sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:4893 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mpnet-base
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4893
- loss:TripletLoss
widget:
- source_sentence: I almost envy you your assignment. I see in your mind that you
    are tempted to take my place. Not correct, Doctor, although I am aware of your
    mind attempting to contact mine. Were you born a telepath? Yes. That is why I
    had to study on Vulcan. I understand. May I show you to your quarters?[SEP]I think
    I'll stay here a bit. Ambassador Kollos often finds the process of transport somewhat
    unsettling.
  sentences:
  - ' I don''t see anything, do you?'
  - I understand. Our ship's surgeon often makes the same complaint. Do call when
    you are ready.
  - Irrelevant, since we are here.
- source_sentence: Aye, sir. Aye, sir. Return to your station, Sub-commander. The
    boarding action on the Enterprise will begin with my command. If they resist,
    destroy her. Execution of state criminals is both painful and unpleasant. I believe
    the details are unnecessary. The sentence will be carried out immediately after
    the charges have been recorded. I demand the Right of Statement first.[SEP]You
    understand Romulan tradition well. The right is granted.
  sentences:
  - ' No, I... it''s just... you''re just coming off the surgery and you''re not yourself
    yet and I work for you and Even though last year''s... [Frustrated sigh as House
    starts smiling smugly.] you''re smiling! I''m saying no and you''re smiling!'
  - I wish to ask a question. What of Sarek's family, his wife and son?
  - Thank you. I shall not require much time. No more than twenty minutes, I should
    say.
- source_sentence: Mankind, ready to kill. That's the way it was in 1881. I wonder
    how humanity managed to survive. We overcame our instinct for violence. Some desk-bound
    Starfleet bureaucrat cut these cloak-and-dagger orders.[SEP]Aye, but why the secrecy?
    This star system's under Federation control.
  sentences:
  - It's in a border area, Mister Scott. The Klingons also claim jurisdiction.
  - ' That''s your argument? Better outcome?'
  - Are you all right, Captain?
- source_sentence: We're trying to help you, Oxmyx. Nobody helps nobody but himself.
    Sir, you are employing a double negative. Huh? I fail to see why you do not understand
    us. You yourself have stated the need for unity of authority on this planet. We
    agree.[SEP]Yeah, but I got to be the unity.
  sentences:
  - Co-operation, sir, would inevitably result
  - Quite right, Mister Scott. There's somebody holding us down. All systems are go,
    but we're not moving.
  - ' So today I''m jailbait but in 22 weeks anybody can do anything to me. Will I
    be so different in 22 weeks?'
- source_sentence: What happened? Where have I been? Right here, it seems. But that
    girl. She was so beautiful. So real. Do you remember anything else? No.[SEP]Good.
    Perhaps that explains why he's here. Nothing was real to him except the girl.
  sentences:
  - Sweeping the area of Outpost two. Sensor reading indefinite. Double-checking Outpost
    three. I read dust and debris. Both Earth outposts gone, and the asteroids they
    were constructed on, pulverised.
  - ' It''s killing you.'
  - Captain, the Melkotian object.
model-index:
- name: SentenceTransformer based on microsoft/mpnet-base
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: evaluator enc
      type: evaluator_enc
    metrics:
    - type: cosine_accuracy
      value: 0.9989781379699707
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: evaluator val
      type: evaluator_val
    metrics:
    - type: cosine_accuracy
      value: 0.9930555820465088
      name: Cosine Accuracy
---

# SentenceTransformer based on microsoft/mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 256 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': 256, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("greatakela/gennlp_hw1_encoder2025")
# Run inference
sentences = [
    "What happened? Where have I been? Right here, it seems. But that girl. She was so beautiful. So real. Do you remember anything else? No.[SEP]Good. Perhaps that explains why he's here. Nothing was real to him except the girl.",
    'Captain, the Melkotian object.',
    " It's killing you.",
]
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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## Evaluation

### Metrics

#### Triplet

* Datasets: `evaluator_enc` and `evaluator_val`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | evaluator_enc | evaluator_val |
|:--------------------|:--------------|:--------------|
| **cosine_accuracy** | **0.999**     | **0.9931**    |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 4,893 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        | sentence_2                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                             |
  | details | <ul><li>min: 2 tokens</li><li>mean: 90.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.64 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.14 tokens</li><li>max: 199 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | sentence_1                                                                                                                                                                                | sentence_2                                                                      |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | <code>Oh, well, if that's all. Mister Scott, transport the glommer over to the Klingon ship. Aye, sir. You can't do this to me. Under space salvage laws, he's mine. A planetary surface is not covered by space salvage laws. But if you want the little beastie that bad, Mister Jones, we'll transport you over with it. I withdraw my claim.[SEP]Well, at least we can report the stasis field is not as effective a weapon as we thought. The power drain is too high and takes too long for the Klingon ship to recover to make it practical.</code>                                                                                | <code>Agreed, Captain. Tribbles appear to be a much more effective weapon.</code>                                                                                                         | <code> [protesting] I give him...</code>                                        |
  | <code>Do you mean that's what the Kelvans really are? Undoubtedly. Well, if they look that way normally, why did they adapt themselves to our bodies? Perhaps practicality. They chose the Enterprise as the best vessel for the trip. Immense beings with a hundred tentacles would have difficulty with the turbolift. We've got to stop them. We outnumber them. Their only hold on us is the paralysis field. Well, that's enough. One wrong move, and they jam all our neural circuits.[SEP]Jam. Spock, if you reverse the circuits on McCoy's neuro-analyser, can you set up a counter field to jam the paralysis projector?</code> | <code>I'm dubious of the possibilities of success, Captain. The medical equipment is not designed to put out a great deal of power. The polarized elements would burn out quickly.</code> | <code> The next step would be a type of brain surgery.</code>                   |
  | <code>Well, speculation isn't much help. We have to get in there. Perhaps there is a way open on the far side. There is much less activity there. That building in the centre. It seems to be important. You stand before the Ruling Tribunal of the Aquans. I am Domar, the High Tribune. I'm Captain Kirk of the starship Enterprise. This is my first officer, Mister Spock.[SEP]You are air-breather enemies from the surface. We have expected spies for a long time.</code>                                                                                                                                                         | <code>We came here in peace, Tribune.</code>                                                                                                                                              | <code> Which is why we need to look at the nerve that you didn't biopsy.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

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

- `eval_strategy`: steps
- `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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 3
- `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 | Training Loss | evaluator_enc_cosine_accuracy | evaluator_val_cosine_accuracy |
|:------:|:----:|:-------------:|:-----------------------------:|:-----------------------------:|
| -1     | -1   | -             | 0.5494                        | -                             |
| 0.4902 | 300  | -             | 0.9808                        | -                             |
| 0.8170 | 500  | 1.4249        | -                             | -                             |
| 0.9804 | 600  | -             | 0.9912                        | -                             |
| 1.0    | 612  | -             | 0.9931                        | -                             |
| 1.4706 | 900  | -             | 0.9963                        | -                             |
| 1.6340 | 1000 | 0.2269        | -                             | -                             |
| 1.9608 | 1200 | -             | 0.9990                        | -                             |
| 2.0    | 1224 | -             | 0.9990                        | -                             |
| 2.4510 | 1500 | 0.1054        | 0.9990                        | -                             |
| 2.9412 | 1800 | -             | 0.9990                        | -                             |
| 3.0    | 1836 | -             | 0.9990                        | -                             |
| -1     | -1   | -             | -                             | 0.9931                        |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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