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HoshinoSSR/m3e_doctor

sourceHugging Faceupdated 2y agoView on Hugging Face
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Model Card

SentenceTransformer

This is a sentence-transformers model trained. 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: Unknown -->
  • —Maximum Sequence Length: 512 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    '最近蹲下一会在起身时，会感觉头很晕，眼前还发黑，过一段时间就好了。这是怎么回事？以前我也经常起蹲，都没这种反应~~・',
    '根据你的描述你应该是有点直立性低血压，最根本的原因还是血虚导致的建议你用补血的中药治疗，可以用四物汤治疗，另外你平时注意加强营养，多吃大枣，枸杞',
    '你做唐筛有一项高风险只能说明宝宝患先天性愚型的可能性大一点，但不知确诊就一定会有先天性愚型的。你若想确诊需要做羊水穿刺检查或无创DNA检查来确诊，定期做产前检查，保持心情愉悦，多喝水，多吃蔬菜水果，尽量不要吃辛辣刺激性食品及生冷食品。',
]
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)

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 84,000 training samples
  • —Columns: <code>sentence0</code> and <code>sentence1</code>
  • —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 13 tokens</li><li>mean: 50.14 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 99.34 tokens</li><li>max: 251 tokens</li></ul> |
  • —Samples: | sentence0 | sentence1 | |:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | <code>怎么计算怀孕天数11月8号身上来的12月19号检查经医生说怀孕42天42天是正确的吗</code> | <code>一般计算怀孕时间都是从末次月经的第一天算起。所以，考虑你应该是怀孕在42天左右。</code> | | <code>头皮红红很痒头皮红红的一片很痒刚洗完头头皮是红的可过了一会儿红的那一片慢慢的被成头皮屑然后很痒经常掉头发什么原因要用什么药才可以治好</code> | <code>脂溢性皮炎是一种好发于皮脂溢出部位的慢性皮炎。口服B族维生素，抗组胺类药物，外用硫磺、抗真菌及皮质类固醇激素制剂。</code> | | <code>用溴隐亭半年泌乳素正常但是月经还是很少（3年了吃药后也毫无改观）服用溴隐亭半年泌乳素正常但是月经还是很少（3年了吃药后也毫无改观）无排卵期请问伽马刀治疗能否根除肿瘤？（</code> | <code>将直径≤10mm的垂体瘤称为垂体微腺瘤一般可口服多巴胺激动剂－嗅隐亭治疗青年女性在在服用多巴胺激动剂治疗后妊娠怀孕期间可能会出现垂体腺瘤卒中或明显增大必要时需紧急手术</code> |
  • —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —per_device_train_batch_size: 16
  • —per_device_eval_batch_size: 16
  • —num_train_epochs: 1
  • —multi_dataset_batch_sampler: round_robin
All Hyperparameters

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

  • —overwrite_output_dir: False
  • —do_predict: False
  • —eval_strategy: no
  • —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
  • —num_train_epochs: 1
  • —max_steps: -1
  • —lr_scheduler_type: linear
  • —lr_scheduler_kwargs: {}
  • —warmup_ratio: 0.0
  • —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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —fsdp_transformer_layer_cls_to_wrap: None
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: round_robin

</details>

Training Logs

EpochStepTraining Loss
0.09525000.4876
0.190510000.3743
0.285715000.3377
0.381020000.3399
0.476225000.3299
0.571430000.3067
0.666735000.3032
0.761940000.2935
0.857145000.2949
0.952450000.2825

Framework Versions

  • —Python: 3.12.3
  • —Sentence Transformers: 3.4.1
  • —Transformers: 4.49.0
  • —PyTorch: 2.5.1+cu124
  • —Accelerate: 1.4.0
  • —Datasets: 3.3.2
  • —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",
}
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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