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ModernBERT-SimCSE-multitask_v03

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results
Inference Endpoints
Model card Files Community
CocoRoF commited on
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Training in progress, step 300

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. 2_Dense/config.json +1 -0
  3. 2_Dense/model.safetensors +3 -0
  4. README.md +494 -0
  5. config.json +47 -0
  6. config_sentence_transformers.json +10 -0
  7. model.safetensors +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +945 -0
  13. training_args.bin +3 -0
1_Pooling/config.json ADDED
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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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+ }
2_Dense/config.json ADDED
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+ {"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
2_Dense/model.safetensors ADDED
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README.md ADDED
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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:5749
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+ - loss:CosineSimilarityLoss
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+ base_model: CocoRoF/ModernBERT-SimCSE_v02
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+ widget:
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+ - source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
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+ 쪽으로. "
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+ sentences:
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+ - 두 마리의 독수리가 가지에 앉는다.
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+ - 다른 물체와는 관련이 없는 '정지'는 없다.
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+ - 소녀는 버스의 열린 문 앞에 서 있다.
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+ - source_sentence: 숲에는 개들이 있다.
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+ sentences:
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+ - 양을 보는 아이들.
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+ - 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
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+ - 개들은 숲속에 혼자 있다.
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+ - source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
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+ sentences:
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+ - 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
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+ - 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
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+ - 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
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+ - source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
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+ sentences:
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+ - 한 남자가 수영장으로 뛰어들었다.
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+ - 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
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+ - 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
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+ - source_sentence: 버스가 바쁜 길을 따라 운전한다.
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+ sentences:
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+ - 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
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+ - 그 여자는 데이트하러 가는 중이다.
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+ - 녹색 버스가 도로를 따라 내려간다.
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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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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ model-index:
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+ - name: SentenceTransformer based on CocoRoF/ModernBERT-SimCSE_v02
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev
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+ type: sts_dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.805539118357127
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8061033061285413
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+ name: Spearman Cosine
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+ - type: pearson_euclidean
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+ value: 0.7633523638911596
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.7628951831481233
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+ name: Spearman Euclidean
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+ - type: pearson_manhattan
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+ value: 0.7652880535446602
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.7657560923304267
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+ name: Spearman Manhattan
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+ - type: pearson_dot
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+ value: 0.7133686434266335
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.7015065203951969
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.805539118357127
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.8061033061285413
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on CocoRoF/ModernBERT-SimCSE_v02
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [CocoRoF/ModernBERT-SimCSE_v02](https://huggingface.co/CocoRoF/ModernBERT-SimCSE_v02). 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:** [CocoRoF/ModernBERT-SimCSE_v02](https://huggingface.co/CocoRoF/ModernBERT-SimCSE_v02) <!-- at revision de4148c764893843e15a4e0b241fe308147a9aaa -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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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': 512, '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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+ (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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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("CocoRoF/ModernBERT-SimCSE-multitask_v02")
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+ # Run inference
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+ sentences = [
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+ '버스가 바쁜 길을 따라 운전한다.',
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+ '녹색 버스가 도로를 따라 내려간다.',
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+ '그 여자는 데이트하러 가는 중이다.',
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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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+
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+ </details>
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+ -->
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+
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+ <!--
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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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+
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+ <details><summary>Click to expand</summary>
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+
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+ </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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+ #### Semantic Similarity
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+
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+ * Dataset: `sts_dev`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:-------------------|:-----------|
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+ | pearson_cosine | 0.8055 |
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+ | spearman_cosine | 0.8061 |
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+ | pearson_euclidean | 0.7634 |
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+ | spearman_euclidean | 0.7629 |
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+ | pearson_manhattan | 0.7653 |
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+ | spearman_manhattan | 0.7658 |
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+ | pearson_dot | 0.7134 |
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+ | spearman_dot | 0.7015 |
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+ | pearson_max | 0.8055 |
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+ | **spearman_max** | **0.8061** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *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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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
214
+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 5,749 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 7 tokens</li><li>mean: 13.52 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.41 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:------------------------------------|:------------------------------------------|:------------------|
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+ | <code>비행기가 이륙하고 있다.</code> | <code>비행기가 이륙하고 있다.</code> | <code>1.0</code> |
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+ | <code>한 남자가 큰 플루트를 연주하고 있다.</code> | <code>남자가 플루트를 연주하고 있다.</code> | <code>0.76</code> |
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+ | <code>한 남자가 피자에 치즈를 뿌려놓고 있다.</code> | <code>한 남자가 구운 피자에 치즈 조각을 뿌려놓고 있다.</code> | <code>0.76</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
237
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
238
+ }
239
+ ```
240
+
241
+ ### Evaluation Dataset
242
+
243
+ #### Unnamed Dataset
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+
245
+
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+ * Size: 1,500 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.52 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:-------------------------------------|:------------------------------------|:------------------|
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+ | <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> | <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> | <code>1.0</code> |
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+ | <code>어린아이가 말을 타고 있다.</code> | <code>아이가 말을 타고 있다.</code> | <code>0.95</code> |
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+ | <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
263
+ }
264
+ ```
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+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `overwrite_output_dir`: True
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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+ - `gradient_accumulation_steps`: 8
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+ - `learning_rate`: 5e-07
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+ - `num_train_epochs`: 10.0
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+ - `warmup_ratio`: 0.1
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+ - `push_to_hub`: True
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+ - `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v02
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+ - `hub_strategy`: checkpoint
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
284
+
285
+ - `overwrite_output_dir`: True
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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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`: 8
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 5e-07
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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`: 10.0
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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
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
315
+ - `restore_callback_states_from_checkpoint`: False
316
+ - `no_cuda`: False
317
+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: False
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: True
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
342
+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
345
+ - `fsdp_min_num_params`: 0
346
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
347
+ - `fsdp_transformer_layer_cls_to_wrap`: None
348
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
349
+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
351
+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
357
+ - `ddp_bucket_cap_mb`: None
358
+ - `ddp_broadcast_buffers`: False
359
+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: True
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v02
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+ - `hub_strategy`: checkpoint
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+ - `hub_private_repo`: None
368
+ - `hub_always_push`: False
369
+ - `gradient_checkpointing`: False
370
+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `include_for_metrics`: []
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
376
+ - `push_to_hub_organization`: None
377
+ - `mp_parameters`:
378
+ - `auto_find_batch_size`: False
379
+ - `full_determinism`: False
380
+ - `torchdynamo`: None
381
+ - `ray_scope`: last
382
+ - `ddp_timeout`: 1800
383
+ - `torch_compile`: False
384
+ - `torch_compile_backend`: None
385
+ - `torch_compile_mode`: None
386
+ - `dispatch_batches`: None
387
+ - `split_batches`: None
388
+ - `include_tokens_per_second`: False
389
+ - `include_num_input_tokens_seen`: False
390
+ - `neftune_noise_alpha`: None
391
+ - `optim_target_modules`: None
392
+ - `batch_eval_metrics`: False
393
+ - `eval_on_start`: False
394
+ - `use_liger_kernel`: False
395
+ - `eval_use_gather_object`: False
396
+ - `average_tokens_across_devices`: False
397
+ - `prompts`: None
398
+ - `batch_sampler`: no_duplicates
399
+ - `multi_dataset_batch_sampler`: proportional
400
+
401
+ </details>
402
+
403
+ ### Training Logs
404
+ | Epoch | Step | Training Loss | Validation Loss | sts_dev_spearman_max |
405
+ |:------:|:----:|:-------------:|:---------------:|:--------------------:|
406
+ | 0.2228 | 10 | 0.0284 | - | - |
407
+ | 0.4457 | 20 | 0.0346 | - | - |
408
+ | 0.6685 | 30 | 0.0305 | 0.0317 | 0.7927 |
409
+ | 0.8914 | 40 | 0.0495 | - | - |
410
+ | 1.1337 | 50 | 0.04 | - | - |
411
+ | 1.3565 | 60 | 0.0295 | 0.0316 | 0.7930 |
412
+ | 1.5794 | 70 | 0.0352 | - | - |
413
+ | 1.8022 | 80 | 0.042 | - | - |
414
+ | 2.0446 | 90 | 0.0476 | 0.0314 | 0.7933 |
415
+ | 2.2674 | 100 | 0.0289 | - | - |
416
+ | 2.4903 | 110 | 0.0345 | - | - |
417
+ | 2.7131 | 120 | 0.0339 | 0.0311 | 0.7940 |
418
+ | 2.9359 | 130 | 0.0493 | - | - |
419
+ | 3.1783 | 140 | 0.0341 | - | - |
420
+ | 3.4011 | 150 | 0.0332 | 0.0308 | 0.7952 |
421
+ | 3.6240 | 160 | 0.0303 | - | - |
422
+ | 3.8468 | 170 | 0.045 | - | - |
423
+ | 4.0891 | 180 | 0.0422 | 0.0305 | 0.7961 |
424
+ | 4.3120 | 190 | 0.0278 | - | - |
425
+ | 4.5348 | 200 | 0.0338 | - | - |
426
+ | 4.7577 | 210 | 0.0372 | 0.0302 | 0.7968 |
427
+ | 4.9805 | 220 | 0.0469 | - | - |
428
+ | 5.2228 | 230 | 0.0303 | - | - |
429
+ | 5.4457 | 240 | 0.0328 | 0.0297 | 0.7982 |
430
+ | 5.6685 | 250 | 0.0295 | - | - |
431
+ | 5.8914 | 260 | 0.0458 | - | - |
432
+ | 6.1337 | 270 | 0.0363 | 0.0295 | 0.7997 |
433
+ | 6.3565 | 280 | 0.0265 | - | - |
434
+ | 6.5794 | 290 | 0.0341 | - | - |
435
+ | 6.8022 | 300 | 0.0384 | 0.0291 | 0.8007 |
436
+ | 7.0446 | 310 | 0.0431 | - | - |
437
+ | 7.2674 | 320 | 0.0256 | - | - |
438
+ | 7.4903 | 330 | 0.0321 | 0.0287 | 0.8022 |
439
+ | 7.7131 | 340 | 0.0315 | - | - |
440
+ | 7.9359 | 350 | 0.0438 | - | - |
441
+ | 8.1783 | 360 | 0.0301 | 0.0284 | 0.8038 |
442
+ | 8.4011 | 370 | 0.0301 | - | - |
443
+ | 8.6240 | 380 | 0.0285 | - | - |
444
+ | 8.8468 | 390 | 0.0394 | 0.0282 | 0.8049 |
445
+ | 9.0891 | 400 | 0.0374 | - | - |
446
+ | 9.3120 | 410 | 0.0245 | - | - |
447
+ | 9.5348 | 420 | 0.0316 | 0.0279 | 0.8061 |
448
+ | 9.7577 | 430 | 0.0331 | - | - |
449
+ | 9.9805 | 440 | 0.0411 | - | - |
450
+
451
+
452
+ ### Framework Versions
453
+ - Python: 3.11.10
454
+ - Sentence Transformers: 3.3.1
455
+ - Transformers: 4.48.0.dev0
456
+ - PyTorch: 2.5.1+cu121
457
+ - Accelerate: 1.1.0
458
+ - Datasets: 3.1.0
459
+ - Tokenizers: 0.21.0
460
+
461
+ ## Citation
462
+
463
+ ### BibTeX
464
+
465
+ #### Sentence Transformers
466
+ ```bibtex
467
+ @inproceedings{reimers-2019-sentence-bert,
468
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
469
+ author = "Reimers, Nils and Gurevych, Iryna",
470
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
471
+ month = "11",
472
+ year = "2019",
473
+ publisher = "Association for Computational Linguistics",
474
+ url = "https://arxiv.org/abs/1908.10084",
475
+ }
476
+ ```
477
+
478
+ <!--
479
+ ## Glossary
480
+
481
+ *Clearly define terms in order to be accessible across audiences.*
482
+ -->
483
+
484
+ <!--
485
+ ## Model Card Authors
486
+
487
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
488
+ -->
489
+
490
+ <!--
491
+ ## Model Card Contact
492
+
493
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
494
+ -->
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