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---
base_model: dunzhang/stella_en_400M_v5
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:491850
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: f"What constitutes 'Received Information' as defined in this contract?"
sentences:
- Notwithstanding the foregoing, it is understood that Auriemma has no control or
influence over any decisions by the University of Connecticut to enter into any
arrangement or agreement with any Berkshire Competitor.
- 'Received Information may include any of the following: geo-location, IP address,
device ID or unique identifier, device manufacturer and type, device and hardware
settings, ID for advertising, ad data, operating system, operator, IMSI (international
mobile subscriber identity), certain information regarding contacts contained
in user device phone books ("Contacts"), phone number, connection information,
screen resolution, usage statistics, device log and event information, incoming
and outgoing calls and messages, times and date of calls, duration of calls, behavioral
information, version of the Software used, and other information based on interactions
with the Services.'
- '"Received Information" means a user''s private, personal or personally identifying
or identifiable data or information, including content and contact information
such as name, email address, or social network identifier.'
- source_sentence: f"What constitutes 'Received Information' as defined in this contract?"
sentences:
- (a) "Confidential Information" means nonpublic information that a party to this
Agreement ("Disclosing Party") designates as being confidential to the party that
receives such information ("Receiving Party") or which, under the circumstances
surrounding disclosure ought to be treated as confidential by the Receiving Party.
- '"Received Information" means a user''s private, personal or personally identifying
or identifiable data or information, including content and contact information
such as name, email address, or social network identifier.'
- 'Facebook Connect: If you use one of our applications and connect to your Facebook
account within such application, you will be providing us with basic account information
i.e., user ID, name, email, gender, birthday, current city, profile picture URL
and the user IDs of your friends who have also connected with our applications.
In addition, we will cache data we receive from the Facebook APIs to improve our
user experience. If you want us to delete the data we receive from Facebook about
you, please contact us through support.storm8.com.'
- source_sentence: Is Google considered a third-party vendor in this context?
sentences:
- 1.10 "Purchase Order" shall mean a written purchase order issued to ESTABLISHMENT
by APOLLO for the purchase of Product under this Agreement.
- 'Banking and Joint Venture Funds
The funds of the Joint Venture will be placed in such investments and banking
accounts as will be designated by the Participants. Joint Venture funds will be
held in the name of the Joint Venture and will not be commingled with those of
any other person or entity.'
- Google, as a third party vendor, uses cookies to serve ads on our site. Google's
use of the DART cookie enables it to serve ads to our users based on their visit
to our site and other sites on the Internet. Users may opt out of the use of the
DART cookie by visiting the Google ad and content network privacy policy.
- source_sentence: What purposes does the entity have for processing the data gathered
from its clientele?
sentences:
- Metavante hereby grants to Customer a personal, nonexclusive, and nontransferable
license and right, for the duration of this Agreement, to use the Incidental Software
solely in accordance with the applicable Documentation and for no other purposes.
- 'B. HOW WE USE COLLECTED INFORMATION a. Any of the information (Personal and Non-personal)
we collect from you may be used in one of the following ways: To personalize user
experience- We may use Information to understand demographics, customer interest,
and other trends among our Users;'
- To the extent that the Parties have jointly developed any New Amorphous Alloy
Technology and they have agreed that such New Amorphous Alloy Technology will
be jointly owned, as set forth in Section 8.2 above, each Party hereby assigns
to the other, and will cause its employees, contractors, representatives, successors,
assigns, Affiliates, parents, subsidiaries, officers and directors to assign to
the other, a co-equal right, title and interest in and to any such jointly developed
New Amorphous Alloy Technology. T
- source_sentence: How might an individual residing in the western coastal state of
the U.S. obtain a record of the entities to which a particular application has
provided their personal data for marketing use within the last calendar year?
sentences:
- The term “Confidential Information” means any and all tangible and intangible
information disclosed to Receiver in oral, written, graphic, recorded, photographic,
any machine-readable or in any other medium or form relating to the intellectual
property, management, operations, products, inventions, suppliers, customers,
financials of VIDAR or any present or contemplated project, contract or relationship
between VIDAR and Receiver, including without limitation, any and all plans, Intellectual
Property (defined below), know-how, computer programs, software (source and object
code), algorithms, computer processing systems, techniques, methodologies, formulae,
compilations of information, designs, drawings, schematics, analyses, evaluations,
formulations, ingredients, samples, processes, machines, prototypes, mock-ups,
product performance data, proposals, job notes, reports, records, specifications,
manuals, supplier and customer lists and information, licenses, the prices it
obtains or has obtained for the licensing of its software products and services,
purchase and sales records, marketing information or any other information concerning
the business and goodwill of VIDAR and any information which is identified as
being of a confidential or proprietary nature or should be considered confidential
under the circumstances.
- 'Specific Location Practices: California, EU residents California Privacy Rights
Residents of the State of California can request a list of all third-parties to
which our App has disclosed certain personal information (as defined by California
law) during the preceding year for those third-parties'' direct marketing purposes.
If you are a California resident and want such a list, please contact us at CaliforniaRequest@viber.com.
For all requests, please ensure you put the statement "Your California Privacy
Rights" in the body of your request, as well as your name, street address, city,
state, and zip code. In the body of your request, please provide enough information
for us to determine if this applies to you. You need to attest to the fact that
you are a California resident and provide a current California address for our
response. Please note that we will not accept requests via the telephone, mail,
or by facsimile, and we are not responsible for notices that are not labeled or
sent properly, or that do not have complete information. Viber does not currently
take actions to respond to Do Not Track signals because a uniform technological
standard has not yet been developed. We continue to review new technologies and
may adopt a standard once one is created.'
- Neither party may assign this Agreement or any rights and obligations under this
Agreement to any third party without the written consent of the other party.
model-index:
- name: SentenceTransformer based on dunzhang/stella_en_400M_v5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: stella en 400M v5
type: stella_en_400M_v5
metrics:
- type: cosine_accuracy@1
value: 0.5986368799697085
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7519878833775085
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8008330177962892
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8527073078379401
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5986368799697085
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2506626277925028
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16016660355925785
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08527073078379402
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5986368799697085
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7519878833775085
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8008330177962892
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8527073078379401
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7263474307341174
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6857685280347147
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6903360937337177
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.5937145020825445
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7425217720560394
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8008330177962892
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8511927300265051
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5937145020825445
name: Dot Precision@1
- type: dot_precision@3
value: 0.24750725735201312
name: Dot Precision@3
- type: dot_precision@5
value: 0.16016660355925785
name: Dot Precision@5
- type: dot_precision@10
value: 0.0851192730026505
name: Dot Precision@10
- type: dot_recall@1
value: 0.5937145020825445
name: Dot Recall@1
- type: dot_recall@3
value: 0.7425217720560394
name: Dot Recall@3
- type: dot_recall@5
value: 0.8008330177962892
name: Dot Recall@5
- type: dot_recall@10
value: 0.8511927300265051
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7219180873294574
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6804977070974791
name: Dot Mrr@10
- type: dot_map@100
value: 0.685154909034552
name: Dot Map@100
---
# SentenceTransformer based on dunzhang/stella_en_400M_v5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5). 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:** [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5) <!-- at revision 1bb50bc7bb726810eac2140e62155b88b0df198f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: NewModel
(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): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## 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/stella_en_400M_v5_MultipleNegativesRankingLoss")
# Run inference
sentences = [
'How might an individual residing in the western coastal state of the U.S. obtain a record of the entities to which a particular application has provided their personal data for marketing use within the last calendar year?',
'Specific Location Practices: California, EU residents California Privacy Rights Residents of the State of California can request a list of all third-parties to which our App has disclosed certain personal information (as defined by California law) during the preceding year for those third-parties\' direct marketing purposes. If you are a California resident and want such a list, please contact us at CaliforniaRequest@viber.com. For all requests, please ensure you put the statement "Your California Privacy Rights" in the body of your request, as well as your name, street address, city, state, and zip code. In the body of your request, please provide enough information for us to determine if this applies to you. You need to attest to the fact that you are a California resident and provide a current California address for our response. Please note that we will not accept requests via the telephone, mail, or by facsimile, and we are not responsible for notices that are not labeled or sent properly, or that do not have complete information. Viber does not currently take actions to respond to Do Not Track signals because a uniform technological standard has not yet been developed. We continue to review new technologies and may adopt a standard once one is created.',
'The term “Confidential Information” means any and all tangible and intangible information disclosed to Receiver in oral, written, graphic, recorded, photographic, any machine-readable or in any other medium or form relating to the intellectual property, management, operations, products, inventions, suppliers, customers, financials of VIDAR or any present or contemplated project, contract or relationship between VIDAR and Receiver, including without limitation, any and all plans, Intellectual Property (defined below), know-how, computer programs, software (source and object code), algorithms, computer processing systems, techniques, methodologies, formulae, compilations of information, designs, drawings, schematics, analyses, evaluations, formulations, ingredients, samples, processes, machines, prototypes, mock-ups, product performance data, proposals, job notes, reports, records, specifications, manuals, supplier and customer lists and information, licenses, the prices it obtains or has obtained for the licensing of its software products and services, purchase and sales records, marketing information or any other information concerning the business and goodwill of VIDAR and any information which is identified as being of a confidential or proprietary nature or should be considered confidential under the circumstances.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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: `stella_en_400M_v5`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5986 |
| cosine_accuracy@3 | 0.752 |
| cosine_accuracy@5 | 0.8008 |
| cosine_accuracy@10 | 0.8527 |
| cosine_precision@1 | 0.5986 |
| cosine_precision@3 | 0.2507 |
| cosine_precision@5 | 0.1602 |
| cosine_precision@10 | 0.0853 |
| cosine_recall@1 | 0.5986 |
| cosine_recall@3 | 0.752 |
| cosine_recall@5 | 0.8008 |
| cosine_recall@10 | 0.8527 |
| cosine_ndcg@10 | 0.7263 |
| cosine_mrr@10 | 0.6858 |
| **cosine_map@100** | **0.6903** |
| dot_accuracy@1 | 0.5937 |
| dot_accuracy@3 | 0.7425 |
| dot_accuracy@5 | 0.8008 |
| dot_accuracy@10 | 0.8512 |
| dot_precision@1 | 0.5937 |
| dot_precision@3 | 0.2475 |
| dot_precision@5 | 0.1602 |
| dot_precision@10 | 0.0851 |
| dot_recall@1 | 0.5937 |
| dot_recall@3 | 0.7425 |
| dot_recall@5 | 0.8008 |
| dot_recall@10 | 0.8512 |
| dot_ndcg@10 | 0.7219 |
| dot_mrr@10 | 0.6805 |
| dot_map@100 | 0.6852 |
<!--
## 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: 491,850 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: 17.09 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 102.69 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 96.04 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What safeguards are in place to protect the information obtained from third-party sources?</code> | <code>Information We Collect From Other Sources We may also receive information from other sources and combine that with information we collect through our Services. For example: If you choose to link, create, or log in to your Uber account with a payment provider (e.g., Google Wallet) or social media service (e.g., Facebook), or if you engage with a separate app or website that uses our API (or whose API we use), we may receive information about you or your connections from that site or app.</code> | <code>We receive data from Public Resources (as defined under the Terms of Service) associated with users and user Contacts, including from social networks to which users or user Contacts are registered, such as Facebook, Google+, Linkedin, Twitter, and Foursquare.</code> |
| <code>What safeguards are in place to protect the information obtained from third-party sources?</code> | <code>Information We Collect From Other Sources We may also receive information from other sources and combine that with information we collect through our Services. For example: If you choose to link, create, or log in to your Uber account with a payment provider (e.g., Google Wallet) or social media service (e.g., Facebook), or if you engage with a separate app or website that uses our API (or whose API we use), we may receive information about you or your connections from that site or app.</code> | <code>You also may be able to link an account from a social networking service (e.g., Facebook, Google+, Yahoo!) to an account through our Services. This may allow you to use your credentials from the other site or service to sign in to certain features on our Services. If you link your account from a third-party site or service, we may collect information from those third-party accounts, and any information that we collect will be governed by this Privacy Policy.</code> |
| <code>What safeguards are in place to protect the information obtained from third-party sources?</code> | <code>Information We Collect From Other Sources We may also receive information from other sources and combine that with information we collect through our Services. For example: If you choose to link, create, or log in to your Uber account with a payment provider (e.g., Google Wallet) or social media service (e.g., Facebook), or if you engage with a separate app or website that uses our API (or whose API we use), we may receive information about you or your connections from that site or app.</code> | <code>Information We Collect Personal data ("Personal Information") may be required to use some services offered by PSafe, or users may have the option of providing it, including name, home address, email address and contact telephone number. We may collect Personal Information about you from third parties and add this information to the information we have already collected from you via our services. PSafe may confirm the provided Personal Information by consulting with public authorities, specialized companies or databases. The information that PSafe obtains from these entities will be treated confidentially.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 6,000 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: 8 tokens</li><li>mean: 23.16 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 96.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 94.79 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What term is used to describe sensitive materials unique to the involved entities and not accessible by the general populace, regardless of its physical state or the manner of its revelation?</code> | <code>For purposes of this Agreement, "Confidential Information" means any data or information that is proprietary to the Parties and not generally known to the public, whether in tangible or intangible form, whenever and however disclosed, including but not limited to:</code> | <code>A. "Confidential Information" of a party shall mean any trade secrets, know-how, inventions, products, designs, methods, techniques, systems, processes, software programs, works of authorship, business plans, customer lists, projects, plans, pricing, proposals and any other information which a party discloses to the Recipient Party that: (i) if disclosed in writing is clearly marked as confidential or carries a similar legend; or (ii) if disclosed verbally or in tangible form is identified as confidential at the time of disclosure, then summarized in a writing so marked by the Disclosing Party and delivered to the Recipient Party with fifteen (15) days.</code> |
| <code>What term is used to describe sensitive materials unique to the involved entities and not accessible by the general populace, regardless of its physical state or the manner of its revelation?</code> | <code>For purposes of this Agreement, "Confidential Information" means any data or information that is proprietary to the Parties and not generally known to the public, whether in tangible or intangible form, whenever and however disclosed, including but not limited to:</code> | <code>1. Disclosure: Recipient agrees not to disclose and the Discloser agrees to let the Recipient have the access to the Confidential Information as identified and reduced in writing or provided verbally or in any other way not reduced in writing at the time of such disclosure of the information.</code> |
| <code>What term is used to describe sensitive materials unique to the involved entities and not accessible by the general populace, regardless of its physical state or the manner of its revelation?</code> | <code>For purposes of this Agreement, "Confidential Information" means any data or information that is proprietary to the Parties and not generally known to the public, whether in tangible or intangible form, whenever and however disclosed, including but not limited to:</code> | <code>Confidential Information - information of whatever kind and in whatever form contained (and includes in particular but without prejudice to the generality of the foregoing, documents, drawings, computerized information, films, tapes, specifications, designs, models, equipment or data of any kind) which is clearly identified by the Disclosing Party as confidential by an appropriate legend or if orally disclosed then upon disclosure or within 30 days of such oral disclosure identified in writing by the Disclosing Party as confidential.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 2
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | stella_en_400M_v5_cosine_map@100 |
|:-------:|:--------:|:-------------:|:----------:|:--------------------------------:|
| 0 | 0 | - | - | 0.5279 |
| 0.0260 | 100 | 1.5185 | - | - |
| 0.0520 | 200 | 0.9779 | - | - |
| 0.0781 | 300 | 0.828 | - | - |
| 0.1041 | 400 | 0.7038 | - | - |
| 0.1301 | 500 | 0.6537 | - | - |
| 0.1561 | 600 | 0.5801 | - | - |
| 0.1821 | 700 | 0.5588 | - | - |
| 0.2082 | 800 | 0.5124 | - | - |
| 0.2342 | 900 | 0.4827 | - | - |
| 0.2602 | 1000 | 0.4672 | - | - |
| 0.2862 | 1100 | 0.4285 | - | - |
| 0.3123 | 1200 | 0.3965 | - | - |
| 0.3383 | 1300 | 0.3759 | - | - |
| 0.3643 | 1400 | 0.3612 | - | - |
| 0.3903 | 1500 | 0.3209 | - | - |
| 0.4163 | 1600 | 0.3108 | - | - |
| 0.4424 | 1700 | 0.3012 | - | - |
| 0.4684 | 1800 | 0.2837 | - | - |
| 0.4944 | 1900 | 0.2801 | - | - |
| 0.5204 | 2000 | 0.2581 | - | - |
| 0.5464 | 2100 | 0.2502 | - | - |
| 0.5725 | 2200 | 0.2502 | - | - |
| 0.5985 | 2300 | 0.2271 | - | - |
| 0.6245 | 2400 | 0.2265 | - | - |
| 0.6505 | 2500 | 0.2144 | - | - |
| 0.6766 | 2600 | 0.2161 | - | - |
| 0.7026 | 2700 | 0.2071 | - | - |
| 0.7286 | 2800 | 0.197 | - | - |
| 0.7546 | 2900 | 0.1875 | - | - |
| 0.7806 | 3000 | 0.1846 | - | - |
| 0.8067 | 3100 | 0.1827 | - | - |
| 0.8327 | 3200 | 0.1732 | - | - |
| 0.8587 | 3300 | 0.1778 | - | - |
| 0.8847 | 3400 | 0.1679 | - | - |
| 0.9107 | 3500 | 0.1685 | - | - |
| 0.9368 | 3600 | 0.165 | - | - |
| 0.9628 | 3700 | 0.1716 | - | - |
| 0.9888 | 3800 | 0.1593 | - | - |
| **1.0** | **3843** | **-** | **0.9541** | **-** |
| 1.0148 | 3900 | 0.1463 | - | - |
| 1.0409 | 4000 | 0.1482 | - | - |
| 1.0669 | 4100 | 0.1446 | - | - |
| 1.0929 | 4200 | 0.1481 | - | - |
| 1.1189 | 4300 | 0.15 | - | - |
| 1.1449 | 4400 | 0.1446 | - | - |
| 1.1710 | 4500 | 0.1414 | - | - |
| 1.1970 | 4600 | 0.1427 | - | - |
| 1.2230 | 4700 | 0.1385 | - | - |
| 1.2490 | 4800 | 0.134 | - | - |
| 1.2750 | 4900 | 0.1343 | - | - |
| 1.3011 | 5000 | 0.1462 | - | - |
| 1.3271 | 5100 | 0.1343 | - | - |
| 1.3531 | 5200 | 0.1324 | - | - |
| 1.3791 | 5300 | 0.125 | - | - |
| 1.4052 | 5400 | 0.1299 | - | - |
| 1.4312 | 5500 | 0.1237 | - | - |
| 1.4572 | 5600 | 0.1349 | - | - |
| 1.4832 | 5700 | 0.1303 | - | - |
| 1.5092 | 5800 | 0.1272 | - | - |
| 1.5353 | 5900 | 0.1238 | - | - |
| 1.5613 | 6000 | 0.1194 | - | - |
| 1.5873 | 6100 | 0.1267 | - | - |
| 1.6133 | 6200 | 0.1187 | - | - |
| 1.6393 | 6300 | 0.123 | - | - |
| 1.6654 | 6400 | 0.1183 | - | - |
| 1.6914 | 6500 | 0.1245 | - | - |
| 1.7174 | 6600 | 0.1173 | - | - |
| 1.7434 | 6700 | 0.1164 | - | - |
| 1.7695 | 6800 | 0.1169 | - | - |
| 1.7955 | 6900 | 0.1181 | - | - |
| 1.8215 | 7000 | 0.1188 | - | - |
| 1.8475 | 7100 | 0.1166 | - | - |
| 1.8735 | 7200 | 0.1144 | - | - |
| 1.8996 | 7300 | 0.1116 | - | - |
| 1.9256 | 7400 | 0.1149 | - | - |
| 1.9516 | 7500 | 0.1137 | - | - |
| 1.9776 | 7600 | 0.1113 | - | - |
| 2.0 | 7686 | - | 1.0487 | 0.6903 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.4.0+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",
}
```
#### 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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