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bge_embedding_finetune1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3507
loss:GISTEmbedLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3507
- loss:GISTEmbedLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: Is there an option to use ride-sharing apps like Ola or Uber for
travel from the Airport to the Mela?
sentences:
- "Are there towing services available if my vehicle breaks down in the parking\
\ lot?\n Yes, towing services are available if your vehicle breaks down in the\
\ parking lot."
- No, ride-sharing options like Ola or Uber are not available for travel from the
Airport to the Mela. Pilgrims are encouraged to use other transport options like
taxis, buses, or dedicated shuttle services provided for the event.
- Baking bread requires certain key ingredients to achieve a perfect texture. Flour,
water, and yeast are the base, while salt enhances flavor. The dough should be
kneaded until smooth, then allowed to rise in a warm area. After a proper rise,
shaping the loaf is essential for even baking in the oven.
- source_sentence: What is the significance of Akshaywat?
sentences:
- Akshaywat, or the "immortal banyan tree," is a spiritually significant site in
Prayagraj, especially during the Kumbh Mela. Symbolizing immortality and eternal
life, the tree is believed to possess divine qualities that remain unaffected
by creation and destruction cycles. Mythologically, it is associated with Lord
Brahma, who is said to have performed a sacrificial ritual under it, and Lord
Vishnu, who is believed to have blessed devotees there. Akshaywat is also a sacred
spot for performing Pind Daan, rituals for deceased ancestors, thought to help
achieve Moksha (liberation). As a center of spiritual wisdom and pilgrimage for
thousands of years, it continues to be a powerful symbol of divine blessings and
spiritual strength for Hindu devotees.
- 'What are the must-visit spiritual sites near Sangam?
The Sangam area, where the Ganga, Yamuna, and the mystical Saraswati rivers converge,
is surrounded by revered spiritual sites:
Bade Hanumanji Temple:Bade Hanumanji Temple, also known as Lete Hanuman Mandir,
is a unique and revered Hindu shrine located near the Sangam in Prayagraj. This
temple is distinctive for its reclining idol of Lord Hanuman, a one-of-a-kind
depiction of the deity. Each year, during the monsoon floods, the Ganga river
rises to gently wash over the feet of Lord Hanuman—a sacred ritual believed to
be a divine blessing
Patalpuri Temple and Akshayavat Tree: Located within the Allahabad Fort, the ancient
Patalpuri Temple is known for the Akshayavat (Indestructible Banyan Tree), considered
sacred and a symbol of immortality.
Mankameshwar Temple: A dedicated Shiva temple located near the Sangam, known for
its serene atmosphere and the belief that prayers here fulfill desires.'
- The uniqueness of brightly colored seashells lies in their mesmerizing patterns.
Found along coastlines worldwide, these intricate formations tell stories of marine
life and geological processes. Each shell serves as a protective covering, shielding
the delicate organisms within from predators and environmental threats. Fishermen
and beachcombers alike often treasure these natural artifacts, using them for
decoration or as tools in crafting. The vibrant hues seen in shells, ranging from
deep blues to vivid oranges, result from pigments produced by the mollusks themselves,
influenced by their habitat and diet. Collecting seashells can foster a deep appreciation
for marine ecosystems and the roles different species play within them, reminding
us of the intricate balance of nature.
- source_sentence: Allahabad Junction ka matlab
sentences:
- 'Where is Anand Bhavan Museum located?
Anand Bhawan is located on Jawaharlal Nehru Road, about 5 km from Allahabad Junction
Railway Station, Prayagraj, Uttar Pradesh.'
- Aartis are performed both in the mornings and evenings on the riverbanks in Prayagraj
to honor the divine presence of the sacred rivers—Ganga, Yamuna, and mythical
Saraswati—and to seek their blessings. \n The morning Aarti symbolizes the beginning
of a new day, invoking the divine to bestow grace, protection, and spiritual strength
upon the devotees. \n The evening Aarti serves as a ritual of gratitude, marking
the end of the day by thanking the deities for their blessings and guidance.
- 'Where is Khusro Bagh located?
The garden is located approximately 3 km from Allahabad Junction Railway Station,
making it easily accessible by local transport. The address is near the Lukarganj
area, Allahabad, Uttar Pradesh.'
- source_sentence: Do E-Rickshaws have a maximum passenger limit, and what is it?
sentences:
- The ancient art of glassblowing dates back thousands of years. This intricate
craft requires skill and precision, resulting in beautiful works that can be functional
or decorative. From vases to intricate sculptures, the possibilities are endless.
- E-Rickshaws have a maximum passenger limit of 4 people. It is important not to
exceed this limit to ensure safety.
- No, shuttle buses will not have dedicated volunteers specifically, but for assistance,
you can reach out to the nearest information center.
- source_sentence: Tourists visit reason
sentences:
- 'What attractions are closest to the city center?
Near the city center, you’ll find several attractions within a short distance.
Anand Bhavan and Swaraj Bhavan are centrally located and offer insights into the
Nehru family and India’s freedom movement. All Saints’ Cathedral, a magnificent
Gothic-style church also known as the “Patthar Girja,” is located in Civil Lines
and is one of Prayagraj''s architectural gems. Company Bagh, a peaceful park,
is also close by and ideal for a quiet stroll. Chandrashekhar Azad Park and Khusro
Bagh are both centrally located as well, providing green spaces along with historical
importance.'
- "When and where was the last Kumbh held?\n The last Mahakumbh was held in Haridwar\
\ in 2021."
- 'What is All Saints Cathedral, and why is it architecturally significant?
All Saints Cathedral, locally known as Patthar Girja (Stone Church), is a renowned
Anglican Christian Church located on M.G. Marg, Allahabad. Built in the late 19th
century, it is one of the most beautiful and architecturally significant churches
in Uttar Pradesh, attracting both tourists and pilgrims.'
pipeline_tag: sentence-similarity
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
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: 0.3580387685290764
name: Cosine Accuracy@1
- type: cosine_accuracy@5
value: 0.7092360319270239
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7993158494868872
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3580387685290764
name: Cosine Precision@1
- type: cosine_precision@5
value: 0.14184720638540477
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07993158494868871
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3580387685290764
name: Cosine Recall@1
- type: cosine_recall@5
value: 0.7092360319270239
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7993158494868872
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.5538539564761136
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.5832174788373438
name: Cosine Ndcg@10
- type: cosine_ndcg@100
value: 0.6189539076148961
name: Cosine Ndcg@100
- type: cosine_mrr@5
value: 0.5013492968453055
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.5136020162530992
name: Cosine Mrr@10
- type: cosine_mrr@100
value: 0.5210085507064763
name: Cosine Mrr@100
- type: cosine_map@100
value: 0.5210085507064769
name: Cosine 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 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': 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_finetune1")
# Run inference
sentences = [
'Tourists visit reason',
'What is All Saints Cathedral, and why is it architecturally significant?\nAll Saints Cathedral, locally known as Patthar Girja (Stone Church), is a renowned Anglican Christian Church located on M.G. Marg, Allahabad. Built in the late 19th century, it is one of the most beautiful and architecturally significant churches in Uttar Pradesh, attracting both tourists and pilgrims.',
"What attractions are closest to the city center?\nNear the city center, you’ll find several attractions within a short distance. Anand Bhavan and Swaraj Bhavan are centrally located and offer insights into the Nehru family and India’s freedom movement. All Saints’ Cathedral, a magnificent Gothic-style church also known as the “Patthar Girja,” is located in Civil Lines and is one of Prayagraj's architectural gems. Company Bagh, a peaceful park, is also close by and ideal for a quiet stroll. Chandrashekhar Azad Park and Khusro Bagh are both centrally located as well, providing green spaces along with historical importance.",
]
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 | 0.358 |
| cosine_accuracy@5 | 0.7092 |
| cosine_accuracy@10 | 0.7993 |
| cosine_precision@1 | 0.358 |
| cosine_precision@5 | 0.1418 |
| cosine_precision@10 | 0.0799 |
| cosine_recall@1 | 0.358 |
| cosine_recall@5 | 0.7092 |
| cosine_recall@10 | 0.7993 |
| cosine_ndcg@5 | 0.5539 |
| cosine_ndcg@10 | 0.5832 |
| **cosine_ndcg@100** | **0.619** |
| cosine_mrr@5 | 0.5013 |
| cosine_mrr@10 | 0.5136 |
| cosine_mrr@100 | 0.521 |
| cosine_map@100 | 0.521 |
<!--
## 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: 3,507 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: 4 tokens</li><li>mean: 11.76 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 116.82 tokens</li><li>max: 504 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 121.15 tokens</li><li>max: 424 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Where are the shuttle bus pickup points located within the Kumbh Mela grounds?</code> | <code>No, shuttle buses will not have dedicated volunteers specifically, but for assistance, you can reach out to the nearest information center.</code> | <code>The ancient art of weaving has captivated many cultures worldwide. In some regions, artisans use intricate patterns to tell stories, while others focus on vibrant colors that highlight their heritage. Experimentation with different materials can yield unique textures, adding depth to the final product. Workshops often provide insights into traditional techniques, ensuring these skills are passed down through generations.</code> |
| <code>Hotel Ilawart start place</code> | <code>Is hotel pickup and drop-off available for the tours?<br> Fixed pickup points, such as Hotel Ilawart, are provided for all tours. In some cases, pickup and drop-off can be arranged for locations within a 5 km radius of the starting point, but you must confirm this with the tour operator at the time of booking.</code> | <code>What all is included in the trip package?<br>The trip package typically includes transportation, tour guide services, and breakfast. Meals such as lunch and dinner can be purchased separately. Hotel bookings are usually not included in the package, so you will need to arrange accommodation independently.</code> |
| <code>Are there food stalls or restaurants at the Railway Junction that cater to dietary restrictions for pilgrims?</code> | <code>Yes, there are food stalls and restaurants available at the Railway Junction that cater to various dietary needs, including vegetarian and other dietary restrictions suitable for pilgrims.</code> | <code>The sound of the ocean waves rhythmically crashing against the shore creates a soothing symphony that invites relaxation. Seagulls soar above, occasionally diving down to catch a glimpse of fish beneath the surface. Beachgoers spread out their colorful towels, soaking up the sun's golden rays while children build sandcastles, their laughter mingling with the salty breeze. A distant sailboat glides across the horizon, hinting at adventures beyond the vast expanse of blue. As the sun sets, the sky transforms into a canvas of vibrant hues, signaling the end of another beautiful day by the sea.</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: 877 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 877 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 12.21 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 115.93 tokens</li><li>max: 471 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 118.09 tokens</li><li>max: 422 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Ganga bath benefit</code> | <code>What is the ritual of Snan or bathing?<br> 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.<br> <br> 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.<br> <br> 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. <br> List of Aliases: [['Snan', 'bathing'], ]</code> | <code>What benefits will I get by attending the Kumbh Mela?<br>It is believed that bathing in the holy rivers during this time washes away sins and grants liberation from the cycle of life and death.<br> <br> Attending the Kumbh and taking a dip in the sacred rivers provides a unique opportunity for spiritual growth, purification, and selfrealization. ✨</code> |
| <code>Guide provide what</code> | <code>What is the guide-to-participant ratio for each tour?<br> Each tour is led by one guide per group, ensuring a personalized experience with ample opportunity for detailed insights and engagement. The guide will provide context, historical background, and answer any questions during the tour, offering a rich, informative experience for participants.</code> | <code>How many people can join a group tour?<br>Group sizes depend on the type of vehicle selected. For instance, a Dzire accommodates up to 4 people, an Innova is suitable for 5-6 people, and larger groups (minimum 10 people) can travel in a Tempo Traveller. For even larger groups, multiple vehicles can be arranged to ensure everyone can travel together comfortably.</code> |
| <code>How many rules must a Kalpvasi observe?</code> | <code>A Kalpvasi must observe 21 rules during Kalpvas, involving disciplines of the mind, speech, and actions.</code> | <code>The dancing colors of autumn leaves create a tapestry of nature’s beauty, inviting every eye to witness the grandeur of the changing seasons. Every gust of wind carries a whisper of nostalgia as trees shed their vibrant garments.</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`: 30
- `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`: 30
- `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
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | val_evaluator_cosine_ndcg@100 |
|:-----------:|:--------:|:-------------:|:---------------:|:-----------------------------:|
| 0.0909 | 10 | - | 1.0916 | 0.4285 |
| 0.1818 | 20 | - | 1.0683 | 0.4295 |
| 0.2727 | 30 | - | 1.0320 | 0.4301 |
| 0.3636 | 40 | - | 0.9845 | 0.4309 |
| 0.4545 | 50 | 1.8466 | 0.9320 | 0.4340 |
| 0.5455 | 60 | - | 0.8804 | 0.4352 |
| 0.6364 | 70 | - | 0.8284 | 0.4368 |
| 0.7273 | 80 | - | 0.7754 | 0.4420 |
| 0.8182 | 90 | - | 0.7211 | 0.4425 |
| 0.9091 | 100 | 1.4317 | 0.6711 | 0.4442 |
| 1.0 | 110 | - | 0.6193 | 0.4483 |
| 1.0909 | 120 | - | 0.5700 | 0.4555 |
| 1.1818 | 130 | - | 0.5271 | 0.4603 |
| 1.2727 | 140 | - | 0.4892 | 0.4620 |
| 1.3636 | 150 | 1.0007 | 0.4611 | 0.4651 |
| 1.4545 | 160 | - | 0.4276 | 0.4706 |
| 1.5455 | 170 | - | 0.4005 | 0.4698 |
| 1.6364 | 180 | - | 0.3818 | 0.4728 |
| 1.7273 | 190 | - | 0.3573 | 0.4763 |
| 1.8182 | 200 | 0.7585 | 0.3321 | 0.4783 |
| 1.9091 | 210 | - | 0.3091 | 0.4806 |
| 2.0 | 220 | - | 0.2963 | 0.4833 |
| 2.0909 | 230 | - | 0.2875 | 0.4834 |
| 2.1818 | 240 | - | 0.2793 | 0.4842 |
| 2.2727 | 250 | 0.5586 | 0.2729 | 0.4879 |
| 2.3636 | 260 | - | 0.2663 | 0.4885 |
| 2.4545 | 270 | - | 0.2576 | 0.4925 |
| 2.5455 | 280 | - | 0.2477 | 0.5006 |
| 2.6364 | 290 | - | 0.2353 | 0.5058 |
| 2.7273 | 300 | 0.4751 | 0.2278 | 0.5112 |
| 2.8182 | 310 | - | 0.2206 | 0.5096 |
| 2.9091 | 320 | - | 0.2130 | 0.5144 |
| 3.0 | 330 | - | 0.2043 | 0.5202 |
| 3.0909 | 340 | - | 0.1973 | 0.5214 |
| 3.1818 | 350 | 0.381 | 0.1964 | 0.5271 |
| 3.2727 | 360 | - | 0.1968 | 0.5325 |
| 3.3636 | 370 | - | 0.1922 | 0.5289 |
| 3.4545 | 380 | - | 0.1869 | 0.5329 |
| 3.5455 | 390 | - | 0.1789 | 0.5391 |
| 3.6364 | 400 | 0.3886 | 0.1743 | 0.5464 |
| 3.7273 | 410 | - | 0.1730 | 0.5472 |
| 3.8182 | 420 | - | 0.1699 | 0.5479 |
| 3.9091 | 430 | - | 0.1644 | 0.5525 |
| 4.0 | 440 | - | 0.1623 | 0.5511 |
| 4.0909 | 450 | 0.2977 | 0.1600 | 0.5513 |
| 4.1818 | 460 | - | 0.1540 | 0.5519 |
| 4.2727 | 470 | - | 0.1492 | 0.5589 |
| 4.3636 | 480 | - | 0.1450 | 0.5624 |
| 4.4545 | 490 | - | 0.1426 | 0.5644 |
| 4.5455 | 500 | 0.2496 | 0.1407 | 0.5629 |
| 4.6364 | 510 | - | 0.1390 | 0.5663 |
| 4.7273 | 520 | - | 0.1399 | 0.5695 |
| 4.8182 | 530 | - | 0.1377 | 0.5764 |
| 4.9091 | 540 | - | 0.1357 | 0.5753 |
| 5.0 | 550 | 0.2322 | 0.1364 | 0.5827 |
| 5.0909 | 560 | - | 0.1327 | 0.5804 |
| 5.1818 | 570 | - | 0.1300 | 0.5799 |
| 5.2727 | 580 | - | 0.1307 | 0.5816 |
| 5.3636 | 590 | - | 0.1331 | 0.5868 |
| 5.4545 | 600 | 0.2219 | 0.1322 | 0.5839 |
| 5.5455 | 610 | - | 0.1332 | 0.5822 |
| 5.6364 | 620 | - | 0.1323 | 0.5817 |
| 5.7273 | 630 | - | 0.1311 | 0.5845 |
| 5.8182 | 640 | - | 0.1282 | 0.5834 |
| 5.9091 | 650 | 0.1982 | 0.1253 | 0.5870 |
| 6.0 | 660 | - | 0.1242 | 0.5880 |
| 6.0909 | 670 | - | 0.1241 | 0.5859 |
| 6.1818 | 680 | - | 0.1265 | 0.5885 |
| 6.2727 | 690 | - | 0.1287 | 0.5964 |
| 6.3636 | 700 | 0.1613 | 0.1321 | 0.5968 |
| 6.4545 | 710 | - | 0.1332 | 0.5979 |
| 6.5455 | 720 | - | 0.1295 | 0.6016 |
| 6.6364 | 730 | - | 0.1262 | 0.6022 |
| 6.7273 | 740 | - | 0.1242 | 0.6020 |
| 6.8182 | 750 | 0.172 | 0.1238 | 0.6037 |
| 6.9091 | 760 | - | 0.1222 | 0.6036 |
| 7.0 | 770 | - | 0.1213 | 0.6038 |
| 7.0909 | 780 | - | 0.1208 | 0.6038 |
| 7.1818 | 790 | - | 0.1200 | 0.6011 |
| 7.2727 | 800 | 0.1486 | 0.1196 | 0.5979 |
| 7.3636 | 810 | - | 0.1227 | 0.6015 |
| 7.4545 | 820 | - | 0.1225 | 0.6004 |
| 7.5455 | 830 | - | 0.1195 | 0.6045 |
| 7.6364 | 840 | - | 0.1202 | 0.6045 |
| 7.7273 | 850 | 0.1501 | 0.1208 | 0.6044 |
| 7.8182 | 860 | - | 0.1177 | 0.6038 |
| 7.9091 | 870 | - | 0.1161 | 0.6031 |
| 8.0 | 880 | - | 0.1168 | 0.6024 |
| 8.0909 | 890 | - | 0.1175 | 0.6050 |
| 8.1818 | 900 | 0.1563 | 0.1157 | 0.6063 |
| 8.2727 | 910 | - | 0.1146 | 0.6056 |
| 8.3636 | 920 | - | 0.1152 | 0.6073 |
| 8.4545 | 930 | - | 0.1167 | 0.6077 |
| 8.5455 | 940 | - | 0.1172 | 0.6087 |
| 8.6364 | 950 | 0.1247 | 0.1169 | 0.6077 |
| 8.7273 | 960 | - | 0.1159 | 0.6056 |
| 8.8182 | 970 | - | 0.1151 | 0.6066 |
| 8.9091 | 980 | - | 0.1161 | 0.6089 |
| 9.0 | 990 | - | 0.1187 | 0.6071 |
| 9.0909 | 1000 | 0.1497 | 0.1157 | 0.6110 |
| 9.1818 | 1010 | - | 0.1148 | 0.6086 |
| 9.2727 | 1020 | - | 0.1134 | 0.6125 |
| 9.3636 | 1030 | - | 0.1173 | 0.6114 |
| 9.4545 | 1040 | - | 0.1174 | 0.6118 |
| 9.5455 | 1050 | 0.1025 | 0.1159 | 0.6127 |
| 9.6364 | 1060 | - | 0.1118 | 0.6093 |
| 9.7273 | 1070 | - | 0.1114 | 0.6103 |
| 9.8182 | 1080 | - | 0.1128 | 0.6102 |
| 9.9091 | 1090 | - | 0.1142 | 0.6116 |
| 10.0 | 1100 | 0.128 | 0.1147 | 0.6115 |
| 10.0909 | 1110 | - | 0.1143 | 0.6095 |
| 10.1818 | 1120 | - | 0.1134 | 0.6073 |
| 10.2727 | 1130 | - | 0.1137 | 0.6059 |
| 10.3636 | 1140 | - | 0.1143 | 0.6049 |
| 10.4545 | 1150 | 0.1413 | 0.1145 | 0.6047 |
| 10.5455 | 1160 | - | 0.1154 | 0.6032 |
| 10.6364 | 1170 | - | 0.1158 | 0.6044 |
| 10.7273 | 1180 | - | 0.1151 | 0.6060 |
| 10.8182 | 1190 | - | 0.1145 | 0.6081 |
| 10.9091 | 1200 | 0.1223 | 0.1133 | 0.6084 |
| 11.0 | 1210 | - | 0.1121 | 0.6090 |
| 11.0909 | 1220 | - | 0.1130 | 0.6129 |
| 11.1818 | 1230 | - | 0.1134 | 0.6089 |
| 11.2727 | 1240 | - | 0.1136 | 0.6112 |
| 11.3636 | 1250 | 0.1199 | 0.1142 | 0.6134 |
| 11.4545 | 1260 | - | 0.1128 | 0.6145 |
| 11.5455 | 1270 | - | 0.1097 | 0.6148 |
| 11.6364 | 1280 | - | 0.1081 | 0.6122 |
| 11.7273 | 1290 | - | 0.1074 | 0.6126 |
| 11.8182 | 1300 | 0.1143 | 0.1063 | 0.6167 |
| 11.9091 | 1310 | - | 0.1067 | 0.6163 |
| 12.0 | 1320 | - | 0.1067 | 0.6190 |
| 12.0909 | 1330 | - | 0.1075 | 0.6193 |
| 12.1818 | 1340 | - | 0.1092 | 0.6222 |
| 12.2727 | 1350 | 0.0974 | 0.1087 | 0.6199 |
| 12.3636 | 1360 | - | 0.1078 | 0.6183 |
| 12.4545 | 1370 | - | 0.1072 | 0.6180 |
| 12.5455 | 1380 | - | 0.1072 | 0.6172 |
| 12.6364 | 1390 | - | 0.1072 | 0.6209 |
| 12.7273 | 1400 | 0.1257 | 0.1056 | 0.6152 |
| 12.8182 | 1410 | - | 0.1046 | 0.6149 |
| 12.9091 | 1420 | - | 0.1034 | 0.6142 |
| 13.0 | 1430 | - | 0.1034 | 0.6165 |
| 13.0909 | 1440 | - | 0.1046 | 0.6165 |
| 13.1818 | 1450 | 0.0866 | 0.1064 | 0.6177 |
| 13.2727 | 1460 | - | 0.1070 | 0.6158 |
| 13.3636 | 1470 | - | 0.1055 | 0.6151 |
| 13.4545 | 1480 | - | 0.1040 | 0.6182 |
| 13.5455 | 1490 | - | 0.1042 | 0.6144 |
| 13.6364 | 1500 | 0.0757 | 0.1042 | 0.6151 |
| 13.7273 | 1510 | - | 0.1056 | 0.6169 |
| 13.8182 | 1520 | - | 0.1059 | 0.6172 |
| 13.9091 | 1530 | - | 0.1059 | 0.6181 |
| 14.0 | 1540 | - | 0.1042 | 0.6167 |
| 14.0909 | 1550 | 0.0754 | 0.1043 | 0.6198 |
| 14.1818 | 1560 | - | 0.1044 | 0.6215 |
| 14.2727 | 1570 | - | 0.1042 | 0.6205 |
| 14.3636 | 1580 | - | 0.1058 | 0.6196 |
| 14.4545 | 1590 | - | 0.1076 | 0.6212 |
| 14.5455 | 1600 | 0.0901 | 0.1098 | 0.6219 |
| 14.6364 | 1610 | - | 0.1095 | 0.6247 |
| 14.7273 | 1620 | - | 0.1084 | 0.6209 |
| 14.8182 | 1630 | - | 0.1063 | 0.6164 |
| 14.9091 | 1640 | - | 0.1049 | 0.6170 |
| 15.0 | 1650 | 0.1034 | 0.1043 | 0.6199 |
| 15.0909 | 1660 | - | 0.1033 | 0.6216 |
| 15.1818 | 1670 | - | 0.1035 | 0.6244 |
| 15.2727 | 1680 | - | 0.1048 | 0.6286 |
| 15.3636 | 1690 | - | 0.1070 | 0.6239 |
| **15.4545** | **1700** | **0.0821** | **0.1084** | **0.6237** |
| 15.5455 | 1710 | - | 0.1095 | 0.6234 |
| 15.6364 | 1720 | - | 0.1090 | 0.6221 |
| 15.7273 | 1730 | - | 0.1089 | 0.6227 |
| 15.8182 | 1740 | - | 0.1091 | 0.6201 |
| 15.9091 | 1750 | 0.074 | 0.1089 | 0.6195 |
| 16.0 | 1760 | - | 0.1082 | 0.6205 |
| 16.0909 | 1770 | - | 0.1076 | 0.6198 |
| 16.1818 | 1780 | - | 0.1079 | 0.6195 |
| 16.2727 | 1790 | - | 0.1081 | 0.6238 |
| 16.3636 | 1800 | 0.083 | 0.1066 | 0.6219 |
| 16.4545 | 1810 | - | 0.1055 | 0.6201 |
| 16.5455 | 1820 | - | 0.1045 | 0.6217 |
| 16.6364 | 1830 | - | 0.1030 | 0.6198 |
| 16.7273 | 1840 | - | 0.1012 | 0.6192 |
| 16.8182 | 1850 | 0.0569 | 0.1012 | 0.6198 |
| 16.9091 | 1860 | - | 0.1017 | 0.6224 |
| 17.0 | 1870 | - | 0.1024 | 0.6220 |
| 17.0909 | 1880 | - | 0.1038 | 0.6217 |
| 17.1818 | 1890 | - | 0.1046 | 0.6231 |
| 17.2727 | 1900 | 0.1054 | 0.1056 | 0.6191 |
| 17.3636 | 1910 | - | 0.1064 | 0.6220 |
| 17.4545 | 1920 | - | 0.1078 | 0.6213 |
| 17.5455 | 1930 | - | 0.1077 | 0.6228 |
| 17.6364 | 1940 | - | 0.1071 | 0.6194 |
| 17.7273 | 1950 | 0.0588 | 0.1073 | 0.6227 |
| 17.8182 | 1960 | - | 0.1073 | 0.6219 |
| 17.9091 | 1970 | - | 0.1074 | 0.6217 |
| 18.0 | 1980 | - | 0.1073 | 0.6239 |
| 18.0909 | 1990 | - | 0.1074 | 0.6210 |
| 18.1818 | 2000 | 0.0772 | 0.1076 | 0.6226 |
| 18.2727 | 2010 | - | 0.1081 | 0.6215 |
| 18.3636 | 2020 | - | 0.1081 | 0.6206 |
| 18.4545 | 2030 | - | 0.1073 | 0.6229 |
| 18.5455 | 2040 | - | 0.1069 | 0.6221 |
| 18.6364 | 2050 | 0.0669 | 0.1070 | 0.6233 |
| 18.7273 | 2060 | - | 0.1062 | 0.6233 |
| 18.8182 | 2070 | - | 0.1051 | 0.6232 |
| 18.9091 | 2080 | - | 0.1038 | 0.6211 |
| 19.0 | 2090 | - | 0.1028 | 0.6210 |
| 19.0909 | 2100 | 0.0638 | 0.1015 | 0.6214 |
| 19.1818 | 2110 | - | 0.1021 | 0.6208 |
| 19.2727 | 2120 | - | 0.1029 | 0.6205 |
| 19.3636 | 2130 | - | 0.1033 | 0.6205 |
| 19.4545 | 2140 | - | 0.1044 | 0.6206 |
| 19.5455 | 2150 | 0.0805 | 0.1030 | 0.6187 |
| 19.6364 | 2160 | - | 0.1029 | 0.6199 |
| 19.7273 | 2170 | - | 0.1041 | 0.6214 |
| 19.8182 | 2180 | - | 0.1050 | 0.6211 |
| 19.9091 | 2190 | - | 0.1040 | 0.6207 |
| 20.0 | 2200 | 0.0932 | 0.1028 | 0.6201 |
| 20.0909 | 2210 | - | 0.1019 | 0.6212 |
| 20.1818 | 2220 | - | 0.1030 | 0.6202 |
| 20.2727 | 2230 | - | 0.1034 | 0.6212 |
| 20.3636 | 2240 | - | 0.1029 | 0.6224 |
| 20.4545 | 2250 | 0.0655 | 0.1034 | 0.6203 |
| 20.5455 | 2260 | - | 0.1030 | 0.6229 |
| 20.6364 | 2270 | - | 0.1023 | 0.6193 |
| 20.7273 | 2280 | - | 0.1022 | 0.6185 |
| 20.8182 | 2290 | - | 0.1017 | 0.6189 |
| 20.9091 | 2300 | 0.0879 | 0.1011 | 0.6178 |
| 21.0 | 2310 | - | 0.1015 | 0.6175 |
| 21.0909 | 2320 | - | 0.1019 | 0.6182 |
| 21.1818 | 2330 | - | 0.1013 | 0.6198 |
| 21.2727 | 2340 | - | 0.1014 | 0.6187 |
| 21.3636 | 2350 | 0.074 | 0.1022 | 0.6205 |
| 21.4545 | 2360 | - | 0.1038 | 0.6213 |
| 21.5455 | 2370 | - | 0.1043 | 0.6236 |
| 21.6364 | 2380 | - | 0.1044 | 0.6231 |
| 21.7273 | 2390 | - | 0.1045 | 0.6221 |
| 21.8182 | 2400 | 0.0768 | 0.1050 | 0.6224 |
| 21.9091 | 2410 | - | 0.1054 | 0.6222 |
| 22.0 | 2420 | - | 0.1052 | 0.6214 |
| 22.0909 | 2430 | - | 0.1051 | 0.6186 |
| 22.1818 | 2440 | - | 0.1055 | 0.6193 |
| 22.2727 | 2450 | 0.0741 | 0.1055 | 0.6205 |
| 22.3636 | 2460 | - | 0.1053 | 0.6208 |
| 22.4545 | 2470 | - | 0.1052 | 0.6224 |
| 22.5455 | 2480 | - | 0.1037 | 0.6191 |
| 22.6364 | 2490 | - | 0.1032 | 0.6189 |
| 22.7273 | 2500 | 0.0669 | 0.1034 | 0.6189 |
| 22.8182 | 2510 | - | 0.1037 | 0.6224 |
| 22.9091 | 2520 | - | 0.1038 | 0.6226 |
| 23.0 | 2530 | - | 0.1035 | 0.6203 |
| 23.0909 | 2540 | - | 0.1030 | 0.6198 |
| 23.1818 | 2550 | 0.0762 | 0.1029 | 0.6201 |
| 23.2727 | 2560 | - | 0.1025 | 0.6195 |
| 23.3636 | 2570 | - | 0.1024 | 0.6215 |
| 23.4545 | 2580 | - | 0.1028 | 0.6224 |
| 23.5455 | 2590 | - | 0.1036 | 0.6232 |
| 23.6364 | 2600 | 0.0815 | 0.1037 | 0.6227 |
| 23.7273 | 2610 | - | 0.1039 | 0.6227 |
| 23.8182 | 2620 | - | 0.1036 | 0.6211 |
| 23.9091 | 2630 | - | 0.1034 | 0.6192 |
| 24.0 | 2640 | - | 0.1033 | 0.6193 |
| 24.0909 | 2650 | 0.0661 | 0.1033 | 0.6178 |
| 24.1818 | 2660 | - | 0.1027 | 0.6174 |
| 24.2727 | 2670 | - | 0.1024 | 0.6198 |
| 24.3636 | 2680 | - | 0.1025 | 0.6184 |
| 24.4545 | 2690 | - | 0.1020 | 0.6181 |
| 24.5455 | 2700 | 0.0679 | 0.1020 | 0.6194 |
| 24.6364 | 2710 | - | 0.1020 | 0.6185 |
| 24.7273 | 2720 | - | 0.1027 | 0.6196 |
| 24.8182 | 2730 | - | 0.1027 | 0.6191 |
| 24.9091 | 2740 | - | 0.1030 | 0.6196 |
| 25.0 | 2750 | 0.0713 | 0.1035 | 0.6208 |
| 25.0909 | 2760 | - | 0.1042 | 0.6187 |
| 25.1818 | 2770 | - | 0.1049 | 0.6181 |
| 25.2727 | 2780 | - | 0.1051 | 0.6200 |
| 25.3636 | 2790 | - | 0.1051 | 0.6204 |
| 25.4545 | 2800 | 0.0786 | 0.1048 | 0.6184 |
| 25.5455 | 2810 | - | 0.1049 | 0.6198 |
| 25.6364 | 2820 | - | 0.1051 | 0.6200 |
| 25.7273 | 2830 | - | 0.1051 | 0.6198 |
| 25.8182 | 2840 | - | 0.1048 | 0.6190 |
| 25.9091 | 2850 | 0.0613 | 0.1050 | 0.6196 |
| 26.0 | 2860 | - | 0.1050 | 0.6183 |
| 26.0909 | 2870 | - | 0.1047 | 0.6198 |
| 26.1818 | 2880 | - | 0.1046 | 0.6197 |
| 26.2727 | 2890 | - | 0.1045 | 0.6217 |
| 26.3636 | 2900 | 0.0576 | 0.1045 | 0.6208 |
| 26.4545 | 2910 | - | 0.1047 | 0.6192 |
| 26.5455 | 2920 | - | 0.1046 | 0.6220 |
| 26.6364 | 2930 | - | 0.1042 | 0.6189 |
| 26.7273 | 2940 | - | 0.1039 | 0.6204 |
| 26.8182 | 2950 | 0.066 | 0.1036 | 0.6215 |
| 26.9091 | 2960 | - | 0.1032 | 0.6188 |
| 27.0 | 2970 | - | 0.1030 | 0.6209 |
| 27.0909 | 2980 | - | 0.1027 | 0.6203 |
| 27.1818 | 2990 | - | 0.1026 | 0.6215 |
| 27.2727 | 3000 | 0.0681 | 0.1025 | 0.6212 |
| 27.3636 | 3010 | - | 0.1026 | 0.6193 |
| 27.4545 | 3020 | - | 0.1027 | 0.6189 |
| 27.5455 | 3030 | - | 0.1028 | 0.6195 |
| 27.6364 | 3040 | - | 0.1030 | 0.6196 |
| 27.7273 | 3050 | 0.081 | 0.1031 | 0.6187 |
| 27.8182 | 3060 | - | 0.1032 | 0.6181 |
| 27.9091 | 3070 | - | 0.1030 | 0.6177 |
| 28.0 | 3080 | - | 0.1029 | 0.6202 |
| 28.0909 | 3090 | - | 0.1030 | 0.6193 |
| 28.1818 | 3100 | 0.0443 | 0.1031 | 0.6195 |
| 28.2727 | 3110 | - | 0.1031 | 0.6195 |
| 28.3636 | 3120 | - | 0.1032 | 0.6177 |
| 28.4545 | 3130 | - | 0.1034 | 0.6187 |
| 28.5455 | 3140 | - | 0.1035 | 0.6189 |
| 28.6364 | 3150 | 0.0646 | 0.1036 | 0.6187 |
| 28.7273 | 3160 | - | 0.1037 | 0.6199 |
| 28.8182 | 3170 | - | 0.1038 | 0.6208 |
| 28.9091 | 3180 | - | 0.1038 | 0.6190 |
| 29.0 | 3190 | - | 0.1038 | 0.6191 |
| 29.0909 | 3200 | 0.0692 | 0.1038 | 0.6190 |
| 29.1818 | 3210 | - | 0.1038 | 0.6201 |
| 29.2727 | 3220 | - | 0.1038 | 0.6194 |
| 29.3636 | 3230 | - | 0.1037 | 0.6201 |
| 29.4545 | 3240 | - | 0.1037 | 0.6189 |
| 29.5455 | 3250 | 0.084 | 0.1037 | 0.6194 |
| 29.6364 | 3260 | - | 0.1037 | 0.6189 |
| 29.7273 | 3270 | - | 0.1038 | 0.6199 |
| 29.8182 | 3280 | - | 0.1038 | 0.6194 |
| 29.9091 | 3290 | - | 0.1038 | 0.6191 |
| 30.0 | 3300 | 0.0598 | 0.1038 | 0.6190 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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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