Upload 11 files
Browse files- 1_Pooling/config.json +7 -0
- README.md +167 -0
- config.json +32 -0
- config_sentence_transformers.json +36 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +65 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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README.md
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---
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license: mit
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---
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---
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language:
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- ru
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- en
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pipeline_tag: sentence-similarity
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tags:
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- russian
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- pretraining
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- embeddings
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- feature-extraction
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- sentence-similarity
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- sentence-transformers
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- transformers
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datasets:
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- IlyaGusev/gazeta
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- zloelias/lenta-ru
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- HuggingFaceFW/fineweb-2
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- HuggingFaceFW/fineweb
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license: mit
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base_model: sergeyzh/LaBSE-ru-turbo
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---
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## BERTA
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Модель для расчетов эмбеддингов предложений на русском и английском языках получена методом дистилляции эмбеддингов [ai-forever/FRIDA](https://huggingface.co/ai-forever/FRIDA) (размер эмбеддингов - 1536, слоёв - 24) в [sergeyzh/LaBSE-ru-turbo](https://huggingface.co/sergeyzh/LaBSE-ru-turbo) (размер эмбеддингов - 768, слоёв - 12). Основной режим использования FRIDA - CLS pooling заменен на mean pooling. Каких-либо других изменений поведения модели не производилось. Дистиляция выполнена в максимально возможном объеме - эмбеддинги русских и английских предложений, работа префиксов.
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Размер контекста модели соответствует FRIDA - 512 токенов.
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## Префиксы
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Все префиксы унаследованы от FRIDA.
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Оптимальный (обеспечивающий средние результаты) префикс для большинства задач - "categorize_entailment: " прописан по умолчанию в [config_sentence_transformers.json](https://huggingface.co/sergeyzh/BERTA/blob/main/config_sentence_transformers.json)
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Перечень используемых префиксов и их влияние на оценки модели в [encodechka](https://github.com/avidale/encodechka):
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| Префикс | STS | PI | NLI | SA | TI |
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|:-----------------------|:---------:|:---------:|:---------:|:---------:|:---------:|
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| - | 0,842 | 0,757 | 0,463 | **0,830** | 0,985 |
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| search_query: | 0,853 | 0,767 | 0,479 | 0,825 | 0,987 |
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| search_document: | 0,831 | 0,749 | 0,463 | 0,817 | 0,986 |
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| paraphrase: | 0,847 | **0,778** | 0,446 | 0,825 | 0,986 |
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| categorize: | **0,857** | 0,765 | 0,501 | 0,829 | **0,988** |
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| categorize_sentiment: | 0,589 | 0,535 | 0,417 | 0,805 | 0,982 |
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| categorize_topic: | 0,740 | 0,521 | 0,396 | 0,770 | 0,982 |
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| categorize_entailment: | 0,841 | 0,762 | **0,571** | 0,827 | 0,986 |
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**Задачи:**
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- Semantic text similarity (**STS**);
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- Paraphrase identification (**PI**);
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- Natural language inference (**NLI**);
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- Sentiment analysis (**SA**);
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- Toxicity identification (**TI**).
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# Метрики
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Оценки модели на бенчмарке [ruMTEB](https://habr.com/ru/companies/sberdevices/articles/831150/):
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|Model Name | Metric | FRIDA | BERTA | [rubert-mini-frida](https://huggingface.co/sergeyzh/rubert-mini-frida) | multilingual-e5-large-instruct | multilingual-e5-large |
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|:-------------------------------|:--------------------|----------:|----------:|--------------------:|---------------------:|----------------------:|
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|CEDRClassification | Accuracy | **0.646** | 0.622 | 0.552 | 0.500 | 0.448 |
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|GeoreviewClassification | Accuracy | **0.577** | 0.548 | 0.464 | 0.559 | 0.497 |
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|GeoreviewClusteringP2P | V-measure | **0.783** | 0.738 | 0.698 | 0.743 | 0.605 |
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|HeadlineClassification | Accuracy | 0.890 | **0.891** | 0.880 | 0.862 | 0.758 |
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|InappropriatenessClassification | Accuracy | **0.783** | 0.748 | 0.698 | 0.655 | 0.616 |
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|KinopoiskClassification | Accuracy | **0.705** | 0.678 | 0.595 | 0.661 | 0.566 |
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|RiaNewsRetrieval | NDCG@10 | **0.868** | 0.816 | 0.721 | 0.824 | 0.807 |
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|RuBQReranking | MAP@10 | **0.771** | 0.752 | 0.711 | 0.717 | 0.756 |
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|RuBQRetrieval | NDCG@10 | 0.724 | 0.710 | 0.654 | 0.692 | **0.741** |
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|RuReviewsClassification | Accuracy | **0.751** | 0.723 | 0.658 | 0.686 | 0.653 |
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|RuSTSBenchmarkSTS | Pearson correlation | 0.814 | 0.822 | 0.803 | **0.840** | 0.831 |
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|RuSciBenchGRNTIClassification | Accuracy | **0.699** | 0.690 | 0.625 | 0.651 | 0.582 |
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|RuSciBenchGRNTIClusteringP2P | V-measure | **0.670** | 0.650 | 0.586 | 0.622 | 0.520 |
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|RuSciBenchOECDClassification | Accuracy | 0.546 | **0.555** | 0.493 | 0.502 | 0.445 |
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|RuSciBenchOECDClusteringP2P | V-measure | **0.566** | 0.556 | 0.507 | 0.528 | 0.450 |
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|SensitiveTopicsClassification | Accuracy | 0.398 | **0.399** | 0.373 | 0.323 | 0.257 |
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|TERRaClassification | Average Precision | **0.665** | 0.657 | 0.606 | 0.639 | 0.584 |
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|Model Name | Metric | FRIDA | BERTA | rubert-mini-frida | multilingual-e5-large-instruct | multilingual-e5-large |
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|:-------------------------------|:--------------------|----------:|----------:|--------------------:|----------------------:|---------------------:|
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|Classification | Accuracy | **0.707** | 0.698 | 0.631 | 0.654 | 0.588 |
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|Clustering | V-measure | **0.673** | 0.648 | 0.597 | 0.631 | 0.525 |
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|MultiLabelClassification | Accuracy | **0.522** | 0.510 | 0.463 | 0.412 | 0.353 |
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|PairClassification | Average Precision | **0.665** | 0.657 | 0.606 | 0.639 | 0.584 |
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|Reranking | MAP@10 | **0.771** | 0.752 | 0.711 | 0.717 | 0.756 |
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|Retrieval | NDCG@10 | **0.796** | 0.763 | 0.687 | 0.758 | 0.774 |
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|STS | Pearson correlation | 0.814 | 0.822 | 0.803 | **0.840** | 0.831 |
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|Average | Average | **0.707** | 0.693 | 0.643 | 0.664 | 0.630 |
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## Использование модели с библиотекой `transformers`:
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```python
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import torch
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import torch.nn.functional as F
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from transformers import AutoTokenizer, AutoModel
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def pool(hidden_state, mask, pooling_method="mean"):
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if pooling_method == "mean":
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s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
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d = mask.sum(axis=1, keepdim=True).float()
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return s / d
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elif pooling_method == "cls":
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return hidden_state[:, 0]
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inputs = [
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#
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"paraphrase: В Ярославской области разрешили работу бань, но без посетителей",
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"categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.",
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"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
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#
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"paraphrase: Ярославским баням разрешили работать без посетителей",
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"categorize_entailment: Женщину спасают врачи.",
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"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование."
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]
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tokenizer = AutoTokenizer.from_pretrained("sergeyzh/BERTA")
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model = AutoModel.from_pretrained("sergeyzh/BERTA")
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tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**tokenized_inputs)
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embeddings = pool(
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outputs.last_hidden_state,
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tokenized_inputs["attention_mask"],
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pooling_method="mean"
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)
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embeddings = F.normalize(embeddings, p=2, dim=1)
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sim_scores = embeddings[:3] @ embeddings[3:].T
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print(sim_scores.diag().tolist())
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# [0.9530372023582458, 0.866746723651886, 0.7839133143424988]
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# [0.9360030293464661, 0.8591322302818298, 0.728583037853241] - FRIDA
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```
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## Использование с `sentence_transformers` (sentence-transformers>=2.4.0):
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```python
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from sentence_transformers import SentenceTransformer
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# loads model with mean pooling
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model = SentenceTransformer("sergeyzh/BERTA")
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paraphrase = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt="paraphrase: ")
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print(paraphrase[0] @ paraphrase[1].T)
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# 0.9530372
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# 0.9360032 - FRIDA
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categorize_entailment = model.encode(["Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "Женщину спасают врачи."], prompt="categorize_entailment: ")
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print(categorize_entailment[0] @ categorize_entailment[1].T)
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# 0.8667469
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# 0.8591322 - FRIDA
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query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt="search_query: ")
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document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt="search_document: ")
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print(query_embedding @ document_embedding.T)
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# 0.7839136
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# 0.7285831 - FRIDA
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```
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config.json
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{
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"_name_or_path": "sergeyzh/BERTA",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.48.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 55083
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.7.0",
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"transformers": "4.40.1",
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"pytorch": "2.2.1+cu118"
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},
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"prompts": {
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"query": "search_query: ",
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"passage": "search_document: ",
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"CEDRClassification": "categorize_sentiment: ",
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"GeoreviewClassification": "categorize_sentiment: ",
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"GeoreviewClusteringP2P": "categorize_topic: ",
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"HeadlineClassification": "categorize_topic: ",
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"InappropriatenessClassification": "categorize_topic: ",
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"KinopoiskClassification": "categorize_sentiment: ",
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"MassiveIntentClassification": "categorize: ",
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"MassiveScenarioClassification": "paraphrase: ",
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"RuReviewsClassification": "categorize_sentiment: ",
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"RUParaPhraserSTS": "paraphrase: ",
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"RuSTSBenchmarkSTS": "categorize: ",
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"STS22": "paraphrase: ",
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"RuSciBenchGRNTIClassification": "categorize_topic: ",
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"RuSciBenchGRNTIClusteringP2P": "categorize_topic: ",
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+
"RuSciBenchOECDClassification": "categorize_topic: ",
|
25 |
+
"RuSciBenchOECDClusteringP2P": "categorize_topic: ",
|
26 |
+
"SensitiveTopicsClassification": "categorize_topic: ",
|
27 |
+
"TERRa": "categorize_entailment: ",
|
28 |
+
"Classification": "categorize_entailment: ",
|
29 |
+
"MultilabelClassification": "categorize: ",
|
30 |
+
"Clustering": "categorize_topic: ",
|
31 |
+
"PairClassification": "categorize_entailment: ",
|
32 |
+
"STS": "paraphrase: "
|
33 |
+
},
|
34 |
+
"default_prompt_name": "Classification",
|
35 |
+
"similarity_fn_name": null
|
36 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:96bfcb9b24d99f7501528b8d122610ad25f0f87f212e5debb3fb5afe87aa8888
|
3 |
+
size 513402728
|
modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
+
"content": "[MASK]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"3": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"4": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_basic_tokenize": true,
|
47 |
+
"do_lower_case": false,
|
48 |
+
"extra_special_tokens": {},
|
49 |
+
"mask_token": "[MASK]",
|
50 |
+
"max_length": 512,
|
51 |
+
"model_max_length": 512,
|
52 |
+
"never_split": null,
|
53 |
+
"pad_to_multiple_of": null,
|
54 |
+
"pad_token": "[PAD]",
|
55 |
+
"pad_token_type_id": 0,
|
56 |
+
"padding_side": "right",
|
57 |
+
"sep_token": "[SEP]",
|
58 |
+
"stride": 0,
|
59 |
+
"strip_accents": null,
|
60 |
+
"tokenize_chinese_chars": true,
|
61 |
+
"tokenizer_class": "BertTokenizer",
|
62 |
+
"truncation_side": "right",
|
63 |
+
"truncation_strategy": "longest_first",
|
64 |
+
"unk_token": "[UNK]"
|
65 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|