README.md

---
base_model: sentence-transformers/msmarco-distilbert-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:88018
- loss:TripletLoss
widget:
- source_sentence: How should SpinRecords.com notify NETTAXI of a potential indemnifiable
    claim?
  sentences:
  - '4. The Company shall have no obligations to Verenium with respect to the use
    of such information, or disclosure to others not party to this Agreement, of such
    information which:  (d) is rightfully and in good faith developed by Company independently
    of any disclosures made under this Agreement, as evidenced by Company’s competent
    written records; or  '
  - 13. Changes To This Privacy Policy We may update this Privacy Policy to reflect
    changes to our information practices. If we make any material changes we will
    notify you by email (sent to the e-mail address specified in your account) or
    by means of a notice on the Services prior to the change becoming effective. We
    encourage you to periodically review this page for the latest information on our
    privacy practices.
  - 7.2     Indemnification  by  NETTAXI.  NETTAXI  shall defend, indemnify and              ----------------------------
    hold  SpinRecords.com  harmless from any and all damages, liabilities, costs and
    expenses  (including, but not limited to reasonable attorneys' fees) incurred
    by SpinRecords.com  as a result of (1) any breach of this Agreement; (ii) any
    claim that  the  NETTAXI  Brand  Features  or  any  part  thereof,  infringes  or
    misappropriates  any  Intellectual Property Right of a third party; or (iii) any
    claim  arising  out  of  Spinrecords.com's display of the NETTAXI Brand Features
    SpinRecords.com  shall  provide  NETTAXI  with  written  notice of the claim and
    permit  NETTAXI  to control the defense, settlement, adjustment or compromise
    of any such claim.  SpinRecords.com may employ counsel at its own expense to assist
    it  with  respect  to any such claim; provided, however, that if such counsel
    is necessary  because of a conflict of interest of either NETTAXI or its counsel
    or because  NETTAXI  does not assume control, NETTAXI will bear the expense of
    such counsel.
- source_sentence: What types of advertisements does Crazy Labs accept within their
    apps?
  sentences:
  - Each party each agrees that it will not knowingly do anything inconsistent with
    the other party's ownership of such party's intellectual property, including without
    limitation, questioning the validity of that party's Trademarks or registering
    or attempting to register the other party's Trademarks in its own name or that
    of any other firm, person or corporation.
  - '11. Advertisements We accept advertisements, in various formats (such as banners,
    interstitials, rewarded videos, etc.) from third parties ad networks which may
    be displayed in our Crazy Labs Apps. These third parties ad networks may collect
    and use, inter alia (i) information about your visits to Crazy Labs Apps in connection
    with such marketing, sales and advertising activities; and (ii) geographic tracking
    and carrier network preferences. (iii) information, such as age, gender and logged
    from device to ensure that appropriate advertising is presented within the App
    and calculate or control the number of views of an ad, and/or deliver advertisements
    relating to User''s interests, and measure the effectiveness of advertisements
    campaigns. The delivery of advertisements to you may be based on IP address, device
    identifiers and other Personal Information gathered during your use of the Crazy
    Labs Apps. Note that third parties ad networks which are referred to in relation
    to the Crazy Labs Apps may include third parties service providers, such as Facebook
    and other ad networks, in addition to those which are listed in the following
    link: https://www.tabtale.com/3rdparties/. Note that if you click on any of these
    advertisements, the advertisers may use cookies and other web-tracking technologies
    (such as tracking pixel agent or visitor identification technology, etc.) on your
    device to collect data regarding advertisement performance, your interaction with
    such advertisements and our Crazy Labs Apps and your interests (which may include,
    non-personal and/or personal information (such as, device and network information,
    unique identifiers, gender, age and geo-location) about you) in order to serve
    you advertisements, including targeted advertisements, and for the legitimate
    business interests of such Third Parties ad networks. We recommend that you review
    the terms of use and privacy policy of any third party advertisers with whom you
    are interacting before doing so. Their privacy policy, not ours, will apply to
    any of those interactions.'
  - We want our advertising to be as relevant and interesting as the other information
    you find on our Services. With this in mind, we use all of the information we
    have about you to show you relevant ads. We do not share information that personally
    identifies you (personally identifiable information is information like name or
    email address that can by itself be used to contact you or identifies who you
    are) with advertising, measurement or analytics partners unless you give us permission.
    We may provide these partners with information about the reach and effectiveness
    of their advertising without providing information that personally identifies
    you, or if we have aggregated the information so that it does not personally identify
    you. For example, we may tell an advertiser how its ads performed, or how many
    people viewed their ads or installed an app after seeing an ad, or provide non-personally
    identifying demographic information (such as 25 year old female, in Madrid, who
    likes software engineering) to these partners to help them understand their audience
    or customers, but only after the advertiser has agreed to abide by our advertiser
    guidelines.
- source_sentence: 9.2     Nature of the Association. The parties herein are engaged
    as independent entities in accordance with this Agreement, and there exists no
    intention to forge any alternate form of association, such as a partnership, franchise,
    joint venture, agency, employer/employee relationship, fiduciary connection, or
    any other specific relationship. Each party is precluded from conducting themselves
    in any way that might suggest or insinuate any association different from that
    of an independent entity, nor shall either party possess the authority to obligate
    or commit the other party in any manner.
  sentences:
  - 'The parties hereby grant to each other non-exclusive, fully-paid, royalty-free
    licenses to utilize the other party''s trademarks, as follows: (a) Biocept Trademarks.
    To facilitate the promotion and performance of Tests, during the Term Biocept
    hereby grants Life Technologies a non-exclusive, royalty-free, non-transferable
    license to use the Biocept Trademarks solely for<omitted>use in connection with
    the promotion and performance of the Tests in the Territory.'
  - This Agreement may not be assigned or otherwise transferred, nor may any right
    or obligations hereunder be assigned or transferred, by either Party without the
    prior written consent of the other Party; provided, however, that Licensor may,
    without such consent, assign this Agreement and its rights and obligations hereunder,
    in whole or in part, to an Affiliate or in connection with the transfer or sale
    of all or substantially all of its assets related to the Licensed Product or the
    business relating thereto, or in the event of its merger or consolidation or change
    in control or similar transaction.
  - 9.2     Relationship  of  Parties.  The parties are independent contractors              -------------------------
    under  this  Agreement  and  no  other  relationship  is  intended,  including
    a partnership,  franchise,  joint  venture,  agency, employer/employee, fiduciary,
    master/servant  relationship, or other special relationship. Neither party shall
    act  in  a  manner  which expresses or implies a relationship other than that
    of independent  contractor,  nor  bind  the  other  party.
- source_sentence: In which section can I find the specifics of the 'Initial Term'?
  sentences:
  - 1.22 "Initial Term" has the meaning set forth in Section 8.1.
  - If the Reseller sells less than 50% of any year's Annual Milestone, Todos, in
    its sole discretion, may either (a) cancel the Reseller's exclusivity, and market,
    distribute, and sell the Products in the Territory directly or indirectly through
    other distributors and resellers, while leaving the Reseller with a non-exclusive
    right to distribute and sell the Products for the remainder of the term, or (b)
    terminate the Agreement upon one hundred eighty (180) days prior written notice,
    provided that the Reseller does not cure its failure to achieve 50% of the applicable
    year's Annual Milestone within the 180-day notice period.
  - 6 Term and Termination.
- source_sentence: In what circumstances can FCE assume responsibility for a Program
    Patent?
  sentences:
  - We may also collect anonymous, statistical data from users of the Services, such
    as a user's browser version, operating system version, country, page loading time,
    type of device, number of visits, time using the Services, network, demographic
    estimates, flow-through the website and/or Services or referral source, which
    may then be aggregated. We may use non-personal data that we collect from you
    to improve the Services or to support advertising services. For registered users,
    this anonymous, statistical data may include that relating to their activities,
    such as high scores, game rankings, league rankings, game challenges, avatars
    etc.
  - Notwithstanding the foregoing, in the event ExxonMobil decides not to prosecute,
    defend, enforce, maintain or decides to abandon any Program Patent, then ExxonMobil
    will provide notice thereof to FCE, and FCE will then have the right, but not
    the obligation, to prosecute or maintain the Program Patent and sole responsibility
    for the continuing costs, taxes, legal fees, maintenance fees and other fees associated
    with that Program Patent.
  - 4. Limitation of Liability of the Sponsor. The Sponsor shall not be liable for
    any error of judgment or mistake of law or for any act or omission in the oversight,
    administration or management of the Trust or the performance of its duties hereunder,
    except for willful misfeasance, bad faith or gross negligence in the performance
    of its duties, or by reason of the reckless disregard of its obligations and duties
    hereunder. As used in this Section 4, the term "Sponsor" shall include Domini
    and/or any of its affiliates and the directors, officers and employees of Domini
    and/or any of its affiliates.
model-index:
- name: SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: msmarco distilbert base v2
      type: msmarco-distilbert-base-v2
    metrics:
    - type: cosine_accuracy@1
      value: 0.6422082459818309
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8230607966457023
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.872816212438854
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9382250174703005
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6422082459818309
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27435359888190075
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1745632424877708
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09382250174703004
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6422082459818309
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8230607966457023
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.872816212438854
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9382250174703005
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.790195916846684
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7427224274289222
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7454587747656682
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.6317260656883298
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.8204053109713487
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.8735150244584207
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.9375262054507337
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.6317260656883298
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.27346843699044954
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.17470300489168414
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09375262054507337
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.6317260656883298
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.8204053109713487
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.8735150244584207
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.9375262054507337
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.7853441093620476
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.7364890242143864
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.7392413927907737
      name: Dot Map@100
---

# SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2). 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:** [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2) <!-- at revision 741fcf2d6eabaf0927bfe49c6d9c577df95d3c40 -->
- **Maximum Sequence Length:** 350 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
)
```

## 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("kperkins411/msmarco-distilbert-base-v2_triplet_legal")
# Run inference
sentences = [
    'In what circumstances can FCE assume responsibility for a Program Patent?',
    'Notwithstanding the foregoing, in the event ExxonMobil decides not to prosecute, defend, enforce, maintain or decides to abandon any Program Patent, then ExxonMobil will provide notice thereof to FCE, and FCE will then have the right, but not the obligation, to prosecute or maintain the Program Patent and sole responsibility for the continuing costs, taxes, legal fees, maintenance fees and other fees associated with that Program Patent.',
    '4. Limitation of Liability of the Sponsor. The Sponsor shall not be liable for any error of judgment or mistake of law or for any act or omission in the oversight, administration or management of the Trust or the performance of its duties hereunder, except for willful misfeasance, bad faith or gross negligence in the performance of its duties, or by reason of the reckless disregard of its obligations and duties hereunder. As used in this Section 4, the term "Sponsor" shall include Domini and/or any of its affiliates and the directors, officers and employees of Domini and/or any of its affiliates.',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `msmarco-distilbert-base-v2`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6422     |
| cosine_accuracy@3   | 0.8231     |
| cosine_accuracy@5   | 0.8728     |
| cosine_accuracy@10  | 0.9382     |
| cosine_precision@1  | 0.6422     |
| cosine_precision@3  | 0.2744     |
| cosine_precision@5  | 0.1746     |
| cosine_precision@10 | 0.0938     |
| cosine_recall@1     | 0.6422     |
| cosine_recall@3     | 0.8231     |
| cosine_recall@5     | 0.8728     |
| cosine_recall@10    | 0.9382     |
| cosine_ndcg@10      | 0.7902     |
| cosine_mrr@10       | 0.7427     |
| **cosine_map@100**  | **0.7455** |
| dot_accuracy@1      | 0.6317     |
| dot_accuracy@3      | 0.8204     |
| dot_accuracy@5      | 0.8735     |
| dot_accuracy@10     | 0.9375     |
| dot_precision@1     | 0.6317     |
| dot_precision@3     | 0.2735     |
| dot_precision@5     | 0.1747     |
| dot_precision@10    | 0.0938     |
| dot_recall@1        | 0.6317     |
| dot_recall@3        | 0.8204     |
| dot_recall@5        | 0.8735     |
| dot_recall@10       | 0.9375     |
| dot_ndcg@10         | 0.7853     |
| dot_mrr@10          | 0.7365     |
| dot_map@100         | 0.7392     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 88,018 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                            | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.42 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 102.85 tokens</li><li>max: 350 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 103.73 tokens</li><li>max: 350 tokens</li></ul> |
* Samples:
  | anchor                                                                                | positive                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                                                                                                                      |
  |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What happens if a Party fails to retain records for the required period?</code> | <code>Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation.</code> | <code>Either party hereto may terminate this Agreement after the Initial Period upon at least six (6) months' prior written notice to the other party thereof.</code>                                                                                                                                                                                                                         |
  | <code>What happens if a Party fails to retain records for the required period?</code> | <code>Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation.</code> | <code>The Agreement may be terminated by both Parties with a notification period of *** before the end of the Initial Term of the Agreement.</code>                                                                                                                                                                                                                                           |
  | <code>What happens if a Party fails to retain records for the required period?</code> | <code>Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation.</code> | <code>For twelve (12) months after delivery of the Master Copy of each Licensed Product to Licensee, Licensor warrants that the media in which the Licensed Products are stored shall be free from defects in materials and workmanship, assuming normal use. Licensee may return any defective media to Licensor for replacement free of charge during such twelve (12) month period.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,084 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 20.24 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 97.01 tokens</li><li>max: 350 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 105.03 tokens</li><li>max: 350 tokens</li></ul> |
* Samples:
  | anchor                                                                                                  | positive                                                                                                                                                                                                                                                                                                                                     | negative                                                                                                                                                                                                                                                                                                                                                                                |
  |:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts?</code> | <code>Capital Accounts<br><br>An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account.</code> | <code>Section 4.3 Deposits and Payments 19</code>                                                                                                                                                                                                                                                                                                                                       |
  | <code>Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts?</code> | <code>Capital Accounts<br><br>An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account.</code> | <code>Section 2.1 The Fund agrees at its own expense to execute any and all documents, to furnish any and all information, and to take any other actions that may be reasonably necessary in connection with the qualification of the Shares for sale in those states that Integrity may designate.</code>                                                                              |
  | <code>Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts?</code> | <code>Capital Accounts<br><br>An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account.</code> | <code>Section 1.9 Integrity shall prepare and deliver reports to the Treasurer of the Fund and to the Investment Adviser on a regular, at least quarterly, basis, showing the distribution expenses incurred pursuant to this Agreement and the Plan and the purposes therefore, as well as any supplemental reports as the Trustees, from time to time, may reasonably request.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step     | Training Loss | loss       | msmarco-distilbert-base-v2_cosine_map@100 |
|:----------:|:--------:|:-------------:|:----------:|:-----------------------------------------:|
| 0          | 0        | -             | -          | 0.6601                                    |
| 0.1453     | 100      | 1.5696        | -          | -                                         |
| 0.2907     | 200      | 0.7941        | -          | -                                         |
| 0.4360     | 300      | 0.6151        | -          | -                                         |
| 0.5814     | 400      | 0.5458        | -          | -                                         |
| 0.7267     | 500      | 0.5085        | -          | -                                         |
| 0.8721     | 600      | 0.4601        | -          | -                                         |
| 1.0131     | 697      | -             | 0.3492     | -                                         |
| 1.0044     | 700      | 0.4055        | -          | -                                         |
| 1.1497     | 800      | 0.3538        | -          | -                                         |
| 1.2951     | 900      | 0.2245        | -          | -                                         |
| 1.4404     | 1000     | 0.1821        | -          | -                                         |
| 1.5858     | 1100     | 0.1761        | -          | -                                         |
| 1.7311     | 1200     | 0.1872        | -          | -                                         |
| 1.8765     | 1300     | 0.169         | -          | -                                         |
| 2.0131     | 1394     | -             | 0.2674     | -                                         |
| 2.0087     | 1400     | 0.1502        | -          | -                                         |
| 2.1541     | 1500     | 0.1416        | -          | -                                         |
| 2.2994     | 1600     | 0.0914        | -          | -                                         |
| 2.4448     | 1700     | 0.0868        | -          | -                                         |
| 2.5901     | 1800     | 0.0854        | -          | -                                         |
| 2.7355     | 1900     | 0.0905        | -          | -                                         |
| 2.8808     | 2000     | 0.0888        | -          | -                                         |
| **2.9738** | **2064** | **-**         | **0.2272** | **0.7455**                                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```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",
}
```

#### TripletLoss
```bibtex
@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}
}
```

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