--- language: - zh license: mit tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:225000 - loss:MultipleNegativesRankingLoss base_model: richinfoai/ritrieve_zh_v1 widget: - source_sentence: 下班后和同事直奔常去的那家火锅店，热热闹闹地涮了一晚上。 sentences: - 联延掩四远，赫弈成洪炉。 - 把酒仰问天，古今谁不死。 - 骑出平阳里，筵开卫尉家。 - source_sentence: 站在山顶看日出时，突然觉得世俗烦恼都不重要了。 sentences: - 郁没二悲魂，萧条犹在否。 - 封疆亲日月，邑里出王公。 - 心朝玉皇帝，貌似紫阳人。 - source_sentence: 隔壁老张家两个儿子都被征走了，现在天天以泪洗面。 sentences: - 若教为女嫁东风，除却黄莺难匹配。 - 山东今岁点行频，几处冤魂哭虏尘。 - 远图尝画地，超拜乃登坛。 - source_sentence: 边境小镇常年没人驻守，只有老李一个人在山脚下种地。 sentences: - 海徼长无戍，湘山独种畬。 - 高名宋玉遗闲丽，作赋兰成绝盛才。 - 九衢南面色，苍翠绝纤尘。 - source_sentence: 微信列表翻到底，能说真心话的居然只剩快递群。 sentences: - 黛消波月空蟾影，歌息梁尘有梵声。 - 代情难重论，人事好乖移。 - 时应记得长安事，曾向文场属思劳。 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # RITRIEVE ZH 微调：古诗 ↔ 现代语 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [richinfoai/ritrieve_zh_v1](https://huggingface.co/richinfoai/ritrieve_zh_v1) on the json dataset. It maps sentences & paragraphs to a 1792-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:** [richinfoai/ritrieve_zh_v1](https://huggingface.co/richinfoai/ritrieve_zh_v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1792 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** zh - **License:** mit ### 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: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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): Dense({'in_features': 1024, 'out_features': 1792, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## 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 = [ '微信列表翻到底，能说真心话的居然只剩快递群。', '代情难重论，人事好乖移。', '时应记得长安事，曾向文场属思劳。', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1792] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### json * Dataset: json * Size: 225,000 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------|:------------------------------|:------------------------------| | 整个人蜷在阳光里，连毛衣都晒出一股蓬松的香味。 | 箕踞拥裘坐，半身在日旸。 | 洛阳女儿对门居，才可容颜十五馀。 | | 好像所有的好事都约好了一样，今天一起找上门来。 | 临终极乐宝华迎，观音势至俱来至。 | 身没南朝宅已荒，邑人犹赏旧风光。 | | 大家都觉得她太娇气，只有你一直小心照顾着她。 | 弱质人皆弃，唯君手自栽。 | 秦筑长城城已摧，汉武北上单于台。 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### json * Dataset: json * Size: 25,000 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------|:--------------------------|:------------------------------| | 看着街边那些孤零零的老人，真怕自己以后也变成那样。 | 垂白乱南翁，委身希北叟。 | 熏香荀令偏怜少，傅粉何郎不解愁。 | | 关了灯，屋里黑漆漆的，就听见外面秋虫和落叶在说话。 | 秋虫与秋叶，一夜隔窗闻。 | 未能穷意义，岂敢求瑕痕。 | | 虽然爷爷不在了，但他教我做人的道理永远记在心里。 | 惟孝虽遥，灵规不朽。 | 巧类鸳机织，光攒麝月团。 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 6 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 2e-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`: 6 - `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} - `tp_size`: 0 - `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 - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0284 | 50 | 4.4241 | - | | 0.0569 | 100 | 3.4415 | - | | 0.0853 | 150 | 2.6725 | - | | 0.1138 | 200 | 2.4137 | 2.2686 | | 0.1422 | 250 | 2.2701 | - | | 0.1706 | 300 | 2.1523 | - | | 0.1991 | 350 | 2.0805 | - | | 0.2275 | 400 | 2.0513 | 1.9506 | | 0.2560 | 450 | 2.0048 | - | | 0.2844 | 500 | 1.9552 | - | | 0.3129 | 550 | 1.8778 | - | | 0.3413 | 600 | 1.8549 | 1.7630 | | 0.3697 | 650 | 1.822 | - | | 0.3982 | 700 | 1.8128 | - | | 0.4266 | 750 | 1.7742 | - | | 0.4551 | 800 | 1.7076 | 1.6331 | | 0.4835 | 850 | 1.6919 | - | | 0.5119 | 900 | 1.64 | - | | 0.5404 | 950 | 1.6291 | - | | 0.5688 | 1000 | 1.5881 | 1.5368 | | 0.5973 | 1050 | 1.6018 | - | | 0.6257 | 1100 | 1.5664 | - | | 0.6542 | 1150 | 1.5545 | - | | 0.6826 | 1200 | 1.5292 | 1.4532 | | 0.7110 | 1250 | 1.5166 | - | | 0.7395 | 1300 | 1.517 | - | | 0.7679 | 1350 | 1.4639 | - | | 0.7964 | 1400 | 1.4729 | 1.3687 | | 0.8248 | 1450 | 1.4501 | - | | 0.8532 | 1500 | 1.3932 | - | | 0.8817 | 1550 | 1.4063 | - | | 0.9101 | 1600 | 1.3825 | 1.3003 | | 0.9386 | 1650 | 1.3647 | - | | 0.9670 | 1700 | 1.3431 | - | | 0.9954 | 1750 | 1.3417 | - | | 1.0239 | 1800 | 1.0839 | 1.2431 | | 1.0523 | 1850 | 1.0801 | - | | 1.0808 | 1900 | 1.0577 | - | | 1.1092 | 1950 | 1.0159 | - | | 1.1377 | 2000 | 1.0239 | 1.2132 | | 1.1661 | 2050 | 1.0335 | - | | 1.1945 | 2100 | 1.0117 | - | | 1.2230 | 2150 | 1.0343 | - | | 1.2514 | 2200 | 1.0193 | 1.1808 | | 1.2799 | 2250 | 1.0235 | - | | 1.3083 | 2300 | 0.9949 | - | | 1.3367 | 2350 | 1.0058 | - | | 1.3652 | 2400 | 1.0039 | 1.1428 | | 1.3936 | 2450 | 1.0164 | - | | 1.4221 | 2500 | 0.9934 | - | | 1.4505 | 2550 | 0.9777 | - | | 1.4790 | 2600 | 0.9753 | 1.1101 | | 1.5074 | 2650 | 0.9621 | - | | 1.5358 | 2700 | 0.9756 | - | | 1.5643 | 2750 | 0.9725 | - | | 1.5927 | 2800 | 0.9649 | 1.0813 | | 1.6212 | 2850 | 0.9652 | - | | 1.6496 | 2900 | 0.9861 | - | | 1.6780 | 2950 | 0.916 | - | | 1.7065 | 3000 | 0.9417 | 1.0523 | | 1.7349 | 3050 | 0.9599 | - | | 1.7634 | 3100 | 0.9275 | - | | 1.7918 | 3150 | 0.9247 | - | | 1.8203 | 3200 | 0.9417 | 1.0306 | | 1.8487 | 3250 | 0.9275 | - | | 1.8771 | 3300 | 0.9431 | - | | 1.9056 | 3350 | 0.9147 | - | | 1.9340 | 3400 | 0.8957 | 1.0051 | | 1.9625 | 3450 | 0.9169 | - | | 1.9909 | 3500 | 0.9079 | - | | 2.0193 | 3550 | 0.7057 | - | | 2.0478 | 3600 | 0.6037 | 0.9944 | | 2.0762 | 3650 | 0.5888 | - | | 2.1047 | 3700 | 0.6134 | - | | 2.1331 | 3750 | 0.6209 | - | | 2.1615 | 3800 | 0.6163 | 0.9836 | | 2.1900 | 3850 | 0.6271 | - | | 2.2184 | 3900 | 0.629 | - | | 2.2469 | 3950 | 0.6041 | - | | 2.2753 | 4000 | 0.622 | 0.9792 | | 2.3038 | 4050 | 0.6175 | - | | 2.3322 | 4100 | 0.627 | - | | 2.3606 | 4150 | 0.6339 | - | | 2.3891 | 4200 | 0.6325 | 0.9643 | | 2.4175 | 4250 | 0.6044 | - | | 2.4460 | 4300 | 0.6124 | - | | 2.4744 | 4350 | 0.6326 | - | | 2.5028 | 4400 | 0.6349 | 0.9462 | | 2.5313 | 4450 | 0.6286 | - | | 2.5597 | 4500 | 0.6325 | - | | 2.5882 | 4550 | 0.6399 | - | | 2.6166 | 4600 | 0.6184 | 0.9317 | | 2.6451 | 4650 | 0.6292 | - | | 2.6735 | 4700 | 0.6017 | - | | 2.7019 | 4750 | 0.6305 | - | | 2.7304 | 4800 | 0.6152 | 0.9213 | | 2.7588 | 4850 | 0.5972 | - | | 2.7873 | 4900 | 0.6048 | - | | 2.8157 | 4950 | 0.6096 | - | | 2.8441 | 5000 | 0.6156 | 0.9073 | | 2.8726 | 5050 | 0.5942 | - | | 2.9010 | 5100 | 0.592 | - | | 2.9295 | 5150 | 0.6088 | - | | 2.9579 | 5200 | 0.5941 | 0.8950 | | 2.9863 | 5250 | 0.6161 | - | | 3.0148 | 5300 | 0.5021 | - | | 3.0432 | 5350 | 0.4116 | - | | 3.0717 | 5400 | 0.3936 | 0.9009 | | 3.1001 | 5450 | 0.4193 | - | | 3.1286 | 5500 | 0.422 | - | | 3.1570 | 5550 | 0.432 | - | | 3.1854 | 5600 | 0.4281 | 0.8985 | | 3.2139 | 5650 | 0.4091 | - | | 3.2423 | 5700 | 0.4305 | - | | 3.2708 | 5750 | 0.4203 | - | | 3.2992 | 5800 | 0.4193 | 0.8869 | | 3.3276 | 5850 | 0.4238 | - | | 3.3561 | 5900 | 0.4274 | - | | 3.3845 | 5950 | 0.4124 | - | | 3.4130 | 6000 | 0.4241 | 0.8842 | | 3.4414 | 6050 | 0.427 | - | | 3.4699 | 6100 | 0.4275 | - | | 3.4983 | 6150 | 0.4152 | - | | 3.5267 | 6200 | 0.4247 | 0.8733 | | 3.5552 | 6250 | 0.4111 | - | | 3.5836 | 6300 | 0.4396 | - | | 3.6121 | 6350 | 0.4122 | - | | 3.6405 | 6400 | 0.4252 | 0.8657 | | 3.6689 | 6450 | 0.4167 | - | | 3.6974 | 6500 | 0.4282 | - | | 3.7258 | 6550 | 0.411 | - | | 3.7543 | 6600 | 0.4273 | 0.8540 | | 3.7827 | 6650 | 0.4327 | - | | 3.8111 | 6700 | 0.431 | - | | 3.8396 | 6750 | 0.4347 | - | | 3.8680 | 6800 | 0.4264 | 0.8523 | | 3.8965 | 6850 | 0.4213 | - | | 3.9249 | 6900 | 0.4285 | - | | 3.9534 | 6950 | 0.4138 | - | | 3.9818 | 7000 | 0.4051 | 0.8407 | | 4.0102 | 7050 | 0.3779 | - | | 4.0387 | 7100 | 0.2957 | - | | 4.0671 | 7150 | 0.2939 | - | | 4.0956 | 7200 | 0.3065 | 0.8590 | | 4.1240 | 7250 | 0.3081 | - | | 4.1524 | 7300 | 0.3043 | - | | 4.1809 | 7350 | 0.3176 | - | | 4.2093 | 7400 | 0.3067 | 0.8487 | | 4.2378 | 7450 | 0.299 | - | | 4.2662 | 7500 | 0.3106 | - | | 4.2947 | 7550 | 0.3062 | - | | 4.3231 | 7600 | 0.3153 | 0.8498 | | 4.3515 | 7650 | 0.3206 | - | | 4.3800 | 7700 | 0.3202 | - | | 4.4084 | 7750 | 0.3167 | - | | 4.4369 | 7800 | 0.3044 | 0.8426 | | 4.4653 | 7850 | 0.3015 | - | | 4.4937 | 7900 | 0.3157 | - | | 4.5222 | 7950 | 0.3109 | - | | 4.5506 | 8000 | 0.3164 | 0.8385 | | 4.5791 | 8050 | 0.2996 | - | | 4.6075 | 8100 | 0.3247 | - | | 4.6359 | 8150 | 0.3093 | - | | 4.6644 | 8200 | 0.3017 | 0.8294 | | 4.6928 | 8250 | 0.3075 | - | | 4.7213 | 8300 | 0.3006 | - | | 4.7497 | 8350 | 0.3134 | - | | 4.7782 | 8400 | 0.3111 | 0.8249 | | 4.8066 | 8450 | 0.3165 | - | | 4.8350 | 8500 | 0.3071 | - | | 4.8635 | 8550 | 0.3017 | - | | 4.8919 | 8600 | 0.3092 | 0.8225 | | 4.9204 | 8650 | 0.3 | - | | 4.9488 | 8700 | 0.2999 | - | | 4.9772 | 8750 | 0.3116 | - | | 5.0057 | 8800 | 0.3046 | 0.8173 | | 5.0341 | 8850 | 0.2501 | - | | 5.0626 | 8900 | 0.2443 | - | | 5.0910 | 8950 | 0.2338 | - | | 5.1195 | 9000 | 0.2382 | 0.8248 | | 5.1479 | 9050 | 0.2524 | - | | 5.1763 | 9100 | 0.2427 | - | | 5.2048 | 9150 | 0.2512 | - | | 5.2332 | 9200 | 0.2377 | 0.8218 | | 5.2617 | 9250 | 0.2458 | - | | 5.2901 | 9300 | 0.2515 | - | | 5.3185 | 9350 | 0.2453 | - | | 5.3470 | 9400 | 0.244 | 0.8226 | | 5.3754 | 9450 | 0.2389 | - | | 5.4039 | 9500 | 0.253 | - | | 5.4323 | 9550 | 0.2509 | - | | 5.4608 | 9600 | 0.2492 | 0.8198 | | 5.4892 | 9650 | 0.2379 | - | | 5.5176 | 9700 | 0.247 | - | | 5.5461 | 9750 | 0.2419 | - | | 5.5745 | 9800 | 0.244 | 0.8150 | | 5.6030 | 9850 | 0.2498 | - | | 5.6314 | 9900 | 0.2381 | - | | 5.6598 | 9950 | 0.2425 | - | | 5.6883 | 10000 | 0.2451 | 0.8148 | | 5.7167 | 10050 | 0.2468 | - | | 5.7452 | 10100 | 0.2404 | - | | 5.7736 | 10150 | 0.2397 | - | | 5.8020 | 10200 | 0.2417 | 0.8124 | | 5.8305 | 10250 | 0.2446 | - | | 5.8589 | 10300 | 0.2443 | - | | 5.8874 | 10350 | 0.2465 | - | | 5.9158 | 10400 | 0.2472 | 0.8121 |
### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.0+cu126 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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", } ``` #### 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} } ```