Модель для расчетов эмбеддингов предложений на русском и английском языках получена методом дистилляции эмбеддингов ai-forever/FRIDA (размер эмбеддингов - 1536, слоёв - 24) в sergeyzh/rubert-mini-sts (размер эмбеддингов - 312, слоёв - 7). Основной режим использования FRIDA - CLS pooling заменен на mean pooling. Каких-либо других изменений поведения модели (модификации или фильтрации эмбеддингов, использования дополнительной модели) не производилось. Дистиляция выполнена в максимально возможном объеме - эмбеддинги русских и английских предложений, работа префиксов. Рекомендуемый размер контекста модели соответствует FRIDA и не превышает 512 токенов (фактический унаследованный от исходной модели - 2048). Все префиксы унаследованы от FRIDA. Оптимальный (обеспечивающий средние результаты) для большинства задач - "categorize: " прописан по умолчанию в config_sentence_transformers.json
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View details ↗Базовая модель BERT для расчетов компактных эмбеддингов предложений на русском языке. Модель основана на cointegrated/rubert-tiny2 - имеет аналогичные размеры контекста (2048) и ембеддинга (312), количество слоев увеличено с 3 до 7.
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View details ↗Быстрая модель BERT для русского языка с размером ембеддинга 256 и длиной контекста 512. Модель получена методом последовательной дистилляции моделей sergeyzh/rubert-tiny-turbo и BAAI/bge-m3. Выигрывает по скорости у rubert-tiny-turbo при аналогичном качестве на CPU в ~x1.4, на GPU в ~x1.2 раза. Оценки модели на бенчмарке ruMTEB:
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View details ↗Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval. This is migrated from the v2 model on the TF Hub, which uses dict-based input. The embeddings produced by both the versions of the model are equivalent. Details about data, training, evaluation and performance metrics are available in the original paper.
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View details ↗⚠️ This is just the PyTorch version of `hyperonym/xlm-roberta-longformer-base-16384` without any modifications. **xlm-roberta-longformer** is a multilingual Longformer initialized with XLM-RoBERTa's weights without further pretraining. It is intended to be fine-tuned on a downstream task. The notebook for replicating the model is available on GitHub: https://github.com/hyperonym/dirge/blob/master/models/xlm-roberta-longformer/convert.ipynb
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View details ↗⚠️ This is just the PyTorch version of `hyperonym/xlm-roberta-longformer-large-16384` without any modifications. **xlm-roberta-longformer** is a multilingual Longformer initialized with XLM-RoBERTa's weights without further pretraining. It is intended to be fine-tuned on a downstream task.
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View details ↗It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗It maps sentences to a 384 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. For an automated evaluation of this model, see the *Evaluation Benchmark*: text2vec Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集shibing624/nli-zh-all/text2vec-base-multilingual-dataset训练得到，并在中英文测试集评估相对于原模型效果有提升，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 - QPS的GPU测试环境是Tesla V100，显存32GB
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