---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:7786
- loss:ContrastiveLossWithInBatchAndHardNegatives
base_model: google/embeddinggemma-300m
widget:
- source_sentence: '<ROUTE_START>

    <SEG> ST_1 -> ST_2 <DIR:RIGHT> <LIGHT> <POI: Pie Face / amenity: cafe / cuisine:
    coffee_shop>

    <SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Zoob
    Zib Aura Thai / amenity: restaurant / cuisine: thai> <POI_LEFT: Pie Face / amenity:
    cafe / cuisine: coffee_shop> <POI_LEFT: Burgers and Cupcakes / amenity: restaurant
    / cuisine: burger>

    <SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Zoob
    Zib Aura Thai / amenity: restaurant / cuisine: thai> <POI_LEFT: Burgers and Cupcakes
    / amenity: restaurant / cuisine: burger> <POI_LEFT: Sergimmo Slumeria / amenity:
    restaurant / cuisine: italian>

    <SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Burgers
    and Cupcakes / amenity: restaurant / cuisine: burger> <POI_LEFT: Sergimmo Slumeria
    / amenity: restaurant / cuisine: italian> <POI_LEFT: shop: convenience>

    <SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <LIGHT> <POI_LEFT: Golden City / amenity: restaurant>
    <POI_LEFT: Deli & Pizzeria / amenity: fast_food / cuisine: pizza>

    <SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_LEFT: Hudson Station Bar and Grill / amenity:
    restaurant / cuisine: steak_house> <POI_LEFT: Uncle Jack''s / amenity: restaurant
    / cuisine: steak_house>

    <SEG> ST_7 -> ST_8 <DIR:STRAIGHT> <POI_RIGHT: Café Bistro / amenity: fast_food
    / cuisine: deli,_buffet,_Asian,_salad,_pizza>

    <SEG> ST_8 -> ST_9 <DIR:STRAIGHT> <POI_RIGHT: Café Bistro / amenity: fast_food
    / cuisine: deli,_buffet,_Asian,_salad,_pizza> <POI_LEFT: Five Guys / amenity:
    fast_food>

    <ROUTE_END>'
  sentences:
  - Walk to the light where Pie Face may be on the corner and turn right. Pass a parking
    lot on the right and go through a light. At the following light where Golden City
    and Deli & Pizzeria may be on the corners turn left. Stop 1/2 down the block next
    to Cafe Bistro just before Five Guys on the right side.
  - Go straight past M408 Performing Arts high school then at the intersection where
    Chaan Teng and take a left.
  - Walk to the light where Pie Face may be on the corner and turn right. Pass a parking
    lot on the right and go through a light. At the following light where Golden City
    and Deli & Pizzeria may be on the corners turn left. Stop 1/2 down the block next
    to Cafe Bistro just before Five Guys on the right side.
- source_sentence: '<ROUTE_START>

    <SEG> ST_1 -> ST_2 <DIR:LEFT> <LIGHT> <POI: Brooks Brothers / shop: clothes>

    <SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Brooks Brothers / shop: clothes>

    <SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Brooks Brothers / shop: clothes>

    <SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Radio City Music Hall / amenity: theatre / tourism:
    yes>

    <SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Radio City Music Hall / amenity: theatre / tourism:
    yes>

    <SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Radio City Music Hall / amenity: theatre / tourism:
    yes>

    <SEG> ST_7 -> ST_8 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Radio City Music Hall / amenity: theatre / tourism:
    yes>

    <SEG> ST_8 -> ST_9 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes> <POI_RIGHT: Radio City Music Hall / amenity: theatre / tourism:
    yes>

    <SEG> ST_9 -> ST_10 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism: attraction
    / historic: yes>

    <SEG> ST_10 -> ST_11 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes> <POI_LEFT: Teresa''s / amenity: restaurant / cuisine:
    pizza>

    <SEG> ST_11 -> ST_12 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes>

    <SEG> ST_12 -> ST_13 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes> <POI_RIGHT: FDNY shop / shop: gift>

    <SEG> ST_13 -> ST_14 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes>

    <SEG> ST_14 -> ST_15 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes>

    <SEG> ST_15 -> ST_16 <DIR:STRAIGHT> <POI_RIGHT: Rockefeller Center / tourism:
    attraction / historic: yes> <POI_RIGHT: Del Frisco''s / amenity: restaurant>

    <SEG> ST_16 -> ST_17 <DIR:STRAIGHT> <POI_RIGHT: Del Frisco''s / amenity: restaurant>

    <SEG> ST_17 -> ST_18 <DIR:STRAIGHT> <POI_RIGHT: Del Frisco''s / amenity: restaurant>
    <POI_LEFT: Club Quarters / tourism: hotel>

    <ROUTE_END>'
  sentences:
  - Start going through 2 traffic lights and at the following light, Brooks Brothers
    and a large building with fountains in front should be on the corners. Turn left.
    Stop a few steps before the next light, adjacent to Bill's Bar or Columbia Photo
    Studio which is the 2nd to last building on the block counting from the left.
  - Go straight through the next intersection past San Marzano. At the 2nd intersection
    where Oiji is take a left. Then stop at Foot gear plus.
  - Start going through 2 traffic lights and at the following light, Brooks Brothers
    and a large building with fountains in front should be on the corners. Turn left.
    Go through the next light, and you will pass Walgreens on the left.
- source_sentence: '<ROUTE_START>

    <SEG> ST_1 -> ST_2 <DIR:LEFT> <LIGHT>

    <ROUTE_END>'
  sentences:
  - Walk to the 1st light with American Apparel on the right and turn left. At the
    next light with AMC on the far left corner, turn right. Go through the next light
    with Paragon Sports on the right corner. Stop at the following traffic light with
    Union Square on the far left corner.
  - Turn left at the lights.
  - Go in to the intersection and stop.
- source_sentence: '<ROUTE_START>

    <SEG> ST_1 -> ST_2 <DIR:STRAIGHT>

    <SEG> ST_2 -> ST_3 <DIR:STRAIGHT>

    <SEG> ST_3 -> ST_4 <DIR:RIGHT> <LIGHT>

    <ROUTE_END>'
  sentences:
  - Go straight and take a right at the first intersection.
  - Go through the light after AM-PM Deli and Grocery on the right.
  - then a left at the next one and go past New York Family Court.
- source_sentence: '<ROUTE_START>

    <SEG> ST_1 -> ST_2 <DIR:LEFT> <LIGHT> <POI: Citi Bike / amenity: bicycle_rental>
    <POI: Hampton Inn / tourism: hotel>

    <SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_LEFT: Santander / amenity: bank> <POI_LEFT:
    Santander / amenity: atm> <POI_RIGHT: Hampton Inn / tourism: hotel> <POI_RIGHT:
    T-Mobile / shop: mobile_phone>

    <SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_LEFT: Chase / amenity: bank> <POI_LEFT:
    Chase / amenity: atm> <POI_LEFT: Pret A Manger / amenity: fast_food / cuisine:
    sandwich>

    <SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_LEFT: Chase / amenity: bank> <POI_LEFT:
    Chase / amenity: atm> <POI_LEFT: Pret A Manger / amenity: fast_food / cuisine:
    sandwich>

    <SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <POI_LEFT: Santander / amenity: bank> <POI_LEFT:
    Santander / amenity: atm> <POI_RIGHT: Hampton Inn / tourism: hotel> <POI_RIGHT:
    T-Mobile / shop: mobile_phone>

    <SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_RIGHT: Sheraton New York Times Square Hotel
    / tourism: hotel>

    <SEG> ST_7 -> ST_8 <DIR:STRAIGHT> <POI_RIGHT: Sheraton New York Times Square Hotel
    / tourism: hotel>

    <ROUTE_END>'
  sentences:
  - After this block take a left at the third light and stop halfway down the block.
    After the second intersection you'll go down a block with several theaters. A
    Hampton Inn will be on your right and a bank ahead on the corner. Start by going
    straight through two intersections.
  - At the next light with a playground on the far left, turn left. Stop in the middle
    of the playground before a fire station on the left side. Pass a T-intersection
    with Chase on the far left corner. Walk by a small church to the end of the street
    and turn left.
  - After this block take a left at the third light and stop halfway down the block.
    After the second intersection you'll go down a block with several theaters. A
    Hampton Inn will be on your right and a bank ahead on the corner. Start by going
    straight through two intersections.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on google/embeddinggemma-300m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
- **Maximum Sequence Length:** 2048 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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("0xFarzad/embeddinggemma-graph-instructions-hard-negatives-final")
# Run inference
queries = [
    "\u003cROUTE_START\u003e\n\u003cSEG\u003e ST_1 -\u003e ST_2 \u003cDIR:LEFT\u003e \u003cLIGHT\u003e \u003cPOI: Citi Bike / amenity: bicycle_rental\u003e \u003cPOI: Hampton Inn / tourism: hotel\u003e\n\u003cSEG\u003e ST_2 -\u003e ST_3 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Santander / amenity: bank\u003e \u003cPOI_LEFT: Santander / amenity: atm\u003e \u003cPOI_RIGHT: Hampton Inn / tourism: hotel\u003e \u003cPOI_RIGHT: T-Mobile / shop: mobile_phone\u003e\n\u003cSEG\u003e ST_3 -\u003e ST_4 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Chase / amenity: bank\u003e \u003cPOI_LEFT: Chase / amenity: atm\u003e \u003cPOI_LEFT: Pret A Manger / amenity: fast_food / cuisine: sandwich\u003e\n\u003cSEG\u003e ST_4 -\u003e ST_5 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Chase / amenity: bank\u003e \u003cPOI_LEFT: Chase / amenity: atm\u003e \u003cPOI_LEFT: Pret A Manger / amenity: fast_food / cuisine: sandwich\u003e\n\u003cSEG\u003e ST_5 -\u003e ST_6 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Santander / amenity: bank\u003e \u003cPOI_LEFT: Santander / amenity: atm\u003e \u003cPOI_RIGHT: Hampton Inn / tourism: hotel\u003e \u003cPOI_RIGHT: T-Mobile / shop: mobile_phone\u003e\n\u003cSEG\u003e ST_6 -\u003e ST_7 \u003cDIR:STRAIGHT\u003e \u003cPOI_RIGHT: Sheraton New York Times Square Hotel / tourism: hotel\u003e\n\u003cSEG\u003e ST_7 -\u003e ST_8 \u003cDIR:STRAIGHT\u003e \u003cPOI_RIGHT: Sheraton New York Times Square Hotel / tourism: hotel\u003e\n\u003cROUTE_END\u003e",
]
documents = [
    "After this block take a left at the third light and stop halfway down the block. After the second intersection you'll go down a block with several theaters. A Hampton Inn will be on your right and a bank ahead on the corner. Start by going straight through two intersections.",
    "After this block take a left at the third light and stop halfway down the block. After the second intersection you'll go down a block with several theaters. A Hampton Inn will be on your right and a bank ahead on the corner. Start by going straight through two intersections.",
    'At the next light with a playground on the far left, turn left. Stop in the middle of the playground before a fire station on the left side. Pass a T-intersection with Chase on the far left corner. Walk by a small church to the end of the street and turn left.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.3893,  0.3893, -0.0502]])
```

<!--
### 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.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 7,786 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: 16 tokens</li><li>mean: 257.72 tokens</li><li>max: 1468 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 44.19 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 40.97 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | positive                                                                                                                                                                                                        | negative                                                                                                                                                                                                        |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT> <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Brooks Brothers / shop: clothes><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Brooks Brothers / shop: clothes><br><SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes><br><SEG> ST_4 -> ST_5 <DIR:LEFT> <LIGHT><br><SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Nine West / shop: clothes><br><ROUTE_END></code>                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>Turn left at the 2nd light with Nine West on the left corner.</code>                                                                                                                                      | <code>Walk to the light with the fountain on your left and turn right.</code>                                                                                                                                   |
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT> <LIGHT> <POI_LEFT: highway: bus_stop> <POI_LEFT: Houndstooth Pub / amenity: bar><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_LEFT: highway: bus_stop> <POI_LEFT: Houndstooth Pub / amenity: bar><br><SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international><br><SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White Castle / amenity: fast_food / cuisine: burger><br><SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White Castle / amenity: fast_food / cuisine: burger><br><SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White ...</code> | <code>Cooper's Tavern is on the right corner. Go about half way down the block. Go to the lights and turn right. Go through the following three sets of lights. Stop right after McDonald's on the left.</code> | <code>Go through the following three sets of lights. Go about half way down the block. Cooper's Tavern is on the right corner. Stop right after McDonald's on the left. Go to the lights and turn right.</code> |
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT><br><SEG> ST_3 -> ST_4 <DIR:RIGHT> <LIGHT><br><SEG> ST_4 -> ST_5 <DIR:STRAIGHT><br><SEG> ST_5 -> ST_6 <DIR:STRAIGHT><br><SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_LEFT: Exki / amenity: restaurant / cuisine: fast_food><br><ROUTE_END></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>Go straight and take a right at the intersection. Continue straight through 2 intersections. then your destination will be right before Exki on the left.</code>                                          | <code>Go straight and take a right at the intersection. Head to the first light and make a left. then your destination will be right before Exki on the left.</code>                                            |
* Loss: <code>contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives</code>

### Evaluation Dataset

#### Unnamed Dataset

* Size: 866 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 866 samples:
  |         | anchor                                                                                | positive                                                                           | negative                                                                           |
  |:--------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                             | string                                                                             |
  | details | <ul><li>min: 16 tokens</li><li>mean: 262.37 tokens</li><li>max: 1270 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 43.59 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 40.63 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | positive                                                                                                                                                                                                                                                                         | negative                                                                                                                                                                                                                                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT> <POI_LEFT: Chelsea Square Restaurant / amenity: restaurant><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT><br><ROUTE_END></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>stop in the middle of the intersection at the next light.</code>                                                                                                                                                                                                           | <code>Go through another light past Rail Line Diner.</code>                                                                                                                                                                                                                      |
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT> <POI_LEFT: Insomnia Cookies / shop: bakery><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT><br><SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <LIGHT><br><SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_LEFT: Atmi / shop: alcohol><br><SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <POI_LEFT: Atmi / shop: alcohol> <POI_RIGHT: leisure: park> <POI_LEFT: Urban Outfitters / shop: clothes><br><SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_LEFT: highway: bus_stop><br><SEG> ST_7 -> ST_8 <DIR:RIGHT> <LIGHT><br><SEG> ST_8 -> ST_9 <DIR:RIGHT> <LIGHT><br><SEG> ST_9 -> ST_10 <DIR:STRAIGHT> <POI_RIGHT: Momofuku Ssäm Bar / amenity: restaurant / cuisine: asian><br><SEG> ST_10 -> ST_11 <DIR:STRAIGHT><br><ROUTE_END></code> | <code>stop a little more than half way down the block where Abe Lebewohl Park on the right begins between Atmi and Urban Outfitters will be on the left. Go to the traffic light where there is a bus stop on the near left corner and turn right. Go through two lights.</code> | <code>Go to the traffic light where there is a bus stop on the near left corner and turn right. Go through two lights. stop a little more than half way down the block where Abe Lebewohl Park on the right begins between Atmi and Urban Outfitters will be on the left.</code> |
  | <code><ROUTE_START><br><SEG> ST_1 -> ST_2 <DIR:STRAIGHT><br><SEG> ST_2 -> ST_3 <DIR:STRAIGHT><br><SEG> ST_3 -> ST_4 <DIR:RIGHT> <LIGHT><br><ROUTE_END></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>Turn right and go through the light immediately after.</code>                                                                                                                                                                                                              | <code>Go through the next intersection and pass the park on your right.</code>                                                                                                                                                                                                   |
* Loss: <code>contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives</code>

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

- `eval_strategy`: steps
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: True
- `dataloader_num_workers`: 2
- `load_best_model_at_end`: True
- `prompts`: task: sentence similarity | query: 

#### 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`: 16
- `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`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `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
- `bf16`: True
- `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`: True
- `dataloader_num_workers`: 2
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: task: sentence similarity | query: 
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch      | Step     | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.1028     | 100      | 1.168         | -               |
| 0.2055     | 200      | 0.856         | -               |
| 0.3083     | 300      | 0.7349        | -               |
| 0.4111     | 400      | 0.7102        | -               |
| 0.5139     | 500      | 0.7844        | 1.0139          |
| 0.6166     | 600      | 0.8398        | -               |
| 0.7194     | 700      | 0.6467        | -               |
| 0.8222     | 800      | 0.7531        | -               |
| 0.9250     | 900      | 0.7469        | -               |
| 1.0277     | 1000     | 0.7163        | 0.8791          |
| 1.1305     | 1100     | 0.6351        | -               |
| 1.2333     | 1200     | 0.5194        | -               |
| 1.3361     | 1300     | 0.5055        | -               |
| 1.4388     | 1400     | 0.5584        | -               |
| 1.5416     | 1500     | 0.5229        | 0.7156          |
| 1.6444     | 1600     | 0.4807        | -               |
| 1.7472     | 1700     | 0.5424        | -               |
| 1.8499     | 1800     | 0.5885        | -               |
| 1.9527     | 1900     | 0.5101        | -               |
| **2.0555** | **2000** | **0.4153**    | **0.6557**      |
| 2.1583     | 2100     | 0.3408        | -               |
| 2.2610     | 2200     | 0.356         | -               |
| 2.3638     | 2300     | 0.3845        | -               |
| 2.4666     | 2400     | 0.3322        | -               |
| 2.5694     | 2500     | 0.3347        | 0.6803          |
| 2.6721     | 2600     | 0.3297        | -               |
| 2.7749     | 2700     | 0.3092        | -               |
| 2.8777     | 2800     | 0.3704        | -               |
| 2.9805     | 2900     | 0.3689        | -               |
| 3.0832     | 3000     | 0.2179        | 0.7215          |
| 3.1860     | 3100     | 0.1906        | -               |
| 3.2888     | 3200     | 0.2338        | -               |
| 3.3916     | 3300     | 0.1779        | -               |
| 3.4943     | 3400     | 0.2546        | -               |
| 3.5971     | 3500     | 0.2066        | 0.6497          |
| 3.6999     | 3600     | 0.237         | -               |
| 3.8027     | 3700     | 0.239         | -               |
| 3.9054     | 3800     | 0.2162        | -               |
| 4.0082     | 3900     | 0.2095        | -               |
| 4.1110     | 4000     | 0.1101        | 0.6722          |
| 4.2138     | 4100     | 0.1108        | -               |
| 4.3165     | 4200     | 0.1059        | -               |
| 4.4193     | 4300     | 0.1235        | -               |
| 4.5221     | 4400     | 0.1061        | -               |
| 4.6249     | 4500     | 0.1231        | 0.6879          |
| 4.7276     | 4600     | 0.1017        | -               |
| 4.8304     | 4700     | 0.1236        | -               |
| 4.9332     | 4800     | 0.0849        | -               |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.9.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.2.0
- Tokenizers: 0.22.1

## 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",
}
```

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