sentence-transformers tensorboard safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:500 loss:MarginDistillationLoss arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:500
- loss:MarginDistillationLoss
widget:
- source_sentence: qualify leads job description
  sentences:
  - job description Qualify leads – can the customers use our solutions and do they
    have budget
  - 'job description QUALIFICATION STANDARDS:'
  - job description Has passion and convictions and the innate ability to inspire
    passion in others
- source_sentence: what kind of work can you do in a fast paced work environment
  sentences:
  - 'job description Customer Care:'
  - cv Worked effectively in a heavily cross-functional, fast paced environment
  - job description Ability to work in a fast-paced, team environment to meet required
    deadlines
- source_sentence: what is the job description of architect at cisco
  sentences:
  - job description Proven track record of meeting quota
  - job description You will provide an architectural perspective across the Cisco
    product portfolio and can use your technical specialization for specific opportunities.
  - job description Work with network architect to direct network engineering within
    the US/ Canada region.
- source_sentence: what is a product partner for a bank
  sentences:
  - job description Assist customers with product features and installation needs
    for TV, Internet, phone and security services
  - job description Partners effectively with Credit, Product Partners, Closers, Servicing,
    Technical Services, and other partners to identify cross-sell opportunities and
    deepen client relationships as well as solve internal obstacles and deliver a
    seamless execution.
  - job description Partner with the ad product/experience team to ensure strategies
    can be effectively executed
- source_sentence: what is the job description of outcome-oriented
  sentences:
  - job description Outcome-oriented -- results-focused with strong performance culture
  - job description Results oriented—takes personal accountability and drives to “get
    it done” (solve customer problems and close deals) and “do it right” (act with
    integrity and sustain strong relationships)
  - job description Provide consistent assessment of each associate’s sales performance
    and work within the store to give feedback on areas of strength and opportunity
    while keeping in line with Company objectives.
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'what is the job description of outcome-oriented',
    'job description Outcome-oriented -- results-focused with strong performance culture',
    'job description Results oriented—takes personal accountability and drives to “get it done” (solve customer problems and close deals) and “do it right” (act with integrity and sustain strong relationships)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 500 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, and <code>label</code>
* Approximate statistics based on the first 500 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         | sentence_2                                                                        | label                                                               |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                            | float                                                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.23 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 26.02 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: -13.07</li><li>mean: 3.46</li><li>max: 21.29</li></ul> |
* Samples:
  | sentence_0                                                               | sentence_1                                                                                                                                  | sentence_2                                          | label                           |
  |:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:--------------------------------|
  | <code>what is a cv</code>                                                | <code>cv Jan 2003 to Jan 2005 Company Name Implemented a database package for the nuclear power plant historical design calculations</code> | <code>cv Member and Provider Services</code>        | <code>-2.188136577606201</code> |
  | <code>what is the minimum requirement to work in a warehouse</code>      | <code>job description Experience/Minimum Requirements</code>                                                                                | <code>job description Minimum Requirements:</code>  | <code>-2.951946973800659</code> |
  | <code>what type of education does a client service executive need</code> | <code>job description Bachelor’s degree from an accredited college/university or equivalent B2B client service experience</code>            | <code>job description Education Requirements</code> | <code>9.929327964782715</code>  |
* Loss: <code>gpl.toolkit.loss.MarginDistillationLoss</code>

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

- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 1
- `max_steps`: 50
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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`: 1
- `max_steps`: 50
- `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`: False
- `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>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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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