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

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,388 @@
+---
+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]])
+```
+
+<!--
+### 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
+
+#### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "transformers_version": "5.2.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "5.2.0",
+    "pytorch": "2.10.0"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:6065b335bec1546c010f6615af545f442614f46841947c827c2f557cb0a6243f
+size 470637392

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

tokenizer.json

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+oid sha256:ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
+size 17083053

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>"
+}