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whitemouse84
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ModernBERT-base-en-ru-v1

Sentence Similarity
sentence-transformers
Safetensors
English
Russian
modernbert
feature-extraction
Generated from Trainer
dataset_size:3072899
loss:MSELoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community
whitemouse84 commited on
Commit
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Add new SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +570 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +952 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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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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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,570 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:3072899
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+ - loss:MSELoss
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+ widget:
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+ - source_sentence: That means you can see that disc 80 feet down.
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+ sentences:
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+ - Он также сказал, что наводнение, идущее вниз по течению в приходе Ассеншен, является
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+ угрозой, так как эти вздувшиеся реки будут медленно стекать в озеро Морпа. «В
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+ киберфутбол играют десятки миллионов людей по всему континенту, и мы рады дать
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+ шанс участникам состязания из наших национальных ассоциаций представлять свою
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+ страну на самом высоком уровне», – заявил директор по маркетингу УЕФА Ги-Лоран
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+ Эпстейн.
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+ - Компания Нортэма также заменяет замки в домах и машинах на совместимые с чипом
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+ по цене в 300 фунтов за один замок.
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+ - Это значит, что диск можно увидеть на глубине 80 футов.
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+ - source_sentence: There, you can also take baths in wine, pearls, iodine-bromine,
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+ selenium, and sage-liquorice, depending on what the doctor prescribes for you.
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+ sentences:
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+ - Организация даже учредила первый и единственный заповедник летучих мышей в поместье
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+ Трив в Дамфрис-энд-Галловей, который является домом для восьми из десяти видов
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+ летучих мышей в Шотландии.
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+ - Вместе мы гораздо сильнее, чем по отдельности. Экспертный звуковой анализ всех
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+ записей установит частоту криков летучих мышей, а также какой вид что делает.
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+ - Там можно принимать также ванны винные, жемчужные, йодобромные, селеновые, шалфейно-лакричные,
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+ в зависимости от того, что вам назначит врача.
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+ - source_sentence: But on Pine Ridge, I will always be what is called "wasichu."
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+ sentences:
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+ - И я много думал о том, как это может быть применимо к разным уровням реальности,
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+ скажем, в плане экологии.
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+ - я всегда буду тем, кого называют ващичу,
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+ - Так что если мы можем сделать это, то мы можем высвободить ресурсы для закупки
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+ лекарств, которые действительно нужны для лечения СПИДа, и ВИЧ, и малярии, и для
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+ предотвращения птичьего гриппа. Спасибо.
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+ - source_sentence: And Bertie County is no exception to this.
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+ sentences:
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+ - И округ Берти - не исключение.
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+ - Кажется, в природе существует закон о том, что подходить слишком близко к месту,
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+ откуда ты произошел, опасно.
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+ - Они устали от договоренностей. Они устали от священных холмов.
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+ - source_sentence: Transparency is absolutely critical to this.
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+ sentences:
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+ - 'Первая: непреклонность местных лидеров к установлению чего-либо меньшего, чем
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+ их максимальные требования.'
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+ - Прозрачность - абсолютно критична в этом процессе.
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+ - Мы покупаем его нашим детям.
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - negative_mse
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+ - src2trg_accuracy
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+ - trg2src_accuracy
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+ - mean_accuracy
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+ model-index:
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+ - name: SentenceTransformer
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+ results:
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+ - task:
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+ type: knowledge-distillation
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+ name: Knowledge Distillation
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+ dataset:
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+ name: small content
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+ type: small_content
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+ metrics:
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+ - type: negative_mse
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+ value: -4.356895923614502
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+ name: Negative Mse
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+ - task:
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+ type: translation
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+ name: Translation
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+ dataset:
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+ name: small content
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+ type: small_content
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+ metrics:
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+ - type: src2trg_accuracy
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+ value: 0.7375
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+ name: Src2Trg Accuracy
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+ - type: trg2src_accuracy
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+ value: 0.665
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+ name: Trg2Src Accuracy
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+ - type: mean_accuracy
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+ value: 0.70125
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+ name: Mean Accuracy
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+ - task:
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+ type: knowledge-distillation
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+ name: Knowledge Distillation
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+ dataset:
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+ name: big content
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+ type: big_content
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+ metrics:
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+ - type: negative_mse
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+ value: -3.541424036026001
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+ name: Negative Mse
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+ - task:
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+ type: translation
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+ name: Translation
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+ dataset:
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+ name: big content
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+ type: big_content
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+ metrics:
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+ - type: src2trg_accuracy
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+ value: 0.8285
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+ name: Src2Trg Accuracy
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+ - type: trg2src_accuracy
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+ value: 0.668
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+ name: Trg2Src Accuracy
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+ - type: mean_accuracy
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+ value: 0.7482500000000001
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+ name: Mean Accuracy
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the corpus dataset. 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.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - corpus
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
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+ (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})
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("whitemouse84/ModernBERT-base-en-ru-v1")
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+ # Run inference
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+ sentences = [
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+ 'Transparency is absolutely critical to this.',
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+ 'Прозрачность - абсолютно критична в этом процессе.',
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+ 'Мы покупаем его нашим детям.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
184
+ </details>
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+ -->
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+
187
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
192
+ <details><summary>Click to expand</summary>
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+
194
+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Knowledge Distillation
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+
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+ * Datasets: `small_content` and `big_content`
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+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
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+
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+ | Metric | small_content | big_content |
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+ |:-----------------|:--------------|:------------|
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+ | **negative_mse** | **-4.3569** | **-3.5414** |
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+
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+ #### Translation
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+
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+ * Datasets: `small_content` and `big_content`
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+ * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
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+
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+ | Metric | small_content | big_content |
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+ |:------------------|:--------------|:------------|
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+ | src2trg_accuracy | 0.7375 | 0.8285 |
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+ | trg2src_accuracy | 0.665 | 0.668 |
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+ | **mean_accuracy** | **0.7013** | **0.7483** |
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+
227
+ <!--
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+ ## Bias, Risks and Limitations
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+
230
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
233
+ <!--
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+ ### Recommendations
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+
236
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
237
+ -->
238
+
239
+ ## Training Details
240
+
241
+ ### Training Dataset
242
+
243
+ #### corpus
244
+
245
+ * Dataset: corpus
246
+ * Size: 3,072,899 training samples
247
+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | english | non_english | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | string | list |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 29.26 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 71.46 tokens</li><li>max: 285 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
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+ * Samples:
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+ | english | non_english | label |
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+ |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Hence it can be said that Voit is a well-satisfied customer, and completely convinced of the potential offered by Voortman machines for his firm.</code> | <code>В конечном итоге можно утверждать, что компания Voit довольна своим выбором, ведь она имела возможность убедиться в качественных характеристиках оборудования Voortman.</code> | <code>[0.1702279895544052, -0.6711388826370239, -0.5062062740325928, 0.14078450202941895, 0.15188495814800262, ...]</code> |
257
+ | <code>We want to feel good, we want to be happy, in fact happiness is our birthright.</code> | <code>Мы хотим чувствовать себя хорошо, хотим быть счастливы.</code> | <code>[0.556108295917511, -0.42819586396217346, -0.25372204184532166, 0.099883534014225, 0.7299532294273376, ...]</code> |
258
+ | <code>In Germany, Arcandor - a major holding company in the mail order, retail and tourism industries that reported €21 billion in 2007 sales - threatens to become the first victim of tighter credit terms.</code> | <code>В Германии Arcandor - ключевая холдинговая компания в сфере посылочной и розничной торговли, а также индустрии туризма, в финансовых отчетах которой за 2007 год значился торговый оборот в размере €21 миллиардов - грозит стать первой жертвой ужесточения условий кредитования.</code> | <code>[-0.27140647172927856, -0.5173773169517517, -0.6571329236030579, 0.21765929460525513, -0.01978394016623497, ...]</code> |
259
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
260
+
261
+ ### Evaluation Datasets
262
+
263
+ #### small_content
264
+
265
+ * Dataset: small_content
266
+ * Size: 2,000 evaluation samples
267
+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
268
+ * Approximate statistics based on the first 1000 samples:
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+ | | english | non_english | label |
270
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | string | list |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 24.13 tokens</li><li>max: 252 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 53.83 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
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+ * Samples:
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+ | english | non_english | label |
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+ |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Thank you so much, Chris.</code> | <code>Спасибо, Крис.</code> | <code>[1.0408389568328857, 0.3253674805164337, -0.12651680409908295, 0.45153331756591797, 0.4052223563194275, ...]</code> |
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+ | <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>Это огромная честь, получить возможность выйти на эту сцену дважды. Я неимоверно благодарен.</code> | <code>[0.6990637183189392, -0.4462655782699585, -0.5292129516601562, 0.23709823191165924, 0.32307693362236023, ...]</code> |
278
+ | <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>Я в восторге от этой конференции, и я хочу поблагодарить вас всех за благожелательные отзывы о моем позавчерашнем выступлении.</code> | <code>[0.8470447063446045, -0.17461800575256348, -0.7178670167922974, 0.6488378047943115, 0.6101466417312622, ...]</code> |
279
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
280
+
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+ #### big_content
282
+
283
+ * Dataset: big_content
284
+ * Size: 2,000 evaluation samples
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+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
286
+ * Approximate statistics based on the first 1000 samples:
287
+ | | english | non_english | label |
288
+ |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
289
+ | type | string | string | list |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 43.84 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 107.9 tokens</li><li>max: 411 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
291
+ * Samples:
292
+ | english | non_english | label |
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+ |:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>India has recorded a surge in COVID-19 cases in the past weeks, with over 45,000 new cases detected every day since July 23.</code> | <code>Индия зафиксировала резкий всплеск случаев заражения COVID-19 за последние недели, с 23 июля каждый день выявляется более 45 000 новых случаев.</code> | <code>[-0.12528948485851288, -0.49428656697273254, -0.07556094229221344, 0.8069225549697876, 0.20946118235588074, ...]</code> |
295
+ | <code>A bloom the Red Tide extends approximately 130 miles of coastline from northern Pinellas to southern Lee counties.</code> | <code>Цветение Красного Прилива простирается примерно на 130 миль дволь береговой линии от Пинеллас на севере до округа Ли на юге.</code> | <code>[0.027262285351753235, -0.4401558041572571, -0.3353440463542938, 0.11166133731603622, -0.2294958084821701, ...]</code> |
296
+ | <code>Among those affected by the new rules is Transport Secretary Grant Shapps, who began his holiday in Spain on Saturday.</code> | <code>Среди тех, кого затронули новые правила, оказался министр транспорта Грант Шэппс, у которого в субботу начался отпуск в Испании.</code> | <code>[0.1868007630109787, -0.18781621754169464, -0.48890581727027893, 0.328614205121994, 0.36041054129600525, ...]</code> |
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+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
298
+
299
+ ### Training Hyperparameters
300
+ #### Non-Default Hyperparameters
301
+
302
+ - `eval_strategy`: steps
303
+ - `per_device_train_batch_size`: 4
304
+ - `per_device_eval_batch_size`: 4
305
+ - `gradient_accumulation_steps`: 16
306
+ - `learning_rate`: 2e-05
307
+ - `num_train_epochs`: 1
308
+ - `warmup_ratio`: 0.1
309
+ - `bf16`: True
310
+
311
+ #### All Hyperparameters
312
+ <details><summary>Click to expand</summary>
313
+
314
+ - `overwrite_output_dir`: False
315
+ - `do_predict`: False
316
+ - `eval_strategy`: steps
317
+ - `prediction_loss_only`: True
318
+ - `per_device_train_batch_size`: 4
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+ - `per_device_eval_batch_size`: 4
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 16
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 2e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
338
+ - `log_level_replica`: warning
339
+ - `log_on_each_node`: True
340
+ - `logging_nan_inf_filter`: True
341
+ - `save_safetensors`: True
342
+ - `save_on_each_node`: False
343
+ - `save_only_model`: False
344
+ - `restore_callback_states_from_checkpoint`: False
345
+ - `no_cuda`: False
346
+ - `use_cpu`: False
347
+ - `use_mps_device`: False
348
+ - `seed`: 42
349
+ - `data_seed`: None
350
+ - `jit_mode_eval`: False
351
+ - `use_ipex`: False
352
+ - `bf16`: True
353
+ - `fp16`: False
354
+ - `fp16_opt_level`: O1
355
+ - `half_precision_backend`: auto
356
+ - `bf16_full_eval`: False
357
+ - `fp16_full_eval`: False
358
+ - `tf32`: None
359
+ - `local_rank`: 0
360
+ - `ddp_backend`: None
361
+ - `tpu_num_cores`: None
362
+ - `tpu_metrics_debug`: False
363
+ - `debug`: []
364
+ - `dataloader_drop_last`: False
365
+ - `dataloader_num_workers`: 0
366
+ - `dataloader_prefetch_factor`: None
367
+ - `past_index`: -1
368
+ - `disable_tqdm`: False
369
+ - `remove_unused_columns`: True
370
+ - `label_names`: None
371
+ - `load_best_model_at_end`: False
372
+ - `ignore_data_skip`: False
373
+ - `fsdp`: []
374
+ - `fsdp_min_num_params`: 0
375
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
376
+ - `fsdp_transformer_layer_cls_to_wrap`: None
377
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
378
+ - `deepspeed`: None
379
+ - `label_smoothing_factor`: 0.0
380
+ - `optim`: adamw_torch
381
+ - `optim_args`: None
382
+ - `adafactor`: False
383
+ - `group_by_length`: False
384
+ - `length_column_name`: length
385
+ - `ddp_find_unused_parameters`: None
386
+ - `ddp_bucket_cap_mb`: None
387
+ - `ddp_broadcast_buffers`: False
388
+ - `dataloader_pin_memory`: True
389
+ - `dataloader_persistent_workers`: False
390
+ - `skip_memory_metrics`: True
391
+ - `use_legacy_prediction_loop`: False
392
+ - `push_to_hub`: False
393
+ - `resume_from_checkpoint`: None
394
+ - `hub_model_id`: None
395
+ - `hub_strategy`: every_save
396
+ - `hub_private_repo`: None
397
+ - `hub_always_push`: False
398
+ - `gradient_checkpointing`: False
399
+ - `gradient_checkpointing_kwargs`: None
400
+ - `include_inputs_for_metrics`: False
401
+ - `include_for_metrics`: []
402
+ - `eval_do_concat_batches`: True
403
+ - `fp16_backend`: auto
404
+ - `push_to_hub_model_id`: None
405
+ - `push_to_hub_organization`: None
406
+ - `mp_parameters`:
407
+ - `auto_find_batch_size`: False
408
+ - `full_determinism`: False
409
+ - `torchdynamo`: None
410
+ - `ray_scope`: last
411
+ - `ddp_timeout`: 1800
412
+ - `torch_compile`: False
413
+ - `torch_compile_backend`: None
414
+ - `torch_compile_mode`: None
415
+ - `dispatch_batches`: None
416
+ - `split_batches`: None
417
+ - `include_tokens_per_second`: False
418
+ - `include_num_input_tokens_seen`: False
419
+ - `neftune_noise_alpha`: None
420
+ - `optim_target_modules`: None
421
+ - `batch_eval_metrics`: False
422
+ - `eval_on_start`: False
423
+ - `use_liger_kernel`: False
424
+ - `eval_use_gather_object`: False
425
+ - `average_tokens_across_devices`: False
426
+ - `prompts`: None
427
+ - `batch_sampler`: batch_sampler
428
+ - `multi_dataset_batch_sampler`: proportional
429
+
430
+ </details>
431
+
432
+ ### Training Logs
433
+ | Epoch | Step | Training Loss | small content loss | big content loss | small_content_negative_mse | small_content_mean_accuracy | big_content_negative_mse | big_content_mean_accuracy |
434
+ |:------:|:----:|:-------------:|:------------------:|:----------------:|:--------------------------:|:---------------------------:|:------------------------:|:-------------------------:|
435
+ | 0.0128 | 100 | 0.4387 | - | - | - | - | - | - |
436
+ | 0.0256 | 200 | 0.4445 | - | - | - | - | - | - |
437
+ | 0.0384 | 300 | 0.4366 | - | - | - | - | - | - |
438
+ | 0.0512 | 400 | 0.444 | - | - | - | - | - | - |
439
+ | 0.064 | 500 | 0.4539 | - | - | - | - | - | - |
440
+ | 0.0768 | 600 | 0.4586 | - | - | - | - | - | - |
441
+ | 0.0896 | 700 | 0.452 | - | - | - | - | - | - |
442
+ | 0.1024 | 800 | 0.455 | - | - | - | - | - | - |
443
+ | 0.1152 | 900 | 0.4566 | - | - | - | - | - | - |
444
+ | 0.128 | 1000 | 0.464 | - | - | - | - | - | - |
445
+ | 0.1408 | 1100 | 0.4606 | - | - | - | - | - | - |
446
+ | 0.1536 | 1200 | 0.4545 | - | - | - | - | - | - |
447
+ | 0.1664 | 1300 | 0.452 | - | - | - | - | - | - |
448
+ | 0.1792 | 1400 | 0.4572 | - | - | - | - | - | - |
449
+ | 0.192 | 1500 | 0.449 | - | - | - | - | - | - |
450
+ | 0.2048 | 1600 | 0.441 | - | - | - | - | - | - |
451
+ | 0.2176 | 1700 | 0.4409 | - | - | - | - | - | - |
452
+ | 0.2304 | 1800 | 0.4518 | - | - | - | - | - | - |
453
+ | 0.2432 | 1900 | 0.4522 | - | - | - | - | - | - |
454
+ | 0.256 | 2000 | 0.4551 | 0.0242 | 0.0196 | -4.4742 | 0.6918 | -3.6582 | 0.6813 |
455
+ | 0.2688 | 2100 | 0.4517 | - | - | - | - | - | - |
456
+ | 0.2816 | 2200 | 0.4504 | - | - | - | - | - | - |
457
+ | 0.2944 | 2300 | 0.4435 | - | - | - | - | - | - |
458
+ | 0.3072 | 2400 | 0.4445 | - | - | - | - | - | - |
459
+ | 0.32 | 2500 | 0.4356 | - | - | - | - | - | - |
460
+ | 0.3328 | 2600 | 0.4358 | - | - | - | - | - | - |
461
+ | 0.3456 | 2700 | 0.4442 | - | - | - | - | - | - |
462
+ | 0.3584 | 2800 | 0.4453 | - | - | - | - | - | - |
463
+ | 0.3712 | 2900 | 0.4519 | - | - | - | - | - | - |
464
+ | 0.384 | 3000 | 0.4328 | - | - | - | - | - | - |
465
+ | 0.3968 | 3100 | 0.4449 | - | - | - | - | - | - |
466
+ | 0.4096 | 3200 | 0.4463 | - | - | - | - | - | - |
467
+ | 0.4224 | 3300 | 0.4374 | - | - | - | - | - | - |
468
+ | 0.4352 | 3400 | 0.4377 | - | - | - | - | - | - |
469
+ | 0.448 | 3500 | 0.4461 | - | - | - | - | - | - |
470
+ | 0.4608 | 3600 | 0.4506 | - | - | - | - | - | - |
471
+ | 0.4736 | 3700 | 0.4382 | - | - | - | - | - | - |
472
+ | 0.4864 | 3800 | 0.4407 | - | - | - | - | - | - |
473
+ | 0.4992 | 3900 | 0.4481 | - | - | - | - | - | - |
474
+ | 0.512 | 4000 | 0.4332 | 0.0239 | 0.0196 | -4.4136 | 0.7030 | -3.6931 | 0.6658 |
475
+ | 0.5248 | 4100 | 0.4444 | - | - | - | - | - | - |
476
+ | 0.5376 | 4200 | 0.4444 | - | - | - | - | - | - |
477
+ | 0.5504 | 4300 | 0.4389 | - | - | - | - | - | - |
478
+ | 0.5632 | 4400 | 0.4385 | - | - | - | - | - | - |
479
+ | 0.576 | 4500 | 0.4399 | - | - | - | - | - | - |
480
+ | 0.5888 | 4600 | 0.4306 | - | - | - | - | - | - |
481
+ | 0.6016 | 4700 | 0.435 | - | - | - | - | - | - |
482
+ | 0.6144 | 4800 | 0.4307 | - | - | - | - | - | - |
483
+ | 0.6272 | 4900 | 0.4399 | - | - | - | - | - | - |
484
+ | 0.64 | 5000 | 0.4401 | - | - | - | - | - | - |
485
+ | 0.6528 | 5100 | 0.4337 | - | - | - | - | - | - |
486
+ | 0.6656 | 5200 | 0.4369 | - | - | - | - | - | - |
487
+ | 0.6784 | 5300 | 0.4392 | - | - | - | - | - | - |
488
+ | 0.6912 | 5400 | 0.4356 | - | - | - | - | - | - |
489
+ | 0.704 | 5500 | 0.4266 | - | - | - | - | - | - |
490
+ | 0.7168 | 5600 | 0.4303 | - | - | - | - | - | - |
491
+ | 0.7296 | 5700 | 0.4241 | - | - | - | - | - | - |
492
+ | 0.7424 | 5800 | 0.4361 | - | - | - | - | - | - |
493
+ | 0.7552 | 5900 | 0.4256 | - | - | - | - | - | - |
494
+ | 0.768 | 6000 | 0.4303 | 0.0234 | 0.0188 | -4.3569 | 0.7013 | -3.5414 | 0.7483 |
495
+ | 0.7808 | 6100 | 0.4221 | - | - | - | - | - | - |
496
+ | 0.7936 | 6200 | 0.421 | - | - | - | - | - | - |
497
+ | 0.8064 | 6300 | 0.4418 | - | - | - | - | - | - |
498
+ | 0.8192 | 6400 | 0.4225 | - | - | - | - | - | - |
499
+ | 0.832 | 6500 | 0.4271 | - | - | - | - | - | - |
500
+ | 0.8448 | 6600 | 0.4291 | - | - | - | - | - | - |
501
+ | 0.8576 | 6700 | 0.4192 | - | - | - | - | - | - |
502
+ | 0.8704 | 6800 | 0.4335 | - | - | - | - | - | - |
503
+ | 0.8832 | 6900 | 0.4291 | - | - | - | - | - | - |
504
+ | 0.896 | 7000 | 0.432 | - | - | - | - | - | - |
505
+ | 0.9088 | 7100 | 0.432 | - | - | - | - | - | - |
506
+ | 0.9216 | 7200 | 0.4247 | - | - | - | - | - | - |
507
+ | 0.9344 | 7300 | 0.4259 | - | - | - | - | - | - |
508
+ | 0.9472 | 7400 | 0.4235 | - | - | - | - | - | - |
509
+ | 0.96 | 7500 | 0.4181 | - | - | - | - | - | - |
510
+ | 0.9728 | 7600 | 0.43 | - | - | - | - | - | - |
511
+ | 0.9856 | 7700 | 0.4189 | - | - | - | - | - | - |
512
+ | 0.9984 | 7800 | 0.4188 | - | - | - | - | - | - |
513
+
514
+
515
+ ### Framework Versions
516
+ - Python: 3.13.2
517
+ - Sentence Transformers: 3.4.1
518
+ - Transformers: 4.49.0
519
+ - PyTorch: 2.6.0+cu126
520
+ - Accelerate: 1.4.0
521
+ - Datasets: 3.3.2
522
+ - Tokenizers: 0.21.0
523
+
524
+ ## Citation
525
+
526
+ ### BibTeX
527
+
528
+ #### Sentence Transformers
529
+ ```bibtex
530
+ @inproceedings{reimers-2019-sentence-bert,
531
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
532
+ author = "Reimers, Nils and Gurevych, Iryna",
533
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
534
+ month = "11",
535
+ year = "2019",
536
+ publisher = "Association for Computational Linguistics",
537
+ url = "https://arxiv.org/abs/1908.10084",
538
+ }
539
+ ```
540
+
541
+ #### MSELoss
542
+ ```bibtex
543
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
544
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
545
+ author = "Reimers, Nils and Gurevych, Iryna",
546
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
547
+ month = "11",
548
+ year = "2020",
549
+ publisher = "Association for Computational Linguistics",
550
+ url = "https://arxiv.org/abs/2004.09813",
551
+ }
552
+ ```
553
+
554
+ <!--
555
+ ## Glossary
556
+
557
+ *Clearly define terms in order to be accessible across audiences.*
558
+ -->
559
+
560
+ <!--
561
+ ## Model Card Authors
562
+
563
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
564
+ -->
565
+
566
+ <!--
567
+ ## Model Card Contact
568
+
569
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
570
+ -->
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+ "gradient_checkpointing": false,
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+ "hidden_activation": "gelu",
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+ "hidden_size": 768,
23
+ "initializer_cutoff_factor": 2.0,
24
+ "initializer_range": 0.02,
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+ "intermediate_size": 1152,
26
+ "layer_norm_eps": 1e-05,
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+ "local_attention": 128,
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+ "local_rope_theta": 10000.0,
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+ "max_position_embeddings": 8192,
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+ "model_type": "modernbert",
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+ "num_hidden_layers": 22,
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42
+ "sparse_pred_ignore_index": -100,
43
+ "sparse_prediction": false,
44
+ "torch_dtype": "float32",
45
+ "transformers_version": "4.49.0",
46
+ "vocab_size": 50368
47
+ }
config_sentence_transformers.json ADDED
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1
+ {
2
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3
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4
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+ "pytorch": "2.6.0+cu126"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
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+ oid sha256:7584a5f14b2e4f7a1bfad3b934cd805b6061cf6596f458b61beb0233ec13471d
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+ size 596070136
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+ "type": "sentence_transformers.models.Pooling"
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+ }
14
+ ]
sentence_bert_config.json ADDED
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1
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+ "do_lower_case": false
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+ }
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The diff for this file is too large to render. See raw diff
 
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