sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:7005 loss:MultipleNegativesRankingLoss_with_logging custom_code arxiv:1908.10084 base_model:Alibaba-NLP/gte-large-en-v1.5 base_model:finetune:Alibaba-NLP/gte-large-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Alibaba-NLP/gte-large-en-v1.5
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_accuracy@30
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_precision@30
- cosine_precision@50
- cosine_precision@100
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_recall@30
- cosine_recall@50
- cosine_recall@100
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_accuracy@30
- dot_accuracy@50
- dot_accuracy@100
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_precision@30
- dot_precision@50
- dot_precision@100
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_recall@30
- dot_recall@50
- dot_recall@100
- 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:7005
- loss:MultipleNegativesRankingLoss_with_logging
widget:
- source_sentence: What are the client's target industries?
  sentences:
  - 'Right.

    And also, you know, heavy equipment.

    Okay, I understand.'
  - 'And there''s a full spectrum.

    It''s all about your order offering.

    Right.

    If you''re offering, like, a full design platform where now we have way more engagement
    in terms of employee being able to get it from one place, and that could be.

    That could take away again, like, my pitch would be basically being on the show.'
  - 'Our competitors are billion dollar corporations.

    So Experian Epsilon, which is owned by IPG or publicis, big french company, Axiom,
    which is owned by IPG.

    Inter public group, huge agency.

    So it''s nice competing against multibillion dollar corporations because they
    move at the speed of the Statue of Liberty.'
- source_sentence: What is the strategy for heating products?
  sentences:
  - 'Then when you go in to take a look, you say, okay, I''ve got this.

    Now I need to record my test results so that we do down here.

    And we say, okay, this is me, so I''ll pick myself.

    And here we go.

    So once you''re in here, you say, okay, it''s inspector me.'
  - 'I don''t think we make any margin on these products.

    I''m going to put it on here, though, because I want to add different ones.

    So three in one and then.

    Underfloor heating?'
  - 'How are others using it?

    Use cases like.

    Yeah, for example, we have one, one partner, there''s climbo.'
- source_sentence: What feature did Aseel request regarding budget information display?
  sentences:
  - 'So you want to do your west coast.

    Do you want to do 10:00 a.m.

    on the morning of 13th?'
  - 'But the only thing that I just was thinking about is, for example, if I was a
    head teacher and I''m about to approve an order and obviously I go and click on
    the three dots and it tells me my geo budget department by GL budget and obviously
    tells you what your total budget is, your spend and what''s remaining.

    Is there a way in which I can see what actually went under proof expenditure?

    So it should be.

    So to see how much has been committed against the budget?'
  - 'Awesome.

    And speaking of releases, is there any way I''m not getting the.

    And I''m sure Chris probably is.'
- source_sentence: Does the customer have any other EAP-like resources available?
  sentences:
  - 'Every time I make a post, I get.

    I get just a ton of inquiries, you know?

    And we''re just.

    We''re doing a bunch of cool operational stuff right now, so we''re just trying
    to get that all figured out, you know?

    Yeah.

    Well, hey, let me give you a rundown of a couple things I''m doing with, like,
    people in your kind of peripheral.

    Just so you know what we''re trying to do to boost the voices of you and agencies
    like you.'
  - 'So we need Kim and Manju.

    We need to account that for production downtime for on 16th.

    No cutover plan.'
  - 'They''re thinking, well, there we have them already, and they offer all these
    things.

    This is pretty great, you know, because we also use, so we have Voya life insurance,
    and through Voya, they offer a couple eap type of resources, too.

    Right.

    So we have additional assistance with another program.

    Right.

    But with our eap, which is through Magellan, they would just usually would just
    be better than the other comparisons when it came down to it.'
- source_sentence: What was Nathan's response to the initial proposal from Global
    Air U?
  sentences:
  - But I was listening to everything that you were talking about.
  - 'And hopefully that should update now in your account in a second.

    Yeah.

    If you give that a go now, you should see all the way to August 2025.'
  - 'I don''t see on the proposal.

    I don''t see anything class or the class related.

    Um.

    Oh, so for the course.

    No, no.'
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.32793959007551243
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48975188781014023
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5663430420711975
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6612729234088457
      name: Cosine Accuracy@10
    - type: cosine_accuracy@30
      value: 0.7669902912621359
      name: Cosine Accuracy@30
    - type: cosine_accuracy@50
      value: 0.8155339805825242
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.8597626752966558
      name: Cosine Accuracy@100
    - type: cosine_precision@1
      value: 0.32793959007551243
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1902193455591514
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13829557713052856
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08716289104638619
      name: Cosine Precision@10
    - type: cosine_precision@30
      value: 0.038439410284070476
      name: Cosine Precision@30
    - type: cosine_precision@50
      value: 0.025717367853290186
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.014282632146709814
      name: Cosine Precision@100
    - type: cosine_recall@1
      value: 0.19877399359600004
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.32606462218112703
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.39100529100529097
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.475571479940412
      name: Cosine Recall@10
    - type: cosine_recall@30
      value: 0.6031369325867708
      name: Cosine Recall@30
    - type: cosine_recall@50
      value: 0.660217290799815
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.7195099398982894
      name: Cosine Recall@100
    - type: cosine_ndcg@10
      value: 0.3784769275629581
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.42950420369514186
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3193224907975288
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.3290183387270766
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.4886731391585761
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.5717367853290184
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.6634304207119741
      name: Dot Accuracy@10
    - type: dot_accuracy@30
      value: 0.7669902912621359
      name: Dot Accuracy@30
    - type: dot_accuracy@50
      value: 0.8133764832793959
      name: Dot Accuracy@50
    - type: dot_accuracy@100
      value: 0.8619201725997843
      name: Dot Accuracy@100
    - type: dot_precision@1
      value: 0.3290183387270766
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.18985976267529667
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.1387270765911543
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.08737864077669903
      name: Dot Precision@10
    - type: dot_precision@30
      value: 0.038511326860841424
      name: Dot Precision@30
    - type: dot_precision@50
      value: 0.025652642934196335
      name: Dot Precision@50
    - type: dot_precision@100
      value: 0.0143042071197411
      name: Dot Precision@100
    - type: dot_recall@1
      value: 0.19940326364274585
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.32588483073919966
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.39370216263420144
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.4770997071967946
      name: Dot Recall@10
    - type: dot_recall@30
      value: 0.6043595143918767
      name: Dot Recall@30
    - type: dot_recall@50
      value: 0.659138542148251
      name: Dot Recall@50
    - type: dot_recall@100
      value: 0.7219987671443983
      name: Dot Recall@100
    - type: dot_ndcg@10
      value: 0.3791495475200093
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.4305302991387128
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.31951258454174397
      name: Dot Map@100
---

# SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 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:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) <!-- at revision 104333d6af6f97649377c2afbde10a7704870c7b -->
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (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})
)
```

## 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("model_3")
# Run inference
sentences = [
    "What was Nathan's response to the initial proposal from Global Air U?",
    "I don't see on the proposal.\nI don't see anything class or the class related.\nUm.\nOh, so for the course.\nNo, no.",
    'And hopefully that should update now in your account in a second.\nYeah.\nIf you give that a go now, you should see all the way to August 2025.',
]
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]
```

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</details>
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You can finetune this model on your own dataset.

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

</details>
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## 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    | 0.3279     |
| cosine_accuracy@3    | 0.4898     |
| cosine_accuracy@5    | 0.5663     |
| cosine_accuracy@10   | 0.6613     |
| cosine_accuracy@30   | 0.767      |
| cosine_accuracy@50   | 0.8155     |
| cosine_accuracy@100  | 0.8598     |
| cosine_precision@1   | 0.3279     |
| cosine_precision@3   | 0.1902     |
| cosine_precision@5   | 0.1383     |
| cosine_precision@10  | 0.0872     |
| cosine_precision@30  | 0.0384     |
| cosine_precision@50  | 0.0257     |
| cosine_precision@100 | 0.0143     |
| cosine_recall@1      | 0.1988     |
| cosine_recall@3      | 0.3261     |
| cosine_recall@5      | 0.391      |
| cosine_recall@10     | 0.4756     |
| cosine_recall@30     | 0.6031     |
| cosine_recall@50     | 0.6602     |
| cosine_recall@100    | 0.7195     |
| cosine_ndcg@10       | 0.3785     |
| cosine_mrr@10        | 0.4295     |
| **cosine_map@100**   | **0.3193** |
| dot_accuracy@1       | 0.329      |
| dot_accuracy@3       | 0.4887     |
| dot_accuracy@5       | 0.5717     |
| dot_accuracy@10      | 0.6634     |
| dot_accuracy@30      | 0.767      |
| dot_accuracy@50      | 0.8134     |
| dot_accuracy@100     | 0.8619     |
| dot_precision@1      | 0.329      |
| dot_precision@3      | 0.1899     |
| dot_precision@5      | 0.1387     |
| dot_precision@10     | 0.0874     |
| dot_precision@30     | 0.0385     |
| dot_precision@50     | 0.0257     |
| dot_precision@100    | 0.0143     |
| dot_recall@1         | 0.1994     |
| dot_recall@3         | 0.3259     |
| dot_recall@5         | 0.3937     |
| dot_recall@10        | 0.4771     |
| dot_recall@30        | 0.6044     |
| dot_recall@50        | 0.6591     |
| dot_recall@100       | 0.722      |
| dot_ndcg@10          | 0.3791     |
| dot_mrr@10           | 0.4305     |
| dot_map@100          | 0.3195     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 7,005 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 14.59 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 60.98 tokens</li><li>max: 170 tokens</li></ul> |
* Samples:
  | anchor                                                                           | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  |:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What progress has been made with setting up Snowflake share?</code>        | <code>He finally got around to giving me the information necessary to set up Snowflake share.<br>I will be submitting the application to get back set up.<br>Once the database is set up, then we just need to figure out how to configure Snowflake share, which it's going to be in the documentation.<br>We should be set on that end.<br>We also are going to have a conversation with someone named Peter Tsanghen, who's, who owns Jira platform.<br>Great.</code> |
  | <code>Who is Peter Tsanghen and what is the planned interaction with him?</code> | <code>He finally got around to giving me the information necessary to set up Snowflake share.<br>I will be submitting the application to get back set up.<br>Once the database is set up, then we just need to figure out how to configure Snowflake share, which it's going to be in the documentation.<br>We should be set on that end.<br>We also are going to have a conversation with someone named Peter Tsanghen, who's, who owns Jira platform.<br>Great.</code> |
  | <code>Who is Peter Tsanghen and what is the planned interaction with him?</code> | <code>Uh, and so now we just have to meet with Peter.<br>Peter is someone who I used to work with on, he used to work on, uh, syndicated data products.<br>So I used to work with him on that.</code>                                                                                                                                                                                                                                                                    |
* Loss: <code>__main__.MultipleNegativesRankingLoss_with_logging</code>

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

- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `num_train_epochs`: 2
- `max_steps`: 1751
- `disable_tqdm`: True
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 2
- `max_steps`: 1751
- `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
- `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`: True
- `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}
- `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
- `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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 0.0114 | 20   | 0.2538         |
| 0.0228 | 40   | 0.2601         |
| 0.0342 | 60   | 0.2724         |
| 0.0457 | 80   | 0.2911         |
| 0.0571 | 100  | 0.2976         |
| 0.0685 | 120  | 0.3075         |
| 0.0799 | 140  | 0.3071         |
| 0.0913 | 160  | 0.3111         |
| 0.1027 | 180  | 0.3193         |


### Framework Versions
- Python: 3.10.9
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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",
}
```

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