modelo_finetuneadoX2 / README.md

tomasravel

Add new SentenceTransformer model.

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	---
	base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:87757
	- loss:CoSENTLoss
	widget:
	- source_sentence: buenos aires berazategui calle 22 desde 3801 hasta 3899
	sentences:
	- buenos aires berazategui bullrich desde 3801 hasta 3899
	- capital federal general pueyrredon mar del plata juan jose castelli desde 8502
	hasta 8600
	- buenos aires general pueyrredon mar del plata bravo desde 2001 hasta 2099
	- source_sentence: capital federal ciudad autonoma buenos aires arenales desde 3402
	hasta 3500
	sentences:
	- capital federal ciudad autonoma buenos aires arenales desde 3702 hasta 3800
	- buenos aires moreno pablo acosta desde 401 hasta 499
	- buenos aires valle hermoso mar del plata tripulantes del fournier desde 4001 hasta
	4099
	- source_sentence: buenos aires la matanza la tablada irigoyen desde 1001 hasta 1099
	sentences:
	- santiago del estero lomas de zamora a lugano desde 502 hasta 600
	- buenos aires lomas de zamora ingeniero budge mayor eduardo olivero 3400
	- buenos aires la matanza la tablada irigoyen 2599
	- source_sentence: buenos aires avellaneda villa dominico alberto barcelo desde 302
	hasta 400
	sentences:
	- buenos aires avellaneda villa dominico barcelo alberto desde 302 hasta 400
	- buenos aires hurlingham concepcion arenal desde 6902 hasta 7000
	- buenos aires la tablada pje laplace desde 301 hasta 399
	- source_sentence: buenos aires general pueyrredon mar del plata av patricio peralta
	ramos desde 6101 hasta 6199
	sentences:
	- bahia blanca buenos aires estacion algarrobo desde 1301 hasta 1399
	- buenos aires general pueyrredon mar del plata ing c chapeaurouge desde 6101 hasta
	6199
	- buenos aires general pueyrredon mar del plata pje jacaranda desde 4001 hasta 4099
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) <!-- at revision 3bf4ae7445aa77c8daaef06518dd78baffff53c9 -->
	- 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})
	)
	```

	## 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("tomasravel/modelo_finetuneadoX2")
	# Run inference
	sentences = [
	'buenos aires general pueyrredon mar del plata av patricio peralta ramos desde 6101 hasta 6199',
	'buenos aires general pueyrredon mar del plata ing c chapeaurouge desde 6101 hasta 6199',
	'buenos aires general pueyrredon mar del plata pje jacaranda desde 4001 hasta 4099',
	]
	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>
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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 87,757 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: 13 tokens</li><li>mean: 21.0 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 19.59 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 0.5</li><li>mean: 0.77</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>buenos aires general pueyrredon mar del plata p albarracin desde 1902 hasta 2000</code> \| <code>buenos aires general pueyrredon mar del plata albarracin paula desde 1902 hasta 2000</code> \| <code>1.0</code> \|
	\| <code>buenos aires berazategui calle 11 desde 2001 hasta 2099</code> \| <code>capital federal berazategui calle 11 desde 2001 hasta 2099</code> \| <code>0.72</code> \|
	\| <code>buenos aires bahia blanca gral alvear desde 1901 hasta 1999</code> \| <code>buenos aires bahia blanca gral alvear 1974</code> \| <code>1.0</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"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 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`: 3
	- `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`: 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`: 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 \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0912 \| 500 \| 4.2287 \|
	\| 0.1823 \| 1000 \| 3.6868 \|
	\| 0.2735 \| 1500 \| 3.4965 \|
	\| 0.3646 \| 2000 \| 3.3966 \|
	\| 0.4558 \| 2500 \| 3.3262 \|
	\| 0.5469 \| 3000 \| 3.2206 \|
	\| 0.6381 \| 3500 \| 3.1346 \|
	\| 0.7293 \| 4000 \| 3.0975 \|
	\| 0.8204 \| 4500 \| 2.988 \|
	\| 0.9116 \| 5000 \| 3.0538 \|
	\| 1.0027 \| 5500 \| 2.9717 \|
	\| 1.0939 \| 6000 \| 2.9248 \|
	\| 1.1851 \| 6500 \| 2.8625 \|
	\| 1.2762 \| 7000 \| 2.8606 \|
	\| 1.3674 \| 7500 \| 2.762 \|
	\| 1.4585 \| 8000 \| 2.8183 \|
	\| 1.5497 \| 8500 \| 2.705 \|
	\| 1.6408 \| 9000 \| 2.7019 \|
	\| 1.7320 \| 9500 \| 2.623 \|
	\| 1.8232 \| 10000 \| 2.6409 \|
	\| 1.9143 \| 10500 \| 2.709 \|
	\| 2.0055 \| 11000 \| 2.6223 \|
	\| 2.0966 \| 11500 \| 2.6085 \|
	\| 2.1878 \| 12000 \| 2.6152 \|
	\| 2.2789 \| 12500 \| 2.5679 \|
	\| 2.3701 \| 13000 \| 2.533 \|
	\| 2.4613 \| 13500 \| 2.5537 \|
	\| 2.5524 \| 14000 \| 2.5063 \|
	\| 2.6436 \| 14500 \| 2.4698 \|
	\| 2.7347 \| 15000 \| 2.4349 \|
	\| 2.8259 \| 15500 \| 2.4058 \|
	\| 2.9170 \| 16000 \| 2.5143 \|


	### Framework Versions
	- Python: 3.9.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.44.2
	- PyTorch: 2.2.2
	- Accelerate: 0.34.2
	- Datasets: 2.21.0
	- 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",
	}
	```

	#### 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},
	}
	```

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