 0.9808     | 0.9802     | 0.9796     | 0.9772     |
| cosine_recall@10    | 0.9927     | 0.9926     | 0.9923     | 0.9917     | 0.9906     |
| **cosine_ndcg@10**  | **0.9235** | **0.9233** | **0.9224** | **0.9205** | **0.9159** |
| cosine_mrr@10       | 0.9003     | 0.9        | 0.8989     | 0.8965     | 0.8908     |
| cosine_map@100      | 0.9007     | 0.9004     | 0.8993     | 0.897      | 0.8913     |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 56,041 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 15 tokens</li><li>mean: 30.56 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 109.65 tokens</li><li>max: 298 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What does the 'dataStatProps' attribute represent in the 'AnalyticsMetadataInfo' type, and what is its data type?</code>            | <code>The 'dataStatProps' attribute in the 'AnalyticsMetadataInfo' type represents a list of dataset statistical properties of the data used to generate the analytics. It is defined as an optional attribute with a data type of 'array(DatasetStatisticalProperty)' and a cardinality of 1..N, meaning it can contain one or more elements.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                  |
  | <code>Why is it important to have standardized methods for resource management in the Nudm_SubscriberDataManagement Service API?</code>   | <code>Standardized methods for resource management in the Nudm_SubscriberDataManagement Service API are important because they ensure uniformity, predictability, and compatibility across different implementations and systems. This standardization facilitates seamless integration, reduces errors, and enhances the efficiency of managing subscriber data, which is critical for maintaining reliable communication services.</code>                                                                                                                                                                                                                                                                                                                                            |
  | <code>What is the purpose of the Nsmf_PDUSession_SMContextStatusNotify service operation in the context of I-SMF context transfer?</code> | <code>The Nsmf_PDUSession_SMContextStatusNotify service operation is used by the SMF (Session Management Function) to notify its consumers about the status of an SM (Session Management) context related to a PDU (Packet Data Unit) Session. In the context of I-SMF (Intermediate SMF) context transfer, this service operation is used to indicate the transfer of the SM context to a new I-SMF or SMF set. It also allows the SMF to update the SMF-derived CN (Core Network) assisted RAN (Radio Access Network) parameters tuning in the AMF (Access and Mobility Management Function). Additionally, it can report DDN (Downlink Data Notification) failures and provide target DNAI (Data Network Access Identifier) information for the current or next PDU session.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "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`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: 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}
- `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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.0913     | 10      | 1.4273        | -                      | -                      | -                      | -                      | -                     |
| 0.1826     | 20      | 0.5399        | -                      | -                      | -                      | -                      | -                     |
| 0.2740     | 30      | 0.1252        | -                      | -                      | -                      | -                      | -                     |
| 0.3653     | 40      | 0.0625        | -                      | -                      | -                      | -                      | -                     |
| 0.4566     | 50      | 0.0507        | -                      | -                      | -                      | -                      | -                     |
| 0.5479     | 60      | 0.0366        | -                      | -                      | -                      | -                      | -                     |
| 0.6393     | 70      | 0.029         | -                      | -                      | -                      | -                      | -                     |
| 0.7306     | 80      | 0.0239        | -                      | -                      | -                      | -                      | -                     |
| 0.8219     | 90      | 0.0252        | -                      | -                      | -                      | -                      | -                     |
| 0.9132     | 100     | 0.0237        | -                      | -                      | -                      | -                      | -                     |
| 0.9954     | 109     | -             | 0.9199                 | 0.9195                 | 0.9180                 | 0.9150                 | 0.9081                |
| 1.0046     | 110     | 0.026         | -                      | -                      | -                      | -                      | -                     |
| 1.0959     | 120     | 0.017         | -                      | -                      | -                      | -                      | -                     |
| 1.1872     | 130     | 0.02          | -                      | -                      | -                      | -                      | -                     |
| 1.2785     | 140     | 0.0125        | -                      | -                      | -                      | -                      | -                     |
| 1.3699     | 150     | 0.0134        | -                      | -                      | -                      | -                      | -                     |
| 1.4612     | 160     | 0.0128        | -                      | -                      | -                      | -                      | -                     |
| 1.5525     | 170     | 0.0123        | -                      | -                      | -                      | -                      | -                     |
| 1.6438     | 180     | 0.0097        | -                      | -                      | -                      | -                      | -                     |
| 1.7352     | 190     | 0.0101        | -                      | -                      | -                      | -                      | -                     |
| 1.8265     | 200     | 0.0124        | -                      | -                      | -                      | -                      | -                     |
| 1.9178     | 210     | 0.0116        | -                      | -                      | -                      | -                      | -                     |
| 2.0        | 219     | -             | 0.9220                 | 0.9216                 | 0.9206                 | 0.9184                 | 0.9130                |
| 2.0091     | 220     | 0.012         | -                      | -                      | -                      | -                      | -                     |
| 2.1005     | 230     | 0.0111        | -                      | -                      | -                      | -                      | -                     |
| 2.1918     | 240     | 0.0101        | -                      | -                      | -                      | -                      | -                     |
| 2.2831     | 250     | 0.0101        | -                      | -                      | -                      | -                      | -                     |
| 2.3744     | 260     | 0.009         | -                      | -                      | -                      | -                      | -                     |
| 2.4658     | 270     | 0.0103        | -                      | -                      | -                      | -                      | -                     |
| 2.5571     | 280     | 0.009         | -                      | -                      | -                      | -                      | -                     |
| 2.6484     | 290     | 0.0083        | -                      | -                      | -                      | -                      | -                     |
| 2.7397     | 300     | 0.0076        | -                      | -                      | -                      | -                      | -                     |
| 2.8311     | 310     | 0.0093        | -                      | -                      | -                      | -                      | -                     |
| 2.9224     | 320     | 0.0104        | -                      | -                      | -                      | -                      | -                     |
| 2.9954     | 328     | -             | 0.9234                 | 0.9230                 | 0.9221                 | 0.9201                 | 0.9156                |
| 3.0137     | 330     | 0.0104        | -                      | -                      | -                      | -                      | -                     |
| 3.1050     | 340     | 0.0089        | -                      | -                      | -                      | -                      | -                     |
| 3.1963     | 350     | 0.0084        | -                      | -                      | -                      | -                      | -                     |
| 3.2877     | 360     | 0.0082        | -                      | -                      | -                      | -                      | -                     |
| 3.3790     | 370     | 0.0089        | -                      | -                      | -                      | -                      | -                     |
| 3.4703     | 380     | 0.0083        | -                      | -                      | -                      | -                      | -                     |
| 3.5616     | 390     | 0.0061        | -                      | -                      | -                      | -                      | -                     |
| 3.6530     | 400     | 0.0065        | -                      | -                      | -                      | -                      | -                     |
| 3.7443     | 410     | 0.0063        | -                      | -                      | -                      | -                      | -                     |
| 3.8356     | 420     | 0.0084        | -                      | -                      | -                      | -                      | -                     |
| 3.9269     | 430     | 0.0083        | -                      | -                      | -                      | -                      | -                     |
| **3.9817** | **436** | **-**         | **0.9235**             | **0.9233**             | **0.9224**             | **0.9205**             | **0.9159**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.2.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@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
```bibtex
@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
```bibtex
@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}
}
```

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