tomaarsen
/

mpnet-base-all-nli-triplet

@@ -1,547 +1,553 @@
----
-language:
-- en
-license: apache-2.0
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- 100K<n<1M
-- loss:MultipleNegativesRankingLoss
-base_model: microsoft/mpnet-base
-metrics:
-- cosine_accuracy
-- dot_accuracy
-- manhattan_accuracy
-- euclidean_accuracy
-- max_accuracy
-widget:
-- source_sentence: The truth?
-  sentences:
-  - Is that true?
-  - Two kids are outdoors.
-  - The dog is sleeping.
-- source_sentence: It did not.
-  sentences:
-  - It is not, of course.
-  - The boy is in the sand.
-  - Men are napping.
-- source_sentence: Impossible.
-  sentences:
-  - Entirely possible.
-  - The people are athletes
-  - The young man is sleeping.
-- source_sentence: Just a bike
-  sentences:
-  - A person on a bike
-  - yeah i can believe that
-  - The man is wearing jeans.
-- source_sentence: Then he ran.
-  sentences:
-  - The people are running.
-  - The man is on his bike.
-  - The boy is sleeping.
-pipeline_tag: sentence-similarity
-co2_eq_emissions:
-  emissions: 118.81134392463773
-  energy_consumed: 0.30566177669432554
-  source: codecarbon
-  training_type: fine-tuning
-  on_cloud: false
-  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
-  ram_total_size: 31.777088165283203
-  hours_used: 1.661
-  hardware_used: 1 x NVIDIA GeForce RTX 3090
-model-index:
-- name: MPNet base trained on AllNLI triplets
-  results:
-  - task:
-      type: triplet
-      name: Triplet
-    dataset:
-      name: all nli dev
-      type: all-nli-dev
-    metrics:
-    - type: cosine_accuracy
-      value: 0.9003645200486027
-      name: Cosine Accuracy
-    - type: dot_accuracy
-      value: 0.09705346294046173
-      name: Dot Accuracy
-    - type: manhattan_accuracy
-      value: 0.8968712029161604
-      name: Manhattan Accuracy
-    - type: euclidean_accuracy
-      value: 0.8974787363304981
-      name: Euclidean Accuracy
-    - type: max_accuracy
-      value: 0.9003645200486027
-      name: Max Accuracy
-  - task:
-      type: triplet
-      name: Triplet
-    dataset:
-      name: all nli test
-      type: all-nli-test
-    metrics:
-    - type: cosine_accuracy
-      value: 0.9149644424269935
-      name: Cosine Accuracy
-    - type: dot_accuracy
-      value: 0.08564079285822364
-      name: Dot Accuracy
-    - type: manhattan_accuracy
-      value: 0.911484339536995
-      name: Manhattan Accuracy
-    - type: euclidean_accuracy
-      value: 0.9134513542139506
-      name: Euclidean Accuracy
-    - type: max_accuracy
-      value: 0.9149644424269935
-      name: Max Accuracy
----
-# MPNet base trained on AllNLI triplets
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** 512 tokens
-- **Output Dimensionality:** 768 tokens
-- **Similarity Function:** Cosine Similarity
-- **Training Dataset:**
-    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
-- **Language:** en
-- **License:** apache-2.0
-### 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: 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("tomaarsen/mpnet-base-all-nli-triplet")
-# Run inference
-sentences = [
-    'Then he ran.',
-    'The people are running.',
-    'The man is on his bike.',
-]
-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]
-```
-<!--
-### 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.*
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-## Evaluation
-### Metrics
-#### Triplet
-* Dataset: `all-nli-dev`
-* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
-| Metric             | Value      |
-|:-------------------|:-----------|
-| cosine_accuracy    | 0.9004     |
-| dot_accuracy       | 0.0971     |
-| manhattan_accuracy | 0.8969     |
-| euclidean_accuracy | 0.8975     |
-| **max_accuracy**   | **0.9004** |
-#### Triplet
-* Dataset: `all-nli-test`
-* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
-| Metric             | Value     |
-|:-------------------|:----------|
-| cosine_accuracy    | 0.915     |
-| dot_accuracy       | 0.0856    |
-| manhattan_accuracy | 0.9115    |
-| euclidean_accuracy | 0.9135    |
-| **max_accuracy**   | **0.915** |
-<!--
-## 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
-#### sentence-transformers/all-nli
-* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
-* Size: 100,000 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: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
-* Samples:
-  | anchor                                                                     | positive                                         | negative                                                   |
-  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
-  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
-  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
-  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
-#### sentence-transformers/all-nli
-* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
-* Size: 6,584 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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
-* Samples:
-  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
-  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
-  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
-  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
-  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.1
-- `fp16`: True
-- `batch_sampler`: no_duplicates
-#### 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`: 16
-- `per_device_eval_batch_size`: 16
-- `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`: 1.0
-- `num_train_epochs`: 1
-- `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`: 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
-- `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`: no_duplicates
-- `multi_dataset_batch_sampler`: proportional
-</details>
-### Training Logs
-| Epoch | Step | Training Loss | loss   | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
-|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
-| 0     | 0    | -             | -      | 0.6832                   | -                         |
-| 0.016 | 100  | 2.6355        | 1.0725 | 0.7924                   | -                         |
-| 0.032 | 200  | 0.9206        | 0.8342 | 0.8080                   | -                         |
-| 0.048 | 300  | 1.2567        | 0.7855 | 0.8133                   | -                         |
-| 0.064 | 400  | 0.7949        | 0.8857 | 0.7974                   | -                         |
-| 0.08  | 500  | 0.7583        | 0.9487 | 0.7872                   | -                         |
-| 0.096 | 600  | 1.0022        | 1.1312 | 0.7848                   | -                         |
-| 0.112 | 700  | 0.8178        | 1.2282 | 0.7895                   | -                         |
-| 0.128 | 800  | 0.9997        | 1.5132 | 0.7488                   | -                         |
-| 0.144 | 900  | 1.1173        | 1.4605 | 0.7473                   | -                         |
-| 0.16  | 1000 | 1.0089        | 1.3794 | 0.7543                   | -                         |
-| 0.176 | 1100 | 1.0235        | 1.4188 | 0.7640                   | -                         |
-| 0.192 | 1200 | 1.0031        | 1.2465 | 0.7570                   | -                         |
-| 0.208 | 1300 | 0.8286        | 1.4176 | 0.7426                   | -                         |
-| 0.224 | 1400 | 0.8411        | 1.1914 | 0.7600                   | -                         |
-| 0.24  | 1500 | 0.8389        | 1.1719 | 0.7820                   | -                         |
-| 0.256 | 1600 | 0.7144        | 1.1167 | 0.7691                   | -                         |
-| 0.272 | 1700 | 0.881         | 1.0747 | 0.7902                   | -                         |
-| 0.288 | 1800 | 0.8657        | 1.1576 | 0.7966                   | -                         |
-| 0.304 | 1900 | 0.7323        | 1.0122 | 0.8322                   | -                         |
-| 0.32  | 2000 | 0.6578        | 1.1248 | 0.8273                   | -                         |
-| 0.336 | 2100 | 0.6037        | 1.1194 | 0.8269                   | -                         |
-| 0.352 | 2200 | 0.641         | 1.1410 | 0.8341                   | -                         |
-| 0.368 | 2300 | 0.7843        | 1.0600 | 0.8328                   | -                         |
-| 0.384 | 2400 | 0.8222        | 0.9988 | 0.8161                   | -                         |
-| 0.4   | 2500 | 0.7287        | 1.2026 | 0.8395                   | -                         |
-| 0.416 | 2600 | 0.6035        | 0.8802 | 0.8273                   | -                         |
-| 0.432 | 2700 | 0.8275        | 1.1631 | 0.8458                   | -                         |
-| 0.448 | 2800 | 0.8483        | 0.9218 | 0.8316                   | -                         |
-| 0.464 | 2900 | 0.8813        | 1.1187 | 0.8147                   | -                         |
-| 0.48  | 3000 | 0.7408        | 0.9582 | 0.8246                   | -                         |
-| 0.496 | 3100 | 0.7886        | 0.9364 | 0.8261                   | -                         |
-| 0.512 | 3200 | 0.6064        | 0.8338 | 0.8302                   | -                         |
-| 0.528 | 3300 | 0.6415        | 0.7895 | 0.8650                   | -                         |
-| 0.544 | 3400 | 0.5766        | 0.7525 | 0.8571                   | -                         |
-| 0.56  | 3500 | 0.6212        | 0.8605 | 0.8572                   | -                         |
-| 0.576 | 3600 | 0.5773        | 0.7460 | 0.8419                   | -                         |
-| 0.592 | 3700 | 0.6104        | 0.7480 | 0.8580                   | -                         |
-| 0.608 | 3800 | 0.5754        | 0.7215 | 0.8657                   | -                         |
-| 0.624 | 3900 | 0.5525        | 0.7900 | 0.8630                   | -                         |
-| 0.64  | 4000 | 0.7802        | 0.7443 | 0.8612                   | -                         |
-| 0.656 | 4100 | 0.9796        | 0.7756 | 0.8748                   | -                         |
-| 0.672 | 4200 | 0.9355        | 0.6917 | 0.8796                   | -                         |
-| 0.688 | 4300 | 0.7081        | 0.6442 | 0.8832                   | -                         |
-| 0.704 | 4400 | 0.6868        | 0.6395 | 0.8891                   | -                         |
-| 0.72  | 4500 | 0.5964        | 0.5983 | 0.8820                   | -                         |
-| 0.736 | 4600 | 0.6618        | 0.5754 | 0.8861                   | -                         |
-| 0.752 | 4700 | 0.6957        | 0.6177 | 0.8803                   | -                         |
-| 0.768 | 4800 | 0.6375        | 0.5577 | 0.8881                   | -                         |
-| 0.784 | 4900 | 0.5481        | 0.5496 | 0.8835                   | -                         |
-| 0.8   | 5000 | 0.6626        | 0.5728 | 0.8949                   | -                         |
-| 0.816 | 5100 | 0.5192        | 0.5329 | 0.8935                   | -                         |
-| 0.832 | 5200 | 0.5856        | 0.5188 | 0.8935                   | -                         |
-| 0.848 | 5300 | 0.5142        | 0.5252 | 0.8920                   | -                         |
-| 0.864 | 5400 | 0.6404        | 0.5641 | 0.8885                   | -                         |
-| 0.88  | 5500 | 0.5466        | 0.5209 | 0.8929                   | -                         |
-| 0.896 | 5600 | 0.575         | 0.5170 | 0.8961                   | -                         |
-| 0.912 | 5700 | 0.626         | 0.5095 | 0.9001                   | -                         |
-| 0.928 | 5800 | 0.5631        | 0.4817 | 0.8984                   | -                         |
-| 0.944 | 5900 | 0.7301        | 0.4996 | 0.8984                   | -                         |
-| 0.96  | 6000 | 0.7712        | 0.5160 | 0.9014                   | -                         |
-| 0.976 | 6100 | 0.6203        | 0.5000 | 0.9007                   | -                         |
-| 0.992 | 6200 | 0.0005        | 0.4996 | 0.9004                   | -                         |
-| 1.0   | 6250 | -             | -      | -                        | 0.9150                    |
-### Environmental Impact
-Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
-- **Energy Consumed**: 0.306 kWh
-- **Carbon Emitted**: 0.119 kg of CO2
-- **Hours Used**: 1.661 hours
-### Training Hardware
-- **On Cloud**: No
-- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
-- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
-- **RAM Size**: 31.78 GB
-### Framework Versions
-- Python: 3.11.6
-- Sentence Transformers: 3.0.0.dev0
-- Transformers: 4.41.1
-- PyTorch: 2.3.0+cu121
-- Accelerate: 0.30.1
-- 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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+---
+language:
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- 100K<n<1M
+- loss:MultipleNegativesRankingLoss
+base_model: microsoft/mpnet-base
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+widget:
+- source_sentence: The strangely dressed guys, one wearing an orange wig, sunglasses
+    with peace signs, and a karate costume with an orannge belt, another wearing a
+    curly blue wig, heart shaped sunglasses, and a karate outfit painted with leaves,
+    and the third wearing pink underwear, a black afro, and giant sunglasses.
+  sentences:
+  - A blonde female is reaching into a golf hole while holding two golf balls.
+  - There are people wearing outfits.
+  - The people are naked.
+- source_sentence: A group of children playing and having a good time.
+  sentences:
+  - The kids are together.
+  - The children are reading books.
+  - People are pointing at a Middle-aged woman.
+- source_sentence: Three children dressed in winter clothes are walking through the
+    woods while pushing cargo along.
+  sentences:
+  - A woman is sitting.
+  - Three childre are dressed in summer clothes.
+  - Three children are dressed in winter clothes.
+- source_sentence: A young child is enjoying the water and rock scenery with their
+    dog.
+  sentences:
+  - The child and dog are enjoying some fresh air.
+  - The teenage boy is taking his cat for a walk beside the water.
+  - A lady in blue has birds around her.
+- source_sentence: 'Boca da Corrida Encumeada (moderate; 5 hours): views of Curral
+    das Freiras and the valley of Ribeiro do Poco.'
+  sentences:
+  - 'Boca da Corrida Encumeada is a moderate text that takes 5 hours to complete. '
+  - This chapter is in the advance category.
+  - I think it is something that we need.
+pipeline_tag: sentence-similarity
+co2_eq_emissions:
+  emissions: 118.81134392463773
+  energy_consumed: 0.30566177669432554
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 1.661
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: MPNet base trained on AllNLI triplets
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9003645200486027
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.09705346294046173
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8968712029161604
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8974787363304981
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9003645200486027
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9149644424269935
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.08564079285822364
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.911484339536995
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9134513542139506
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9149644424269935
+      name: Max Accuracy
+---
+# MPNet base trained on AllNLI triplets
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+- **License:** apache-2.0
+### 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: 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("tomaarsen/mpnet-base-all-nli-triplet")
+# Run inference
+sentences = [
+    'Then he ran.',
+    'The people are running.',
+    'The man is on his bike.',
+]
+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]
+```
+<!--
+### 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
+#### Triplet
+* Dataset: `all-nli-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9004     |
+| dot_accuracy       | 0.0971     |
+| manhattan_accuracy | 0.8969     |
+| euclidean_accuracy | 0.8975     |
+| **max_accuracy**   | **0.9004** |
+#### Triplet
+* Dataset: `all-nli-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+| Metric             | Value     |
+|:-------------------|:----------|
+| cosine_accuracy    | 0.915     |
+| dot_accuracy       | 0.0856    |
+| manhattan_accuracy | 0.9115    |
+| euclidean_accuracy | 0.9135    |
+| **max_accuracy**   | **0.915** |
+<!--
+## 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
+#### sentence-transformers/all-nli
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 100,000 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: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                         | negative                                                   |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
+#### sentence-transformers/all-nli
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 6,584 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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
+  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+#### 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 1.0
+- `num_train_epochs`: 1
+- `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`: 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
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+</details>
+### Training Logs
+| Epoch | Step | Training Loss | loss   | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
+|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
+| 0     | 0    | -             | -      | 0.6832                   | -                         |
+| 0.016 | 100  | 2.6355        | 1.0725 | 0.7924                   | -                         |
+| 0.032 | 200  | 0.9206        | 0.8342 | 0.8080                   | -                         |
+| 0.048 | 300  | 1.2567        | 0.7855 | 0.8133                   | -                         |
+| 0.064 | 400  | 0.7949        | 0.8857 | 0.7974                   | -                         |
+| 0.08  | 500  | 0.7583        | 0.9487 | 0.7872                   | -                         |
+| 0.096 | 600  | 1.0022        | 1.1312 | 0.7848                   | -                         |
+| 0.112 | 700  | 0.8178        | 1.2282 | 0.7895                   | -                         |
+| 0.128 | 800  | 0.9997        | 1.5132 | 0.7488                   | -                         |
+| 0.144 | 900  | 1.1173        | 1.4605 | 0.7473                   | -                         |
+| 0.16  | 1000 | 1.0089        | 1.3794 | 0.7543                   | -                         |
+| 0.176 | 1100 | 1.0235        | 1.4188 | 0.7640                   | -                         |
+| 0.192 | 1200 | 1.0031        | 1.2465 | 0.7570                   | -                         |
+| 0.208 | 1300 | 0.8286        | 1.4176 | 0.7426                   | -                         |
+| 0.224 | 1400 | 0.8411        | 1.1914 | 0.7600                   | -                         |
+| 0.24  | 1500 | 0.8389        | 1.1719 | 0.7820                   | -                         |
+| 0.256 | 1600 | 0.7144        | 1.1167 | 0.7691                   | -                         |
+| 0.272 | 1700 | 0.881         | 1.0747 | 0.7902                   | -                         |
+| 0.288 | 1800 | 0.8657        | 1.1576 | 0.7966                   | -                         |
+| 0.304 | 1900 | 0.7323        | 1.0122 | 0.8322                   | -                         |
+| 0.32  | 2000 | 0.6578        | 1.1248 | 0.8273                   | -                         |
+| 0.336 | 2100 | 0.6037        | 1.1194 | 0.8269                   | -                         |
+| 0.352 | 2200 | 0.641         | 1.1410 | 0.8341                   | -                         |
+| 0.368 | 2300 | 0.7843        | 1.0600 | 0.8328                   | -                         |
+| 0.384 | 2400 | 0.8222        | 0.9988 | 0.8161                   | -                         |
+| 0.4   | 2500 | 0.7287        | 1.2026 | 0.8395                   | -                         |
+| 0.416 | 2600 | 0.6035        | 0.8802 | 0.8273                   | -                         |
+| 0.432 | 2700 | 0.8275        | 1.1631 | 0.8458                   | -                         |
+| 0.448 | 2800 | 0.8483        | 0.9218 | 0.8316                   | -                         |
+| 0.464 | 2900 | 0.8813        | 1.1187 | 0.8147                   | -                         |
+| 0.48  | 3000 | 0.7408        | 0.9582 | 0.8246                   | -                         |
+| 0.496 | 3100 | 0.7886        | 0.9364 | 0.8261                   | -                         |
+| 0.512 | 3200 | 0.6064        | 0.8338 | 0.8302                   | -                         |
+| 0.528 | 3300 | 0.6415        | 0.7895 | 0.8650                   | -                         |
+| 0.544 | 3400 | 0.5766        | 0.7525 | 0.8571                   | -                         |
+| 0.56  | 3500 | 0.6212        | 0.8605 | 0.8572                   | -                         |
+| 0.576 | 3600 | 0.5773        | 0.7460 | 0.8419                   | -                         |
+| 0.592 | 3700 | 0.6104        | 0.7480 | 0.8580                   | -                         |
+| 0.608 | 3800 | 0.5754        | 0.7215 | 0.8657                   | -                         |
+| 0.624 | 3900 | 0.5525        | 0.7900 | 0.8630                   | -                         |
+| 0.64  | 4000 | 0.7802        | 0.7443 | 0.8612                   | -                         |
+| 0.656 | 4100 | 0.9796        | 0.7756 | 0.8748                   | -                         |
+| 0.672 | 4200 | 0.9355        | 0.6917 | 0.8796                   | -                         |
+| 0.688 | 4300 | 0.7081        | 0.6442 | 0.8832                   | -                         |
+| 0.704 | 4400 | 0.6868        | 0.6395 | 0.8891                   | -                         |
+| 0.72  | 4500 | 0.5964        | 0.5983 | 0.8820                   | -                         |
+| 0.736 | 4600 | 0.6618        | 0.5754 | 0.8861                   | -                         |
+| 0.752 | 4700 | 0.6957        | 0.6177 | 0.8803                   | -                         |
+| 0.768 | 4800 | 0.6375        | 0.5577 | 0.8881                   | -                         |
+| 0.784 | 4900 | 0.5481        | 0.5496 | 0.8835                   | -                         |
+| 0.8   | 5000 | 0.6626        | 0.5728 | 0.8949                   | -                         |
+| 0.816 | 5100 | 0.5192        | 0.5329 | 0.8935                   | -                         |
+| 0.832 | 5200 | 0.5856        | 0.5188 | 0.8935                   | -                         |
+| 0.848 | 5300 | 0.5142        | 0.5252 | 0.8920                   | -                         |
+| 0.864 | 5400 | 0.6404        | 0.5641 | 0.8885                   | -                         |
+| 0.88  | 5500 | 0.5466        | 0.5209 | 0.8929                   | -                         |
+| 0.896 | 5600 | 0.575         | 0.5170 | 0.8961                   | -                         |
+| 0.912 | 5700 | 0.626         | 0.5095 | 0.9001                   | -                         |
+| 0.928 | 5800 | 0.5631        | 0.4817 | 0.8984                   | -                         |
+| 0.944 | 5900 | 0.7301        | 0.4996 | 0.8984                   | -                         |
+| 0.96  | 6000 | 0.7712        | 0.5160 | 0.9014                   | -                         |
+| 0.976 | 6100 | 0.6203        | 0.5000 | 0.9007                   | -                         |
+| 0.992 | 6200 | 0.0005        | 0.4996 | 0.9004                   | -                         |
+| 1.0   | 6250 | -             | -      | -                        | 0.9150                    |
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.306 kWh
+- **Carbon Emitted**: 0.119 kg of CO2
+- **Hours Used**: 1.661 hours
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.0.0.dev0
+- Transformers: 4.41.1
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.30.1
+- 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}
+}
+```
+<!--
+## Glossary
+*Clearly define terms in order to be accessible across audiences.*
+-->
+<!--
+## Model Card Authors
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+<!--
+## Model Card Contact
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 -->