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base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:396
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How can technographics contribute to predicting consumer behavior
in digital marketing?
sentences:
- Data analysis is essential in predicting consumer behavior in digital marketing.
Analysis of data related to consumer behavior, preferences and needs can reveal
patterns and trends that can be used to forecast future behavior and refine marketing
strategies.
- Technographics enables businesses to understand the technological habits and preferences
of their customers. By analyzing this data, companies can predict how these users
are likely to interact with their digital products or services, and tailor their
marketing strategies accordingly.
- The key components include data collection (gathering data from various sources),
data analysis (using algorithms and models to analyze data), and predictive modelling
(predicting future customer behavior based on analyzed data).
- source_sentence: How is technographic data collected for understanding cross-channel
behavior?
sentences:
- Technographic data for understanding cross-channel behavior is collected through
various data analytics tools that track the customer's interactions across different
digital channels. These tools can monitor website usage, mobile app activity,
social media engagements, and email click-through rates.
- Adobe's "Real-Time Marketing Insights" is a notable case. They utilized technographic
data to identify their customer's most-used digital tools, leading to significant
enhancements in their personalized marketing strategies and an increase in customer
engagement.
- Technology stack analysis helps identify the tools and platforms a company uses
for digital marketing. It enables marketers to understand the infrastructure that
supports their strategies and spot opportunities for innovation or consolidation.
- source_sentence: How does consumer behavior pattern analysis contribute to the effectiveness
of digital marketing campaigns?
sentences:
- Machine learning can identify patterns and trends in technographic data that may
not be obvious to humans. It can also predict future behavior based on these patterns,
allowing businesses to anticipate consumer needs and adjust their strategies accordingly.
- Consumer behavior pattern analysis provides insights into how, when, and why consumers
interact with digital marketing content. These insights can be used to refine
campaign strategies, enhance personalization, and ultimately improve conversion
rates and customer loyalty.
- Predictive analytics can forecast what content will resonate best with certain
audience segments based on past engagement. It can guide topics, formats, and
delivery channels, enabling marketers to create content that is more likely to
attract and engage their target audience.
- source_sentence: What are technographics in the context of digital marketing?
sentences:
- Technographics is a market research analysis method that investigates the technology-related
behaviors and preferences of consumers. This includes their usage, adoption and
purchase of technology, which is crucial in forming a comprehensive understanding
of your target audience's digital landscape.
- Technographics data can be collected through surveys, social media mining, or
purchased from data providers. The data is analyzed using statistical techniques
or machine learning algorithms to identify patterns and insights related to consumer
behavior.
- Technographics can help businesses understand what platforms or technologies their
competitors' customers use, providing insights into competitor tech strengths
and weaknesses. This can guide businesses in differentiating their offers and
positioning themselves more effectively in the market.
- source_sentence: How important is it to update technographic data frequently?
sentences:
- By analyzing a competitor's technology stack, marketers can gain insights into
their strategies, tools, and platforms. This knowledge can help them identify
gaps in their own stack, adopt superior technologies, or find ways to differentiate
their approach.
- It is crucial. Technology trends and usage patterns evolve quickly. Keeping your
technographic data up-to-date ensures that your marketing strategies remain relevant
and effective.
- Technographics is a research methodology that provides data about consumers based
on their technology use, preferences and behavior. This method helps businesses
understand which technologies their audience is using, and how they use them,
thereby, informing the development of more effective and personalized marketing
strategies.
model-index:
- name: Technographics Marketing Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5050505050505051
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7575757575757576
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16835016835016833
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11515151515151514
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07575757575757575
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5050505050505051
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7575757575757576
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5323267552745661
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46469456469456494
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.47723382714423296
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5151515151515151
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7272727272727273
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17171717171717168
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11515151515151514
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0727272727272727
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5151515151515151
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7272727272727273
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5279877868900206
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46727994227994235
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4818097786730832
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.35353535353535354
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48484848484848486
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5858585858585859
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7070707070707071
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.35353535353535354
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16161616161616163
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11717171717171715
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07070707070707069
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.35353535353535354
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48484848484848486
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5858585858585859
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7070707070707071
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5102400942328595
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.44968734968734975
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4654526924283992
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.47474747474747475
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6868686868686869
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15824915824915825
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11515151515151512
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06868686868686867
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47474747474747475
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6868686868686869
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5096813265364254
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.45540724707391383
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4713790516617994
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.3434343434343434
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48484848484848486
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5353535353535354
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6868686868686869
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3434343434343434
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16161616161616163
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10707070707070705
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06868686868686867
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3434343434343434
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48484848484848486
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5353535353535354
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6868686868686869
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4979120019313254
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.44037197370530706
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4556726424225123
name: Cosine Map@100
---
# Technographics Marketing Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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("arad1367/technographics-marketing-matryoshka")
# Run inference
sentences = [
'How important is it to update technographic data frequently?',
'It is crucial. Technology trends and usage patterns evolve quickly. Keeping your technographic data up-to-date ensures that your marketing strategies remain relevant and effective.',
"By analyzing a competitor's technology stack, marketers can gain insights into their strategies, tools, and platforms. This knowledge can help them identify gaps in their own stack, adopt superior technologies, or find ways to differentiate their approach.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.3737 | 0.3737 | 0.3535 | 0.3737 | 0.3434 |
| cosine_accuracy@3 | 0.5051 | 0.5152 | 0.4848 | 0.4747 | 0.4848 |
| cosine_accuracy@5 | 0.5758 | 0.5758 | 0.5859 | 0.5758 | 0.5354 |
| cosine_accuracy@10 | 0.7576 | 0.7273 | 0.7071 | 0.6869 | 0.6869 |
| cosine_precision@1 | 0.3737 | 0.3737 | 0.3535 | 0.3737 | 0.3434 |
| cosine_precision@3 | 0.1684 | 0.1717 | 0.1616 | 0.1582 | 0.1616 |
| cosine_precision@5 | 0.1152 | 0.1152 | 0.1172 | 0.1152 | 0.1071 |
| cosine_precision@10 | 0.0758 | 0.0727 | 0.0707 | 0.0687 | 0.0687 |
| cosine_recall@1 | 0.3737 | 0.3737 | 0.3535 | 0.3737 | 0.3434 |
| cosine_recall@3 | 0.5051 | 0.5152 | 0.4848 | 0.4747 | 0.4848 |
| cosine_recall@5 | 0.5758 | 0.5758 | 0.5859 | 0.5758 | 0.5354 |
| cosine_recall@10 | 0.7576 | 0.7273 | 0.7071 | 0.6869 | 0.6869 |
| **cosine_ndcg@10** | **0.5323** | **0.528** | **0.5102** | **0.5097** | **0.4979** |
| cosine_mrr@10 | 0.4647 | 0.4673 | 0.4497 | 0.4554 | 0.4404 |
| cosine_map@100 | 0.4772 | 0.4818 | 0.4655 | 0.4714 | 0.4557 |
<!--
## 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
#### json
* Dataset: json
* Size: 396 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 396 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.71 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 48.68 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What role does customer segmentation play in predictive analytics?</code> | <code>Customer segmentation within predictive analytics allows marketers to group customers based on similar characteristics. This helps in creating more targeted marketing strategies and predicting behavior patterns for each segment, improving overall campaign effectiveness.</code> |
| <code>How has technographics evolved over the years to accommodate the digital space?</code> | <code>Initially focused on hardware and software usage, technographics has evolved to consider digital platforms and tools. It now investigates consumer behavior across different channels, devices, and even social media platforms to provide a more comprehensive consumer profile.</code> |
| <code>Can you name some common methods of collecting technographic data?</code> | <code>Some common methods include surveys, interviews, online browsing behavior tracking, and direct observation. In addition, databases can be bought from vendors specializing in technographic data collection.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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_fused
- `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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0 | 1 | - | 0.4650 | 0.4667 | 0.4712 | 0.4371 | 0.4151 |
| 2.0 | 3 | - | 0.5316 | 0.5307 | 0.5051 | 0.4810 | 0.4407 |
| **3.0** | **5** | **-** | **0.5256** | **0.5222** | **0.5136** | **0.5104** | **0.4742** |
| 4.0 | 7 | - | 0.5316 | 0.5269 | 0.5120 | 0.5083 | 0.4790 |
| 5.0 | 9 | - | 0.5337 | 0.5280 | 0.5102 | 0.5101 | 0.4983 |
| 6.0 | 10 | 2.9453 | 0.5323 | 0.5280 | 0.5102 | 0.5097 | 0.4979 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.4
- Sentence Transformers: 3.4.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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