| --- |
| base_model: sentence-transformers/all-MiniLM-L12-v2 |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| - pearson_manhattan |
| - spearman_manhattan |
| - pearson_euclidean |
| - spearman_euclidean |
| - pearson_dot |
| - spearman_dot |
| - pearson_max |
| - spearman_max |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:100000 |
| - loss:CosineSimilarityLoss |
| widget: |
| - source_sentence: NIPA personal income includes pension contributions by employers |
| in the year income is earned , and benefits paid at retirement are not a component |
| of NIPA income . |
| sentences: |
| - While not the only makeup of income , NIPA is one of the more well known income |
| distinctions . |
| - Les temples de karnak et de Louxor ont été démolis pour faire place à des projets |
| de construction en Cisjordanie . |
| - Les restaurants sont tenus à des règles strictes pour contenir leur odeur . |
| - source_sentence: right right you know the one that 's one reason we bought a house |
| here in Plano we were hoping you know well the school district 's gonna be good |
| you know for resale value and so on and so forth but |
| sentences: |
| - We moved to Plano because we thought the school district was good . |
| - These and those . |
| - L' obsession a suscité une suggestion que tous étaient des boucs émissaires de |
| la guerre . |
| - source_sentence: Dans l' homme invisible , le talentueux dixième narrateur doit |
| surmonter non seulement les différentes idéologies qui lui sont présentées comme |
| masques ou subversions d' identité , mais aussi les différents rôles et prescriptions |
| pour le leadership que sa propre race lui souhaite de réaliser . |
| sentences: |
| - '" We ''re too uptight now ! " Said Tommy' |
| - Le talentueux dixième narrateur doit surmonter les idéologies . |
| - Saddam is not taking advantage of the current Arab love towards the United States |
| - source_sentence: Les lacunes trouvées au cours de la surveillance en cours ou au |
| moyen d' évaluations distinctes devraient être communiquées à l' individu responsable |
| de la fonction et à au moins un niveau de gestion au-dessus de cet individu . |
| sentences: |
| - L' économie diminuera également si les conditions du marché changent . |
| - The Watergate comparison wasn 't just for Democratic bashing . |
| - Il n' y a pas lieu de signaler les lacunes . |
| - source_sentence: it looks fertile and it it um i mean it rains enough they have |
| the climate and the rain and if not it 's like i 've been to Saint Thomas and |
| it just starts from the ocean up |
| sentences: |
| - Il n' a jamais triché . |
| - They don 't know how to do it . |
| - They have the rain and the climate so I imagine the lands would be fertile . |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: snli dev |
| type: snli-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.3725313255221131 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.3729470854776107 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.3650227128515394 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.37250760289182383 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.36567325497563746 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.37294699995093694 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.3725313190046259 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.3729474276296007 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.3725313255221131 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.3729474276296007 |
| name: Spearman Max |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). 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:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision a05860a77cef7b37e0048a7864658139bc18a854 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 tokens |
| - **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': 128, '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() |
| ) |
| ``` |
|
|
| ## 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("cherifkhalifah/finetuned-snli-MiniLM-L12-v2-100k-en-fr") |
| # Run inference |
| sentences = [ |
| "it looks fertile and it it um i mean it rains enough they have the climate and the rain and if not it 's like i 've been to Saint Thomas and it just starts from the ocean up", |
| 'They have the rain and the climate so I imagine the lands would be fertile .', |
| "They don 't know how to do it .", |
| ] |
| 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 |
|
|
| #### Semantic Similarity |
| * Dataset: `snli-dev` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:-------------------|:-----------| |
| | pearson_cosine | 0.3725 | |
| | spearman_cosine | 0.3729 | |
| | pearson_manhattan | 0.365 | |
| | spearman_manhattan | 0.3725 | |
| | pearson_euclidean | 0.3657 | |
| | spearman_euclidean | 0.3729 | |
| | pearson_dot | 0.3725 | |
| | spearman_dot | 0.3729 | |
| | pearson_max | 0.3725 | |
| | **spearman_max** | **0.3729** | |
| |
| <!-- |
| ## 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: 100,000 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: 5 tokens</li><li>mean: 35.27 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.46 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------| |
| | <code>Natalia M' a regardé .</code> | <code>Natalia a regardé et attend que je lui donne l' épée .</code> | <code>0.5</code> | |
| | <code>And he sounded sincere .</code> | <code>He sounded sincere.He was sounding sincere in his words .</code> | <code>0.0</code> | |
| | <code>There 's a small zoo area where you can see snakes , lizards , birds of prey , wolves , hyenas , foxes , and various desert cats , including cheetahs and leopards .</code> | <code>The zoo is home to some endangered desert animals .</code> | <code>0.5</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 |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 4 |
| - `fp16`: True |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### 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`: 16 |
| - `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`: 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`: 4 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `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`: True |
| - `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`: 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 |
| - `eval_on_start`: False |
| - `eval_use_gather_object`: False |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | snli-dev_spearman_max | |
| |:------:|:-----:|:-------------:|:---------------------:| |
| | 0.08 | 500 | 0.2008 | 0.0433 | |
| | 0.16 | 1000 | 0.1757 | 0.1024 | |
| | 0.24 | 1500 | 0.1732 | 0.1503 | |
| | 0.32 | 2000 | 0.1685 | 0.2168 | |
| | 0.4 | 2500 | 0.1702 | 0.2206 | |
| | 0.48 | 3000 | 0.1676 | 0.2117 | |
| | 0.56 | 3500 | 0.1637 | 0.2624 | |
| | 0.64 | 4000 | 0.1636 | 0.2169 | |
| | 0.72 | 4500 | 0.1608 | 0.0051 | |
| | 0.8 | 5000 | 0.1601 | 0.2236 | |
| | 0.88 | 5500 | 0.1597 | 0.2471 | |
| | 0.96 | 6000 | 0.1596 | 0.2934 | |
| | 1.0 | 6250 | - | 0.2905 | |
| | 1.04 | 6500 | 0.1602 | 0.3001 | |
| | 1.12 | 7000 | 0.1571 | 0.3116 | |
| | 1.2 | 7500 | 0.1588 | 0.3145 | |
| | 1.28 | 8000 | 0.1562 | 0.3304 | |
| | 1.3600 | 8500 | 0.1548 | 0.3376 | |
| | 1.44 | 9000 | 0.156 | 0.3359 | |
| | 1.52 | 9500 | 0.1552 | 0.3194 | |
| | 1.6 | 10000 | 0.153 | 0.3474 | |
| | 1.6800 | 10500 | 0.1529 | 0.3220 | |
| | 1.76 | 11000 | 0.1518 | 0.3255 | |
| | 1.8400 | 11500 | 0.1499 | 0.3332 | |
| | 1.92 | 12000 | 0.1524 | 0.3521 | |
| | 2.0 | 12500 | 0.1512 | 0.3425 | |
| | 2.08 | 13000 | 0.1514 | 0.3462 | |
| | 2.16 | 13500 | 0.1516 | 0.3414 | |
| | 2.24 | 14000 | 0.1532 | 0.3453 | |
| | 2.32 | 14500 | 0.1459 | 0.3699 | |
| | 2.4 | 15000 | 0.1524 | 0.3576 | |
| | 2.48 | 15500 | 0.1506 | 0.3418 | |
| | 2.56 | 16000 | 0.1488 | 0.3559 | |
| | 2.64 | 16500 | 0.1486 | 0.3597 | |
| | 2.7200 | 17000 | 0.1469 | 0.3552 | |
| | 2.8 | 17500 | 0.1448 | 0.3459 | |
| | 2.88 | 18000 | 0.1458 | 0.3503 | |
| | 2.96 | 18500 | 0.1468 | 0.3647 | |
| | 3.0 | 18750 | - | 0.3611 | |
| | 3.04 | 19000 | 0.1472 | 0.3741 | |
| | 3.12 | 19500 | 0.1457 | 0.3603 | |
| | 3.2 | 20000 | 0.147 | 0.3576 | |
| | 3.2800 | 20500 | 0.1451 | 0.3663 | |
| | 3.36 | 21000 | 0.1438 | 0.3734 | |
| | 3.44 | 21500 | 0.1471 | 0.3698 | |
| | 3.52 | 22000 | 0.1462 | 0.3646 | |
| | 3.6 | 22500 | 0.1436 | 0.3740 | |
| | 3.68 | 23000 | 0.1441 | 0.3696 | |
| | 3.76 | 23500 | 0.1423 | 0.3636 | |
| | 3.84 | 24000 | 0.1411 | 0.3713 | |
| | 3.92 | 24500 | 0.1438 | 0.3706 | |
| | 4.0 | 25000 | 0.1421 | 0.3729 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.1.1 |
| - Transformers: 4.44.2 |
| - PyTorch: 2.4.1+cu121 |
| - Accelerate: 0.34.2 |
| - Datasets: 3.0.1 |
| - 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", |
| } |
| ``` |
| |
| <!-- |
| ## 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 |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
