Add new SentenceTransformer model.

6977101 verified 8 months ago

19.7 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:100K<n<1M
	- loss:TripletLoss
	base_model: FacebookAI/xlm-roberta-base
	metrics:
	- cosine_accuracy
	- dot_accuracy
	- manhattan_accuracy
	- euclidean_accuracy
	- max_accuracy
	widget:
	- source_sentence: Skip
	sentences:
	- Ships
	- Kapital akcyjny
	- Other finance income
	- source_sentence: IIII
	sentences:
	- iii
	- Gauti dividendai
	- Loans given
	- source_sentence: IVE
	sentences:
	- HH
	- Koszty finansowe
	- Current borrowings
	- source_sentence: K K
	sentences:
	- TOTAL ACTIF
	- Nuomos mokejimai
	- Accruals
	- source_sentence: Sales
	sentences:
	- Revenue
	- Operating profit
	- Current borrowings
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy
	value: 0.9987885552019722
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.001529316610921369
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.9975135360413657
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.9990958312877694
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.9990958312877694
	name: Max Accuracy
	---

	# SentenceTransformer based on FacebookAI/xlm-roberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). 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: [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(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("slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may")
	# Run inference
	sentences = [
	'Sales',
	'Revenue',
	'Operating profit',
	]
	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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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

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	## Evaluation

	### Metrics

	#### Triplet

	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| cosine_accuracy \| 0.9988 \|
	\| dot_accuracy \| 0.0015 \|
	\| manhattan_accuracy \| 0.9975 \|
	\| euclidean_accuracy \| 0.9991 \|
	\| max_accuracy \| 0.9991 \|

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	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 660,643 training samples
	* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor_label \| pos_hlabel \| neg_hlabel \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 11.86 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 9.06 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 25 tokens</li></ul> \|
	* Samples:
	\| anchor_label \| pos_hlabel \| neg_hlabel \|
	\|:---------------------------------------------\|:-------------------------------------------\|:------------------------------------------------------------------------------\|
	\| <code>Basic earnings (loss) per share</code> \| <code>Tavakasum kahjum aktsia kohta</code> \| <code>II Kapital z nadwyzki wartosci emisyjnej ponad wartosc nominalna</code> \|
	\| <code>Comprehensive income</code> \| <code>Suma dochodow calkowitych</code> \| <code>dont Marques</code> \|
	\| <code>Cash and cash equivalents</code> \| <code>Cash and cash equivalents</code> \| <code>Cars incl prepayments</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 283,133 evaluation samples
	* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor_label \| pos_hlabel \| neg_hlabel \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 11.78 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 9.22 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 8.12 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor_label \| pos_hlabel \| neg_hlabel \|
	\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------\|:-------------------------------------\|
	\| <code>Deferred tax assets</code> \| <code>Deferred tax assets</code> \| <code>Immateriella tillgangar</code> \|
	\| <code>Equity</code> \| <code>EGET KAPITAL inklusive periodens resultat</code> \| <code>Materials</code> \|
	\| <code>Adjustments for decrease (increase) in other operating receivables</code> \| <code>Okning av ovriga rorelsetillgangar</code> \| <code>Rorelseresultat</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

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

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `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
	- `learning_rate`: 5e-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`: 1
	- `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
	- `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}
	- `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
	- `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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| max_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:------:\|:------------:\|
	\| 0.0121 \| 500 \| 3.7705 \| - \| - \|
	\| 0.0242 \| 1000 \| 1.4084 \| - \| - \|
	\| 0.0363 \| 1500 \| 0.7062 \| - \| - \|
	\| 0.0484 \| 2000 \| 0.5236 \| - \| - \|
	\| 0.0605 \| 2500 \| 0.4348 \| - \| - \|
	\| 0.0727 \| 3000 \| 0.3657 \| - \| - \|
	\| 0.0848 \| 3500 \| 0.3657 \| - \| - \|
	\| 0.0969 \| 4000 \| 0.2952 \| - \| - \|
	\| 0.1090 \| 4500 \| 0.3805 \| - \| - \|
	\| 0.1211 \| 5000 \| 0.3255 \| - \| - \|
	\| 0.1332 \| 5500 \| 0.2621 \| - \| - \|
	\| 0.1453 \| 6000 \| 0.2377 \| - \| - \|
	\| 0.1574 \| 6500 \| 0.2139 \| - \| - \|
	\| 0.1695 \| 7000 \| 0.2085 \| - \| - \|
	\| 0.1816 \| 7500 \| 0.1809 \| - \| - \|
	\| 0.1937 \| 8000 \| 0.1711 \| - \| - \|
	\| 0.2059 \| 8500 \| 0.1608 \| - \| - \|
	\| 0.2180 \| 9000 \| 0.1808 \| - \| - \|
	\| 0.2301 \| 9500 \| 0.1553 \| - \| - \|
	\| 0.2422 \| 10000 \| 0.1417 \| - \| - \|
	\| 0.2543 \| 10500 \| 0.1329 \| - \| - \|
	\| 0.2664 \| 11000 \| 0.1689 \| - \| - \|
	\| 0.2785 \| 11500 \| 0.1292 \| - \| - \|
	\| 0.2906 \| 12000 \| 0.1181 \| - \| - \|
	\| 0.3027 \| 12500 \| 0.1223 \| - \| - \|
	\| 0.3148 \| 13000 \| 0.129 \| - \| - \|
	\| 0.3269 \| 13500 \| 0.0911 \| - \| - \|
	\| 0.3391 \| 14000 \| 0.113 \| - \| - \|
	\| 0.3512 \| 14500 \| 0.0955 \| - \| - \|
	\| 0.3633 \| 15000 \| 0.108 \| - \| - \|
	\| 0.3754 \| 15500 \| 0.094 \| - \| - \|
	\| 0.3875 \| 16000 \| 0.0947 \| - \| - \|
	\| 0.3996 \| 16500 \| 0.0748 \| - \| - \|
	\| 0.4117 \| 17000 \| 0.0699 \| - \| - \|
	\| 0.4238 \| 17500 \| 0.0707 \| - \| - \|
	\| 0.4359 \| 18000 \| 0.0768 \| - \| - \|
	\| 0.4480 \| 18500 \| 0.0805 \| - \| - \|
	\| 0.4601 \| 19000 \| 0.0705 \| - \| - \|
	\| 0.4723 \| 19500 \| 0.069 \| - \| - \|
	\| 0.4844 \| 20000 \| 0.072 \| - \| - \|
	\| 0.4965 \| 20500 \| 0.0669 \| - \| - \|
	\| 0.5086 \| 21000 \| 0.066 \| - \| - \|
	\| 0.5207 \| 21500 \| 0.0624 \| - \| - \|
	\| 0.5328 \| 22000 \| 0.0687 \| - \| - \|
	\| 0.5449 \| 22500 \| 0.076 \| - \| - \|
	\| 0.5570 \| 23000 \| 0.0563 \| - \| - \|
	\| 0.5691 \| 23500 \| 0.0594 \| - \| - \|
	\| 0.5812 \| 24000 \| 0.0524 \| - \| - \|
	\| 0.5933 \| 24500 \| 0.0528 \| - \| - \|
	\| 0.6055 \| 25000 \| 0.0448 \| - \| - \|
	\| 0.6176 \| 25500 \| 0.041 \| - \| - \|
	\| 0.6297 \| 26000 \| 0.0397 \| - \| - \|
	\| 0.6418 \| 26500 \| 0.0489 \| - \| - \|
	\| 0.6539 \| 27000 \| 0.0595 \| - \| - \|
	\| 0.6660 \| 27500 \| 0.034 \| - \| - \|
	\| 0.6781 \| 28000 \| 0.0569 \| - \| - \|
	\| 0.6902 \| 28500 \| 0.0467 \| - \| - \|
	\| 0.7023 \| 29000 \| 0.0323 \| - \| - \|
	\| 0.7144 \| 29500 \| 0.0428 \| - \| - \|
	\| 0.7266 \| 30000 \| 0.0344 \| - \| - \|
	\| 0.7387 \| 30500 \| 0.029 \| - \| - \|
	\| 0.7508 \| 31000 \| 0.0418 \| - \| - \|
	\| 0.7629 \| 31500 \| 0.0285 \| - \| - \|
	\| 0.7750 \| 32000 \| 0.0425 \| - \| - \|
	\| 0.7871 \| 32500 \| 0.0266 \| - \| - \|
	\| 0.7992 \| 33000 \| 0.0325 \| - \| - \|
	\| 0.8113 \| 33500 \| 0.0215 \| - \| - \|
	\| 0.8234 \| 34000 \| 0.0316 \| - \| - \|
	\| 0.8355 \| 34500 \| 0.0286 \| - \| - \|
	\| 0.8476 \| 35000 \| 0.0285 \| - \| - \|
	\| 0.8598 \| 35500 \| 0.0284 \| - \| - \|
	\| 0.8719 \| 36000 \| 0.0147 \| - \| - \|
	\| 0.8840 \| 36500 \| 0.0217 \| - \| - \|
	\| 0.8961 \| 37000 \| 0.0311 \| - \| - \|
	\| 0.9082 \| 37500 \| 0.0202 \| - \| - \|
	\| 0.9203 \| 38000 \| 0.0236 \| - \| - \|
	\| 0.9324 \| 38500 \| 0.0201 \| - \| - \|
	\| 0.9445 \| 39000 \| 0.0246 \| - \| - \|
	\| 0.9566 \| 39500 \| 0.0177 \| - \| - \|
	\| 0.9687 \| 40000 \| 0.0173 \| - \| - \|
	\| 0.9808 \| 40500 \| 0.0202 \| - \| - \|
	\| 0.9930 \| 41000 \| 0.017 \| - \| - \|
	\| 1.0 \| 41291 \| - \| 0.0140 \| 0.9991 \|


	### Framework Versions
	- Python: 3.10.13
	- Sentence Transformers: 3.0.0
	- Transformers: 4.39.3
	- PyTorch: 2.1.2
	- Accelerate: 0.28.0
	- Datasets: 2.18.0
	- Tokenizers: 0.15.2

	## 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",
	}
	```

	#### TripletLoss
	```bibtex
	@misc{hermans2017defense,
	title={In Defense of the Triplet Loss for Person Re-Identification},
	author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
	year={2017},
	eprint={1703.07737},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```

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