sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:56 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-l base_model:finetune:Snowflake/snowflake-arctic-embed-l model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Snowflake/snowflake-arctic-embed-l
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:56
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How did the fans of KKR feel about McCullum's return to the team?
  sentences:
  - are not seeing.At KKR, they get back together and they have a great run in one
    period, even though Morgan is clearly washed and is never going to make runs again.At
    this stage – unless you are from KKR, or you've listened to Morgan a lot, or you’ve
    got McCullum peeled back in the 2015 World Cup – McCullum was an important player,
    but not the most important. His team didn’t win the World Cup final, and they
    didn’t go to No.1 in the world. KKR were a really good team when he played for
    them, but they weren’t a powerhouse. England’s success was linked to McCullum
    at times, but he wasn't actually the one in charge. Then too, when he came back
    for KKR, there were lots of KKR fans who weren't particularly happy with the way
    that the team was
  - right-hand lead by a boxer. Even if someone tells you they’re going to do it beforehand,
    it’s such a weird thing to happen that it catches you off guard.And if they hit
    you in the face, they've hit you in the face. And that's what a lot of Bazball's
    early success was built on. But now, there's no surprise anymore, it’s completely
    gone. I assumed that England would bowl more 90-mile-an-hour bowlers, they would
    bowl more bouncers, and they would up the explosiveness. Even though they were
    pretty explosive in 2015-19, and even in the period when they were failing after
    that. They were pretty explosive all the way through, and we knew exactly how
    England were going to play cricket.That's one of the most fascinating things about
    all this. Its
  - 'We all know what it is, even if some of us still misunderstand it at times and
    others get a little too excited by it.We also know what Bazball is going to be.
    Look at what the Australians did when they played that Test series against England.
    They put their fielders out on the boundary early on and went, okay, now you''re
    going to have to play a slightly different kind of cricket.You can scheme for
    Bazball. You can plan for Bazball. We understand its strengths and its limits
    at this point. We also understand that because England believe so much in it,
    sometimes you can allow them to Bazball themselves out of a game. And that brings
    us back to England adopting Bazball in limited-overs cricket.To recap: the first
    version of Bazball was in the'
- source_sentence: How did Eoin Morgan's relationship with McCullum influence England's
    performance in white-ball cricket between 2015 and 2019?
  sentences:
  - that the team was playing.But the one place that seemed to think about him completely
    differently was always England.Some of that is just linked to the Morgan relationship.
    So when McCullum was brought in to be the coach, it was of course to be the coach
    of the white-ball team. And he said, ‘Nah, I want to try the red-ball team.’This
    is where it gets really, really fascinating.When he coached the Test team, all
    he really did was go back and use their plan from 2015-2019. What he essentially
    did was copying an England plan from 2015-19, based on the England talent pool
    that was available at that time, that was copied from his original plan. It is
    a bit of a copy of a copy at that point.Bazball has been discussed more than any
    cricket
  - were so good in this period is that they had two quality spinners, and they had
    a bunch of people who could play spin.By the time they got to white-ball sides
    also officially adopting Bazball, their ability to bowl spin and play spin had
    almost completely disappeared. They've tried to disrupt as much as possible, and
    they stole a Test in India doing it. But, we've also seen the limitations of that,
    and Pakistan embarrassed them recently. Now, they're back trying to play spin
    again, and we can see that they don't have it.The genius of Bazball and Brendon
    McCullum has always been to highlight your strengths and ignore your weaknesses.
    How do you do that when just playing spin or bowling spin is one of them? That's
    a hard thing to overcome
  - he's really good at, which is thrashing the ball around everywhere, played a big
    part too. So his leadership of New Zealand and the IPL experience actually started
    to develop together.This is where the third part of it all comes in. Because along
    comes Eoin Morgan, an Irish cricketer playing in England, who also ended up at
    KKR. He built a relationship with McCullum and imbibed his ideas. That led to
    one of the best periods in white-ball cricket ever for England, between 2015 and
    2019. They only won one ICC event, which is ridiculous considering how they played
    in that period. Of course, Carlos Brathwaite took one away from them, and they
    had a shocking game against Pakistan in the Champions Trophy 2017 semifinal. But
    if you look at their
- source_sentence: What is the main topic discussed in Jarrod Kimber's article "Do
    teams even play ODIs anymore?"
  sentences:
  - about all this. Its main success came from surprise, even if there were lots of
    tactical nuggets within it. But if you can't play spin, and everyone else knows
    exactly how you're going to play, you do what India did. In those T20I games,
    even when India got behind, they waited for England to take chances that they
    didn't need to take. Because everyone knows how England are going to play. They
    tell us every chance they get.Bazball, the term? That's new.Bazball, the cricket?
    Oh we've done it. We've got a prequel in McCullum and Wagner. The cult film that
    no one watched, when McCullum was doing it with New Zealand subsequently. The
    sequel that's often been forgotten with the KKR years. The spin off, which was
    Morgan and England doing it in
  - shoulder. Over and over again. And you had to make batting decisions.From that,
    we got the first version of what we would see as Bazball. So this is really a
    Bazball origins story.And remember that New Zealand were doing terribly, as we
    said, in South Africa and India, and they started to win a lot. We know how much
    they eventually won, especially in Test cricket, because they ended up as the
    world's No.1 team and they won the World Test Championship.Now, a lot of that
    is not under McCullum, but the very basics that McCullum started with is what
    they went on with. For instance, Wagner and the funky fields. The other thing
    that McCullum really brought to that New Zealand side was finding a role for each
    player based on their strengths. So
  - Do teams even play ODIs anymore? - by Jarrod Kimber
- source_sentence: What innovative bowling technique did Neil Wagner use during his
    match against India that contributed to his rise as an important cricketer?
  sentences:
  - he's really good at, which is thrashing the ball around everywhere, played a big
    part too. So his leadership of New Zealand and the IPL experience actually started
    to develop together.This is where the third part of it all comes in. Because along
    comes Eoin Morgan, an Irish cricketer playing in England, who also ended up at
    KKR. He built a relationship with McCullum and imbibed his ideas. That led to
    one of the best periods in white-ball cricket ever for England, between 2015 and
    2019. They only won one ICC event, which is ridiculous considering how they played
    in that period. Of course, Carlos Brathwaite took one away from them, and they
    had a shocking game against Pakistan in the Champions Trophy 2017 semifinal. But
    if you look at their
  - than any cricket concept. Almost ever. We don't have to go through it. Essentially
    it boils down to two key parts. Disrupt when you bat. And then if you've disrupted
    well enough, you should be able to pick up singles, like the middle overs back
    in the 2015-19 years. And when you bowl, try and manufacture wickets. Maybe that
    is your spinner bowling without a deep backward square leg to encourage someone
    who doesn't normally sweep to sweep more. Usually, it just means bowling bouncers.
    In fact, most of Bazball is just bowling bouncers.And if you go back to 2015-19,
    they had Liam Plunkett, Jofra Archer and Mark Wood all bowling lots of bouncers.
    Remember, Wagner started with the bouncers. So bouncers are very important, and
    the disruptive
  - gloves, and most important to the renaissance was Neil Wagner. At that stage,
    no one really thought that Wagner was about to become one of the more important
    cricketers in the world.He kind of became that when he was playing against India
    in a match, and was struggling to get wickets conventionally. And so, he went
    with short-pitched bowling. Of course, lots of people have tried short-pitched
    bowling before. What Wagner did though, was bowl incredibly long spells of short-pitched
    bowling, and he was very accurate with it. Meanwhile, McCullum set fields that
    were only set for short-pitched bowling. As a batter, that’s all that was going
    to happen. You were going to face 60 balls, at 135 kmph, aimed probably at your
    left shoulder. Over and
- source_sentence: Who did the captaincy of the New Zealand cricket team transition
    from, and how was this change perceived?
  sentences:
  - and the disruptive batting is very important.Those are kind of the two key frameworks
    of what Bazball is. It had some success and some failure, and you can go anywhere
    you want to have a look at that. We've probably done about 19 videos and pieces
    looking at it and breaking it down, because it is one of the more fascinating
    things that has happened in modern cricket.But then what happens after this, of
    course, is that the white-ball team – which had been really, really good – suddenly
    starts to suck. And so, they ask McCullum to come over and take over the white-ball
    sides too. It’s a circle of life, this evolution of Brendon McCullum and his own
    tactics. Even when other people were using them and changing them, he incorporated
    their
  - to be captain, but didn’t get it. Then, he kind of just took the captaincy from
    Ross Taylor in a bloodless coup. Although some people might say it wasn't particularly
    bloodless.And then New Zealand went to South Africa, and they had just the worst
    time. It was really, really bad. So all the players got together and asked themselves
    hard questions. “What are we doing? Are we going to become freelance players in
    franchise cricket? Are we going to take New Zealand cricket seriously?”Arising
    from that, they went all-in on a method that I suppose we would now call Bazball.
    But it did take a little while to come together. There were lots of different
    aspects that needed to fall in place. McCullum had to eventually give up the gloves,
    and most
  - were so good in this period is that they had two quality spinners, and they had
    a bunch of people who could play spin.By the time they got to white-ball sides
    also officially adopting Bazball, their ability to bowl spin and play spin had
    almost completely disappeared. They've tried to disrupt as much as possible, and
    they stole a Test in India doing it. But, we've also seen the limitations of that,
    and Pakistan embarrassed them recently. Now, they're back trying to play spin
    again, and we can see that they don't have it.The genius of Bazball and Brendon
    McCullum has always been to highlight your strengths and ignore your weaknesses.
    How do you do that when just playing spin or bowling spin is one of them? That's
    a hard thing to overcome
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.8571428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9642857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8571428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32142857142857145
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8571428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9642857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9354477166148681
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9136904761904762
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9136904761904763
      name: Cosine Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). 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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("martintgc/finetuned_arctic_naive_ft-legal-ft-v0")
# Run inference
sentences = [
    'Who did the captaincy of the New Zealand cricket team transition from, and how was this change perceived?',
    "to be captain, but didn’t get it. Then, he kind of just took the captaincy from Ross Taylor in a bloodless coup. Although some people might say it wasn't particularly bloodless.And then New Zealand went to South Africa, and they had just the worst time. It was really, really bad. So all the players got together and asked themselves hard questions. “What are we doing? Are we going to become freelance players in franchise cricket? Are we going to take New Zealand cricket seriously?”Arising from that, they went all-in on a method that I suppose we would now call Bazball. But it did take a little while to come together. There were lots of different aspects that needed to fall in place. McCullum had to eventually give up the gloves, and most",
    "were so good in this period is that they had two quality spinners, and they had a bunch of people who could play spin.By the time they got to white-ball sides also officially adopting Bazball, their ability to bowl spin and play spin had almost completely disappeared. They've tried to disrupt as much as possible, and they stole a Test in India doing it. But, we've also seen the limitations of that, and Pakistan embarrassed them recently. Now, they're back trying to play spin again, and we can see that they don't have it.The genius of Bazball and Brendon McCullum has always been to highlight your strengths and ignore your weaknesses. How do you do that when just playing spin or bowling spin is one of them? That's a hard thing to overcome",
]
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

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8571     |
| cosine_accuracy@3   | 0.9643     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8571     |
| cosine_precision@3  | 0.3214     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8571     |
| cosine_recall@3     | 0.9643     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9354** |
| cosine_mrr@10       | 0.9137     |
| cosine_map@100      | 0.9137     |

<!--
## 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: 56 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 56 samples:
  |         | sentence_0                                                                         | sentence_1                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 12 tokens</li><li>mean: 21.68 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 161.11 tokens</li><li>max: 213 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  |:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the main focus of Jarrod Kimber's "Bazball's mission to Moscow"?</code>                     | <code>Bazball's mission to Moscow - by Jarrod Kimber - Good Areas</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>How does the concept of "Good Areas" relate to Bazball in the context of the article?</code>        | <code>Bazball's mission to Moscow - by Jarrod Kimber - Good Areas</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>What is the significance of Brendon McCullum and England’s Test side in relation to Bazball?</code> | <code>SubscribeSign inShare this postGood AreasBazball's mission to MoscowCopy linkFacebookEmailNotesMoreBazball's mission to MoscowIt created a revolution when Brendon McCullum and England’s Test side adopted it. But we’ve seen this before.Jarrod KimberFeb 13, 202513Share this postGood AreasBazball's mission to MoscowCopy linkFacebookEmailNotesMore1SharePre-order The Art of BattingSubscribeI've been thinking about Bazball a lot recently because Bazball’s back. Alright. We had Test Bazball, and now we have limited-overs Bazball. Which, I suppose, is Test Bazball on a slightly different frequency. But Bazball is much weirder when you think about it. I don’t know which version of Bazball we’re on, maybe it’s version three. Or maybe it’s version</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: False
- `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`: 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
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | cosine_ndcg@10 |
|:------:|:----:|:--------------:|
| 1.0    | 6    | 0.9232         |
| 2.0    | 12   | 0.9354         |
| 3.0    | 18   | 0.9486         |
| 4.0    | 24   | 0.9643         |
| 5.0    | 30   | 0.9643         |
| 6.0    | 36   | 0.9643         |
| 7.0    | 42   | 0.9643         |
| 8.0    | 48   | 0.9643         |
| 8.3333 | 50   | 0.9643         |
| 9.0    | 54   | 0.9643         |
| 10.0   | 60   | 0.9643         |
| 1.0    | 6    | 0.9643         |
| 2.0    | 12   | 0.9643         |
| 3.0    | 18   | 0.9379         |
| 4.0    | 24   | 0.9354         |
| 5.0    | 30   | 0.9354         |
| 6.0    | 36   | 0.9354         |
| 7.0    | 42   | 0.9339         |
| 8.0    | 48   | 0.9339         |
| 8.3333 | 50   | 0.9339         |
| 9.0    | 54   | 0.9339         |
| 10.0   | 60   | 0.9339         |
| 1.0    | 6    | 0.9486         |
| 2.0    | 12   | 0.9486         |
| 3.0    | 18   | 0.9486         |
| 4.0    | 24   | 0.9486         |
| 5.0    | 30   | 0.9486         |
| 6.0    | 36   | 0.9486         |
| 7.0    | 42   | 0.9354         |
| 8.0    | 48   | 0.9354         |
| 8.3333 | 50   | 0.9354         |
| 9.0    | 54   | 0.9354         |
| 10.0   | 60   | 0.9354         |


### Framework Versions
- Python: 3.9.21
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## Citation

### BibTeX

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

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

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

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