metadata

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. 


        Likewise, 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.


        The 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. 


        The 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.
         
         While every day during the Mahakumbh is considered auspicious for bathing, the main bathing festivals are as follows:
         
         1. Paush Purnima – 13 January
         2. Makar Sankranti – 14 January
         3. Mauni Amavasya – 29 January
         4. Vasant Panchami – 3 February
         5. Maghi Purnima – 12 February
         6. Mahashivratri – 26 February
         
         Out of these, three dates are Shahi Snan festivals, when the Akharas and saints proceed with grand processions for the bath:
         
         1. Makar Sankranti – 14 January
         2. Mauni Amavasya – 29 January
         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
            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
            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
            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
            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 model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Information Retrieval

Dataset: val_evaluator
Evaluated with 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

Training Details

Training Dataset

Unnamed Dataset

Size: 563 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 563 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 16.33 tokens max: 30 tokens	min: 3 tokens mean: 93.51 tokens max: 402 tokens	min: 16 tokens mean: 109.62 tokens max: 269 tokens

Samples:

anchor	positive	negative
`Are there attached bathrooms in tents?`	`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`	`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.`
`Are there any discounts for senior citizens or children on buses traveling from the Bus Stand to the Mela?`	`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.`	`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.`
`Are there any luggage porter services available at Prayagraj Junction for pilgrims heading to the Mela?`	`Yes, luggage porter services are available at Prayagraj Junction for pilgrims heading to the Mela. These porters, often referred to as coolies`	`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.`

Loss: GISTEmbedLoss with these parameters:

{'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: anchor, positive, and negative

Approximate statistics based on the first 141 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 16.05 tokens max: 30 tokens	min: 8 tokens mean: 88.91 tokens max: 324 tokens	min: 27 tokens mean: 104.84 tokens max: 262 tokens

Samples:

anchor	positive	negative
`What family-friendly tours are available?`	`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. Specific tours for senior citizens are also available. To explore them, click here : https://bit.ly/4eWFRoH`	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.
`What are the charges for a private taxi or cab from Prayagraj Airport to the Mela grounds?`	`Private taxi charges are not fixed`	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.
`What are the options for traveling to the Kumbh Mela if I arrive late at night at Prayagraj Junction?`	`If you arrive late at night at Prayagraj Junction for the Kumbh Mela, you have majorly 2 options for travel. 1. Taxi/Cabs: You can easily find 24/7 taxi services outside the railway station. Prepaid taxis are the most convenient and safe option. 2. Auto Rickshaws:Auto rickshaws are readily available outside the railway station.`	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.

Loss: GISTEmbedLoss with these parameters:

{'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

Click to expand

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

Training Logs

Click to expand

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.

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

@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

@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}
}