Sts Distilcamembert Base

🚀 句子轉換器（sentence-transformers）模型

本項目基於 sentence-transformers 庫微調得到了一個可用於特徵提取的模型，能夠將句子或段落編碼為向量，在句子相似度任務上有良好表現。

🚀 快速開始

本模型可通過 sentence-transformers 或 transformers 庫使用，以下是使用步驟。

✨ 主要特性

• 基於 DistilCamemBERT 模型微調，參數數量減半，推理速度提升。
• 能夠將句子或段落（最多 514 個標記）編碼為 768 維的向量。

📦 安裝指南

使用 sentence-transformers 庫

pip install -U sentence-transformers

使用 transformers 庫

pip install -U transformers

💻 使用示例

基礎用法

使用 sentence-transformers 庫

from sentence_transformers import SentenceTransformer
sentences = ["Ceci est un exemple", "deuxième exemple"]

model = SentenceTransformer('h4c5/sts-distilcamembert-base')
embeddings = model.encode(sentences)
print(embeddings)

使用 transformers 庫

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("h4c5/sts-distilcamembert-base")
model = AutoModel.from_pretrained("h4c5/sts-distilcamembert-base")
model.eval()


# Mean Pooling
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[
        0
    ]  # First element of model_output contains all token embeddings
    input_mask_expanded = (
        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    )
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
        input_mask_expanded.sum(1), min=1e-9
    )

# Tokenization et calcul des embeddings des tokens
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
model_output = model(**encoded_input)

# Mean pooling
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])

print(sentence_embeddings)

📚 詳細文檔

評估

本模型在 STSb fr 數據集上進行了評估，以下是評估代碼：

from datasets import load_dataset
from sentence_transformers import InputExample, evaluation


def dataset_to_input_examples(dataset):
    return [
        InputExample(
            texts=[example["sentence1"], example["sentence2"]],
            label=example["similarity_score"] / 5.0,
        )
        for example in dataset
    ]


sts_test_dataset = load_dataset("stsb_multi_mt", name="fr", split="test")
sts_test_examples = dataset_to_input_examples(sts_test_dataset)

sts_test_evaluator = evaluation.EmbeddingSimilarityEvaluator.from_input_examples(
    sts_test_examples, name="sts-test"
)

sts_test_evaluator(model, ".")

評估結果

以下是模型在 stsb_multi_mt 數據集（法語數據，測試集）上的評估結果：

訓練

模型使用以下參數進行訓練：

數據加載器

torch.utils.data.dataloader.DataLoader，長度為 180，參數如下：

{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

損失函數

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

fit() 方法的參數：

{
    "epochs": 10,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 500,
    "weight_decay": 0.01
}

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel 
  (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})
)

引用

@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",
    journal={"https://arxiv.org/abs/1908.10084"},
}

@inproceedings{sanh2019distilbert,
    title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
    author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
    booktitle={NeurIPS EMC^2 Workshop},
    journal={https://arxiv.org/abs/1910.01108},
    year={2019}
}

@inproceedings{martin2020camembert,
    title={CamemBERT: a Tasty French Language Model},
    author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
    booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
    journal={https://arxiv.org/abs/1911.03894},
    year={2020}
}

@inproceedings{delestre:hal-03674695,
    TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
    AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
    URL = {https://hal.archives-ouvertes.fr/hal-03674695},
    BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
    ADDRESS = {Vannes, France},
    YEAR = {2022},
    MONTH = Jul,
    KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
    PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
    HAL_ID = {hal-03674695},
    HAL_VERSION = {v1},
    journal={https://arxiv.org/abs/2205.11111},
}

📄 許可證

本項目採用 MIT 許可證。