Add new SentenceTransformer model

777f300 verified 12 days ago

25.1 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:3560698
	- loss:ModifiedMatryoshkaLoss
	base_model: google-bert/bert-base-multilingual-cased
	widget:
	- source_sentence: And then finally, turn it back to the real world.
	sentences:
	- Y luego, finalmente, devolver eso al mundo real.
	- Parece que el único rasgo que sobrevive a la decapitación es la vanidad.
	- y yo digo que no estoy seguro. Voy a pensarlo a groso modo.
	- source_sentence: Figure out some of the other options that are much better.
	sentences:
	- Piensen en otras de las opciones que son mucho mejores.
	- Éste solía ser un tema bipartidista, y sé que en este grupo realmente lo es.
	- El acuerdo general de paz para Sudán firmado en 2005 resultó ser menos amplio
	que lo previsto, y sus disposiciones aún podrían engendrar un retorno a gran escala
	de la guerra entre el norte y el sur.
	- source_sentence: 'The call to action I offer today -- my TED wish -- is this: Honor
	the treaties.'
	sentences:
	- Esta es la intersección más directa, obvia, de las dos cosas.
	- 'El llamado a la acción que propongo hoy, mi TED Wish, es el siguiente: Honrar
	los tratados.'
	- Los restaurantes del condado se pueden contar con los dedos de una mano... Barbacoa
	Bunn es mi favorito.
	- source_sentence: So for us, this was a graphic public campaign called Connect Bertie.
	sentences:
	- Para nosotros esto era una campaña gráfica llamada Conecta a Bertie.
	- En cambio, los líderes locales se comprometieron a revisarlos más adelante.
	- Con el tiempo, la gente hace lo que se le paga por hacer.
	- source_sentence: And in the audio world that's when the microphone gets too close
	to its sound source, and then it gets in this self-destructive loop that creates
	a very unpleasant sound.
	sentences:
	- Esta es una mina de Zimbabwe en este momento.
	- Estábamos en la I-40.
	- Y, en el mundo del audio, es cuando el micrófono se acerca demasiado a su fuente
	de sonido, y entra en este bucle autodestructivo que crea un sonido muy desagradable.
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- negative_mse
	model-index:
	- name: SentenceTransformer based on google-bert/bert-base-multilingual-cased
	results:
	- task:
	type: knowledge-distillation
	name: Knowledge Distillation
	dataset:
	name: MSE val en es
	type: MSE-val-en-es
	metrics:
	- type: negative_mse
	value: -29.5114666223526
	name: Negative Mse
	- task:
	type: knowledge-distillation
	name: Knowledge Distillation
	dataset:
	name: MSE val en pt
	type: MSE-val-en-pt
	metrics:
	- type: negative_mse
	value: -29.913604259490967
	name: Negative Mse
	- task:
	type: knowledge-distillation
	name: Knowledge Distillation
	dataset:
	name: MSE val en pt br
	type: MSE-val-en-pt-br
	metrics:
	- type: negative_mse
	value: -27.732226252555847
	name: Negative Mse
	---

	# SentenceTransformer based on google-bert/bert-base-multilingual-cased

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). 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: [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 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/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': 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("luanafelbarros/TriLingual-BERT-Distil")
	# Run inference
	sentences = [
	"And in the audio world that's when the microphone gets too close to its sound source, and then it gets in this self-destructive loop that creates a very unpleasant sound.",
	'Y, en el mundo del audio, es cuando el micrófono se acerca demasiado a su fuente de sonido, y entra en este bucle autodestructivo que crea un sonido muy desagradable.',
	'Esta es una mina de Zimbabwe en este momento.',
	]
	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]
	```

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	## Evaluation

	### Metrics

	#### Knowledge Distillation

	* Datasets: `MSE-val-en-es`, `MSE-val-en-pt` and `MSE-val-en-pt-br`
	* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

	\| Metric \| MSE-val-en-es \| MSE-val-en-pt \| MSE-val-en-pt-br \|
	\|:-----------------\|:--------------\|:--------------\|:-----------------\|
	\| negative_mse \| -29.5115 \| -29.9136 \| -27.7322 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 3,560,698 training samples
	* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| english \| non_english \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| string \| list \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 25.46 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 26.67 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \|
	* Samples:
	\| english \| non_english \| label \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> \| <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> \| <code>[-0.04180986061692238, 0.12620249390602112, -0.14501447975635529, 0.09695684909820557, -0.10850819200277328, ...]</code> \|
	\| <code>One thing I often ask about is ancient Greek and how this relates.</code> \| <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code> \| <code>[0.0034368489868938923, -0.02741478756070137, -0.09426739811897278, 0.04873204976320267, -0.008266829885542393, ...]</code> \|
	\| <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code> \| <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code> \| <code>[-0.05048828944563866, 0.2713043689727783, 0.024581076577305794, -0.07316197454929352, -0.044288791716098785, ...]</code> \|
	* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters:
	```json
	{
	"loss": "MSELoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 6,974 evaluation samples
	* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| english \| non_english \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| string \| list \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 25.68 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 27.31 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \|
	* Samples:
	\| english \| non_english \| label \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Thank you so much, Chris.</code> \| <code>Muchas gracias Chris.</code> \| <code>[-0.1432434469461441, -0.10335833579301834, -0.07549277693033218, -0.1542435735464096, 0.009247343055903912, ...]</code> \|
	\| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> \| <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code> \| <code>[0.02740730345249176, -0.0601208470761776, -0.023767368867993355, 0.02245006151497364, 0.007412586361169815, ...]</code> \|
	\| <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> \| <code>He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche.</code> \| <code>[-0.09117366373538971, 0.08627621084451675, -0.05912208557128906, -0.007647979073226452, 0.0008422975661233068, ...]</code> \|
	* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters:
	```json
	{
	"loss": "MSELoss",
	"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`: steps
	- `per_device_train_batch_size`: 200
	- `per_device_eval_batch_size`: 200
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 2
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `label_names`: ['label']

	#### 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`: 200
	- `per_device_eval_batch_size`: 200
	- `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`: 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`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `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`: 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`: ['label']
	- `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
	- `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`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| MSE-val-en-es_negative_mse \| MSE-val-en-pt_negative_mse \| MSE-val-en-pt-br_negative_mse \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|
	\| 0.0562 \| 1000 \| 0.0626 \| 0.0513 \| -21.2968 \| -20.7534 \| -24.2460 \|
	\| 0.1123 \| 2000 \| 0.0478 \| 0.0432 \| -22.1192 \| -21.8663 \| -23.2775 \|
	\| 0.1685 \| 3000 \| 0.0423 \| 0.0391 \| -21.6697 \| -21.5869 \| -21.6856 \|
	\| 0.0562 \| 1000 \| 0.0396 \| 0.0376 \| -21.7666 \| -21.7181 \| -21.6779 \|
	\| 0.1123 \| 2000 \| 0.0381 \| 0.0358 \| -23.4969 \| -23.5022 \| -22.9817 \|
	\| 0.1685 \| 3000 \| 0.0362 \| 0.0339 \| -24.7639 \| -24.8878 \| -23.8888 \|
	\| 0.2247 \| 4000 \| 0.0347 \| 0.0323 \| -26.5721 \| -26.7422 \| -25.4072 \|
	\| 0.2808 \| 5000 \| 0.0332 \| 0.0310 \| -27.6024 \| -27.8268 \| -26.4132 \|
	\| 0.3370 \| 6000 \| 0.0321 \| 0.0299 \| -27.7974 \| -28.0294 \| -26.6213 \|
	\| 0.3932 \| 7000 \| 0.0312 \| 0.0292 \| -28.2719 \| -28.4834 \| -27.0468 \|
	\| 0.4493 \| 8000 \| 0.0305 \| 0.0285 \| -28.2561 \| -28.5574 \| -26.8752 \|
	\| 0.5055 \| 9000 \| 0.0299 \| 0.0280 \| -28.6342 \| -28.9112 \| -27.2933 \|
	\| 0.5617 \| 10000 \| 0.0294 \| 0.0275 \| -28.5512 \| -28.8469 \| -27.1072 \|
	\| 0.6178 \| 11000 \| 0.029 \| 0.0271 \| -28.6788 \| -28.9608 \| -27.2056 \|
	\| 0.6740 \| 12000 \| 0.0286 \| 0.0267 \| -29.0159 \| -29.3281 \| -27.4770 \|
	\| 0.7302 \| 13000 \| 0.0283 \| 0.0264 \| -28.9224 \| -29.2461 \| -27.3500 \|
	\| 0.7863 \| 14000 \| 0.028 \| 0.0261 \| -29.1044 \| -29.4303 \| -27.4377 \|
	\| 0.8425 \| 15000 \| 0.0277 \| 0.0259 \| -29.2340 \| -29.5758 \| -27.6223 \|
	\| 0.8987 \| 16000 \| 0.0275 \| 0.0257 \| -29.1356 \| -29.4699 \| -27.4667 \|
	\| 0.9548 \| 17000 \| 0.0273 \| 0.0255 \| -29.3281 \| -29.6671 \| -27.7174 \|
	\| 1.0110 \| 18000 \| 0.0271 \| 0.0253 \| -29.2991 \| -29.6635 \| -27.6675 \|
	\| 1.0672 \| 19000 \| 0.0268 \| 0.0251 \| -29.3581 \| -29.7326 \| -27.6587 \|
	\| 1.1233 \| 20000 \| 0.0266 \| 0.0250 \| -29.4233 \| -29.7941 \| -27.7913 \|
	\| 1.1795 \| 21000 \| 0.0265 \| 0.0248 \| -29.3941 \| -29.7583 \| -27.6951 \|
	\| 1.2357 \| 22000 \| 0.0264 \| 0.0247 \| -29.5963 \| -29.9737 \| -27.9191 \|
	\| 1.2918 \| 23000 \| 0.0262 \| 0.0245 \| -29.4587 \| -29.8472 \| -27.7702 \|
	\| 1.3480 \| 24000 \| 0.0262 \| 0.0244 \| -29.4977 \| -29.8868 \| -27.8142 \|
	\| 1.4042 \| 25000 \| 0.026 \| 0.0244 \| -29.5356 \| -29.9184 \| -27.8426 \|
	\| 1.4603 \| 26000 \| 0.0259 \| 0.0243 \| -29.5614 \| -29.9388 \| -27.8360 \|
	\| 1.5165 \| 27000 \| 0.0259 \| 0.0242 \| -29.5362 \| -29.9353 \| -27.8223 \|
	\| 1.5727 \| 28000 \| 0.0258 \| 0.0241 \| -29.5088 \| -29.9043 \| -27.7884 \|
	\| 1.6288 \| 29000 \| 0.0258 \| 0.0241 \| -29.4550 \| -29.8543 \| -27.6788 \|
	\| 1.6850 \| 30000 \| 0.0257 \| 0.0240 \| -29.5373 \| -29.9282 \| -27.7855 \|
	\| 1.7412 \| 31000 \| 0.0256 \| 0.0239 \| -29.5195 \| -29.9096 \| -27.7866 \|
	\| 1.7973 \| 32000 \| 0.0256 \| 0.0239 \| -29.5292 \| -29.9266 \| -27.7579 \|
	\| 1.8535 \| 33000 \| 0.0256 \| 0.0239 \| -29.5202 \| -29.9196 \| -27.7408 \|
	\| 1.9097 \| 34000 \| 0.0255 \| 0.0239 \| -29.5090 \| -29.9126 \| -27.7311 \|
	\| 1.9659 \| 35000 \| 0.0255 \| 0.0238 \| -29.5115 \| -29.9136 \| -27.7322 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.46.3
	- PyTorch: 2.5.1+cu121
	- Accelerate: 1.1.1
	- Datasets: 3.2.0
	- Tokenizers: 0.20.3

	## 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",
	}
	```

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