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himanshu23099
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bge_embedding_finetune_v3

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3507
loss:GISTEmbedLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
bge_embedding_finetune_v3 / README.md
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---
base_model: BAAI/bge-small-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@5
- cosine_ndcg@10
- cosine_ndcg@100
- cosine_mrr@5
- cosine_mrr@10
- cosine_mrr@100
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@5
- dot_recall@10
- dot_ndcg@5
- dot_ndcg@10
- dot_ndcg@100
- dot_mrr@5
- dot_mrr@10
- dot_mrr@100
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:563
- loss:GISTEmbedLoss
widget:
- source_sentence: Can I pay for parking using digital payment methods like UPI, credit/debit
cards, or mobile wallets?
sentences:
- The vibrant colors of autumn leaves create a breathtaking tapestry across the
landscape, reminding us of nature's artistry. Many people enjoy taking strolls
through parks to appreciate the crisp air and the sound of crunching leaves underfoot.
Some choose to photograph the scenery, capturing fleeting moments of beauty, while
others might indulge in seasonal treats like pumpkin spice lattes. Embracing the
change in seasons also encourages us to reflect on personal growth and the passage
of time as we move towards the winter months.
- Yes, most parking areas accept digital payment methods such as UPI, credit/debit
cards, or mobile wallets to facilitate cashless transactions. However, it is recommended
to carry some cash as a backup because digital payments might not always work
due to network issues and high crowd density during peak times.
- Mahakumbh 2025 will start on 13 January with the Paush Purnima bath and end on
26 February with the Mahashivratri bath.
- source_sentence: What is Aarti
sentences:
- No, shuttle buses will not have dedicated volunteers specifically, but for assistance,
you can reach out to the nearest information center.
- "In India, since ancient times, rivers are worshipped due to their importance\
\ to the human life. \n\nLikewise, in Tirathraj Prayagraj, Aartis’ are performed\
\ on the banks of Ganga, Yamuna and at Sangam with great admiration, deep-rooted\
\ honor and devotion. In Prayagraj, Prayagraj Mela Authority and various other\
\ communities make grand arrangements for these Aartis.\n\nThe Aartis are performed\
\ in the mornings and evenings, in which priests (Batuks), normally 5 to 7 in\
\ number, chant hymns with great fervor, holding meticulously designed lamps and\
\ worship the rivers with utmost devotion. \n\nThe lamps held by the batuks represent\
\ the importance of panchtatva. On one hand, flames of the lamps signify bowing\
\ to the waters of the sacred rivers and on the other, the holy fumes emanating\
\ from the lamps appear to play the mystic of heaven on earth."
- 'In the realm of celestial bodies, the moons of Jupiter captivate astronomers
with their striking variations. These natural satellites exhibit a diverse range
of landscapes, from the icy crust of Europa to the volcanic surface of Io, each
revealing secrets about the formation of our solar system.
In laboratories around the world, researchers utilize advanced telescopes, funded
by international space agencies, to monitor these moons, collecting data that
aids in understanding their geological processes. They examine topographical maps
and analyze spectrographs, revealing rich insights into the chemical compositions
present on these distant worlds.
Collaborations between scientists and institutions have led to remarkable discoveries,
including the potential for subsurface oceans beneath the icy shell of Europa,
stirring excitement about the possibility of extraterrestrial life. Meanwhile,
rumors of missions planned to explore these enigmatic moons intensify interest
in the ongoing quest for knowledge beyond our home planet.'
- source_sentence: Which all companies offer tour services?
sentences:
- There are no specific facilities exclusively for senior citizens at the Railway
Junction in relation to the Mela. However, most railway stations generally offer
basic amenities like wheelchairs, assistance for boarding and de-boarding, and
special seating areas for senior citizens or those with mobility issues. It is
advisable for senior citizens to check with the railway authorities for any additional
support that might be available during the Mela.
- The art of origami has captivated many enthusiasts around the world. Crafting
intricate designs from simple sheets of paper showcases creativity and precision.
Essential tools include sharp scissors, bone folders, and high-quality paper to
achieve the best results. Workshops often focus on advanced techniques, leading
to beautiful decorative pieces and useful items, enhancing the enjoyment of this
timeless craft.
- All information provided here includes tour services provided by UPSTDC (Uttar
Pradesh State Tourism Development Corporation). Additionally, popular platforms
like MakeMyTrip and other travel websites offer their own tour packages for Kumbh
Mela and nearby attractions. For a wider range of options, you can check these
services directly on their websites to find a tour that best suits your needs.
- source_sentence: From when to when is the Mela?
sentences:
- "Mahakumbh Mela 2025 will begin on 13 January with the Paush Purnima bath and\
\ will conclude on 26 February with the Mahashivratri bath.\n \n While every day\
\ during the Mahakumbh is considered auspicious for bathing, the main bathing\
\ festivals are as follows:\n \n 1. Paush Purnima – 13 January\n 2. Makar Sankranti\
\ – 14 January\n 3. Mauni Amavasya – 29 January\n 4. Vasant Panchami – 3 February\n\
\ 5. Maghi Purnima – 12 February\n 6. Mahashivratri – 26 February\n \n Out of\
\ these, three dates are Shahi Snan festivals, when the Akharas and saints proceed\
\ with grand processions for the bath:\n \n 1. Makar Sankranti – 14 January\n\
\ 2. Mauni Amavasya – 29 January\n 3. Vasant Panchami – 3 February"
- 'The sky today is filled with vibrant clouds, where shades of orange and pink
blend seamlessly into vast expanses of blue. The wind carries the sounds of distant
laughter, as children chase each other through sprawling fields of lush green
grass. Nearby, an old oak tree stands tall, its branches swaying gently and offering
shade to those seeking respite from the warmth of the sun.
A stream meanders through the landscape, its clear waters reflecting the brilliant
hues of the sky above. Dragonflies dart about, their iridescent wings catching
the light as they flit from flower to flower. In the distance, a family prepares
a picnic, the aroma of freshly baked bread mingling with the sweet scent of blooming
wildflowers.
As the afternoon stretches on, the sun begins its slow descent, painting the horizon
in richer tones. The air is filled with a sense of peace and joy, moments warm
with the laughter of friends and the thrill of nature''s beauty all around.'
- No, there is no special bus service specifically for women or families traveling
from the Bus Stand to the Mela. Shuttle buses would be available with fixed timings
and route plans which offer convenient travel
- source_sentence: What is the ritual of Snan or bathing?
sentences:
- Yes, luggage porter services are available at Prayagraj Junction for pilgrims
heading to the Mela. These porters, often referred to as coolies
- 'Taking bath at the confluence of Ganga, Yamuna and invisible Saraswati during
Mahakumbh has special significance. It is believed that by bathing in this holy
confluence, all the sins of a person are washed away and he attains salvation.
Bathing not only symbolizes personal purification, but it also conveys the message
of social harmony and unity, where people from different cultures and communities
come together to participate in this sacred ritual.
It is considered that in special circumstances, the water of rivers also acquires
a special life-giving quality, i.e. nectar, which not only leads to spiritual
development along with purification of the mind, but also gives physical benefits
by getting health.'
- 'The art of knitting is a fascinating hobby that allows individuals to create
beautiful and functional pieces from yarn. By intertwining strands of wool or
cotton, one can produce items ranging from scarves to intricate sweaters. This
craft has been passed down through generations, often bringing family members
together for cozy evenings filled with creativity and conversation.
Knitting not only provides a sense of accomplishment with every completed project
but also promotes focus and relaxation, making it an excellent activity for reducing
stress. Furthermore, the choice of colors and patterns can result in vibrant works
of art, showcasing the unique style and personality of the knitter. Engaging in
this craft often leads to new friendships within community groups that gather
to share techniques and ideas, fostering a sense of belonging among enthusiasts.'
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: val evaluator
type: val_evaluator
metrics:
- type: cosine_accuracy@1
value: 0.8156028368794326
name: Cosine Accuracy@1
- type: cosine_accuracy@5
value: 0.9929078014184397
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8156028368794326
name: Cosine Precision@1
- type: cosine_precision@5
value: 0.1985815602836879
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8156028368794326
name: Cosine Recall@1
- type: cosine_recall@5
value: 0.9929078014184397
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.9154696629317853
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.9179959550389344
name: Cosine Ndcg@10
- type: cosine_ndcg@100
value: 0.9179959550389344
name: Cosine Ndcg@100
- type: cosine_mrr@5
value: 0.8891252955082741
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.8903073286052008
name: Cosine Mrr@10
- type: cosine_mrr@100
value: 0.8903073286052008
name: Cosine Mrr@100
- type: cosine_map@100
value: 0.8903073286052009
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.8156028368794326
name: Dot Accuracy@1
- type: dot_accuracy@5
value: 0.9929078014184397
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8156028368794326
name: Dot Precision@1
- type: dot_precision@5
value: 0.1985815602836879
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.8156028368794326
name: Dot Recall@1
- type: dot_recall@5
value: 0.9929078014184397
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@5
value: 0.9154696629317853
name: Dot Ndcg@5
- type: dot_ndcg@10
value: 0.9179959550389344
name: Dot Ndcg@10
- type: dot_ndcg@100
value: 0.9179959550389344
name: Dot Ndcg@100
- type: dot_mrr@5
value: 0.8891252955082741
name: Dot Mrr@5
- type: dot_mrr@10
value: 0.8903073286052008
name: Dot Mrr@10
- type: dot_mrr@100
value: 0.8903073286052008
name: Dot Mrr@100
- type: dot_map@100
value: 0.8903073286052009
name: Dot Map@100
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): 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("himanshu23099/bge_embedding_finetune_v3")
# Run inference
sentences = [
'What is the ritual of Snan or bathing?',
'Taking bath at the confluence of Ganga, Yamuna and invisible Saraswati during Mahakumbh has special significance. It is believed that by bathing in this holy confluence, all the sins of a person are washed away and he attains salvation.\n\nBathing not only symbolizes personal purification, but it also conveys the message of social harmony and unity, where people from different cultures and communities come together to participate in this sacred ritual.\n\nIt is considered that in special circumstances, the water of rivers also acquires a special life-giving quality, i.e. nectar, which not only leads to spiritual development along with purification of the mind, but also gives physical benefits by getting health.',
'The art of knitting is a fascinating hobby that allows individuals to create beautiful and functional pieces from yarn. By intertwining strands of wool or cotton, one can produce items ranging from scarves to intricate sweaters. This craft has been passed down through generations, often bringing family members together for cozy evenings filled with creativity and conversation.\n\nKnitting not only provides a sense of accomplishment with every completed project but also promotes focus and relaxation, making it an excellent activity for reducing stress. Furthermore, the choice of colors and patterns can result in vibrant works of art, showcasing the unique style and personality of the knitter. Engaging in this craft often leads to new friendships within community groups that gather to share techniques and ideas, fostering a sense of belonging among enthusiasts.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Information Retrieval
* Dataset: `val_evaluator`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.8156 |
| cosine_accuracy@5 | 0.9929 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8156 |
| cosine_precision@5 | 0.1986 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8156 |
| cosine_recall@5 | 0.9929 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@5 | 0.9155 |
| cosine_ndcg@10 | 0.918 |
| cosine_ndcg@100 | 0.918 |
| cosine_mrr@5 | 0.8891 |
| cosine_mrr@10 | 0.8903 |
| cosine_mrr@100 | 0.8903 |
| **cosine_map@100** | **0.8903** |
| dot_accuracy@1 | 0.8156 |
| dot_accuracy@5 | 0.9929 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.8156 |
| dot_precision@5 | 0.1986 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.8156 |
| dot_recall@5 | 0.9929 |
| dot_recall@10 | 1.0 |
| dot_ndcg@5 | 0.9155 |
| dot_ndcg@10 | 0.918 |
| dot_ndcg@100 | 0.918 |
| dot_mrr@5 | 0.8891 |
| dot_mrr@10 | 0.8903 |
| dot_mrr@100 | 0.8903 |
| dot_map@100 | 0.8903 |
<!--
## 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: 563 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 563 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.33 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 93.51 tokens</li><li>max: 402 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 109.62 tokens</li><li>max: 269 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Are there attached bathrooms in tents?</code> | <code>Attached bathroom facilities in tents vary by vendor and tent type. To know more about the availability of attached bathrooms, please reach out to your chosen Tent City vendor. For more information about these vendors and their services, please click here</code> | <code>The colors of the rainbow blend seamlessly across the canvas of the sky, creating a stunning visual display. Enjoying the beauty of nature can greatly enhance one's mood and inspire creativity. Take a moment to appreciate the vibrant hues and how they interact, as this can lead to a greater understanding of art and light. Exploring different forms of expression allows for personal growth and emotional exploration.</code> |
| <code>Are there any discounts for senior citizens or children on buses traveling from the Bus Stand to the Mela?</code> | <code>No, there are no specific discounts available for senior citizens or children on buses traveling from the Bus Stand to the Mela. Standard ticket prices generally apply to all passengers.</code> | <code>The vibrant colors of autumn leaves create a breathtaking scene as they cascade gently to the ground. Local parks become havens for photographers and nature enthusiasts alike, capturing the fleeting beauty of the season. Crisp air invigorates leisurely strolls, while children gather acorns and pinecones, crafting treasures from nature’s bounty.</code> |
| <code>Are there any luggage porter services available at Prayagraj Junction for pilgrims heading to the Mela?</code> | <code>Yes, luggage porter services are available at Prayagraj Junction for pilgrims heading to the Mela. These porters, often referred to as coolies</code> | <code> can be hired directly at the station to assist with carrying luggage from the train platform to your onward transport or directly to the Mela area.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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): Normalize()
), 'temperature': 0.01}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 141 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 141 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 88.91 tokens</li><li>max: 324 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 104.84 tokens</li><li>max: 262 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What family-friendly tours are available?</code> | <code>All tours are designed with families in mind, ensuring a safe, comfortable, and enjoyable experience for all age groups. Whether traveling with children or elderly family members, the tours are structured to accommodate the needs of everyone in the group.<br><br>Specific tours for senior citizens are also available. To explore them, click here : https://bit.ly/4eWFRoH</code> | <code>The majestic mountains rise against the azure sky, their peaks adorned with glistening snow that sparkles in the sunlight. deep valleys shelter hidden waterfalls, where crystal-clear waters cascade gracefully over rocks, creating a tranquil sound reverberating through the lush landscape. Wildlife thrives here, and one may spot elusive deer grazing in the early morning mist. As dusk settles, the horizon transforms into a canvas of vibrant hues, painting a breathtaking sunset that captivates the soul. Each season unveils unique beauty, inviting adventurers to explore its wonders.</code> |
| <code>What are the charges for a private taxi or cab from Prayagraj Airport to the Mela grounds?</code> | <code>Private taxi charges are not fixed</code> | <code>The garden blooms vibrantly with colors and fragrances that attract butterflies and bees. Each petal holds a story from the earth, whispering tales of growth and resilience. Nearby, a small pond reflects the blue sky, while frogs leap joyfully on lily pads, creating ripples that dance across the surface. The sound of rustling leaves accompanies the gentle breeze, making nature's symphony a soothing backdrop for all who pause and appreciate this serene setting. As the sun sets, golden hues envelop the scene, inviting evening creatures to awaken under the twilight.</code> |
| <code>What are the options for traveling to the Kumbh Mela if I arrive late at night at Prayagraj Junction?</code> | <code>If you arrive late at night at Prayagraj Junction for the Kumbh Mela, you have majorly 2 options for travel. <br><br>1. Taxi/Cabs: You can easily find 24/7 taxi services outside the railway station. Prepaid taxis are the most convenient and safe option.<br><br>2. Auto Rickshaws:Auto rickshaws are readily available outside the railway station.</code> | <code>The blooming desert blooms with vibrant colors as dusk approaches. Amidst the sands, ancient stories whisper through the wind, recalling journeys of nomads who tread lightly upon the earth. Some dance beneath the starlit skies, celebrating the beauty of freedom and the vastness of their surroundings. The nocturnal creatures awaken, each sound echoing tales of survival and adventure. Beyond the horizon, a tapestry of dreams unfurls, where every grain of sand holds the promise of a new discovery waiting to be unveiled.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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): Normalize()
), 'temperature': 0.01}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `gradient_accumulation_steps`: 2
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 90
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
#### 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`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-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`: 90
- `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`: 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`: 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}
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | val_evaluator_cosine_map@100 |
|:-----------:|:--------:|:-------------:|:---------------:|:----------------------------:|
| 0.5556 | 10 | 0.9623 | 0.5803 | 0.7676 |
| 1.1111 | 20 | 0.8653 | 0.5278 | 0.7684 |
| 1.6667 | 30 | 0.9346 | 0.4556 | 0.7692 |
| 2.2222 | 40 | 0.8058 | 0.3928 | 0.7687 |
| 2.7778 | 50 | 0.6639 | 0.3282 | 0.7723 |
| 3.3333 | 60 | 0.4974 | 0.2657 | 0.7784 |
| 3.8889 | 70 | 0.4447 | 0.2130 | 0.7877 |
| 4.4444 | 80 | 0.4309 | 0.1753 | 0.7922 |
| 5.0 | 90 | 0.2755 | 0.1320 | 0.7951 |
| 5.5556 | 100 | 0.3105 | 0.0826 | 0.8029 |
| 6.1111 | 110 | 0.1539 | 0.0479 | 0.8106 |
| 6.6667 | 120 | 0.22 | 0.0312 | 0.8141 |
| 7.2222 | 130 | 0.235 | 0.0173 | 0.8245 |
| 7.7778 | 140 | 0.1517 | 0.0119 | 0.8257 |
| 8.3333 | 150 | 0.1328 | 0.0095 | 0.8311 |
| 8.8889 | 160 | 0.1175 | 0.0055 | 0.8319 |
| 9.4444 | 170 | 0.1178 | 0.0037 | 0.8308 |
| 10.0 | 180 | 0.0598 | 0.0034 | 0.8338 |
| 10.5556 | 190 | 0.0958 | 0.0030 | 0.8324 |
| 11.1111 | 200 | 0.0681 | 0.0019 | 0.8331 |
| 11.6667 | 210 | 0.069 | 0.0013 | 0.8406 |
| 12.2222 | 220 | 0.0327 | 0.0009 | 0.8522 |
| 12.7778 | 230 | 0.0833 | 0.0006 | 0.8589 |
| 13.3333 | 240 | 0.0806 | 0.0005 | 0.8596 |
| 13.8889 | 250 | 0.0714 | 0.0004 | 0.8658 |
| 14.4444 | 260 | 0.0813 | 0.0004 | 0.8659 |
| 15.0 | 270 | 0.0512 | 0.0003 | 0.8676 |
| 15.5556 | 280 | 0.043 | 0.0003 | 0.8677 |
| 16.1111 | 290 | 0.0526 | 0.0003 | 0.8677 |
| 16.6667 | 300 | 0.0291 | 0.0002 | 0.8651 |
| 17.2222 | 310 | 0.0487 | 0.0002 | 0.8662 |
| 17.7778 | 320 | 0.054 | 0.0002 | 0.8621 |
| 18.3333 | 330 | 0.067 | 0.0002 | 0.8652 |
| 18.8889 | 340 | 0.0415 | 0.0002 | 0.8652 |
| 19.4444 | 350 | 0.0484 | 0.0002 | 0.8652 |
| 20.0 | 360 | 0.0304 | 0.0002 | 0.8690 |
| 20.5556 | 370 | 0.025 | 0.0002 | 0.8697 |
| 21.1111 | 380 | 0.0549 | 0.0002 | 0.8697 |
| 21.6667 | 390 | 0.0375 | 0.0002 | 0.8736 |
| 22.2222 | 400 | 0.0293 | 0.0002 | 0.8749 |
| 22.7778 | 410 | 0.0558 | 0.0002 | 0.8728 |
| 23.3333 | 420 | 0.0458 | 0.0002 | 0.8730 |
| 23.8889 | 430 | 0.0235 | 0.0002 | 0.8730 |
| 24.4444 | 440 | 0.0515 | 0.0002 | 0.8730 |
| 25.0 | 450 | 0.0337 | 0.0002 | 0.8734 |
| 25.5556 | 460 | 0.0376 | 0.0002 | 0.8734 |
| 26.1111 | 470 | 0.0189 | 0.0003 | 0.8734 |
| 26.6667 | 480 | 0.032 | 0.0002 | 0.8734 |
| 27.2222 | 490 | 0.025 | 0.0002 | 0.8695 |
| 27.7778 | 500 | 0.0258 | 0.0002 | 0.8704 |
| 28.3333 | 510 | 0.0351 | 0.0002 | 0.8681 |
| 28.8889 | 520 | 0.0285 | 0.0002 | 0.8679 |
| 29.4444 | 530 | 0.0263 | 0.0002 | 0.8679 |
| 30.0 | 540 | 0.0901 | 0.0002 | 0.8679 |
| 30.5556 | 550 | 0.0323 | 0.0001 | 0.8686 |
| 31.1111 | 560 | 0.0406 | 0.0001 | 0.8728 |
| 31.6667 | 570 | 0.0302 | 0.0001 | 0.8712 |
| 32.2222 | 580 | 0.0195 | 0.0001 | 0.8718 |
| 32.7778 | 590 | 0.0665 | 0.0001 | 0.8718 |
| 33.3333 | 600 | 0.0153 | 0.0001 | 0.8728 |
| 33.8889 | 610 | 0.0378 | 0.0001 | 0.8728 |
| 34.4444 | 620 | 0.0369 | 0.0001 | 0.8763 |
| 35.0 | 630 | 0.0238 | 0.0001 | 0.8706 |
| 35.5556 | 640 | 0.0275 | 0.0001 | 0.8720 |
| 36.1111 | 650 | 0.0469 | 0.0001 | 0.8708 |
| 36.6667 | 660 | 0.0438 | 0.0001 | 0.8788 |
| 37.2222 | 670 | 0.0333 | 0.0001 | 0.8800 |
| 37.7778 | 680 | 0.0186 | 0.0001 | 0.8765 |
| 38.3333 | 690 | 0.0308 | 0.0001 | 0.8765 |
| 38.8889 | 700 | 0.0713 | 0.0001 | 0.8767 |
| 39.4444 | 710 | 0.0188 | 0.0001 | 0.8767 |
| 40.0 | 720 | 0.0205 | 0.0001 | 0.8767 |
| 40.5556 | 730 | 0.0261 | 0.0001 | 0.8767 |
| 41.1111 | 740 | 0.0193 | 0.0001 | 0.8755 |
| 41.6667 | 750 | 0.0367 | 0.0000 | 0.8755 |
| 42.2222 | 760 | 0.0515 | 0.0000 | 0.8755 |
| 42.7778 | 770 | 0.0649 | 0.0000 | 0.8844 |
| 43.3333 | 780 | 0.0333 | 0.0000 | 0.8879 |
| 43.8889 | 790 | 0.0498 | 0.0000 | 0.8868 |
| 44.4444 | 800 | 0.0324 | 0.0000 | 0.8832 |
| 45.0 | 810 | 0.0321 | 0.0000 | 0.8832 |
| 45.5556 | 820 | 0.0354 | 0.0000 | 0.8832 |
| 46.1111 | 830 | 0.04 | 0.0000 | 0.8868 |
| 46.6667 | 840 | 0.0176 | 0.0000 | 0.8868 |
| 47.2222 | 850 | 0.0297 | 0.0000 | 0.8868 |
| 47.7778 | 860 | 0.0469 | 0.0000 | 0.8868 |
| 48.3333 | 870 | 0.025 | 0.0000 | 0.8868 |
| 48.8889 | 880 | 0.0425 | 0.0000 | 0.8868 |
| 49.4444 | 890 | 0.0475 | 0.0000 | 0.8868 |
| 50.0 | 900 | 0.0529 | 0.0000 | 0.8868 |
| 50.5556 | 910 | 0.0312 | 0.0000 | 0.8868 |
| 51.1111 | 920 | 0.0385 | 0.0000 | 0.8832 |
| 51.6667 | 930 | 0.0316 | 0.0000 | 0.8832 |
| 52.2222 | 940 | 0.0361 | 0.0000 | 0.8832 |
| 52.7778 | 950 | 0.053 | 0.0000 | 0.8832 |
| 53.3333 | 960 | 0.0226 | 0.0000 | 0.8868 |
| 53.8889 | 970 | 0.0781 | 0.0000 | 0.8868 |
| 54.4444 | 980 | 0.03 | 0.0000 | 0.8868 |
| 55.0 | 990 | 0.0349 | 0.0000 | 0.8832 |
| 55.5556 | 1000 | 0.0539 | 0.0000 | 0.8832 |
| 56.1111 | 1010 | 0.0351 | 0.0000 | 0.8832 |
| 56.6667 | 1020 | 0.0506 | 0.0000 | 0.8832 |
| 57.2222 | 1030 | 0.0204 | 0.0000 | 0.8832 |
| 57.7778 | 1040 | 0.0254 | 0.0000 | 0.8844 |
| 58.3333 | 1050 | 0.0274 | 0.0000 | 0.8844 |
| 58.8889 | 1060 | 0.001 | 0.0000 | 0.8844 |
| 59.4444 | 1070 | 0.049 | 0.0000 | 0.8844 |
| 60.0 | 1080 | 0.028 | 0.0000 | 0.8844 |
| 60.5556 | 1090 | 0.0477 | 0.0000 | 0.8844 |
| 61.1111 | 1100 | 0.0304 | 0.0000 | 0.8844 |
| 61.6667 | 1110 | 0.0188 | 0.0000 | 0.8844 |
| 62.2222 | 1120 | 0.0247 | 0.0000 | 0.8879 |
| 62.7778 | 1130 | 0.0428 | 0.0000 | 0.8879 |
| 63.3333 | 1140 | 0.0218 | 0.0000 | 0.8879 |
| 63.8889 | 1150 | 0.0476 | 0.0000 | 0.8868 |
| 64.4444 | 1160 | 0.021 | 0.0000 | 0.8868 |
| 65.0 | 1170 | 0.0435 | 0.0000 | 0.8856 |
| 65.5556 | 1180 | 0.0311 | 0.0000 | 0.8856 |
| 66.1111 | 1190 | 0.0275 | 0.0000 | 0.8856 |
| 66.6667 | 1200 | 0.0405 | 0.0000 | 0.8891 |
| 67.2222 | 1210 | 0.0009 | 0.0000 | 0.8891 |
| 67.7778 | 1220 | 0.0506 | 0.0000 | 0.8891 |
| 68.3333 | 1230 | 0.0538 | 0.0000 | 0.8891 |
| 68.8889 | 1240 | 0.0251 | 0.0000 | 0.8891 |
| 69.4444 | 1250 | 0.0168 | 0.0000 | 0.8891 |
| 70.0 | 1260 | 0.0527 | 0.0000 | 0.8903 |
| 70.5556 | 1270 | 0.0491 | 0.0000 | 0.8903 |
| 71.1111 | 1280 | 0.0092 | 0.0000 | 0.8903 |
| 71.6667 | 1290 | 0.0257 | 0.0000 | 0.8903 |
| **72.2222** | **1300** | **0.0455** | **0.0** | **0.8903** |
| 72.7778 | 1310 | 0.0271 | 0.0000 | 0.8903 |
| 73.3333 | 1320 | 0.04 | 0.0000 | 0.8903 |
| 73.8889 | 1330 | 0.0171 | 0.0000 | 0.8903 |
| 74.4444 | 1340 | 0.0157 | 0.0000 | 0.8903 |
| 75.0 | 1350 | 0.0323 | 0.0000 | 0.8903 |
| 75.5556 | 1360 | 0.0372 | 0.0000 | 0.8903 |
| 76.1111 | 1370 | 0.0109 | 0.0000 | 0.8903 |
| 76.6667 | 1380 | 0.0358 | 0.0000 | 0.8903 |
| 77.2222 | 1390 | 0.0279 | 0.0000 | 0.8903 |
| 77.7778 | 1400 | 0.0173 | 0.0000 | 0.8903 |
| 78.3333 | 1410 | 0.0409 | 0.0000 | 0.8903 |
| 78.8889 | 1420 | 0.0139 | 0.0000 | 0.8903 |
| 79.4444 | 1430 | 0.0123 | 0.0000 | 0.8903 |
| 80.0 | 1440 | 0.0232 | 0.0000 | 0.8903 |
| 80.5556 | 1450 | 0.0145 | 0.0000 | 0.8903 |
| 81.1111 | 1460 | 0.0261 | 0.0000 | 0.8903 |
| 81.6667 | 1470 | 0.0137 | 0.0000 | 0.8903 |
| 82.2222 | 1480 | 0.0146 | 0.0000 | 0.8903 |
| 82.7778 | 1490 | 0.0096 | 0.0000 | 0.8903 |
| 83.3333 | 1500 | 0.0245 | 0.0000 | 0.8903 |
| 83.8889 | 1510 | 0.0312 | 0.0000 | 0.8903 |
| 84.4444 | 1520 | 0.0174 | 0.0000 | 0.8903 |
| 85.0 | 1530 | 0.0437 | 0.0000 | 0.8903 |
| 85.5556 | 1540 | 0.0301 | 0.0000 | 0.8903 |
| 86.1111 | 1550 | 0.0119 | 0.0000 | 0.8903 |
| 86.6667 | 1560 | 0.0554 | 0.0000 | 0.8903 |
| 87.2222 | 1570 | 0.021 | 0.0000 | 0.8903 |
| 87.7778 | 1580 | 0.029 | 0.0000 | 0.8903 |
| 88.3333 | 1590 | 0.0132 | 0.0000 | 0.8903 |
| 88.8889 | 1600 | 0.0339 | 0.0000 | 0.8903 |
| 89.4444 | 1610 | 0.0412 | 0.0000 | 0.8903 |
| 90.0 | 1620 | 0.0847 | 0.0000 | 0.8903 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- 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",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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