Upload fine-tuned multilingual-e5-small for HS code classification

b711fac verified 5 days ago

15.4 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:9829
	- loss:MultipleNegativesRankingLoss
	base_model: intfloat/multilingual-e5-small
	widget:
	- source_sentence: 'query: DAIRY PRODUCE; CHEESE (NOT GRATED, POWDERED OR PROCESSED),
	N.E.C. IN HEADING NO. 0406 POWDERED IN VACUUM PACKS 14290 PCS'
	sentences:
	- 'passage: Tôm đông lạnh, sơ chế, bỏ đầu bỏ vỏ, để xuất khẩu theo điều kiện thương
	mại tiêu chuẩn, điều kiện giao hàng FOB'
	- 'passage: Phô mai loại khác, để thông quan và khai báo nhập khẩu, kèm hóa đơn
	thương mại và phiếu đóng gói'
	- 'passage: Organic fresh tomatoes, hydroponic, for bulk procurement program, palletized
	for container shipment'
	- source_sentence: 'query: Tôm thẻ chân trắng đông lạnh xuất khẩu'
	sentences:
	- 'passage: Red Delicious apples, fresh, for export'
	- 'passage: Cá nước ngọt đông lạnh, đóng thùng'
	- 'passage: กุ้งแช่แข็ง IQF ส่งออก สำหรับการขนส่งข้ามพรมแดน เงื่อนไขการขนส่ง CIF'
	- source_sentence: 'query: 新鲜脐橙加州进口，用于国际批发分销，托盘装集装箱运输'
	sentences:
	- 'passage: VEGETABLES; TOMATOES, FRESH OR CHILLED SIZE 72MM IN REEFER CONTAINER'
	- 'passage: CONVENTIONAL FRUIT, EDIBLE; ORANGES, FRESH OR DRIED IN BULK BAGS, for
	industrial procurement contract, shipping term FOB'
	- 'passage: Thịt bò đông lạnh không xương, Halal'
	- source_sentence: 'query: MEAT; OF BOVINE ANIMALS, BONELESS CUTS, FRESH OR CHILLED
	IN CONTAINER, for cross-border shipment, shipping term FOB'
	sentences:
	- 'passage: Fresh plum tomatoes for Italian cooking, for bulk procurement program,
	palletized for container shipment'
	- 'passage: Boneless beef sirloin, fresh, not frozen, for bonded warehouse delivery,
	palletized for container shipment'
	- 'passage: ORGANIC VEGETABLES, ALLIACEOUS; ONIONS AND SHALLOTS, FRESH OR CHILLED
	WHITE ONION VARIETY IN CARTONS'
	- source_sentence: 'query: CRUSTACEANS; FROZEN, SHRIMPS AND PRAWNS, EXCLUDING COLD-WATER
	VARIETIES, IN SHELL OR NOT, SMOKED, COOKED OR NOT BEFORE OR DURING SMOKING; IN
	SHELL, COOKED BY STEAMING OR BY BOILING IN WATER 21/25 COUNT IN SACKS 8576.9 KG'
	sentences:
	- 'passage: กุ้งแช่แข็ง IQF ส่งออก สำหรับการขนส่งข้ามพรมแดน เงื่อนไขการขนส่ง CIF'
	- 'passage: DAIRY PRODUCE; MILK AND CREAM, CONCENTRATED OR CONTAINING ADDED SUGAR
	OR OTHER SWEETENING MATTER, IN POWDER, GRANULES OR OTHER SOLID FORMS, OF A FAT
	CONTENT NOT EXCEEDING 1.5% (BY WEIGHT) FAT CONTENT 3.5% IN VACUUM PACKS'
	- 'passage: CRUSTACEANS; FROZEN, SHRIMPS AND PRAWNS, EXCLUDING COLD-WATER VARIETIES,
	IN SHELL OR NOT, SMOKED, COOKED OR NOT BEFORE OR DURING SMOKING; IN SHELL, COOKED
	BY STEAMING OR BY BOILING IN WATER'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on intfloat/multilingual-e5-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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: [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 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/huggingface/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, 'architecture': '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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'query: CRUSTACEANS; FROZEN, SHRIMPS AND PRAWNS, EXCLUDING COLD-WATER VARIETIES, IN SHELL OR NOT, SMOKED, COOKED OR NOT BEFORE OR DURING SMOKING; IN SHELL, COOKED BY STEAMING OR BY BOILING IN WATER 21/25 COUNT IN SACKS 8576.9 KG',
	'passage: CRUSTACEANS; FROZEN, SHRIMPS AND PRAWNS, EXCLUDING COLD-WATER VARIETIES, IN SHELL OR NOT, SMOKED, COOKED OR NOT BEFORE OR DURING SMOKING; IN SHELL, COOKED BY STEAMING OR BY BOILING IN WATER',
	'passage: กุ้งแช่แข็ง IQF ส่งออก สำหรับการขนส่งข้ามพรมแดน เงื่อนไขการขนส่ง CIF',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[1.0000, 0.9576, 0.7030],
	# [0.9576, 1.0000, 0.6773],
	# [0.7030, 0.6773, 1.0000]])
	```

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	### 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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	### Out-of-Scope Use

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 9,829 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 36.3 tokens</li><li>max: 114 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 34.27 tokens</li><li>max: 113 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:---------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>query: Chilled beef tenderloin, boneless, vacuum packed</code> \| <code>passage: Thịt bò không xương tươi cho nhà hàng, cho hợp đồng mua sắm công nghiệp, hàng lô hỗn hợp</code> \|
	\| <code>query: 优质鲜牛肉无骨出口级别</code> \| <code>passage: 优质鲜牛肉无骨出口级别，用于国际批发分销，装20尺集装箱</code> \|
	\| <code>query: 冷却去骨黄牛肉真空包装</code> \| <code>passage: FROZEN MEAT; OF BOVINE ANIMALS, BONELESS CUTS, FRESH OR CHILLED SKIN-ON IN TINS 15204.2 KG, for industrial procurement contract, shipping term CIF</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"gather_across_devices": false
	}
	```

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

	- `per_device_train_batch_size`: 4
	- `num_train_epochs`: 2
	- `learning_rate`: 2e-05
	- `warmup_steps`: 0.1
	- `gradient_accumulation_steps`: 16
	- `warmup_ratio`: 0.1

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

	- `per_device_train_batch_size`: 4
	- `num_train_epochs`: 2
	- `max_steps`: -1
	- `learning_rate`: 2e-05
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_steps`: 0.1
	- `optim`: adamw_torch_fused
	- `optim_args`: None
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `optim_target_modules`: None
	- `gradient_accumulation_steps`: 16
	- `average_tokens_across_devices`: True
	- `max_grad_norm`: 1.0
	- `label_smoothing_factor`: 0.0
	- `bf16`: False
	- `fp16`: False
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `use_cache`: False
	- `neftune_noise_alpha`: None
	- `torch_empty_cache_steps`: None
	- `auto_find_batch_size`: False
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `include_num_input_tokens_seen`: no
	- `log_level`: passive
	- `log_level_replica`: warning
	- `disable_tqdm`: False
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `eval_strategy`: no
	- `per_device_eval_batch_size`: 8
	- `prediction_loss_only`: True
	- `eval_on_start`: False
	- `eval_do_concat_batches`: True
	- `eval_use_gather_object`: False
	- `eval_accumulation_steps`: None
	- `include_for_metrics`: []
	- `batch_eval_metrics`: False
	- `save_only_model`: False
	- `save_on_each_node`: False
	- `enable_jit_checkpoint`: False
	- `push_to_hub`: False
	- `hub_private_repo`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_always_push`: False
	- `hub_revision`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `restore_callback_states_from_checkpoint`: False
	- `full_determinism`: False
	- `seed`: 42
	- `data_seed`: None
	- `use_cpu`: False
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: None
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `dataloader_prefetch_factor`: None
	- `remove_unused_columns`: True
	- `label_names`: None
	- `train_sampling_strategy`: random
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `ddp_backend`: None
	- `ddp_timeout`: 1800
	- `fsdp`: []
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `deepspeed`: None
	- `debug`: []
	- `skip_memory_metrics`: True
	- `do_predict`: False
	- `resume_from_checkpoint`: None
	- `warmup_ratio`: 0.1
	- `local_rank`: -1
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.0651 \| 10 \| 0.9040 \|
	\| 0.1302 \| 20 \| 0.7323 \|
	\| 0.1953 \| 30 \| 0.4439 \|
	\| 0.2604 \| 40 \| 0.2618 \|
	\| 0.3255 \| 50 \| 0.2630 \|
	\| 0.3906 \| 60 \| 0.2398 \|
	\| 0.4557 \| 70 \| 0.1878 \|
	\| 0.5207 \| 80 \| 0.2271 \|
	\| 0.5858 \| 90 \| 0.2237 \|
	\| 0.6509 \| 100 \| 0.2180 \|
	\| 0.7160 \| 110 \| 0.2125 \|
	\| 0.7811 \| 120 \| 0.2067 \|
	\| 0.8462 \| 130 \| 0.1925 \|
	\| 0.9113 \| 140 \| 0.1952 \|
	\| 0.9764 \| 150 \| 0.1932 \|
	\| 1.0391 \| 160 \| 0.1368 \|
	\| 1.1041 \| 170 \| 0.1737 \|
	\| 1.1692 \| 180 \| 0.1815 \|
	\| 1.2343 \| 190 \| 0.1724 \|
	\| 1.2994 \| 200 \| 0.1525 \|
	\| 1.3645 \| 210 \| 0.1699 \|
	\| 1.4296 \| 220 \| 0.1592 \|
	\| 1.4947 \| 230 \| 0.1661 \|
	\| 1.5598 \| 240 \| 0.1606 \|
	\| 1.6249 \| 250 \| 0.1218 \|
	\| 1.6900 \| 260 \| 0.1586 \|
	\| 1.7551 \| 270 \| 0.1517 \|
	\| 1.8202 \| 280 \| 0.1458 \|
	\| 1.8853 \| 290 \| 0.1550 \|
	\| 1.9504 \| 300 \| 0.1352 \|


	### Framework Versions
	- Python: 3.14.3
	- Sentence Transformers: 5.2.3
	- Transformers: 5.2.0
	- PyTorch: 2.10.0
	- Accelerate: 1.12.0
	- Datasets: 4.5.0
	- Tokenizers: 0.22.2

	## 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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