| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:5749 |
| | - loss:CosineSimilarityLoss |
| | base_model: microsoft/Phi-3-mini-4k-instruct |
| | widget: |
| | - source_sentence: A man is playing the guitar. |
| | sentences: |
| | - A man plays an acoustic guitar. |
| | - Ryanair chief hails report findings |
| | - 2 British soldiers killed in Afghan insider attack |
| | - source_sentence: You're "a bunch of cells." |
| | sentences: |
| | - I'm "a bunch of cells." |
| | - Four arrested as Bangladesh building toll rises to 352 |
| | - Five cows grazing on a patch of grass between two roadways. |
| | - source_sentence: The skateboarder gets to the top of the ramp. |
| | sentences: |
| | - Kroger's Ralphs chain and Albertsons immediately locked out their grocery workers |
| | in a show of solidarity. |
| | - The man took a piece of pepperoni pizza out of the box. |
| | - The skateboarder rides the pipe wall at a skater park. |
| | - source_sentence: Iran dissidents 'killed in Iraq' |
| | sentences: |
| | - A baby is crawling across the floor. |
| | - The Mets took Lastings Milledge, an outfielder from Florida, with the 12th pick. |
| | - France loses influence in Europe |
| | - source_sentence: N Korea warns of retaliation for South Korea drill |
| | sentences: |
| | - North warns of retaliation for Seoul's naval drill plan |
| | - Bangladeshi Islamists rally to demand action against atheist bloggers |
| | - India's Anti-Graft Party Forms Government in Delhi |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - pearson_cosine |
| | - spearman_cosine |
| | model-index: |
| | - name: SentenceTransformer based on microsoft/Phi-3-mini-4k-instruct |
| | results: |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: stsb dev |
| | type: stsb-dev |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.8730412567769437 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.8731618839441975 |
| | name: Spearman Cosine |
| | --- |
| | |
| | # SentenceTransformer based on microsoft/Phi-3-mini-4k-instruct |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). It maps sentences & paragraphs to a 3072-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:** [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) <!-- at revision f39ac1d28e925b323eae81227eaba4464caced4e --> |
| | - **Maximum Sequence Length:** 128 tokens |
| | - **Output Dimensionality:** 3072 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/huggingface/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': 128, 'do_lower_case': False, 'architecture': 'Phi3Model'}) |
| | (1): Pooling({'word_embedding_dimension': 3072, '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("AntoineGourru/FT_phi_cos") |
| | # Run inference |
| | sentences = [ |
| | 'N Korea warns of retaliation for South Korea drill', |
| | "North warns of retaliation for Seoul's naval drill plan", |
| | 'Bangladeshi Islamists rally to demand action against atheist bloggers', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 3072] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities) |
| | # tensor([[1.0000, 0.7161, 0.1516], |
| | # [0.7161, 1.0000, 0.1610], |
| | # [0.1516, 0.1610, 1.0000]]) |
| | ``` |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | #### Semantic Similarity |
| |
|
| | * Dataset: `stsb-dev` |
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | pearson_cosine | 0.873 | |
| | | **spearman_cosine** | **0.8732** | |
| | |
| | <!-- |
| | ## 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: 5,749 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 4 tokens</li><li>mean: 15.28 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.04 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
| | | <code>The results were released at Tuesday's meeting in Seattle of the American Thoracic Society and will be published in Thursday's New England Journal of Medicine.</code> | <code>The study results were released at a meeting in Seattle of the American Thoracic Society and also will be published in tomorrow's issue of The New England Journal of Medicine.</code> | <code>0.8727999687194824</code> | |
| | | <code>Put a Little Love in your Heart We are all vessels filled with many wonders.</code> | <code>Landon And So This is Christmas We are all vessels filled with many wonders.</code> | <code>0.5599999904632569</code> | |
| | | <code>Wall Street analysts had expected 22 cents a share, according to Thomson First Call.</code> | <code>The results were 3 cents a share lower than the forecast of analysts surveyed by Thomson First Call.</code> | <code>0.4400000095367432</code> | |
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| | ```json |
| | { |
| | "loss_fct": "torch.nn.modules.loss.MSELoss" |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `per_device_train_batch_size`: 4 |
| | - `per_device_eval_batch_size`: 4 |
| | - `num_train_epochs`: 1 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `do_predict`: False |
| | - `eval_strategy`: no |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 4 |
| | - `per_device_eval_batch_size`: 4 |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 5e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: None |
| | - `warmup_ratio`: None |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `enable_jit_checkpoint`: False |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `use_cpu`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `bf16`: False |
| | - `fp16`: False |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: -1 |
| | - `ddp_backend`: None |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `parallelism_config`: None |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch_fused |
| | - `optim_args`: None |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `project`: huggingface |
| | - `trackio_space_id`: trackio |
| | - `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 |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: None |
| | - `hub_always_push`: False |
| | - `hub_revision`: None |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_for_metrics`: [] |
| | - `eval_do_concat_batches`: True |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `include_num_input_tokens_seen`: no |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `liger_kernel_config`: None |
| | - `eval_use_gather_object`: False |
| | - `average_tokens_across_devices`: True |
| | - `use_cache`: False |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | - `router_mapping`: {} |
| | - `learning_rate_mapping`: {} |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | stsb-dev_spearman_cosine | |
| | |:------:|:----:|:-------------:|:------------------------:| |
| | | 0.3477 | 500 | 0.0533 | - | |
| | | 0.6954 | 1000 | 0.0263 | - | |
| | | 1.0 | 1438 | - | 0.8732 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.12.12 |
| | - Sentence Transformers: 5.2.3 |
| | - Transformers: 5.0.0 |
| | - PyTorch: 2.10.0+cu128 |
| | - Accelerate: 1.12.0 |
| | - Datasets: 4.0.0 |
| | - Tokenizers: 0.22.2 |
| | |
| | ## 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", |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| |
|
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| |
|
| | <!-- |
| | ## Model Card Authors |
| |
|
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| |
|
| | <!-- |
| | ## Model Card Contact |
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| | *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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