Instructions to use IoannisKat1/intfloat-multilingual-e5-large-new2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IoannisKat1/intfloat-multilingual-e5-large-new2 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("IoannisKat1/intfloat-multilingual-e5-large-new2")

sentences = [
    "When should supervisory authorities monitor the application of the provisions?",
    "**Court (Civil/Criminal): Civil**  \n**Provisions:**  \n**Time of commission of the act:**  \n**Outcome (not guilty, guilty):**  \n**Reasoning:** Partially accepts the lawsuit.  \n**Facts:** The plaintiff, who works as a lawyer, maintains a savings account with the defendant banking corporation under account number GR.............. Pursuant to a contract dated June 11, 2010, established in Thessaloniki between the defendant and the plaintiff, the plaintiff was granted access to the electronic banking system (e-banking) to conduct banking transactions remotely. On October 10, 2020, the plaintiff fell victim to electronic fraud through the \"phishing\" method, whereby an unknown perpetrator managed to extract and transfer €3,000.00 from the plaintiff’s account to another account of the same bank. Specifically, on that day at 6:51 a.m., the plaintiff received an email from the sender \".........\", with the address ..........., informing him that his debit card had been suspended and that online payments and cash withdrawals could not be made until the issue was resolved. The email urged him to confirm his details within the next 72 hours by following a link titled \"card activation.\"  \nThe plaintiff read the above email on his mobile phone around 8:00 a.m., and believing it came from the defendant, he followed the instructions and accessed a website that was identical (a clone) to that of the defendant. On this page, he was asked to enter his login credentials to connect to the service, which he did, and he was subsequently asked to input his debit card details for the alleged activation, which he also provided. Then, to complete the process, a number was sent to his mobile phone at 8:07 a.m. from the sender ........, which he entered, and two minutes later he received a message from the same sender in English stating that the quick access code had been activated on his mobile. A few minutes later, at 8:18 a.m., he received an email from the defendant informing him of the transfer of €3,000.00 from his account to account number GR ........... held at the same bank, with the beneficiary's details being .......... As soon as the plaintiff read this, he immediately called the defendant's call center and canceled his debit card, the access codes for the service ......., and locked the application .......... At the same time, he verbally submitted a request to dispute and cancel the contested transaction, and in a subsequent phone call, he also canceled his credit card. On the same day, he also sent an email to the defendant informing them in writing of the above and requesting the cancellation of the transaction and the return of the amount of €3,000.00 to his account, as this transfer was not made by him but by an unknown perpetrator through electronic fraud and was not approved by him. It should also be noted that the plaintiff, as the sole beneficiary according to the aforementioned contract for using the defendant's Internet Banking service, never received any update via SMS or the VIBER application from the bank regarding the transaction details before its completion, nor did he receive a one-time code (OTP) to approve the contested transaction. He subsequently filed a complaint against unknown persons at the Cyber Crime Division for the crime of fraud. The defendant sent an email to the plaintiff on October 16, 2020, informing him that his request had been forwarded to the appropriate department of the bank for investigation, stating that the bank would never send him an email or SMS asking him to enter his personal data and that as of October 7, 2020, there was a notice posted for its customers regarding malicious attempts to steal personal data in the \"Our News\" section on ....... A month after the disputed incident, on November 10, 2020, an amount of €2,296.82 was transferred to the plaintiff's account from the account to which the fraudulent credit had been made. The plaintiff immediately sent an email to the defendant asking to be informed whether this transfer was a return of part of the amount that had been illegally withdrawn from his account and requested the return of the remaining amount of €703.18. In its response dated January 13, 2021, the defendant confirmed that the aforementioned amount indeed came from the account to which the fraudulent credit had been made, following a freeze of that account initiated by the defendant during the investigation of the incident, but refused to return the remaining amount, claiming it bore no responsibility for the leak of the personal codes to third parties, according to the terms of the service contract established between them.  \nFrom the entirety of the evidence presented to the court, there is no indication of the authenticity of the contested transaction, as the plaintiff did not give his consent for the execution of the transfer of the amount of €3,000.00, especially in light of the provision in Article 72 paragraph 2 of Law 4537/2018 stating that the mere use of the Internet Banking service by the plaintiff does not necessarily constitute sufficient evidence that the payer approved the payment action. Specifically, it was proven that the contested transaction was not carried out following a strong identification of the plaintiff – the sole beneficiary of the account – and his approval, as the latter may have entered his personal codes on the counterfeit website; however, he was never informed, before the completion of the contested transaction, of the amount that would be transferred from his account to a third-party account, nor did he receive on his mobile phone, either via SMS or through the VIBER application or any other means, the one-time code - extra PIN for its completion, which he was required to enter to approve the contested transaction (payment action) and thus complete his identification, a fact that was not countered by any evidence from the defendant. Furthermore, it is noted that the defendant's claims that it bears no responsibility under the terms of the banking services contract, whereby it is not liable for any damage to its customer in cases of unauthorized use of their personal access codes to the Internet Banking service, are to be rejected as fundamentally unfounded. This is because the aforementioned contractual terms are invalid according to the provision of Article 103 of Law 4537/2018, as they contradict the provisions of Articles 71, 73, and 92 of the same Law, which provide for the provider's universal liability and its exemption only for unusual and unforeseen circumstances that are beyond the control of the party invoking them and whose consequences could not have been avoided despite all efforts to the contrary; these provisions establish mandatory law in favor of users, as according to Article 103 of Law 4537/2018, payment service providers are prohibited from deviating from the provisions to the detriment of payment service users, unless the possibility of deviation is explicitly provided and they can decide to offer only more favorable terms to payment service users; the aforementioned contractual terms do not constitute more favorable terms but rather disadvantageous terms for the payment service user. In this case, however, the defendant did not prove the authenticity of the transaction and its approval by the plaintiff and did not invoke, nor did any unusual and unforeseen circumstances beyond its control, the consequences of which could not have been avoided despite all efforts to the contrary, come to light. Therefore, the contested transaction transferring the amount of €3,000.00 is considered, in the absence of demonstrable consent from the plaintiff, unapproved according to the provisions of Article 64 of Law 4537/2018, and the defendant's contrary claims are rejected, especially since the plaintiff proceeded, according to Article 71 paragraph 1 of Law 4537/2018, without undue delay to notify the defendant regarding the contested unapproved payment action. Consequently, the defendant is liable for compensating the plaintiff for the positive damage he suffered under Article 73 of Law 4537/2018 and is obliged to pay him the requested amount of €703.18, while the plaintiff’s fault in the occurrence of this damage cannot be established, as he entered his personal details in an online environment that was a faithful imitation of that of the defendant, as evidenced by the comparison of the screenshots of the fake website and the real website provided by the plaintiff, a fact that he could not have known while being fully convinced that he was transacting with the defendant. Furthermore, the defendant’s liability to compensate the plaintiff is based on the provision of Article 8 of Law 2251/1994, which applies in this case, as the plaintiff's damage resulted from inadequate fulfillment of its obligations in the context of providing its services, but also on the provision of Article 914 of the Civil Code in the sense of omission on its part of unlawfully and culpably imposed actions. In this case, given that during the relevant period there had been a multitude of similar incidents of fraud against the defendant's customers, the latter, as a service provider to the consumer public and bearing transactional obligations of care and security towards them, displayed gross negligence regarding the security provided for electronic transaction services, which was compromised by the fraudulent theft of funds, as it did not comply with all required high-security measures for executing the contested transaction, failing to implement the strict customer identification verification process and to check the authenticity of the account to which the funds were sent, thus not assuming the suspicious nature of the transaction, did not adopt comprehensive and improved protective measures to fully protect its customers against malicious attacks and online fraud and to prevent the infiltration of unauthorized third parties, nor did it fulfill its obligations to inform, accurately inform, and warn its consumers - customers, as it failed to adequately inform them of attempts to steal their personal data through the sending of informative emails or SMS, while merely posting in a section rather than on a central banner (as it later did) does not constitute adequate information such that it meets the requirement of protecting its customers and the increased safeguarding of their interests. Although the plaintiff acted promptly and informed the defendant on the same day about the contested incident, the defendant did not act as promptly regarding the investigation of the incident and the freezing of the account that held the fraudulent credit to prevent the plaintiff's loss, but only returned part of the funds to the plaintiff a month later. This behavior, beyond being culpable due to gross negligence, was also unlawful, as it would have been illegal even without the contractual relationship, as contrary to the provisions of Law 4537/2018 and Law 2251/1994, regarding the lack of security of the services that the consumer is legitimately entitled to expect, as well as the building of trust that is essential in banking transactions, elements that it was obligated to provide within the sphere of the services offered, and contrary to the principles of good faith and commercial ethics, as crystallized in the provision of Article 288 of the Civil Code, as well as the general duty imposed by Article 914 of the Civil Code not to cause harm to another culpably. This resulted not only in positive damage to the plaintiff but also in causing him moral harm consisting of his mental distress and the disruption, agitation, and sorrow he experienced, for which he must be awarded financial compensation. Taking into account all the general circumstances of the case, the extent of the plaintiff's damage, the severity of the defendant's fault, the mental distress suffered by the plaintiff, the insecurity he felt regarding his deposits, the sorrow he experienced, and the stress caused by his financial loss, which occurred during the pandemic period when his earnings from his professional activity had significantly decreased, as well as the financial and social situation of the parties, it is the court's opinion that he should be granted, as financial compensation for his moral harm, an amount of €250.00, which is deemed reasonable and fair. Therefore, the total monetary amount that the plaintiff is entitled to for his positive damage and financial compensation for the moral harm suffered amounts to a total of (€703.18 + €250.00) = €953.18.",
    "Court (Civil/Criminal): Criminal  \nProvisions: Article 42 paragraphs 1, 2, 3, and 7 of Law 4557/2018  \nTime of commission of the act:  \nOutcome (not guilty, guilty):  \nReasoning: Obligation of the payment service provider, such as banks, to inform their contracting customer after receiving a relevant order for a payment to be made on their behalf. Content of the above notification at the stage of receiving the payment order and during its execution. Terms of liability for the provider regarding compensation for non-execution, erroneous, or delayed execution of payment transactions. In particular, in the case of an unauthorized or erroneous payment, the user is required to notify the provider within a specified timeframe as soon as they become aware of the corresponding transaction. The provisions of Law 4357/2018 establish mandatory legal regulations in favor of users of payment services and cannot be contractually modified to their detriment, but only to their benefit. Defenses available to payment service providers to relieve them of liability. Burden of proof distribution between the parties. This responsibility of banks may also stem from Law 2251/1994, as they provide services to the public and are considered suppliers. Conditions for supplier liability under the aforementioned legislation. Distribution of the burden of proof between the litigants to demonstrate liability for compensation under Law 2251/1994. Terms of concurrency between contractual and tort liability for compensation. The court partially accepts the lawsuit.  \nFacts: On 01/09/2021, an unknown perpetrator sent an email to her from the electronic address “...............”, in which they stated that for security reasons she needed to confirm her account with the bank ....... Not realizing that it was a scam, she followed the attached hyperlink, entered her personal information, resulting in an unknown perpetrator intercepting her online banking credentials and making a transfer totaling 7,000.00 euros to account number ................ of the bank .........  \nC) The beneficiary of the aforementioned account is ......... born on 16/05/1995 in the Municipality of ........., residing at .............., with ID number .................... issued on 02/10/2009 by T.A .............. and tax number ............. from the tax office ...................  \nD) The criminal proceeds reportedly arising from the above criminal activity amount to a total of seven thousand euros (7,000.00€). Following the above, serious suspicions arise that the aforementioned criminal proceeds transferred to the aforementioned bank account were unlawfully appropriated by her and subsequently mixed with other legally held assets, which she used in her overall economic activities, aiming to launder them, thus concealing their true origin and making it impossible for them to be seized. Therefore, there are reasonable suspicions that the aforementioned individual committed not only the primary offense but also the criminal act of “Money Laundering” (Article 2 §1 a, d of Law 4557/2018, in conjunction with Article 4 subparagraph z of the same law as it stands, as well as Article 39 paragraph 1 subparagraphs a & c of the same Law 4557/2018). Because there are serious suspicions that the bank account numbered .......... maintained at the bank ....................., whose beneficiary is ..... (Tax ID ....................), contains part of the monetary amount from the aforementioned criminal activity that was placed behind banking secrecy to conceal its true origin and ultimately to launder it. Because, in this case, part of the criminal proceeds has been found while the remainder has not been found in its entirety, there is a lawful reason and an urgent case for prohibiting the sale or any other transfer of the following assets, given that they are subject to seizure and forfeiture according to Articles 40 and 42 of Law 4557/2018 as they currently stand.",
    "The supervisory authorities should monitor the application of the provisions pursuant to this Regulation and contribute to its consistent application throughout the Union, in order to protect natural persons in relation to the processing of their personal data and to facilitate the free flow of personal data within the internal market. For that purpose, the supervisory authorities should cooperate with each other and with the Commission, without the need for any agreement between Member States on the provision of mutual assistance or on such cooperation."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large. It maps sentences & paragraphs to a 1024-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: intfloat/multilingual-e5-large
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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): 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("IoannisKat1/intfloat-multilingual-e5-large-new2")
# Run inference
sentences = [
    'What can the contract be based on, besides individual contracts, in part according to this excerpt?',
    "1.Where processing is to be carried out on behalf of a controller, the controller shall use only processors providing sufficient guarantees to implement appropriate technical and organisational measures in such a manner that processing will meet the requirements of this Regulation and ensure the protection of the rights of the data subject.\n2.The processor shall not engage another processor without prior specific or general written authorisation of the controller. In the case of general written authorisation, the processor shall inform the controller of any intended changes concerning the addition or replacement of other processors, thereby giving the controller the opportunity to object to such changes.\n3.Processing by a processor shall be governed by a contract or other legal act under Union or Member State law, that is binding on the processor with regard to the controller and that sets out the subject-matter and duration of the processing, the nature and purpose of the processing, the type of personal data and categories of data subjects and the obligations and rights of the controller. That contract or other legal act shall stipulate, in particular, that the processor: (a)  processes the personal data only on documented instructions from the controller, including with regard to transfers of personal data to a third country or an international organisation, unless required to do so by Union or Member State law to which the processor is subject; in such a case, the processor shall inform the controller of that legal requirement before processing, unless that law prohibits such information on important grounds of public interest; (b)  ensures that persons authorised to process the personal data have committed themselves to confidentiality or are under an appropriate statutory obligation of confidentiality; (c)  takes all measures required pursuant to Article 32; (d)  respects the conditions referred to in paragraphs 2 and 4 for engaging another processor; (e)  taking into account the nature of the processing, assists the controller by appropriate technical and organisational measures, insofar as this is possible, for the fulfilment of the controller's obligation to respond to requests for exercising the data subject's rights laid down in Chapter III; (f)  assists the controller in ensuring compliance with the obligations pursuant to Articles 32 to 36 taking into account the nature of processing and the information available to the processor; (g)  at the choice of the controller, deletes or returns all the personal data to the controller after the end of the provision of services relating to processing, and deletes existing copies unless Union or Member State law requires storage of the personal data; (h)  makes available to the controller all information necessary to demonstrate compliance with the obligations laid down in this Article and allow for and contribute to audits, including inspections, conducted by the controller or another auditor mandated by the controller. 4.5.2016 L 119/49   With regard to point (h) of the first subparagraph, the processor shall immediately inform the controller if, in its opinion, an instruction infringes this Regulation or other Union or Member State data protection provisions.\n4.Where a processor engages another processor for carrying out specific processing activities on behalf of the controller, the same data protection obligations as set out in the contract or other legal act between the controller and the processor as referred to in paragraph 3 shall be imposed on that other processor by way of a contract or other legal act under Union or Member State law, in particular providing sufficient guarantees to implement appropriate technical and organisational measures in such a manner that the processing will meet the requirements of this Regulation. Where that other processor fails to fulfil its data protection obligations, the initial processor shall remain fully liable to the controller for the performance of that other processor's obligations.\n5.Adherence of a processor to an approved code of conduct as referred to in Article 40 or an approved certification mechanism as referred to in Article 42 may be used as an element by which to demonstrate sufficient guarantees as referred to in paragraphs 1 and 4 of this Article.\n6.Without prejudice to an individual contract between the controller and the processor, the contract or the other legal act referred to in paragraphs 3 and 4 of this Article may be based, in whole or in part, on standard contractual clauses referred to in paragraphs 7 and 8 of this Article, including when they are part of a certification granted to the controller or processor pursuant to Articles 42 and 43\n7.The Commission may lay down standard contractual clauses for the matters referred to in paragraph 3 and 4 of this Article and in accordance with the examination procedure referred to in Article 93(2).\n8.A supervisory authority may adopt standard contractual clauses for the matters referred to in paragraph 3 and 4 of this Article and in accordance with the consistency mechanism referred to in Article 63\n9.The contract or the other legal act referred to in paragraphs 3 and 4 shall be in writing, including in electronic form.\n10.Without prejudice to Articles 82, 83 and 84, if a processor infringes this Regulation by determining the purposes and means of processing, the processor shall be considered to be a controller in respect of that processing.",
    '1.The Board shall draw up an annual report regarding the protection of natural persons with regard to processing in the Union and, where relevant, in third countries and international organisations. The report shall be made public and be transmitted to the European Parliament, to the Council and to the Commission.\n2.The annual report shall include a review of the practical application of the guidelines, recommendations and best practices referred to in point (l) of Article 70(1) as well as of the binding decisions referred to in Article 65.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4491, 0.1215],
#         [0.4491, 1.0000, 0.2320],
#         [0.1215, 0.2320, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 1024
}
```

Metric	Value
cosine_accuracy@1	0.3965
cosine_accuracy@3	0.4419
cosine_accuracy@5	0.4773
cosine_accuracy@10	0.5303
cosine_precision@1	0.3965
cosine_precision@3	0.3864
cosine_precision@5	0.3682
cosine_precision@10	0.329
cosine_recall@1	0.083
cosine_recall@3	0.2081
cosine_recall@5	0.2836
cosine_recall@10	0.3941
cosine_ndcg@10	0.4599
cosine_mrr@10	0.4271
cosine_map@100	0.5169

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.3939
cosine_accuracy@3	0.4419
cosine_accuracy@5	0.5025
cosine_accuracy@10	0.5505
cosine_precision@1	0.3939
cosine_precision@3	0.3855
cosine_precision@5	0.3763
cosine_precision@10	0.3447
cosine_recall@1	0.0817
cosine_recall@3	0.2045
cosine_recall@5	0.2833
cosine_recall@10	0.4024
cosine_ndcg@10	0.4717
cosine_mrr@10	0.431
cosine_map@100	0.5281

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.3788
cosine_accuracy@3	0.4318
cosine_accuracy@5	0.4848
cosine_accuracy@10	0.5303
cosine_precision@1	0.3788
cosine_precision@3	0.3729
cosine_precision@5	0.3606
cosine_precision@10	0.326
cosine_recall@1	0.0797
cosine_recall@3	0.2026
cosine_recall@5	0.2789
cosine_recall@10	0.3943
cosine_ndcg@10	0.4537
cosine_mrr@10	0.4149
cosine_map@100	0.5082

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.3712
cosine_accuracy@3	0.399
cosine_accuracy@5	0.4419
cosine_accuracy@10	0.4949
cosine_precision@1	0.3712
cosine_precision@3	0.3552
cosine_precision@5	0.3328
cosine_precision@10	0.2995
cosine_recall@1	0.0798
cosine_recall@3	0.1976
cosine_recall@5	0.26
cosine_recall@10	0.3649
cosine_ndcg@10	0.4262
cosine_mrr@10	0.3972
cosine_map@100	0.4892

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.3359
cosine_accuracy@3	0.3763
cosine_accuracy@5	0.4192
cosine_accuracy@10	0.4798
cosine_precision@1	0.3359
cosine_precision@3	0.3266
cosine_precision@5	0.3111
cosine_precision@10	0.2803
cosine_recall@1	0.0742
cosine_recall@3	0.1853
cosine_recall@5	0.2527
cosine_recall@10	0.3572
cosine_ndcg@10	0.4004
cosine_mrr@10	0.3675
cosine_map@100	0.4576

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.2778
cosine_accuracy@3	0.3157
cosine_accuracy@5	0.3434
cosine_accuracy@10	0.3864
cosine_precision@1	0.2778
cosine_precision@3	0.2685
cosine_precision@5	0.253
cosine_precision@10	0.2222
cosine_recall@1	0.0651
cosine_recall@3	0.1629
cosine_recall@5	0.2204
cosine_recall@10	0.3079
cosine_ndcg@10	0.3306
cosine_mrr@10	0.303
cosine_map@100	0.3986

Training Details

Training Dataset

Unnamed Dataset

Size: 1,580 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 17.21 tokens
max: 43 tokens

min: 27 tokens
mean: 373.71 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 7 tokens mean: 17.21 tokens max: 43 tokens	min: 27 tokens mean: 373.71 tokens max: 512 tokens

Samples:

anchor	positive
`What measures should each supervisory authority take to facilitate the submission of complaints?`	Every data subject should have the right to lodge a complaint with a single supervisory authority, in particular in the Member State of his or her habitual residence, and the right to an effective judicial remedy in accordance 4.5.2016 L 119/25 Official Journal of the European Union EN with Article 47 of the Charter if the data subject considers that his or her rights under this Regulation are infringed or where the supervisory authority does not act on a complaint, partially or wholly rejects or dismisses a complaint or does not act where such action is necessary to protect the rights of the data subject. The investigation following a complaint should be carried out, subject to judicial review, to the extent that is appropriate in the specific case. The supervisory authority should inform the data subject of the progress and the outcome of the complaint within a reasonable period. If the case requires further investigation or coordination with another supervisory authority, intermed...
`What did the evidence not indicate?`	Court (Civil/Criminal): Civil Provisions: Time of commission of the act: Outcome (not guilty, guilty): Reasoning: Claim for compensation and monetary satisfaction due to moral damage against a mobile phone company and a credit institution within the framework of inadequate fulfillment of a payment services contract for "web banking." Appropriate actions for mobile phone companies in case of a request for a "sim" card replacement due to wear or loss. They must verify the customer's identity based on the personal details and identification information registered in their system but are not liable for any changes in the latter that were not timely communicated to them. Further security measures such as phone communication or sending an SMS to the mobile number holder are not required. Payment services under Law 4357/2018. Obligation of the payment service provider, such as banks, to inform the payer after receiving a relevant order for making a payment. The con...
`What was the amount transferred from her account?`	Court (Civil/Criminal): Criminal Provisions: Article 42 paragraphs 1, 2, 3, and 7 of Law 4557/2018 Time of commission of the act: Outcome (not guilty, guilty): Reasoning: Obligation of the payment service provider, such as banks, to inform their contracting customer after receiving a relevant order for a payment to be made on their behalf. Content of the above notification at the stage of receiving the payment order and during its execution. Terms of liability for the provider regarding compensation for non-execution, erroneous, or delayed execution of payment transactions. In particular, in the case of an unauthorized or erroneous payment, the user is required to notify the provider within a specified timeframe as soon as they become aware of the corresponding transaction. The provisions of Law 4357/2018 establish mandatory legal regulations in favor of users of payment services and cannot be contractually modified to their detriment, but only to their benefit. Defenses availa...

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 12
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 2
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 12
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: True
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}
tp_size: 0
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: None
hub_always_push: False
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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	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
-1	-1	-	0.4214	0.4041	0.3894	0.3254	0.2396	0.1752
0.1013	10	20.5156	-	-	-	-	-	-
0.2025	20	19.5068	-	-	-	-	-	-
0.3038	30	17.3704	-	-	-	-	-	-
0.4051	40	17.1827	-	-	-	-	-	-
0.5063	50	16.6068	-	-	-	-	-	-
0.6076	60	16.4217	-	-	-	-	-	-
0.7089	70	15.5364	-	-	-	-	-	-
0.8101	80	13.3384	-	-	-	-	-	-
0.9114	90	15.6398	-	-	-	-	-	-
0.9924	98	-	0.4681	0.4793	0.4615	0.4166	0.3638	0.2744
1.0203	100	14.2832	-	-	-	-	-	-
1.1215	110	10.0518	-	-	-	-	-	-
1.2228	120	10.3808	-	-	-	-	-	-
1.3241	130	10.9265	-	-	-	-	-	-
1.4253	140	10.2787	-	-	-	-	-	-
1.5266	150	10.9999	-	-	-	-	-	-
1.6278	160	6.8139	-	-	-	-	-	-
1.7291	170	7.986	-	-	-	-	-	-
1.8304	180	9.2866	-	-	-	-	-	-
1.9316	190	9.2912	-	-	-	-	-	-
1.9924	196	-	0.4772	0.4612	0.4645	0.3945	0.3636	0.2986
2.0405	200	9.9778	-	-	-	-	-	-
2.1418	210	7.8425	-	-	-	-	-	-
2.2430	220	7.7307	-	-	-	-	-	-
2.3443	230	6.6603	-	-	-	-	-	-
2.4456	240	5.8628	-	-	-	-	-	-
2.5468	250	7.5488	-	-	-	-	-	-
2.6481	260	8.5646	-	-	-	-	-	-
2.7494	270	7.7542	-	-	-	-	-	-
2.8506	280	6.046	-	-	-	-	-	-
2.9519	290	4.2612	-	-	-	-	-	-
2.9924	294	-	0.4663	0.4403	0.4505	0.4067	0.3673	0.3267
3.0608	300	4.7943	-	-	-	-	-	-
3.1620	310	7.1236	-	-	-	-	-	-
3.2633	320	7.8359	-	-	-	-	-	-
3.3646	330	7.2883	-	-	-	-	-	-
3.4658	340	6.8383	-	-	-	-	-	-
3.5671	350	6.1145	-	-	-	-	-	-
3.6684	360	5.8697	-	-	-	-	-	-
3.7696	370	5.3551	-	-	-	-	-	-
3.8709	380	7.7562	-	-	-	-	-	-
3.9722	390	4.1286	-	-	-	-	-	-
3.9924	392	-	0.5004	0.4837	0.4654	0.4095	0.3771	0.3238
4.0810	400	6.6456	-	-	-	-	-	-
4.1823	410	7.8539	-	-	-	-	-	-
4.2835	420	5.2917	-	-	-	-	-	-
4.3848	430	5.5573	-	-	-	-	-	-
4.4861	440	6.957	-	-	-	-	-	-
4.5873	450	6.3068	-	-	-	-	-	-
4.6886	460	6.0006	-	-	-	-	-	-
4.7899	470	6.1419	-	-	-	-	-	-
4.8911	480	5.0808	-	-	-	-	-	-
4.9924	490	6.0219	0.4752	0.4754	0.4581	0.4243	0.3931	0.3410
5.1013	500	3.7305	-	-	-	-	-	-
5.2025	510	4.827	-	-	-	-	-	-
5.3038	520	3.1179	-	-	-	-	-	-
5.4051	530	6.141	-	-	-	-	-	-
5.5063	540	6.3686	-	-	-	-	-	-
5.6076	550	4.9029	-	-	-	-	-	-
5.7089	560	3.6987	-	-	-	-	-	-
5.8101	570	5.5046	-	-	-	-	-	-
5.9114	580	5.0166	-	-	-	-	-	-
5.9924	588	-	0.4737	0.4748	0.4567	0.4185	0.3890	0.3435
6.0203	590	3.9625	-	-	-	-	-	-
6.1215	600	6.7869	-	-	-	-	-	-
6.2228	610	3.6329	-	-	-	-	-	-
6.3241	620	6.2702	-	-	-	-	-	-
6.4253	630	3.3559	-	-	-	-	-	-
6.5266	640	4.0666	-	-	-	-	-	-
6.6278	650	3.5322	-	-	-	-	-	-
6.7291	660	4.8831	-	-	-	-	-	-
6.8304	670	6.6302	-	-	-	-	-	-
6.9316	680	5.7623	-	-	-	-	-	-
6.9924	686	-	0.4687	0.4713	0.4520	0.4194	0.3950	0.3338
7.0405	690	5.5453	-	-	-	-	-	-
7.1418	700	2.8097	-	-	-	-	-	-
7.2430	710	3.5171	-	-	-	-	-	-
7.3443	720	3.5449	-	-	-	-	-	-
7.4456	730	4.6169	-	-	-	-	-	-
7.5468	740	3.567	-	-	-	-	-	-
7.6481	750	5.7251	-	-	-	-	-	-
7.7494	760	3.7201	-	-	-	-	-	-
7.8506	770	3.1051	-	-	-	-	-	-
7.9519	780	3.9642	-	-	-	-	-	-
7.9924	784	-	0.4599	0.4717	0.4537	0.4262	0.4004	0.3306
8.0608	790	3.923	-	-	-	-	-	-
8.1620	800	3.52	-	-	-	-	-	-
8.2633	810	3.1567	-	-	-	-	-	-
8.3646	820	6.1725	-	-	-	-	-	-
8.4658	830	3.259	-	-	-	-	-	-
8.5671	840	6.6232	-	-	-	-	-	-
8.6684	850	3.7085	-	-	-	-	-	-
8.7696	860	4.0311	-	-	-	-	-	-
8.8709	870	7.2503	-	-	-	-	-	-
8.9722	880	2.2984	-	-	-	-	-	-
8.9924	882	-	0.4632	0.4752	0.4550	0.4247	0.3953	0.3281
9.0810	890	4.519	-	-	-	-	-	-
9.1823	900	2.99	-	-	-	-	-	-
9.2835	910	5.3026	-	-	-	-	-	-
9.3848	920	3.8492	-	-	-	-	-	-
9.4861	930	1.9454	-	-	-	-	-	-
9.5873	940	3.538	-	-	-	-	-	-
9.6886	950	4.1874	-	-	-	-	-	-
9.7899	960	4.2356	-	-	-	-	-	-
9.8911	970	4.5356	-	-	-	-	-	-
9.9924	980	4.0243	0.4665	0.4681	0.4507	0.4236	0.3937	0.3288
-1	-1	-	0.4599	0.4717	0.4537	0.4262	0.4004	0.3306

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.51.3
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for IoannisKat1/intfloat-multilingual-e5-large-new2

Base model

intfloat/multilingual-e5-large

Finetuned

(170)

this model

Papers for IoannisKat1/intfloat-multilingual-e5-large-new2

Evaluation results

Cosine Accuracy@1 on dim 1024
self-reported

0.396
Cosine Accuracy@3 on dim 1024
self-reported

0.442
Cosine Accuracy@5 on dim 1024
self-reported

0.477
Cosine Accuracy@10 on dim 1024
self-reported

0.530
Cosine Precision@1 on dim 1024
self-reported

0.396
Cosine Precision@3 on dim 1024
self-reported

0.386
Cosine Precision@5 on dim 1024
self-reported

0.368
Cosine Precision@10 on dim 1024
self-reported

0.329