huudan123 commited on
Commit
78216f6
1 Parent(s): fb97f20

Add new SentenceTransformer model.

Browse files
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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+ }
README.md ADDED
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+ ---
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+ base_model: huudan123/model_stage1
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+ datasets: []
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+ language: []
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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_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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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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+ pipeline_tag: sentence-similarity
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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:183796
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+ - loss:MultipleNegativesRankingLoss
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+ widget:
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+ - source_sentence: nếu thời_gian đến mà họ phải có một cuộc đấu_tranh johny shanon
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+ có_thể là một người ngạc_nhiên
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+ sentences:
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+ - johny nghĩ anh ta là người giỏi nhất trong thị_trấn
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+ - nếu một cuộc đấu_tranh đã xảy ra johny có_thể ngạc_nhiên đấy
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+ - tất_cả bằng_chứng về văn_hóa từ xã_hội của umbria đã bị mất
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+ - source_sentence: chèn jay leno đùa ở đây
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+ sentences:
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+ - mathews đã chỉ ra rằng sẽ không cần phải tuyển_dụng luật_sư địa_phương
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+ - đây là nơi mà một trò_đùa jay leno sẽ đi
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+ - jay leno không phải là một diễn_viên hài
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+ - source_sentence: đúng_vậy tất_cả là lỗi của họ
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+ sentences:
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+ - bạn bị giới_hạn bởi số_lượng bộ_nhớ bạn đã có
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+ - phải tất_cả đều là lỗi của họ
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+ - rõ_ràng là tất_cả những lỗi của công_nhân
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+ - source_sentence: 6 mặc_dù mỗi cơ_quan phát_triển và triển_khai các thỏa_thuận hiệu_quả
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+ phản_ánh các ưu_tiên tổ_chức cụ_thể cấu_trúc và nền văn_hóa các thỏa_thuận hiệu_quả
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+ đã gặp các đặc_điểm sau
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+ sentences:
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+ - các thỏa_thuận hiệu_quả đã được phát_hành từ mỗi đại_lý
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+ - kế_hoạch hiệu_quả loại_trừ bất_cứ điều gì để làm với các cấu_trúc
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+ - không có gì bên trong sảnh trên đồi cả
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+ - source_sentence: hay na uy hay gì đó
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+ sentences:
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+ - na uy hay cái gì đó khác
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+ - điều đó hoàn_toàn không đúng
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+ - na uy hoặc từ một trong những quốc_gia scandinavia
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+ model-index:
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+ - name: SentenceTransformer based on huudan123/model_stage1
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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 evaluator
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+ type: sts-evaluator
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.6279986884327646
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.6257861952118347
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.6286844662908612
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.6309663003206769
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.6277475064516767
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.6297451268540156
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.588316765453479
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.5802157556789215
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.6286844662908612
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.6309663003206769
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on huudan123/model_stage1
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/model_stage1](https://huggingface.co/huudan123/model_stage1). 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:** [huudan123/model_stage1](https://huggingface.co/huudan123/model_stage1) <!-- at revision b7466e583ac080b4f544522adb1647a976398ea1 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 tokens
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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: RobertaModel
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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("huudan123/model_stage2")
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+ # Run inference
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+ sentences = [
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+ 'hay na uy hay gì đó',
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+ 'na uy hoặc từ một trong những quốc_gia scandinavia',
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+ 'na uy hay cái gì đó khác',
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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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+
162
+ <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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+
167
+ <!--
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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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+ * Dataset: `sts-evaluator`
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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 | 0.628 |
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+ | spearman_cosine | 0.6258 |
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+ | pearson_manhattan | 0.6287 |
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+ | spearman_manhattan | 0.631 |
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+ | pearson_euclidean | 0.6277 |
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+ | spearman_euclidean | 0.6297 |
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+ | pearson_dot | 0.5883 |
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+ | spearman_dot | 0.5802 |
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+ | pearson_max | 0.6287 |
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+ | **spearman_max** | **0.631** |
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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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+
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+ ## Training Details
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+
218
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
221
+ - `overwrite_output_dir`: True
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+ - `eval_strategy`: epoch
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+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 256
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+ - `num_train_epochs`: 15
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
229
+ - `gradient_checkpointing`: True
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+
231
+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
234
+ - `overwrite_output_dir`: True
235
+ - `do_predict`: False
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+ - `eval_strategy`: epoch
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 256
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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`: 1
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+ - `eval_accumulation_steps`: None
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+ - `learning_rate`: 5e-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`: 15
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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
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `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`: True
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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
280
+ - `tpu_num_cores`: None
281
+ - `tpu_metrics_debug`: False
282
+ - `debug`: []
283
+ - `dataloader_drop_last`: False
284
+ - `dataloader_num_workers`: 0
285
+ - `dataloader_prefetch_factor`: None
286
+ - `past_index`: -1
287
+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: True
291
+ - `ignore_data_skip`: False
292
+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `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
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
310
+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: True
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
320
+ - `eval_do_concat_batches`: True
321
+ - `fp16_backend`: auto
322
+ - `push_to_hub_model_id`: None
323
+ - `push_to_hub_organization`: None
324
+ - `mp_parameters`:
325
+ - `auto_find_batch_size`: False
326
+ - `full_determinism`: False
327
+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
341
+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
344
+ </details>
345
+
346
+ ### Training Logs
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+ | Epoch | Step | Training Loss | loss | sts-evaluator_spearman_max |
348
+ |:-------:|:--------:|:-------------:|:----------:|:--------------------------:|
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+ | 0 | 0 | - | - | 0.6283 |
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+ | 0.6964 | 500 | 4.3237 | - | - |
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+ | 1.0 | 718 | - | 2.3703 | 0.6500 |
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+ | 1.3928 | 1000 | 2.2259 | - | - |
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+ | **2.0** | **1436** | **-** | **2.2597** | **0.624** |
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+ | 2.0891 | 1500 | 2.0143 | - | - |
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+ | 2.7855 | 2000 | 1.7433 | - | - |
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+ | 3.0 | 2154 | - | 2.3027 | 0.6405 |
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+ | 3.4819 | 2500 | 1.5279 | - | - |
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+ | 4.0 | 2872 | - | 2.3583 | 0.6094 |
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+ | 4.1783 | 3000 | 1.3796 | - | - |
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+ | 4.8747 | 3500 | 1.2096 | - | - |
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+ | 5.0 | 3590 | - | 2.4877 | 0.6069 |
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+ | 5.5710 | 4000 | 1.036 | - | - |
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+ | 6.0 | 4308 | - | 2.5685 | 0.6310 |
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+
365
+ * The bold row denotes the saved checkpoint.
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+
367
+ ### Framework Versions
368
+ - Python: 3.10.12
369
+ - Sentence Transformers: 3.0.1
370
+ - Transformers: 4.42.4
371
+ - PyTorch: 2.3.1+cu121
372
+ - Accelerate: 0.33.0
373
+ - Datasets: 2.20.0
374
+ - Tokenizers: 0.19.1
375
+
376
+ ## Citation
377
+
378
+ ### BibTeX
379
+
380
+ #### Sentence Transformers
381
+ ```bibtex
382
+ @inproceedings{reimers-2019-sentence-bert,
383
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
384
+ author = "Reimers, Nils and Gurevych, Iryna",
385
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
386
+ month = "11",
387
+ year = "2019",
388
+ publisher = "Association for Computational Linguistics",
389
+ url = "https://arxiv.org/abs/1908.10084",
390
+ }
391
+ ```
392
+
393
+ #### MultipleNegativesRankingLoss
394
+ ```bibtex
395
+ @misc{henderson2017efficient,
396
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
397
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
398
+ year={2017},
399
+ eprint={1705.00652},
400
+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
402
+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
added_tokens.json ADDED
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+ {
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+ "<mask>": 64000
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+ }
bpe.codes ADDED
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config.json ADDED
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+ {
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+ "_name_or_path": "./final_output",
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+ "architectures": [
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+ "RobertaModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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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-05,
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+ "max_position_embeddings": 258,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "tokenizer_class": "PhobertTokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.42.4",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 64001
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "3.0.1",
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+ "transformers": "4.42.4",
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+ "pytorch": "2.3.1+cu121"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": null
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