| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:1136292 |
| | - loss:CachedMultipleNegativesRankingLoss |
| | base_model: answerdotai/ModernBERT-base |
| | widget: |
| | - source_sentence: During the 1960s Willard Cochrane was U.S. Department of Agriculture's |
| | head agricultural economist under U.S. Secretary of Agriculture Orville Freeman. |
| | sentences: |
| | - Cosmic Smash publisher Sega, platform Dreamcast. |
| | - Willard Cochrane occupation Economist. |
| | - Willard Cochrane educated at Harvard University, educated at Montana State University, |
| | date of birth 15 May 1914. |
| | - source_sentence: Four Moons stars Antonio Velázquez, Alejandro de la Madrid, César |
| | Ramos, Gustavo Egelhaaf, Alonso Echánove, Alejandro Belmonte, Karina Gidi and |
| | Juan Manuel Bernal. |
| | sentences: |
| | - Four Moons cast member Juan Manuel Bernal, cast member Antonio Velázquez, cast |
| | member Alejandro de la Madrid, RTC film rating C. |
| | - Leukotriene C4 synthase ortholog Ltc4s, ortholog Ltc4s, instance of Gene. |
| | - Four Moons publication date 27 April 2015. |
| | - source_sentence: James B. Kirby (September 28, 1884 - June 9, 1971) was an American |
| | inventor and self-taught electrical engineer who focused Jim Kirby's career on |
| | "eliminating the drudgery of housework". |
| | sentences: |
| | - Jim Kirby sex or gender male. |
| | - Kimberlé Williams Crenshaw notable work Intersectionality, field of work Intersectionality. |
| | - Jim Kirby date of death 09 June 1971, occupation Inventor, date of birth 28 September |
| | 1884. |
| | - source_sentence: Isabel Montero de la Cámara began work in the Foreign Office on |
| | June 18, 1974. and was appointed ambassador on April 9, 1996. |
| | sentences: |
| | - Back in Baby 's Arms publication date 00 1969, instance of Album. |
| | - Isabel Montero de la Cámara occupation Diplomat, country of citizenship Costa |
| | Rica, date of birth 01 January 1942. |
| | - Isabel Montero de la Cámara position held Ambassador. |
| | - source_sentence: In 1842 Alvars married the harpist Melanie Lewy, a member of a |
| | Vienna-based family of musicians with whom Alvars frequently performed. |
| | sentences: |
| | - Elias Parish Alvars place of birth Teignmouth. |
| | - Olivia of Palermo date of death 10 June 0463, sex or gender female, feast day |
| | June 10. |
| | - Elias Parish Alvars spouse Melanie Lewy, place of death Vienna. |
| | datasets: |
| | - YesaOuO/TEKGEN-CTSP |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy |
| | model-index: |
| | - name: SentenceTransformer based on answerdotai/ModernBERT-base |
| | results: |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: YesaOuO/TEKGEN CTSP |
| | type: YesaOuO/TEKGEN-CTSP |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.916620671749115 |
| | name: Cosine Accuracy |
| | --- |
| | |
| | # SentenceTransformer based on answerdotai/ModernBERT-base |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [tekgen-ctsp](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP) 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 --> |
| | - **Maximum Sequence Length:** 8192 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [tekgen-ctsp](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP) |
| | <!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
| | (1): Pooling({'word_embedding_dimension': 768, '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}) |
| | ) |
| | ``` |
| |
|
| | ## 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("YesaOuO/ModernBERT-base-CTSP") |
| | # Run inference |
| | sentences = [ |
| | 'In 1842 Alvars married the harpist Melanie Lewy, a member of a Vienna-based family of musicians with whom Alvars frequently performed.', |
| | 'Elias Parish Alvars spouse Melanie Lewy, place of death Vienna.', |
| | 'Elias Parish Alvars place of birth Teignmouth.', |
| | ] |
| | 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 |
| |
|
| | #### Triplet |
| |
|
| | * Dataset: `YesaOuO/TEKGEN-CTSP` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | **cosine_accuracy** | **0.9166** | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### tekgen-ctsp |
| | |
| | * Dataset: [tekgen-ctsp](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP) at [8d091eb](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP/tree/8d091ebc57b429b55add63e77a0408fa8dc3732b) |
| | * Size: 1,136,292 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: 11 tokens</li><li>mean: 38.01 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.07 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.02 tokens</li><li>max: 47 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------| |
| | | <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code> | <code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code> | <code>1976 Swedish Grand Prix pole position Jody Scheckter, winner Jody Scheckter.</code> | |
| | | <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code> | <code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code> | <code>1976 Swedish Grand Prix point in time 13 June 1976, country Sweden.</code> | |
| | | <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code> | <code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code> | <code>1976 Swedish Grand Prix point in time 13 June 1976.</code> | |
| | * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| | ```json |
| | { |
| | "scale": 20.0, |
| | "similarity_fct": "cos_sim" |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| | |
| | #### tekgen-ctsp |
| | |
| | * Dataset: [tekgen-ctsp](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP) at [8d091eb](https://huggingface.co/datasets/YesaOuO/TEKGEN-CTSP/tree/8d091ebc57b429b55add63e77a0408fa8dc3732b) |
| | * Size: 10,866 evaluation 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: 13 tokens</li><li>mean: 40.18 tokens</li><li>max: 183 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.82 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.82 tokens</li><li>max: 62 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>Two men with prior criminal records, Dieter Degowski and Hans-Jürgen Rösner, went on the run for two days through Germany and the Netherlands.</code> | <code>Gladbeck hostage crisis country Netherlands, country Germany, participant Hans-Jürgen Rösner, participant Dieter Degowski.</code> | <code>Gladbeck hostage crisis end time 18 August 1988, point in time 18 August 1988, country Germany, start time 16 August 1988.</code> | |
| | | <code>The Gladbeck hostage crisis (known in Germany as the Gladbeck hostage drama) was a hostage-taking crisis that happened in August 1988 after an armed bank raid in Gladbeck, North Rhine-Westphalia, West Germany.</code> | <code>Gladbeck hostage crisis end time 18 August 1988, point in time 18 August 1988, country Germany, start time 16 August 1988.</code> | <code>Gladbeck hostage crisis country Netherlands, country Germany, participant Hans-Jürgen Rösner, participant Dieter Degowski.</code> | |
| | | <code>The album was originally released only on cassette tape before later being made available for digital download on iTunes and similar digital media stores.</code> | <code>Vongole Fisarmonica instance of Album.</code> | <code>Vongole Fisarmonica performer Those Darn Accordions, publication date 01 January 1992, instance of Album.</code> | |
| | * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| | ```json |
| | { |
| | "scale": 20.0, |
| | "similarity_fct": "cos_sim" |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `per_device_train_batch_size`: 512 |
| | - `per_device_eval_batch_size`: 512 |
| | - `learning_rate`: 8e-05 |
| | - `num_train_epochs`: 1 |
| | - `warmup_ratio`: 0.05 |
| | - `bf16`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: no |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 512 |
| | - `per_device_eval_batch_size`: 512 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 8e-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`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.05 |
| | - `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`: 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`: False |
| | - `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`: None |
| | - `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`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | YesaOuO/TEKGEN-CTSP_cosine_accuracy | |
| | |:------:|:----:|:-------------:|:-----------------------------------:| |
| | | -1 | -1 | - | 0.6585 | |
| | | 0.2252 | 500 | 0.6404 | - | |
| | | 0.4505 | 1000 | 0.212 | - | |
| | | 0.6757 | 1500 | 0.1764 | - | |
| | | 0.9009 | 2000 | 0.1562 | - | |
| | | -1 | -1 | - | 0.9166 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.11.11 |
| | - Sentence Transformers: 3.4.1 |
| | - Transformers: 4.49.0 |
| | - PyTorch: 2.6.0+cu124 |
| | - Accelerate: 1.5.2 |
| | - Datasets: 3.3.2 |
| | - Tokenizers: 0.21.1 |
| | |
| | ## 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", |
| | } |
| | ``` |
| | |
| | #### CachedMultipleNegativesRankingLoss |
| | ```bibtex |
| | @misc{gao2021scaling, |
| | title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
| | author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
| | year={2021}, |
| | eprint={2101.06983}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.LG} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
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| | <!-- |
| | ## Model Card Authors |
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| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| | ## Model Card Contact |
| | |
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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