--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:19598 - loss:CoSENTLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: soi tỷ lệ Southampton vs Nottingham (21h00, 24/8), vòng 2 Ngoại hạng Anh sentences: - nhận định Oakleigh Cannons vs Macarthur - dự đoán Mallorca vs Athletic Bilbao - soi kèo Southampton vs Nottingham Forest - source_sentence: Melbourne Victory vs Macarthur 12h00 ngày 3/11 (VĐQG Australia 2024/25). sentences: - tỷ lệ Tijuana vs Leon - 'Melbourne Victory vs Brisbane Roar ' - Hải Phòng vs SHB Đà Nẵng - source_sentence: Banfield vs Estudiantes 4h00 ngày 8/10 (VĐQG Argentina 2024). sentences: - arsenal vs psg - Shandong Luneng vs Qingdaoyangcheng - 'Boca Juniors vs River Plate ' - source_sentence: 'St Pauli vs Bayern Munich (21h30 ngày 9/11): Khó có bất ngờ.' sentences: - st pauli vs bayern munich - Seattle Sounders vs Houston Dynamo - kyrgyzstan vs triều tiên - source_sentence: 'Juventus vs Napoli (23h00 ngày 21/9): Không dễ cho chủ nhà.' sentences: - cruz azul vs juarez - Real Madrid vs Barcelona - El Salvador vs Montserrat pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: sport query title dev type: sport_query_title_dev metrics: - type: cosine_accuracy value: 0.9943877551020408 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6410836577415466 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9943269726663229 name: Cosine F1 - type: cosine_f1_threshold value: 0.6107593178749084 name: Cosine F1 Threshold - type: cosine_precision value: 0.9958677685950413 name: Cosine Precision - type: cosine_recall value: 0.9927909371781668 name: Cosine Recall - type: cosine_ap value: 0.9995956398472251 name: Cosine Ap --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. 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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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': 256, '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}) (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("Tam1032/MiniLM6-v2-sport") # Run inference sentences = [ 'Juventus vs Napoli (23h00 ngày 21/9): Không dễ cho chủ nhà.', 'Real Madrid vs Barcelona', 'El Salvador vs Montserrat', ] 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] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `sport_query_title_dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9944 | | cosine_accuracy_threshold | 0.6411 | | cosine_f1 | 0.9943 | | cosine_f1_threshold | 0.6108 | | cosine_precision | 0.9959 | | cosine_recall | 0.9928 | | **cosine_ap** | **0.9996** | ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 19,598 training samples * Columns: hypothesis, premise, and label * Approximate statistics based on the first 1000 samples: | | hypothesis | premise | label | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | hypothesis | premise | label | |:---------------------------------------------------------------------------------------|:--------------------------------------------------------|:---------------| | bóng đá Las Palmas vs Girona, 23h30 ngày 26/10: Trừng phạt chủ nhà. | Las Palmas vs Girona | 1 | | Seattle Sounders vs Houston Dynamo 9h30 ngày 29/9 (Nhà nghề Mỹ 2024). | dự đoán Seattle Sounders vs Houston Dynamo | 1 | | bóng đá Tây Ban Nha vs Đan Mạch, 01h45 ngày 13/10: Khuất phục ‘lính chì’. | bóng đá Tây Ban Nha vs Đan Mạch | 1 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### csv * Dataset: csv * Size: 19,598 evaluation samples * Columns: hypothesis, premise, and label * Approximate statistics based on the first 1000 samples: | | hypothesis | premise | label | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | hypothesis | premise | label | |:--------------------------------------------------------------------------|:---------------------------------------|:---------------| | Hải Phòng vs CAHN (19h15 ngày 15/9): Điểm tựa sân nhà. | kết quả Hải Phòng vs CAHN | 1 | | Kuwait vs Jordan 1h15 ngày 20/11 (Vòng loại World Cup 2026). | Kuwait vs Iraq | 0 | | bóng đá Parma vs Empoli 18h30 ngày 27/10 (Serie A 2024/25). | nhận định Parma vs Empoli | 1 | * Loss: [CoSENTLoss](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 - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `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`: 1.0 - `num_train_epochs`: 3 - `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`: 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`: None - `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
### Training Logs | Epoch | Step | Training Loss | Validation Loss | sport_query_title_dev_cosine_ap | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:| | 1.0 | 1103 | - | 0.1376 | 0.9991 | | 1.4506 | 1600 | 0.3994 | - | - | | 2.0 | 2206 | - | 0.0693 | 0.9994 | | 2.9012 | 3200 | 0.0442 | - | - | | 3.0 | 3309 | - | 0.0534 | 0.9996 | ### Framework Versions - Python: 3.11.7 - Sentence Transformers: 3.3.1 - Transformers: 4.47.0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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}, } ```