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RinaChen
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Guwen-nomic-embed-text-v1.5

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:756057
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
Model card Files
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  1. 1_Pooling/config.json +10 -0
  2. README.md +857 -0
  3. config.json +58 -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 +55 -0
  11. vocab.txt +0 -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 +1,857 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: nomic-ai/nomic-embed-text-v1.5
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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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:756057
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+ - loss:MultipleNegativesRankingLoss
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+ widget:
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+ - source_sentence: 府君奈何以蓋世之才欲立忠於垂亡之國
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+ sentences:
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+ - 將遠方進貢來的奇獸飛禽以及白山雞等物縱還山林比起雍畤的祭祀禮數頗有增加
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+ - 您為什麼以蓋絕當世的奇才卻打算向這個面臨滅亡的國家盡效忠心呢
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+ - 大統年間他出任岐州刺史在任不久就因為能力強而聞名
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+ - source_sentence: 將率既至授單于印紱詔令上故印紱
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+ sentences:
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+ - 已經到達的五威將到達後授給單于新印信宣讀詔書要求交回漢朝舊印信
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+ - 於是拜陶隗為西南面招討使
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+ - 司馬錯建議秦惠王攻打蜀國張儀說 還不如進攻韓國
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+ - source_sentence: 行醮禮皇太子詣醴席樂作
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+ sentences:
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+ - 閏七月十七日上宣宗廢除皇后胡氏尊諡
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+ - 等到看見西羌鼠竊狗盜父不父子不子君臣沒有分別四夷之人西羌最為低下
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+ - 行醮禮皇太子來到酒醴席奏樂
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+ - source_sentence: 領軍臧盾太府卿沈僧果等並被時遇孝綽尤輕之
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+ sentences:
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+ - 過了幾天太宰官又來要國書並且說 我國自太宰府以東上國使臣沒有到過今大朝派使臣來若不見國書何以相信
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+ - 所以丹陽葛洪解釋說渾天儀注說 天體像雞蛋地就像是雞蛋中的蛋黃獨處於天體之內天是大的而地是小的
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+ - 領軍臧盾太府卿沈僧果等都是因趕上時機而得到官職的孝綽尤其輕蔑他們每次在朝中集合會面雖然一起做官但從不與他們說話
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+ - source_sentence: 九月辛未太祖曾孫舒國公從式進封安定郡王
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+ sentences:
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+ - 九月初二太祖曾孫舒國公從式進封安定郡王
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+ - 楊難當在漢中大肆燒殺搶劫然後率眾離開了漢中向西返回仇池留下趙溫據守梁州又派他的魏興太守薛健屯駐黃金山
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+ - 正統元年普定蠻夷阿遲等反叛非法稱王四處出擊攻打掠奪
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+ ---
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+
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+ # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision c4f06e01594879a8ccc5c40b0b0a0e2ad46e3a62 -->
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+ - **Maximum Sequence Length:** 8192 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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+
58
+ ### Model Sources
59
+
60
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
61
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
62
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
63
+
64
+ ### Full Model Architecture
65
+
66
+ ```
67
+ SentenceTransformer(
68
+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
69
+ (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})
70
+ )
71
+ ```
72
+
73
+ ## Usage
74
+
75
+ ### Direct Usage (Sentence Transformers)
76
+
77
+ First install the Sentence Transformers library:
78
+
79
+ ```bash
80
+ pip install -U sentence-transformers
81
+ ```
82
+
83
+ Then you can load this model and run inference.
84
+ ```python
85
+ from sentence_transformers import SentenceTransformer
86
+
87
+ # Download from the 🤗 Hub
88
+ model = SentenceTransformer("sentence_transformers_model_id")
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+ # Run inference
90
+ sentences = [
91
+ '九月辛未太祖曾孫舒國公從式進封安定郡王',
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+ '九月初二太祖曾孫舒國公從式進封安定郡王',
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+ '楊難當在漢中大肆燒殺搶劫然後率眾離開了漢中向西返回仇池留下趙溫據守梁州又派他的魏興太守薛健屯駐黃金山',
94
+ ]
95
+ embeddings = model.encode(sentences)
96
+ print(embeddings.shape)
97
+ # [3, 768]
98
+
99
+ # Get the similarity scores for the embeddings
100
+ similarities = model.similarity(embeddings, embeddings)
101
+ print(similarities.shape)
102
+ # [3, 3]
103
+ ```
104
+
105
+ <!--
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+ ### Direct Usage (Transformers)
107
+
108
+ <details><summary>Click to see the direct usage in Transformers</summary>
109
+
110
+ </details>
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+ -->
112
+
113
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
115
+
116
+ You can finetune this model on your own dataset.
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+
118
+ <details><summary>Click to expand</summary>
119
+
120
+ </details>
121
+ -->
122
+
123
+ <!--
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+ ### Out-of-Scope Use
125
+
126
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
128
+
129
+ <!--
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+ ## Bias, Risks and Limitations
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+
132
+ *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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+
135
+ <!--
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+ ### Recommendations
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+
138
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
141
+ ## Training Details
142
+
143
+ ### Training Dataset
144
+
145
+ #### Unnamed Dataset
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+
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+
148
+ * Size: 756,057 training samples
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+ * Columns: <code>anchor</code> and <code>positive</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 20.76 tokens</li><li>max: 199 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 31.48 tokens</li><li>max: 602 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive |
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+ |:------------------------------------------|:------------------------------------------------------------|
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+ | <code>虜懷兼弱之威挾廣地之計強兵大眾親自凌殄旍鼓彌年矢石不息</code> | <code>魏人懷有兼併弱小的威嚴胸藏拓展土地的計謀強人的軍隊親自出徵侵逼消滅旌旗戰鼓連年出動戰事不停息</code> |
159
+ | <code>孟子曰 以善服人者未有能服人者也以善養人然後能服天下</code> | <code>孟子說 用自己的善良使人們服從的人沒有能使人服從的用善良影響教導人們才能使天下的人們都信服</code> |
160
+ | <code>開慶初大元兵渡江理宗議遷都平江慶元后諫不可恐搖動民心乃止</code> | <code>開慶初年大元朝部隊渡過長江理宗打算遷都到平江慶元皇后勸諫不可遷都深恐動搖民心理宗才作罷</code> |
161
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
162
+ ```json
163
+ {
164
+ "scale": 20.0,
165
+ "similarity_fct": "cos_sim"
166
+ }
167
+ ```
168
+
169
+ ### Evaluation Dataset
170
+
171
+ #### Unnamed Dataset
172
+
173
+
174
+ * Size: 84,007 evaluation samples
175
+ * Columns: <code>anchor</code> and <code>positive</code>
176
+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive |
178
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 20.23 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 31.45 tokens</li><li>max: 415 tokens</li></ul> |
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+ * Samples:
182
+ | anchor | positive |
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+ |:--------------------------------------------------|:------------------------------------------------------------------|
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+ | <code>雒陽戶五萬二千八百三十九</code> | <code>雒陽有五萬二千八百三十九戶</code> |
185
+ | <code>拜南青州刺史在任有政績</code> | <code>任南青州刺史很有政績</code> |
186
+ | <code>第六品以下加不得服金釒奠綾錦錦繡七緣綺貂豽裘金叉環鉺及以金校飾器物張絳帳</code> | <code>官位在第六品以下的官員再增加不得穿用金鈿綾錦錦繡七緣綺貂鈉皮衣金叉繯餌以及用金裝飾的器物張絳帳等衣服物品</code> |
187
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
188
+ ```json
189
+ {
190
+ "scale": 20.0,
191
+ "similarity_fct": "cos_sim"
192
+ }
193
+ ```
194
+
195
+ ### Training Hyperparameters
196
+ #### Non-Default Hyperparameters
197
+
198
+ - `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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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+ - `batch_sampler`: no_duplicates
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+
207
+ #### All Hyperparameters
208
+ <details><summary>Click to expand</summary>
209
+
210
+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
212
+ - `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`: 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
226
+ - `num_train_epochs`: 1
227
+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
229
+ - `lr_scheduler_kwargs`: {}
230
+ - `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
236
+ - `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
241
+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
245
+ - `jit_mode_eval`: False
246
+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
249
+ - `fp16_opt_level`: O1
250
+ - `half_precision_backend`: auto
251
+ - `bf16_full_eval`: False
252
+ - `fp16_full_eval`: False
253
+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
257
+ - `tpu_metrics_debug`: False
258
+ - `debug`: []
259
+ - `dataloader_drop_last`: False
260
+ - `dataloader_num_workers`: 0
261
+ - `dataloader_prefetch_factor`: None
262
+ - `past_index`: -1
263
+ - `disable_tqdm`: False
264
+ - `remove_unused_columns`: True
265
+ - `label_names`: None
266
+ - `load_best_model_at_end`: True
267
+ - `ignore_data_skip`: False
268
+ - `fsdp`: []
269
+ - `fsdp_min_num_params`: 0
270
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
271
+ - `fsdp_transformer_layer_cls_to_wrap`: None
272
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
273
+ - `deepspeed`: None
274
+ - `label_smoothing_factor`: 0.0
275
+ - `optim`: adamw_torch
276
+ - `optim_args`: None
277
+ - `adafactor`: False
278
+ - `group_by_length`: False
279
+ - `length_column_name`: length
280
+ - `ddp_find_unused_parameters`: None
281
+ - `ddp_bucket_cap_mb`: None
282
+ - `ddp_broadcast_buffers`: False
283
+ - `dataloader_pin_memory`: True
284
+ - `dataloader_persistent_workers`: False
285
+ - `skip_memory_metrics`: True
286
+ - `use_legacy_prediction_loop`: False
287
+ - `push_to_hub`: False
288
+ - `resume_from_checkpoint`: None
289
+ - `hub_model_id`: None
290
+ - `hub_strategy`: every_save
291
+ - `hub_private_repo`: False
292
+ - `hub_always_push`: False
293
+ - `gradient_checkpointing`: False
294
+ - `gradient_checkpointing_kwargs`: None
295
+ - `include_inputs_for_metrics`: False
296
+ - `eval_do_concat_batches`: True
297
+ - `fp16_backend`: auto
298
+ - `push_to_hub_model_id`: None
299
+ - `push_to_hub_organization`: None
300
+ - `mp_parameters`:
301
+ - `auto_find_batch_size`: False
302
+ - `full_determinism`: False
303
+ - `torchdynamo`: None
304
+ - `ray_scope`: last
305
+ - `ddp_timeout`: 1800
306
+ - `torch_compile`: False
307
+ - `torch_compile_backend`: None
308
+ - `torch_compile_mode`: None
309
+ - `dispatch_batches`: None
310
+ - `split_batches`: None
311
+ - `include_tokens_per_second`: False
312
+ - `include_num_input_tokens_seen`: False
313
+ - `neftune_noise_alpha`: None
314
+ - `optim_target_modules`: None
315
+ - `batch_eval_metrics`: False
316
+ - `eval_on_start`: False
317
+ - `batch_sampler`: no_duplicates
318
+ - `multi_dataset_batch_sampler`: proportional
319
+
320
+ </details>
321
+
322
+ ### Training Logs
323
+ <details><summary>Click to expand</summary>
324
+
325
+ | Epoch | Step | Training Loss | loss |
326
+ |:----------:|:---------:|:-------------:|:----------:|
327
+ | 0.0021 | 100 | 0.4574 | - |
328
+ | 0.0042 | 200 | 0.4089 | - |
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+ | 0.0063 | 300 | 0.2872 | - |
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+ | 0.0085 | 400 | 0.2909 | - |
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+ | 0.0106 | 500 | 0.3076 | - |
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+ | 0.0127 | 600 | 0.2958 | - |
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+ | 0.0148 | 700 | 0.2953 | - |
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+ | 0.0169 | 800 | 0.31 | - |
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+ | 0.0190 | 900 | 0.3031 | - |
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+ | 0.0212 | 1000 | 0.263 | - |
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+ | 0.0233 | 1100 | 0.27 | - |
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+ | 0.0254 | 1200 | 0.3107 | - |
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+ | 0.0275 | 1300 | 0.2453 | - |
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+ | 0.0296 | 1400 | 0.2487 | - |
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+ | 0.0317 | 1500 | 0.2332 | - |
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+ | 0.0339 | 1600 | 0.2708 | - |
343
+ | 0.0360 | 1700 | 0.2731 | - |
344
+ | 0.0381 | 1800 | 0.3102 | - |
345
+ | 0.0402 | 1900 | 0.3385 | - |
346
+ | 0.0423 | 2000 | 0.2802 | - |
347
+ | 0.0444 | 2100 | 0.3348 | - |
348
+ | 0.0466 | 2200 | 0.2527 | - |
349
+ | 0.0487 | 2300 | 0.2916 | - |
350
+ | 0.0508 | 2400 | 0.2671 | - |
351
+ | 0.0529 | 2500 | 0.2187 | - |
352
+ | 0.0550 | 2600 | 0.2624 | - |
353
+ | 0.0571 | 2700 | 0.3061 | - |
354
+ | 0.0593 | 2800 | 0.2439 | - |
355
+ | 0.0614 | 2900 | 0.2831 | - |
356
+ | 0.0635 | 3000 | 0.2948 | - |
357
+ | 0.0656 | 3100 | 0.2828 | - |
358
+ | 0.0677 | 3200 | 0.3079 | - |
359
+ | 0.0698 | 3300 | 0.3194 | - |
360
+ | 0.0720 | 3400 | 0.2768 | - |
361
+ | 0.0741 | 3500 | 0.304 | - |
362
+ | 0.0762 | 3600 | 0.3056 | - |
363
+ | 0.0783 | 3700 | 0.2562 | - |
364
+ | 0.0804 | 3800 | 0.3138 | - |
365
+ | 0.0825 | 3900 | 0.3081 | - |
366
+ | 0.0846 | 4000 | 0.2733 | - |
367
+ | 0.0868 | 4100 | 0.3065 | - |
368
+ | 0.0889 | 4200 | 0.25 | - |
369
+ | 0.0910 | 4300 | 0.3076 | - |
370
+ | 0.0931 | 4400 | 0.2935 | - |
371
+ | 0.0952 | 4500 | 0.2644 | - |
372
+ | 0.0973 | 4600 | 0.2943 | - |
373
+ | 0.0995 | 4700 | 0.316 | - |
374
+ | 0.1016 | 4800 | 0.2616 | - |
375
+ | 0.1037 | 4900 | 0.2985 | - |
376
+ | 0.1058 | 5000 | 0.2962 | 0.2798 |
377
+ | 0.1079 | 5100 | 0.2872 | - |
378
+ | 0.1100 | 5200 | 0.2963 | - |
379
+ | 0.1122 | 5300 | 0.2968 | - |
380
+ | 0.1143 | 5400 | 0.2738 | - |
381
+ | 0.1164 | 5500 | 0.3198 | - |
382
+ | 0.1185 | 5600 | 0.294 | - |
383
+ | 0.1206 | 5700 | 0.3296 | - |
384
+ | 0.1227 | 5800 | 0.2605 | - |
385
+ | 0.1249 | 5900 | 0.3187 | - |
386
+ | 0.1270 | 6000 | 0.2657 | - |
387
+ | 0.1291 | 6100 | 0.3267 | - |
388
+ | 0.1312 | 6200 | 0.3839 | - |
389
+ | 0.1333 | 6300 | 0.3077 | - |
390
+ | 0.1354 | 6400 | 0.205 | - |
391
+ | 0.1376 | 6500 | 0.2839 | - |
392
+ | 0.1397 | 6600 | 0.3037 | - |
393
+ | 0.1418 | 6700 | 0.2694 | - |
394
+ | 0.1439 | 6800 | 0.2956 | - |
395
+ | 0.1460 | 6900 | 0.261 | - |
396
+ | 0.1481 | 7000 | 0.3173 | - |
397
+ | 0.1503 | 7100 | 0.2492 | - |
398
+ | 0.1524 | 7200 | 0.2885 | - |
399
+ | 0.1545 | 7300 | 0.3059 | - |
400
+ | 0.1566 | 7400 | 0.2883 | - |
401
+ | 0.1587 | 7500 | 0.2465 | - |
402
+ | 0.1608 | 7600 | 0.2926 | - |
403
+ | 0.1629 | 7700 | 0.2776 | - |
404
+ | 0.1651 | 7800 | 0.2769 | - |
405
+ | 0.1672 | 7900 | 0.2644 | - |
406
+ | 0.1693 | 8000 | 0.2416 | - |
407
+ | 0.1714 | 8100 | 0.254 | - |
408
+ | 0.1735 | 8200 | 0.2485 | - |
409
+ | 0.1756 | 8300 | 0.3029 | - |
410
+ | 0.1778 | 8400 | 0.2938 | - |
411
+ | 0.1799 | 8500 | 0.2936 | - |
412
+ | 0.1820 | 8600 | 0.2804 | - |
413
+ | 0.1841 | 8700 | 0.2408 | - |
414
+ | 0.1862 | 8800 | 0.2849 | - |
415
+ | 0.1883 | 8900 | 0.2954 | - |
416
+ | 0.1905 | 9000 | 0.2902 | - |
417
+ | 0.1926 | 9100 | 0.2845 | - |
418
+ | 0.1947 | 9200 | 0.3143 | - |
419
+ | 0.1968 | 9300 | 0.2514 | - |
420
+ | 0.1989 | 9400 | 0.2508 | - |
421
+ | 0.2010 | 9500 | 0.2782 | - |
422
+ | 0.2032 | 9600 | 0.291 | - |
423
+ | 0.2053 | 9700 | 0.2464 | - |
424
+ | 0.2074 | 9800 | 0.323 | - |
425
+ | 0.2095 | 9900 | 0.2332 | - |
426
+ | 0.2116 | 10000 | 0.2231 | 0.2521 |
427
+ | 0.2137 | 10100 | 0.245 | - |
428
+ | 0.2159 | 10200 | 0.2883 | - |
429
+ | 0.2180 | 10300 | 0.3097 | - |
430
+ | 0.2201 | 10400 | 0.2303 | - |
431
+ | 0.2222 | 10500 | 0.3194 | - |
432
+ | 0.2243 | 10600 | 0.2836 | - |
433
+ | 0.2264 | 10700 | 0.2727 | - |
434
+ | 0.2286 | 10800 | 0.2542 | - |
435
+ | 0.2307 | 10900 | 0.2708 | - |
436
+ | 0.2328 | 11000 | 0.263 | - |
437
+ | 0.2349 | 11100 | 0.3063 | - |
438
+ | 0.2370 | 11200 | 0.2667 | - |
439
+ | 0.2391 | 11300 | 0.2575 | - |
440
+ | 0.2412 | 11400 | 0.2487 | - |
441
+ | 0.2434 | 11500 | 0.2552 | - |
442
+ | 0.2455 | 11600 | 0.2669 | - |
443
+ | 0.2476 | 11700 | 0.2241 | - |
444
+ | 0.2497 | 11800 | 0.3029 | - |
445
+ | 0.2518 | 11900 | 0.2443 | - |
446
+ | 0.2539 | 12000 | 0.2961 | - |
447
+ | 0.2561 | 12100 | 0.2561 | - |
448
+ | 0.2582 | 12200 | 0.2436 | - |
449
+ | 0.2603 | 12300 | 0.2601 | - |
450
+ | 0.2624 | 12400 | 0.2553 | - |
451
+ | 0.2645 | 12500 | 0.2617 | - |
452
+ | 0.2666 | 12600 | 0.2581 | - |
453
+ | 0.2688 | 12700 | 0.2452 | - |
454
+ | 0.2709 | 12800 | 0.2227 | - |
455
+ | 0.2730 | 12900 | 0.2455 | - |
456
+ | 0.2751 | 13000 | 0.2469 | - |
457
+ | 0.2772 | 13100 | 0.2197 | - |
458
+ | 0.2793 | 13200 | 0.3086 | - |
459
+ | 0.2815 | 13300 | 0.2379 | - |
460
+ | 0.2836 | 13400 | 0.2441 | - |
461
+ | 0.2857 | 13500 | 0.2854 | - |
462
+ | 0.2878 | 13600 | 0.2405 | - |
463
+ | 0.2899 | 13700 | 0.2681 | - |
464
+ | 0.2920 | 13800 | 0.2405 | - |
465
+ | 0.2942 | 13900 | 0.251 | - |
466
+ | 0.2963 | 14000 | 0.2477 | - |
467
+ | 0.2984 | 14100 | 0.231 | - |
468
+ | 0.3005 | 14200 | 0.26 | - |
469
+ | 0.3026 | 14300 | 0.2395 | - |
470
+ | 0.3047 | 14400 | 0.2296 | - |
471
+ | 0.3069 | 14500 | 0.2554 | - |
472
+ | 0.3090 | 14600 | 0.2434 | - |
473
+ | 0.3111 | 14700 | 0.2247 | - |
474
+ | 0.3132 | 14800 | 0.267 | - |
475
+ | 0.3153 | 14900 | 0.2212 | - |
476
+ | 0.3174 | 15000 | 0.2744 | 0.2352 |
477
+ | 0.3195 | 15100 | 0.2168 | - |
478
+ | 0.3217 | 15200 | 0.2042 | - |
479
+ | 0.3238 | 15300 | 0.2187 | - |
480
+ | 0.3259 | 15400 | 0.2368 | - |
481
+ | 0.3280 | 15500 | 0.2693 | - |
482
+ | 0.3301 | 15600 | 0.255 | - |
483
+ | 0.3322 | 15700 | 0.2398 | - |
484
+ | 0.3344 | 15800 | 0.247 | - |
485
+ | 0.3365 | 15900 | 0.2431 | - |
486
+ | 0.3386 | 16000 | 0.2349 | - |
487
+ | 0.3407 | 16100 | 0.212 | - |
488
+ | 0.3428 | 16200 | 0.2875 | - |
489
+ | 0.3449 | 16300 | 0.2571 | - |
490
+ | 0.3471 | 16400 | 0.2513 | - |
491
+ | 0.3492 | 16500 | 0.2729 | - |
492
+ | 0.3513 | 16600 | 0.2755 | - |
493
+ | 0.3534 | 16700 | 0.2079 | - |
494
+ | 0.3555 | 16800 | 0.1997 | - |
495
+ | 0.3576 | 16900 | 0.2217 | - |
496
+ | 0.3598 | 17000 | 0.1887 | - |
497
+ | 0.3619 | 17100 | 0.2623 | - |
498
+ | 0.3640 | 17200 | 0.2049 | - |
499
+ | 0.3661 | 17300 | 0.2 | - |
500
+ | 0.3682 | 17400 | 0.2367 | - |
501
+ | 0.3703 | 17500 | 0.2368 | - |
502
+ | 0.3725 | 17600 | 0.2311 | - |
503
+ | 0.3746 | 17700 | 0.2359 | - |
504
+ | 0.3767 | 17800 | 0.2586 | - |
505
+ | 0.3788 | 17900 | 0.2222 | - |
506
+ | 0.3809 | 18000 | 0.2561 | - |
507
+ | 0.3830 | 18100 | 0.2246 | - |
508
+ | 0.3852 | 18200 | 0.1871 | - |
509
+ | 0.3873 | 18300 | 0.2147 | - |
510
+ | 0.3894 | 18400 | 0.2741 | - |
511
+ | 0.3915 | 18500 | 0.2079 | - |
512
+ | 0.3936 | 18600 | 0.2399 | - |
513
+ | 0.3957 | 18700 | 0.2375 | - |
514
+ | 0.3978 | 18800 | 0.2502 | - |
515
+ | 0.4000 | 18900 | 0.2385 | - |
516
+ | 0.4021 | 19000 | 0.2647 | - |
517
+ | 0.4042 | 19100 | 0.1847 | - |
518
+ | 0.4063 | 19200 | 0.2367 | - |
519
+ | 0.4084 | 19300 | 0.2148 | - |
520
+ | 0.4105 | 19400 | 0.1826 | - |
521
+ | 0.4127 | 19500 | 0.225 | - |
522
+ | 0.4148 | 19600 | 0.2415 | - |
523
+ | 0.4169 | 19700 | 0.2998 | - |
524
+ | 0.4190 | 19800 | 0.2435 | - |
525
+ | 0.4211 | 19900 | 0.2283 | - |
526
+ | 0.4232 | 20000 | 0.2782 | 0.2263 |
527
+ | 0.4254 | 20100 | 0.2786 | - |
528
+ | 0.4275 | 20200 | 0.2695 | - |
529
+ | 0.4296 | 20300 | 0.2112 | - |
530
+ | 0.4317 | 20400 | 0.2006 | - |
531
+ | 0.4338 | 20500 | 0.2031 | - |
532
+ | 0.4359 | 20600 | 0.2335 | - |
533
+ | 0.4381 | 20700 | 0.2154 | - |
534
+ | 0.4402 | 20800 | 0.2225 | - |
535
+ | 0.4423 | 20900 | 0.2234 | - |
536
+ | 0.4444 | 21000 | 0.2233 | - |
537
+ | 0.4465 | 21100 | 0.1851 | - |
538
+ | 0.4486 | 21200 | 0.2009 | - |
539
+ | 0.4508 | 21300 | 0.2337 | - |
540
+ | 0.4529 | 21400 | 0.2175 | - |
541
+ | 0.4550 | 21500 | 0.2564 | - |
542
+ | 0.4571 | 21600 | 0.205 | - |
543
+ | 0.4592 | 21700 | 0.233 | - |
544
+ | 0.4613 | 21800 | 0.2027 | - |
545
+ | 0.4635 | 21900 | 0.209 | - |
546
+ | 0.4656 | 22000 | 0.261 | - |
547
+ | 0.4677 | 22100 | 0.1755 | - |
548
+ | 0.4698 | 22200 | 0.2219 | - |
549
+ | 0.4719 | 22300 | 0.2108 | - |
550
+ | 0.4740 | 22400 | 0.212 | - |
551
+ | 0.4762 | 22500 | 0.2676 | - |
552
+ | 0.4783 | 22600 | 0.2314 | - |
553
+ | 0.4804 | 22700 | 0.1838 | - |
554
+ | 0.4825 | 22800 | 0.1967 | - |
555
+ | 0.4846 | 22900 | 0.2412 | - |
556
+ | 0.4867 | 23000 | 0.2203 | - |
557
+ | 0.4888 | 23100 | 0.2183 | - |
558
+ | 0.4910 | 23200 | 0.239 | - |
559
+ | 0.4931 | 23300 | 0.2273 | - |
560
+ | 0.4952 | 23400 | 0.2335 | - |
561
+ | 0.4973 | 23500 | 0.202 | - |
562
+ | 0.4994 | 23600 | 0.2176 | - |
563
+ | 0.5015 | 23700 | 0.2331 | - |
564
+ | 0.5037 | 23800 | 0.1949 | - |
565
+ | 0.5058 | 23900 | 0.2321 | - |
566
+ | 0.5079 | 24000 | 0.2046 | - |
567
+ | 0.5100 | 24100 | 0.2092 | - |
568
+ | 0.5121 | 24200 | 0.2195 | - |
569
+ | 0.5142 | 24300 | 0.2069 | - |
570
+ | 0.5164 | 24400 | 0.2049 | - |
571
+ | 0.5185 | 24500 | 0.2955 | - |
572
+ | 0.5206 | 24600 | 0.2101 | - |
573
+ | 0.5227 | 24700 | 0.2036 | - |
574
+ | 0.5248 | 24800 | 0.2507 | - |
575
+ | 0.5269 | 24900 | 0.2343 | - |
576
+ | 0.5291 | 25000 | 0.2026 | 0.2072 |
577
+ | 0.5312 | 25100 | 0.2288 | - |
578
+ | 0.5333 | 25200 | 0.2208 | - |
579
+ | 0.5354 | 25300 | 0.1914 | - |
580
+ | 0.5375 | 25400 | 0.1903 | - |
581
+ | 0.5396 | 25500 | 0.2156 | - |
582
+ | 0.5418 | 25600 | 0.216 | - |
583
+ | 0.5439 | 25700 | 0.1909 | - |
584
+ | 0.5460 | 25800 | 0.2265 | - |
585
+ | 0.5481 | 25900 | 0.2447 | - |
586
+ | 0.5502 | 26000 | 0.1879 | - |
587
+ | 0.5523 | 26100 | 0.204 | - |
588
+ | 0.5545 | 26200 | 0.2262 | - |
589
+ | 0.5566 | 26300 | 0.2448 | - |
590
+ | 0.5587 | 26400 | 0.1758 | - |
591
+ | 0.5608 | 26500 | 0.2102 | - |
592
+ | 0.5629 | 26600 | 0.2175 | - |
593
+ | 0.5650 | 26700 | 0.2109 | - |
594
+ | 0.5671 | 26800 | 0.202 | - |
595
+ | 0.5693 | 26900 | 0.2075 | - |
596
+ | 0.5714 | 27000 | 0.2021 | - |
597
+ | 0.5735 | 27100 | 0.1799 | - |
598
+ | 0.5756 | 27200 | 0.2084 | - |
599
+ | 0.5777 | 27300 | 0.2114 | - |
600
+ | 0.5798 | 27400 | 0.1851 | - |
601
+ | 0.5820 | 27500 | 0.22 | - |
602
+ | 0.5841 | 27600 | 0.181 | - |
603
+ | 0.5862 | 27700 | 0.2276 | - |
604
+ | 0.5883 | 27800 | 0.1944 | - |
605
+ | 0.5904 | 27900 | 0.1907 | - |
606
+ | 0.5925 | 28000 | 0.2176 | - |
607
+ | 0.5947 | 28100 | 0.2243 | - |
608
+ | 0.5968 | 28200 | 0.2191 | - |
609
+ | 0.5989 | 28300 | 0.2215 | - |
610
+ | 0.6010 | 28400 | 0.1769 | - |
611
+ | 0.6031 | 28500 | 0.1971 | - |
612
+ | 0.6052 | 28600 | 0.179 | - |
613
+ | 0.6074 | 28700 | 0.2308 | - |
614
+ | 0.6095 | 28800 | 0.2453 | - |
615
+ | 0.6116 | 28900 | 0.2293 | - |
616
+ | 0.6137 | 29000 | 0.2191 | - |
617
+ | 0.6158 | 29100 | 0.1988 | - |
618
+ | 0.6179 | 29200 | 0.1878 | - |
619
+ | 0.6201 | 29300 | 0.2215 | - |
620
+ | 0.6222 | 29400 | 0.2188 | - |
621
+ | 0.6243 | 29500 | 0.1821 | - |
622
+ | 0.6264 | 29600 | 0.1856 | - |
623
+ | 0.6285 | 29700 | 0.1907 | - |
624
+ | 0.6306 | 29800 | 0.1999 | - |
625
+ | 0.6328 | 29900 | 0.1803 | - |
626
+ | 0.6349 | 30000 | 0.201 | 0.1948 |
627
+ | 0.6370 | 30100 | 0.179 | - |
628
+ | 0.6391 | 30200 | 0.2073 | - |
629
+ | 0.6412 | 30300 | 0.2676 | - |
630
+ | 0.6433 | 30400 | 0.1824 | - |
631
+ | 0.6454 | 30500 | 0.1995 | - |
632
+ | 0.6476 | 30600 | 0.2097 | - |
633
+ | 0.6497 | 30700 | 0.2421 | - |
634
+ | 0.6518 | 30800 | 0.1745 | - |
635
+ | 0.6539 | 30900 | 0.2682 | - |
636
+ | 0.6560 | 31000 | 0.1892 | - |
637
+ | 0.6581 | 31100 | 0.2054 | - |
638
+ | 0.6603 | 31200 | 0.23 | - |
639
+ | 0.6624 | 31300 | 0.1711 | - |
640
+ | 0.6645 | 31400 | 0.2163 | - |
641
+ | 0.6666 | 31500 | 0.196 | - |
642
+ | 0.6687 | 31600 | 0.1746 | - |
643
+ | 0.6708 | 31700 | 0.2402 | - |
644
+ | 0.6730 | 31800 | 0.2096 | - |
645
+ | 0.6751 | 31900 | 0.1934 | - |
646
+ | 0.6772 | 32000 | 0.2021 | - |
647
+ | 0.6793 | 32100 | 0.1942 | - |
648
+ | 0.6814 | 32200 | 0.2076 | - |
649
+ | 0.6835 | 32300 | 0.1662 | - |
650
+ | 0.6857 | 32400 | 0.1777 | - |
651
+ | 0.6878 | 32500 | 0.1899 | - |
652
+ | 0.6899 | 32600 | 0.2253 | - |
653
+ | 0.6920 | 32700 | 0.221 | - |
654
+ | 0.6941 | 32800 | 0.1797 | - |
655
+ | 0.6962 | 32900 | 0.1884 | - |
656
+ | 0.6984 | 33000 | 0.2185 | - |
657
+ | 0.7005 | 33100 | 0.193 | - |
658
+ | 0.7026 | 33200 | 0.1975 | - |
659
+ | 0.7047 | 33300 | 0.1774 | - |
660
+ | 0.7068 | 33400 | 0.1709 | - |
661
+ | 0.7089 | 33500 | 0.1753 | - |
662
+ | 0.7111 | 33600 | 0.1834 | - |
663
+ | 0.7132 | 33700 | 0.1853 | - |
664
+ | 0.7153 | 33800 | 0.2155 | - |
665
+ | 0.7174 | 33900 | 0.1837 | - |
666
+ | 0.7195 | 34000 | 0.1655 | - |
667
+ | 0.7216 | 34100 | 0.212 | - |
668
+ | 0.7237 | 34200 | 0.2203 | - |
669
+ | 0.7259 | 34300 | 0.2267 | - |
670
+ | 0.7280 | 34400 | 0.208 | - |
671
+ | 0.7301 | 34500 | 0.1545 | - |
672
+ | 0.7322 | 34600 | 0.2003 | - |
673
+ | 0.7343 | 34700 | 0.2058 | - |
674
+ | 0.7364 | 34800 | 0.1837 | - |
675
+ | 0.7386 | 34900 | 0.2199 | - |
676
+ | 0.7407 | 35000 | 0.1931 | 0.1848 |
677
+ | 0.7428 | 35100 | 0.2456 | - |
678
+ | 0.7449 | 35200 | 0.1996 | - |
679
+ | 0.7470 | 35300 | 0.2145 | - |
680
+ | 0.7491 | 35400 | 0.1915 | - |
681
+ | 0.7513 | 35500 | 0.1734 | - |
682
+ | 0.7534 | 35600 | 0.19 | - |
683
+ | 0.7555 | 35700 | 0.182 | - |
684
+ | 0.7576 | 35800 | 0.1808 | - |
685
+ | 0.7597 | 35900 | 0.1625 | - |
686
+ | 0.7618 | 36000 | 0.1813 | - |
687
+ | 0.7640 | 36100 | 0.1412 | - |
688
+ | 0.7661 | 36200 | 0.2279 | - |
689
+ | 0.7682 | 36300 | 0.2444 | - |
690
+ | 0.7703 | 36400 | 0.1882 | - |
691
+ | 0.7724 | 36500 | 0.1731 | - |
692
+ | 0.7745 | 36600 | 0.1794 | - |
693
+ | 0.7767 | 36700 | 0.2577 | - |
694
+ | 0.7788 | 36800 | 0.169 | - |
695
+ | 0.7809 | 36900 | 0.1725 | - |
696
+ | 0.7830 | 37000 | 0.1788 | - |
697
+ | 0.7851 | 37100 | 0.1783 | - |
698
+ | 0.7872 | 37200 | 0.1764 | - |
699
+ | 0.7894 | 37300 | 0.1616 | - |
700
+ | 0.7915 | 37400 | 0.21 | - |
701
+ | 0.7936 | 37500 | 0.2091 | - |
702
+ | 0.7957 | 37600 | 0.1107 | - |
703
+ | 0.7978 | 37700 | 0.1773 | - |
704
+ | 0.7999 | 37800 | 0.1801 | - |
705
+ | 0.8020 | 37900 | 0.1621 | - |
706
+ | 0.8042 | 38000 | 0.189 | - |
707
+ | 0.8063 | 38100 | 0.182 | - |
708
+ | 0.8084 | 38200 | 0.1912 | - |
709
+ | 0.8105 | 38300 | 0.1731 | - |
710
+ | 0.8126 | 38400 | 0.1646 | - |
711
+ | 0.8147 | 38500 | 0.2037 | - |
712
+ | 0.8169 | 38600 | 0.1418 | - |
713
+ | 0.8190 | 38700 | 0.1485 | - |
714
+ | 0.8211 | 38800 | 0.2221 | - |
715
+ | 0.8232 | 38900 | 0.1886 | - |
716
+ | 0.8253 | 39000 | 0.2082 | - |
717
+ | 0.8274 | 39100 | 0.1742 | - |
718
+ | 0.8296 | 39200 | 0.1589 | - |
719
+ | 0.8317 | 39300 | 0.1959 | - |
720
+ | 0.8338 | 39400 | 0.1517 | - |
721
+ | 0.8359 | 39500 | 0.2049 | - |
722
+ | 0.8380 | 39600 | 0.2187 | - |
723
+ | 0.8401 | 39700 | 0.1801 | - |
724
+ | 0.8423 | 39800 | 0.1735 | - |
725
+ | 0.8444 | 39900 | 0.1881 | - |
726
+ | 0.8465 | 40000 | 0.1778 | 0.1787 |
727
+ | 0.8486 | 40100 | 0.1898 | - |
728
+ | 0.8507 | 40200 | 0.2021 | - |
729
+ | 0.8528 | 40300 | 0.1972 | - |
730
+ | 0.8550 | 40400 | 0.156 | - |
731
+ | 0.8571 | 40500 | 0.1791 | - |
732
+ | 0.8592 | 40600 | 0.188 | - |
733
+ | 0.8613 | 40700 | 0.2177 | - |
734
+ | 0.8634 | 40800 | 0.1287 | - |
735
+ | 0.8655 | 40900 | 0.1797 | - |
736
+ | 0.8677 | 41000 | 0.1533 | - |
737
+ | 0.8698 | 41100 | 0.1668 | - |
738
+ | 0.8719 | 41200 | 0.2047 | - |
739
+ | 0.8740 | 41300 | 0.1619 | - |
740
+ | 0.8761 | 41400 | 0.165 | - |
741
+ | 0.8782 | 41500 | 0.1781 | - |
742
+ | 0.8803 | 41600 | 0.2221 | - |
743
+ | 0.8825 | 41700 | 0.2031 | - |
744
+ | 0.8846 | 41800 | 0.1732 | - |
745
+ | 0.8867 | 41900 | 0.1599 | - |
746
+ | 0.8888 | 42000 | 0.1865 | - |
747
+ | 0.8909 | 42100 | 0.1367 | - |
748
+ | 0.8930 | 42200 | 0.1469 | - |
749
+ | 0.8952 | 42300 | 0.1777 | - |
750
+ | 0.8973 | 42400 | 0.1833 | - |
751
+ | 0.8994 | 42500 | 0.2102 | - |
752
+ | 0.9015 | 42600 | 0.164 | - |
753
+ | 0.9036 | 42700 | 0.1752 | - |
754
+ | 0.9057 | 42800 | 0.2186 | - |
755
+ | 0.9079 | 42900 | 0.1824 | - |
756
+ | 0.9100 | 43000 | 0.1796 | - |
757
+ | 0.9121 | 43100 | 0.1626 | - |
758
+ | 0.9142 | 43200 | 0.1623 | - |
759
+ | 0.9163 | 43300 | 0.2036 | - |
760
+ | 0.9184 | 43400 | 0.1365 | - |
761
+ | 0.9206 | 43500 | 0.1792 | - |
762
+ | 0.9227 | 43600 | 0.1583 | - |
763
+ | 0.9248 | 43700 | 0.1943 | - |
764
+ | 0.9269 | 43800 | 0.1931 | - |
765
+ | 0.9290 | 43900 | 0.1777 | - |
766
+ | 0.9311 | 44000 | 0.1633 | - |
767
+ | 0.9333 | 44100 | 0.1841 | - |
768
+ | 0.9354 | 44200 | 0.1674 | - |
769
+ | 0.9375 | 44300 | 0.1958 | - |
770
+ | 0.9396 | 44400 | 0.1831 | - |
771
+ | 0.9417 | 44500 | 0.1899 | - |
772
+ | 0.9438 | 44600 | 0.177 | - |
773
+ | 0.9460 | 44700 | 0.1881 | - |
774
+ | 0.9481 | 44800 | 0.1643 | - |
775
+ | 0.9502 | 44900 | 0.1462 | - |
776
+ | **0.9523** | **45000** | **0.2118** | **0.1719** |
777
+ | 0.9544 | 45100 | 0.1655 | - |
778
+ | 0.9565 | 45200 | 0.1567 | - |
779
+ | 0.9586 | 45300 | 0.1429 | - |
780
+ | 0.9608 | 45400 | 0.1718 | - |
781
+ | 0.9629 | 45500 | 0.1549 | - |
782
+ | 0.9650 | 45600 | 0.1556 | - |
783
+ | 0.9671 | 45700 | 0.1323 | - |
784
+ | 0.9692 | 45800 | 0.1988 | - |
785
+ | 0.9713 | 45900 | 0.15 | - |
786
+ | 0.9735 | 46000 | 0.1546 | - |
787
+ | 0.9756 | 46100 | 0.1472 | - |
788
+ | 0.9777 | 46200 | 0.196 | - |
789
+ | 0.9798 | 46300 | 0.1913 | - |
790
+ | 0.9819 | 46400 | 0.2261 | - |
791
+ | 0.9840 | 46500 | 0.1842 | - |
792
+ | 0.9862 | 46600 | 0.172 | - |
793
+ | 0.9883 | 46700 | 0.1925 | - |
794
+ | 0.9904 | 46800 | 0.1928 | - |
795
+ | 0.9925 | 46900 | 0.1698 | - |
796
+ | 0.9946 | 47000 | 0.1778 | - |
797
+ | 0.9967 | 47100 | 0.1497 | - |
798
+ | 0.9989 | 47200 | 0.1506 | - |
799
+
800
+ * The bold row denotes the saved checkpoint.
801
+ </details>
802
+
803
+ ### Framework Versions
804
+ - Python: 3.12.4
805
+ - Sentence Transformers: 3.1.0.dev0
806
+ - Transformers: 4.42.4
807
+ - PyTorch: 2.3.1+cpu
808
+ - Accelerate: 0.32.1
809
+ - Datasets: 2.20.0
810
+ - Tokenizers: 0.19.1
811
+
812
+ ## Citation
813
+
814
+ ### BibTeX
815
+
816
+ #### Sentence Transformers
817
+ ```bibtex
818
+ @inproceedings{reimers-2019-sentence-bert,
819
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
820
+ author = "Reimers, Nils and Gurevych, Iryna",
821
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
822
+ month = "11",
823
+ year = "2019",
824
+ publisher = "Association for Computational Linguistics",
825
+ url = "https://arxiv.org/abs/1908.10084",
826
+ }
827
+ ```
828
+
829
+ #### MultipleNegativesRankingLoss
830
+ ```bibtex
831
+ @misc{henderson2017efficient,
832
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
833
+ 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},
834
+ year={2017},
835
+ eprint={1705.00652},
836
+ archivePrefix={arXiv},
837
+ primaryClass={cs.CL}
838
+ }
839
+ ```
840
+
841
+ <!--
842
+ ## Glossary
843
+
844
+ *Clearly define terms in order to be accessible across audiences.*
845
+ -->
846
+
847
+ <!--
848
+ ## Model Card Authors
849
+
850
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
851
+ -->
852
+
853
+ <!--
854
+ ## Model Card Contact
855
+
856
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
857
+ -->
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