--- 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) - **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] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](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 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 396 training samples * Columns: anchor and positive * Approximate statistics based on the first 396 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What role does customer segmentation play in predictive analytics? | 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. | | How has technographics evolved over the years to accommodate the digital space? | 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. | | Can you name some common methods of collecting technographic data? | 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. | * Loss: [MatryoshkaLoss](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
Click to expand - `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
### 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} } ```