| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:478146 |
| - loss:CoSENTLoss |
| widget: |
| - source_sentence: However, its underutilization is mainly due to the absence of a |
| concrete and coherent dissemination strategy. |
| sentences: |
| - At the same time, they need to understand that living in Europe brings great responsibilities |
| in addition to great benefits. |
| - 'The mainstay of any intelligent and patriotic mineral policy can be summed up |
| in the following postulate: "since minerals are exhaustible, they should only |
| be exploited with the maximum return for the economy of the country where they |
| are mined".' |
| - We must move quickly to a shared sustainable energy supply, sustainable transportation |
| and clean air. |
| - source_sentence: Their track record shows they do not support Australia<92>s traditional |
| industries because they are constantly pandering to the Greens. |
| sentences: |
| - An economic dynamic based on the sustainable development of national potential, |
| equitable access to the means of production, social justice, environmental conservation, |
| the incorporation of added value, the promotion of competitiveness and self-management, |
| - the cry "El campo no aguanta más" (The countryside can't take it anymore), of |
| the peasant movement and its proclamation of "Salvemos al Campo para salvar a |
| México" (Let's save the countryside to save Mexico); |
| - On the other hand, increasing defence capacity is directly related to the involvement |
| of all citizens in appropriate programmes, which, together with the acquisition |
| of skills, experience and organisation, also contribute to forging a spirit of |
| militancy and collectivity. |
| - source_sentence: We will prepare the proposals of the United Nations Declaration |
| on the Rights of the Child in line with the commitments made. |
| sentences: |
| - For the presentation of Czech culture, we will also use the upcoming major anniversaries |
| (100 years of the founding of Czechoslovakia, the 30th anniversary of the canonization |
| of Agnes of Bohemia, 600 years since the birth of George of Poděbrady, etc.). |
| - Separate prison units for young people should be established, and special rehabilitation |
| measures should be introduced in these units. |
| - Austrian citizenship is a valuable asset and should not become accessible to those |
| who do not abide by the laws of our state. |
| - source_sentence: Third, CD&V wants to strengthen the social sustainability of our |
| agriculture and horticulture sector. |
| sentences: |
| - We will take a farm-level approach where possible so that low-emissions farmers |
| are rewarded with a lower cost through the ETS, rather than the current approach |
| that assumes each cow, for instance, has the same emissions on every farm. |
| - In addition, 20 billion euros in tax revenues are fraudulently evaded every year |
| (the equivalent of the healthcare budget). |
| - 87 percent of arrested undocumented migrants are released sooner or later, but |
| without papers, in a lawless situation. |
| - source_sentence: This incites social hatred, threatens economic and social stability, |
| and undermines trust in the authorities. |
| sentences: |
| - ' The conditions for a healthy entrepreneurship, where the most innovative and |
| creative win and where the source of enrichment cannot be property speculation |
| or guilds and networks. ' |
| - According to statistics from the Attorney General's Office, since February 2005, |
| when the implementation of the PSD was announced, the rate of violent deaths per |
| 100,000 inhabitants has dropped from 26.41 in December 2005 to 18.43 in December |
| 2007. |
| - As a result, the profits of the oligarchs are more than 400 times what our entire |
| country gets from the exploitation of natural resources. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - json |
| <!-- - **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("LequeuISIR/final-DPR-8e-05") |
| # Run inference |
| sentences = [ |
| 'This incites social hatred, threatens economic and social stability, and undermines trust in the authorities.', |
| '\xa0The conditions for a healthy entrepreneurship, where the most innovative and creative win and where the source of enrichment cannot be property speculation or guilds and networks. ', |
| 'As a result, the profits of the oligarchs are more than 400 times what our entire country gets from the exploitation of natural resources.', |
| ] |
| 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.* |
| --> |
|
|
| <!-- |
| ## 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: 478,146 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 17 tokens</li><li>mean: 33.73 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 33.84 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>0: ~57.50%</li><li>1: ~4.10%</li><li>2: ~38.40%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>There have also been other important structural changes in the countryside, which have come together to form this new, as yet unknown, country.</code> | <code>Meanwhile, investment, which is the way to increase production, employment capacity and competitiveness of the economy, fell from 20% of output in 1974 to only 11.8% on average between 1984 and 1988.</code> | <code>0</code> | |
| | <code>Introduce new visa categories so we can be responsive to humanitarian needs and incentivise greater investment in our domestic infrastructure and regional economies</code> | <code>The purpose of the project is to design and implement public policies aimed at achieving greater and faster inclusion of immigrants.</code> | <code>2</code> | |
| | <code>and economic crimes that seriously and generally affect the fundamental rights of individuals and the international community as a whole.</code> | <code>For the first time in the history, not only of Ecuador, but of the entire world, a government promoted a public audit process of the foreign debt and declared some of its tranches illegitimate and immoral.</code> | <code>0</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
|
|
| ### Evaluation Dataset |
|
|
| #### json |
|
|
| * Dataset: json |
| * Size: 478,146 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 17 tokens</li><li>mean: 33.62 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 34.48 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>0: ~57.30%</li><li>1: ~2.90%</li><li>2: ~39.80%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>The anchoring of the Slovak Republic in the European Union allows citizens to feel: secure politically, secure economically, secure socially.</code> | <code>Radikale Venstre wants Denmark to participate fully and firmly in EU cooperation on immigration, asylum and cross-border crime.</code> | <code>2</code> | |
| | <code>Portugal's participation in the Community's negotiation of the next financial perspective should also be geared in the same direction.</code> | <code>Given the dynamic international framework, safeguarding the national interest requires adjustments to each of these vectors.</code> | <code>2</code> | |
| | <code>On asylum, the Green Party will: Dismantle the direct provision system and replace it with an efficient and humane system for determining the status of asylum seekers</code> | <code>The crisis in the coal sector subsequently forced these immigrant workers to move into other economic sectors such as metallurgy, chemicals, construction and transport.</code> | <code>2</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `learning_rate`: 8e-05 |
| - `num_train_epochs`: 5 |
| - `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`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 5 |
| - `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 | Validation Loss | |
| |:------:|:-----:|:-------------:|:---------------:| |
| | 0.0837 | 500 | 0.7889 | 9.5828 | |
| | 0.1673 | 1000 | 1.2158 | 9.3274 | |
| | 0.2510 | 1500 | 1.8215 | 9.4274 | |
| | 0.3346 | 2000 | 2.3548 | 8.2583 | |
| | 0.4183 | 2500 | 2.7493 | 8.1446 | |
| | 0.5019 | 3000 | 2.8998 | 7.9046 | |
| | 0.5856 | 3500 | 2.9298 | 8.0640 | |
| | 0.6692 | 4000 | 2.9053 | 7.2746 | |
| | 0.7529 | 4500 | 3.0905 | 7.5099 | |
| | 0.8365 | 5000 | 3.1864 | 7.3883 | |
| | 0.9202 | 5500 | 3.2322 | 6.9968 | |
| | 1.0038 | 6000 | 3.1194 | 7.4682 | |
| | 1.0875 | 6500 | 3.0122 | 7.7295 | |
| | 1.1712 | 7000 | 3.0453 | 7.1696 | |
| | 1.2548 | 7500 | 2.9439 | 7.2775 | |
| | 1.3385 | 8000 | 3.1108 | 7.4838 | |
| | 1.4221 | 8500 | 2.8512 | 7.5204 | |
| | 1.5058 | 9000 | 2.9865 | 7.4528 | |
| | 1.5894 | 9500 | 2.9995 | 8.0682 | |
| | 1.6731 | 10000 | 3.1073 | 7.5344 | |
| | 1.7567 | 10500 | 3.0631 | 7.4572 | |
| | 1.8404 | 11000 | 2.9915 | 7.4961 | |
| | 1.9240 | 11500 | 3.0445 | 7.3575 | |
| | 2.0077 | 12000 | 2.9501 | 7.9786 | |
| | 2.0914 | 12500 | 2.3377 | 8.6208 | |
| | 2.1750 | 13000 | 2.2833 | 8.8356 | |
| | 2.2587 | 13500 | 2.2785 | 8.8709 | |
| | 2.3423 | 14000 | 2.3012 | 8.6250 | |
| | 2.4260 | 14500 | 2.3488 | 8.1099 | |
| | 2.5096 | 15000 | 2.095 | 9.2305 | |
| | 2.5933 | 15500 | 2.4123 | 8.6405 | |
| | 2.6769 | 16000 | 2.2236 | 8.7805 | |
| | 2.7606 | 16500 | 2.3367 | 8.7110 | |
| | 2.8442 | 17000 | 2.1159 | 8.6447 | |
| | 2.9279 | 17500 | 2.1622 | 8.7123 | |
| | 3.0115 | 18000 | 2.1916 | 9.0314 | |
| | 3.0952 | 18500 | 1.604 | 9.3373 | |
| | 3.1789 | 19000 | 1.4116 | 9.6509 | |
| | 3.2625 | 19500 | 1.4036 | 9.9127 | |
| | 3.3462 | 20000 | 1.5392 | 9.8093 | |
| | 3.4298 | 20500 | 1.5791 | 9.8325 | |
| | 3.5135 | 21000 | 1.5343 | 9.7822 | |
| | 3.5971 | 21500 | 1.3913 | 9.6243 | |
| | 3.6808 | 22000 | 1.5151 | 9.9644 | |
| | 3.7644 | 22500 | 1.3922 | 9.7816 | |
| | 3.8481 | 23000 | 1.3361 | 9.5338 | |
| | 3.9317 | 23500 | 1.3363 | 9.8282 | |
| | 4.0154 | 24000 | 1.2234 | 10.2117 | |
| | 4.0990 | 24500 | 0.5927 | 10.4107 | |
| | 4.1827 | 25000 | 0.6879 | 10.4405 | |
| | 4.2664 | 25500 | 0.6832 | 10.5138 | |
| | 4.3500 | 26000 | 0.6514 | 10.2798 | |
| | 4.4337 | 26500 | 0.7396 | 10.3250 | |
| | 4.5173 | 27000 | 0.6813 | 10.4115 | |
| | 4.6010 | 27500 | 0.765 | 10.1365 | |
| | 4.6846 | 28000 | 0.5915 | 10.2402 | |
| | 4.7683 | 28500 | 0.5028 | 10.3197 | |
| | 4.8519 | 29000 | 0.5306 | 10.3270 | |
| | 4.9356 | 29500 | 0.5886 | 10.3543 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.9.21 |
| - Sentence Transformers: 3.4.0 |
| - Transformers: 4.48.1 |
| - PyTorch: 2.5.1+cu124 |
| - Accelerate: 1.3.0 |
| - Datasets: 3.2.0 |
| - Tokenizers: 0.21.0 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### CoSENTLoss |
| ```bibtex |
| @online{kexuefm-8847, |
| title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
| author={Su Jianlin}, |
| year={2022}, |
| month={Jan}, |
| url={https://kexue.fm/archives/8847}, |
| } |
| ``` |
|
|
| <!-- |
| ## 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 |
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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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