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sobamchan
/
t5-base-no-mrl

Sentence Similarity
sentence-transformers
Safetensors
English
t5
feature-extraction
dense
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Model card Files
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  1. 1_Pooling/config.json +10 -0
  2. README.md +994 -0
  3. config.json +60 -0
  4. config_sentence_transformers.json +14 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +107 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +939 -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,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "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 +1,994 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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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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+ - dense
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+ - generated_from_trainer
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+ - dataset_size:557850
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+ - loss:MatryoshkaLoss
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: google-t5/t5-base
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+ widget:
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+ - source_sentence: A man is jumping unto his filthy bed.
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+ sentences:
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+ - A young male is looking at a newspaper while 2 females walks past him.
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+ - The bed is dirty.
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+ - The man is on the moon.
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+ - source_sentence: A carefully balanced male stands on one foot near a clean ocean
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+ beach area.
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+ sentences:
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+ - A man is ouside near the beach.
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+ - Three policemen patrol the streets on bikes
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+ - A man is sitting on his couch.
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+ - source_sentence: The man is wearing a blue shirt.
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+ sentences:
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+ - Near the trashcan the man stood and smoked
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+ - A man in a blue shirt leans on a wall beside a road with a blue van and red car
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+ with water in the background.
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+ - A man in a black shirt is playing a guitar.
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+ - source_sentence: The girls are outdoors.
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+ sentences:
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+ - Two girls riding on an amusement part ride.
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+ - a guy laughs while doing laundry
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+ - Three girls are standing together in a room, one is listening, one is writing
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+ on a wall and the third is talking to them.
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+ - source_sentence: A construction worker peeking out of a manhole while his coworker
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+ sits on the sidewalk smiling.
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+ sentences:
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+ - A worker is looking out of a manhole.
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+ - A man is giving a presentation.
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+ - The workers are both inside the manhole.
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+ datasets:
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+ - sentence-transformers/all-nli
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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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+ model-index:
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+ - name: SentenceTransformer based on google-t5/t5-base
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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: 0.8449999807487527
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8463677832895963
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+ name: Spearman Cosine
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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 test
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+ type: sts-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8326553070860506
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.843052740265298
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on google-t5/t5-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
88
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
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+ - **Maximum Sequence Length:** None tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
99
+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
102
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
103
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
104
+
105
+ ### Full Model Architecture
106
+
107
+ ```
108
+ SentenceTransformer(
109
+ (0): Transformer({'max_seq_length': None, 'do_lower_case': False, 'architecture': 'T5EncoderModel'})
110
+ (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})
111
+ )
112
+ ```
113
+
114
+ ## Usage
115
+
116
+ ### Direct Usage (Sentence Transformers)
117
+
118
+ First install the Sentence Transformers library:
119
+
120
+ ```bash
121
+ pip install -U sentence-transformers
122
+ ```
123
+
124
+ Then you can load this model and run inference.
125
+ ```python
126
+ from sentence_transformers import SentenceTransformer
127
+
128
+ # Download from the 🤗 Hub
129
+ model = SentenceTransformer("sentence_transformers_model_id")
130
+ # Run inference
131
+ sentences = [
132
+ 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
133
+ 'A worker is looking out of a manhole.',
134
+ 'The workers are both inside the manhole.',
135
+ ]
136
+ embeddings = model.encode(sentences)
137
+ print(embeddings.shape)
138
+ # [3, 768]
139
+
140
+ # Get the similarity scores for the embeddings
141
+ similarities = model.similarity(embeddings, embeddings)
142
+ print(similarities.shape)
143
+ # [3, 3]
144
+ ```
145
+
146
+ <!--
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+ ### Direct Usage (Transformers)
148
+
149
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
151
+ </details>
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+ -->
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+
154
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
157
+ You can finetune this model on your own dataset.
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+
159
+ <details><summary>Click to expand</summary>
160
+
161
+ </details>
162
+ -->
163
+
164
+ <!--
165
+ ### Out-of-Scope Use
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+
167
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
168
+ -->
169
+
170
+ ## Evaluation
171
+
172
+ ### Metrics
173
+
174
+ #### Semantic Similarity
175
+
176
+ * Datasets: `sts-dev` and `sts-test`
177
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
179
+ | Metric | sts-dev | sts-test |
180
+ |:--------------------|:-----------|:-----------|
181
+ | pearson_cosine | 0.845 | 0.8327 |
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+ | **spearman_cosine** | **0.8464** | **0.8431** |
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+
184
+ <!--
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+ ## Bias, Risks and Limitations
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+
187
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
188
+ -->
189
+
190
+ <!--
191
+ ### Recommendations
192
+
193
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
194
+ -->
195
+
196
+ ## Training Details
197
+
198
+ ### Training Dataset
199
+
200
+ #### all-nli
201
+
202
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
203
+ * Size: 557,850 training samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
205
+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 tokens</li></ul> |
210
+ * Samples:
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+ | anchor | positive | negative |
212
+ |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
213
+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
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+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
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+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
216
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
217
+ ```json
218
+ {
219
+ "loss": "MultipleNegativesRankingLoss",
220
+ "matryoshka_dims": [
221
+ 768
222
+ ],
223
+ "matryoshka_weights": [
224
+ 1
225
+ ],
226
+ "n_dims_per_step": -1
227
+ }
228
+ ```
229
+
230
+ ### Evaluation Dataset
231
+
232
+ #### all-nli
233
+
234
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
235
+ * Size: 6,584 evaluation samples
236
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
237
+ * Approximate statistics based on the first 1000 samples:
238
+ | | anchor | positive | negative |
239
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
240
+ | type | string | string | string |
241
+ | details | <ul><li>min: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 tokens</li></ul> |
242
+ * Samples:
243
+ | anchor | positive | negative |
244
+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
245
+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
246
+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
247
+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
248
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
249
+ ```json
250
+ {
251
+ "loss": "MultipleNegativesRankingLoss",
252
+ "matryoshka_dims": [
253
+ 768
254
+ ],
255
+ "matryoshka_weights": [
256
+ 1
257
+ ],
258
+ "n_dims_per_step": -1
259
+ }
260
+ ```
261
+
262
+ ### Training Hyperparameters
263
+ #### Non-Default Hyperparameters
264
+
265
+ - `eval_strategy`: steps
266
+ - `per_device_train_batch_size`: 32
267
+ - `per_device_eval_batch_size`: 32
268
+ - `num_train_epochs`: 15
269
+ - `warmup_ratio`: 0.1
270
+
271
+ #### All Hyperparameters
272
+ <details><summary>Click to expand</summary>
273
+
274
+ - `overwrite_output_dir`: False
275
+ - `do_predict`: False
276
+ - `eval_strategy`: steps
277
+ - `prediction_loss_only`: True
278
+ - `per_device_train_batch_size`: 32
279
+ - `per_device_eval_batch_size`: 32
280
+ - `per_gpu_train_batch_size`: None
281
+ - `per_gpu_eval_batch_size`: None
282
+ - `gradient_accumulation_steps`: 1
283
+ - `eval_accumulation_steps`: None
284
+ - `torch_empty_cache_steps`: None
285
+ - `learning_rate`: 5e-05
286
+ - `weight_decay`: 0.0
287
+ - `adam_beta1`: 0.9
288
+ - `adam_beta2`: 0.999
289
+ - `adam_epsilon`: 1e-08
290
+ - `max_grad_norm`: 1.0
291
+ - `num_train_epochs`: 15
292
+ - `max_steps`: -1
293
+ - `lr_scheduler_type`: linear
294
+ - `lr_scheduler_kwargs`: {}
295
+ - `warmup_ratio`: 0.1
296
+ - `warmup_steps`: 0
297
+ - `log_level`: passive
298
+ - `log_level_replica`: warning
299
+ - `log_on_each_node`: True
300
+ - `logging_nan_inf_filter`: True
301
+ - `save_safetensors`: True
302
+ - `save_on_each_node`: False
303
+ - `save_only_model`: False
304
+ - `restore_callback_states_from_checkpoint`: False
305
+ - `no_cuda`: False
306
+ - `use_cpu`: False
307
+ - `use_mps_device`: False
308
+ - `seed`: 42
309
+ - `data_seed`: None
310
+ - `jit_mode_eval`: False
311
+ - `bf16`: False
312
+ - `fp16`: False
313
+ - `fp16_opt_level`: O1
314
+ - `half_precision_backend`: auto
315
+ - `bf16_full_eval`: False
316
+ - `fp16_full_eval`: False
317
+ - `tf32`: None
318
+ - `local_rank`: 0
319
+ - `ddp_backend`: None
320
+ - `tpu_num_cores`: None
321
+ - `tpu_metrics_debug`: False
322
+ - `debug`: []
323
+ - `dataloader_drop_last`: False
324
+ - `dataloader_num_workers`: 0
325
+ - `dataloader_prefetch_factor`: None
326
+ - `past_index`: -1
327
+ - `disable_tqdm`: False
328
+ - `remove_unused_columns`: True
329
+ - `label_names`: None
330
+ - `load_best_model_at_end`: False
331
+ - `ignore_data_skip`: False
332
+ - `fsdp`: []
333
+ - `fsdp_min_num_params`: 0
334
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
335
+ - `fsdp_transformer_layer_cls_to_wrap`: None
336
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
337
+ - `parallelism_config`: None
338
+ - `deepspeed`: None
339
+ - `label_smoothing_factor`: 0.0
340
+ - `optim`: adamw_torch_fused
341
+ - `optim_args`: None
342
+ - `adafactor`: False
343
+ - `group_by_length`: False
344
+ - `length_column_name`: length
345
+ - `project`: huggingface
346
+ - `trackio_space_id`: trackio
347
+ - `ddp_find_unused_parameters`: None
348
+ - `ddp_bucket_cap_mb`: None
349
+ - `ddp_broadcast_buffers`: False
350
+ - `dataloader_pin_memory`: True
351
+ - `dataloader_persistent_workers`: False
352
+ - `skip_memory_metrics`: True
353
+ - `use_legacy_prediction_loop`: False
354
+ - `push_to_hub`: False
355
+ - `resume_from_checkpoint`: None
356
+ - `hub_model_id`: None
357
+ - `hub_strategy`: every_save
358
+ - `hub_private_repo`: None
359
+ - `hub_always_push`: False
360
+ - `hub_revision`: None
361
+ - `gradient_checkpointing`: False
362
+ - `gradient_checkpointing_kwargs`: None
363
+ - `include_inputs_for_metrics`: False
364
+ - `include_for_metrics`: []
365
+ - `eval_do_concat_batches`: True
366
+ - `fp16_backend`: auto
367
+ - `push_to_hub_model_id`: None
368
+ - `push_to_hub_organization`: None
369
+ - `mp_parameters`:
370
+ - `auto_find_batch_size`: False
371
+ - `full_determinism`: False
372
+ - `torchdynamo`: None
373
+ - `ray_scope`: last
374
+ - `ddp_timeout`: 1800
375
+ - `torch_compile`: False
376
+ - `torch_compile_backend`: None
377
+ - `torch_compile_mode`: None
378
+ - `include_tokens_per_second`: False
379
+ - `include_num_input_tokens_seen`: no
380
+ - `neftune_noise_alpha`: None
381
+ - `optim_target_modules`: None
382
+ - `batch_eval_metrics`: False
383
+ - `eval_on_start`: False
384
+ - `use_liger_kernel`: False
385
+ - `liger_kernel_config`: None
386
+ - `eval_use_gather_object`: False
387
+ - `average_tokens_across_devices`: True
388
+ - `prompts`: None
389
+ - `batch_sampler`: batch_sampler
390
+ - `multi_dataset_batch_sampler`: proportional
391
+ - `router_mapping`: {}
392
+ - `learning_rate_mapping`: {}
393
+
394
+ </details>
395
+
396
+ ### Training Logs
397
+ <details><summary>Click to expand</summary>
398
+
399
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
400
+ |:-------:|:------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
401
+ | -1 | -1 | - | - | 0.6510 | - |
402
+ | 0.0287 | 500 | 2.5308 | 1.3756 | 0.6760 | - |
403
+ | 0.0574 | 1000 | 2.1199 | 1.0631 | 0.7312 | - |
404
+ | 0.0860 | 1500 | 1.6381 | 0.8444 | 0.7651 | - |
405
+ | 0.1147 | 2000 | 1.3407 | 0.7479 | 0.7763 | - |
406
+ | 0.1434 | 2500 | 1.166 | 0.6835 | 0.7835 | - |
407
+ | 0.1721 | 3000 | 1.0809 | 0.6264 | 0.7883 | - |
408
+ | 0.2008 | 3500 | 1.0059 | 0.5808 | 0.7916 | - |
409
+ | 0.2294 | 4000 | 0.9292 | 0.5431 | 0.7959 | - |
410
+ | 0.2581 | 4500 | 0.8938 | 0.5134 | 0.7996 | - |
411
+ | 0.2868 | 5000 | 0.843 | 0.4859 | 0.8032 | - |
412
+ | 0.3155 | 5500 | 0.7958 | 0.4636 | 0.8064 | - |
413
+ | 0.3442 | 6000 | 0.7612 | 0.4427 | 0.8105 | - |
414
+ | 0.3729 | 6500 | 0.7471 | 0.4230 | 0.8132 | - |
415
+ | 0.4015 | 7000 | 0.7155 | 0.4045 | 0.8146 | - |
416
+ | 0.4302 | 7500 | 0.6817 | 0.3918 | 0.8169 | - |
417
+ | 0.4589 | 8000 | 0.633 | 0.3781 | 0.8197 | - |
418
+ | 0.4876 | 8500 | 0.6551 | 0.3675 | 0.8206 | - |
419
+ | 0.5163 | 9000 | 0.6403 | 0.3585 | 0.8213 | - |
420
+ | 0.5449 | 9500 | 0.6128 | 0.3475 | 0.8229 | - |
421
+ | 0.5736 | 10000 | 0.5791 | 0.3394 | 0.8250 | - |
422
+ | 0.6023 | 10500 | 0.5819 | 0.3298 | 0.8257 | - |
423
+ | 0.6310 | 11000 | 0.559 | 0.3249 | 0.8286 | - |
424
+ | 0.6597 | 11500 | 0.5545 | 0.3163 | 0.8273 | - |
425
+ | 0.6883 | 12000 | 0.5377 | 0.3094 | 0.8314 | - |
426
+ | 0.7170 | 12500 | 0.5308 | 0.3029 | 0.8291 | - |
427
+ | 0.7457 | 13000 | 0.5203 | 0.2968 | 0.8304 | - |
428
+ | 0.7744 | 13500 | 0.5244 | 0.2946 | 0.8284 | - |
429
+ | 0.8031 | 14000 | 0.5046 | 0.2893 | 0.8294 | - |
430
+ | 0.8318 | 14500 | 0.4901 | 0.2843 | 0.8292 | - |
431
+ | 0.8604 | 15000 | 0.4917 | 0.2810 | 0.8319 | - |
432
+ | 0.8891 | 15500 | 0.4915 | 0.2757 | 0.8350 | - |
433
+ | 0.9178 | 16000 | 0.4834 | 0.2711 | 0.8343 | - |
434
+ | 0.9465 | 16500 | 0.4731 | 0.2692 | 0.8343 | - |
435
+ | 0.9752 | 17000 | 0.4547 | 0.2671 | 0.8353 | - |
436
+ | 1.0038 | 17500 | 0.4487 | 0.2640 | 0.8399 | - |
437
+ | 1.0325 | 18000 | 0.4331 | 0.2658 | 0.8368 | - |
438
+ | 1.0612 | 18500 | 0.4304 | 0.2654 | 0.8350 | - |
439
+ | 1.0899 | 19000 | 0.429 | 0.2603 | 0.8366 | - |
440
+ | 1.1186 | 19500 | 0.4207 | 0.2591 | 0.8386 | - |
441
+ | 1.1472 | 20000 | 0.4236 | 0.2570 | 0.8425 | - |
442
+ | 1.1759 | 20500 | 0.4072 | 0.2527 | 0.8435 | - |
443
+ | 1.2046 | 21000 | 0.4196 | 0.2533 | 0.8430 | - |
444
+ | 1.2333 | 21500 | 0.4117 | 0.2503 | 0.8417 | - |
445
+ | 1.2620 | 22000 | 0.3988 | 0.2486 | 0.8446 | - |
446
+ | 1.2907 | 22500 | 0.4016 | 0.2465 | 0.8453 | - |
447
+ | 1.3193 | 23000 | 0.398 | 0.2428 | 0.8438 | - |
448
+ | 1.3480 | 23500 | 0.3993 | 0.2491 | 0.8462 | - |
449
+ | 1.3767 | 24000 | 0.3893 | 0.2446 | 0.8452 | - |
450
+ | 1.4054 | 24500 | 0.3784 | 0.2414 | 0.8482 | - |
451
+ | 1.4341 | 25000 | 0.3789 | 0.2411 | 0.8464 | - |
452
+ | 1.4627 | 25500 | 0.3822 | 0.2368 | 0.8457 | - |
453
+ | 1.4914 | 26000 | 0.3736 | 0.2371 | 0.8456 | - |
454
+ | 1.5201 | 26500 | 0.3598 | 0.2358 | 0.8446 | - |
455
+ | 1.5488 | 27000 | 0.3525 | 0.2368 | 0.8469 | - |
456
+ | 1.5775 | 27500 | 0.3747 | 0.2307 | 0.8478 | - |
457
+ | 1.6061 | 28000 | 0.3649 | 0.2357 | 0.8486 | - |
458
+ | 1.6348 | 28500 | 0.355 | 0.2327 | 0.8475 | - |
459
+ | 1.6635 | 29000 | 0.3501 | 0.2347 | 0.8446 | - |
460
+ | 1.6922 | 29500 | 0.347 | 0.2263 | 0.8499 | - |
461
+ | 1.7209 | 30000 | 0.3565 | 0.2320 | 0.8490 | - |
462
+ | 1.7496 | 30500 | 0.3432 | 0.2271 | 0.8489 | - |
463
+ | 1.7782 | 31000 | 0.3409 | 0.2257 | 0.8483 | - |
464
+ | 1.8069 | 31500 | 0.3522 | 0.2261 | 0.8510 | - |
465
+ | 1.8356 | 32000 | 0.3348 | 0.2270 | 0.8504 | - |
466
+ | 1.8643 | 32500 | 0.339 | 0.2199 | 0.8495 | - |
467
+ | 1.8930 | 33000 | 0.333 | 0.2219 | 0.8502 | - |
468
+ | 1.9216 | 33500 | 0.3103 | 0.2208 | 0.8514 | - |
469
+ | 1.9503 | 34000 | 0.338 | 0.2240 | 0.8541 | - |
470
+ | 1.9790 | 34500 | 0.3276 | 0.2232 | 0.8548 | - |
471
+ | 2.0077 | 35000 | 0.3161 | 0.2248 | 0.8512 | - |
472
+ | 2.0364 | 35500 | 0.2905 | 0.2262 | 0.8528 | - |
473
+ | 2.0650 | 36000 | 0.2928 | 0.2216 | 0.8538 | - |
474
+ | 2.0937 | 36500 | 0.2961 | 0.2175 | 0.8515 | - |
475
+ | 2.1224 | 37000 | 0.2926 | 0.2184 | 0.8543 | - |
476
+ | 2.1511 | 37500 | 0.2923 | 0.2155 | 0.8466 | - |
477
+ | 2.1798 | 38000 | 0.2963 | 0.2201 | 0.8474 | - |
478
+ | 2.2085 | 38500 | 0.2853 | 0.2165 | 0.8492 | - |
479
+ | 2.2371 | 39000 | 0.2778 | 0.2212 | 0.8483 | - |
480
+ | 2.2658 | 39500 | 0.2889 | 0.2170 | 0.8532 | - |
481
+ | 2.2945 | 40000 | 0.2683 | 0.2201 | 0.8499 | - |
482
+ | 2.3232 | 40500 | 0.2812 | 0.2139 | 0.8548 | - |
483
+ | 2.3519 | 41000 | 0.2781 | 0.2193 | 0.8513 | - |
484
+ | 2.3805 | 41500 | 0.2848 | 0.2224 | 0.8482 | - |
485
+ | 2.4092 | 42000 | 0.2725 | 0.2232 | 0.8499 | - |
486
+ | 2.4379 | 42500 | 0.2727 | 0.2209 | 0.8525 | - |
487
+ | 2.4666 | 43000 | 0.2747 | 0.2254 | 0.8535 | - |
488
+ | 2.4953 | 43500 | 0.2785 | 0.2199 | 0.8528 | - |
489
+ | 2.5239 | 44000 | 0.2701 | 0.2181 | 0.8494 | - |
490
+ | 2.5526 | 44500 | 0.273 | 0.2195 | 0.8519 | - |
491
+ | 2.5813 | 45000 | 0.2763 | 0.2160 | 0.8540 | - |
492
+ | 2.6100 | 45500 | 0.2635 | 0.2140 | 0.8534 | - |
493
+ | 2.6387 | 46000 | 0.2722 | 0.2176 | 0.8529 | - |
494
+ | 2.6674 | 46500 | 0.2596 | 0.2136 | 0.8552 | - |
495
+ | 2.6960 | 47000 | 0.2627 | 0.2142 | 0.8524 | - |
496
+ | 2.7247 | 47500 | 0.2673 | 0.2174 | 0.8502 | - |
497
+ | 2.7534 | 48000 | 0.2582 | 0.2147 | 0.8510 | - |
498
+ | 2.7821 | 48500 | 0.256 | 0.2148 | 0.8514 | - |
499
+ | 2.8108 | 49000 | 0.2567 | 0.2122 | 0.8524 | - |
500
+ | 2.8394 | 49500 | 0.2502 | 0.2142 | 0.8526 | - |
501
+ | 2.8681 | 50000 | 0.2514 | 0.2132 | 0.8521 | - |
502
+ | 2.8968 | 50500 | 0.2603 | 0.2134 | 0.8496 | - |
503
+ | 2.9255 | 51000 | 0.2544 | 0.2131 | 0.8520 | - |
504
+ | 2.9542 | 51500 | 0.2526 | 0.2132 | 0.8511 | - |
505
+ | 2.9828 | 52000 | 0.2483 | 0.2131 | 0.8491 | - |
506
+ | 3.0115 | 52500 | 0.2361 | 0.2157 | 0.8522 | - |
507
+ | 3.0402 | 53000 | 0.2258 | 0.2122 | 0.8504 | - |
508
+ | 3.0689 | 53500 | 0.2248 | 0.2138 | 0.8498 | - |
509
+ | 3.0976 | 54000 | 0.2293 | 0.2164 | 0.8497 | - |
510
+ | 3.1263 | 54500 | 0.2328 | 0.2141 | 0.8501 | - |
511
+ | 3.1549 | 55000 | 0.2216 | 0.2109 | 0.8512 | - |
512
+ | 3.1836 | 55500 | 0.2256 | 0.2143 | 0.8529 | - |
513
+ | 3.2123 | 56000 | 0.2189 | 0.2126 | 0.8497 | - |
514
+ | 3.2410 | 56500 | 0.2264 | 0.2131 | 0.8492 | - |
515
+ | 3.2697 | 57000 | 0.2183 | 0.2129 | 0.8478 | - |
516
+ | 3.2983 | 57500 | 0.2155 | 0.2132 | 0.8515 | - |
517
+ | 3.3270 | 58000 | 0.2192 | 0.2133 | 0.8512 | - |
518
+ | 3.3557 | 58500 | 0.221 | 0.2132 | 0.8530 | - |
519
+ | 3.3844 | 59000 | 0.2139 | 0.2146 | 0.8522 | - |
520
+ | 3.4131 | 59500 | 0.2201 | 0.2139 | 0.8533 | - |
521
+ | 3.4417 | 60000 | 0.2058 | 0.2111 | 0.8505 | - |
522
+ | 3.4704 | 60500 | 0.2129 | 0.2116 | 0.8535 | - |
523
+ | 3.4991 | 61000 | 0.2137 | 0.2127 | 0.8513 | - |
524
+ | 3.5278 | 61500 | 0.2074 | 0.2129 | 0.8500 | - |
525
+ | 3.5565 | 62000 | 0.2157 | 0.2135 | 0.8506 | - |
526
+ | 3.5852 | 62500 | 0.2169 | 0.2153 | 0.8491 | - |
527
+ | 3.6138 | 63000 | 0.2052 | 0.2122 | 0.8527 | - |
528
+ | 3.6425 | 63500 | 0.2286 | 0.2144 | 0.8517 | - |
529
+ | 3.6712 | 64000 | 0.2105 | 0.2141 | 0.8491 | - |
530
+ | 3.6999 | 64500 | 0.2117 | 0.2147 | 0.8529 | - |
531
+ | 3.7286 | 65000 | 0.2117 | 0.2148 | 0.8510 | - |
532
+ | 3.7572 | 65500 | 0.2167 | 0.2158 | 0.8523 | - |
533
+ | 3.7859 | 66000 | 0.2176 | 0.2151 | 0.8506 | - |
534
+ | 3.8146 | 66500 | 0.2135 | 0.2131 | 0.8509 | - |
535
+ | 3.8433 | 67000 | 0.2148 | 0.2162 | 0.8532 | - |
536
+ | 3.8720 | 67500 | 0.2049 | 0.2128 | 0.8535 | - |
537
+ | 3.9006 | 68000 | 0.2133 | 0.2132 | 0.8511 | - |
538
+ | 3.9293 | 68500 | 0.2015 | 0.2177 | 0.8473 | - |
539
+ | 3.9580 | 69000 | 0.205 | 0.2165 | 0.8517 | - |
540
+ | 3.9867 | 69500 | 0.2032 | 0.2142 | 0.8548 | - |
541
+ | 4.0154 | 70000 | 0.1992 | 0.2173 | 0.8563 | - |
542
+ | 4.0441 | 70500 | 0.1851 | 0.2173 | 0.8541 | - |
543
+ | 4.0727 | 71000 | 0.1796 | 0.2154 | 0.8547 | - |
544
+ | 4.1014 | 71500 | 0.1836 | 0.2155 | 0.8544 | - |
545
+ | 4.1301 | 72000 | 0.181 | 0.2207 | 0.8513 | - |
546
+ | 4.1588 | 72500 | 0.1923 | 0.2198 | 0.8522 | - |
547
+ | 4.1875 | 73000 | 0.1865 | 0.2201 | 0.8533 | - |
548
+ | 4.2161 | 73500 | 0.1795 | 0.2139 | 0.8548 | - |
549
+ | 4.2448 | 74000 | 0.1813 | 0.2180 | 0.8512 | - |
550
+ | 4.2735 | 74500 | 0.1788 | 0.2147 | 0.8509 | - |
551
+ | 4.3022 | 75000 | 0.179 | 0.2133 | 0.8510 | - |
552
+ | 4.3309 | 75500 | 0.1796 | 0.2162 | 0.8519 | - |
553
+ | 4.3595 | 76000 | 0.1912 | 0.2185 | 0.8509 | - |
554
+ | 4.3882 | 76500 | 0.184 | 0.2162 | 0.8535 | - |
555
+ | 4.4169 | 77000 | 0.1827 | 0.2148 | 0.8535 | - |
556
+ | 4.4456 | 77500 | 0.1786 | 0.2146 | 0.8532 | - |
557
+ | 4.4743 | 78000 | 0.1826 | 0.2146 | 0.8534 | - |
558
+ | 4.5030 | 78500 | 0.1821 | 0.2165 | 0.8525 | - |
559
+ | 4.5316 | 79000 | 0.1781 | 0.2122 | 0.8524 | - |
560
+ | 4.5603 | 79500 | 0.1832 | 0.2147 | 0.8534 | - |
561
+ | 4.5890 | 80000 | 0.1812 | 0.2185 | 0.8534 | - |
562
+ | 4.6177 | 80500 | 0.1839 | 0.2163 | 0.8554 | - |
563
+ | 4.6464 | 81000 | 0.1834 | 0.2158 | 0.8542 | - |
564
+ | 4.6750 | 81500 | 0.1805 | 0.2171 | 0.8523 | - |
565
+ | 4.7037 | 82000 | 0.1818 | 0.2186 | 0.8536 | - |
566
+ | 4.7324 | 82500 | 0.1736 | 0.2176 | 0.8555 | - |
567
+ | 4.7611 | 83000 | 0.1754 | 0.2161 | 0.8525 | - |
568
+ | 4.7898 | 83500 | 0.1808 | 0.2187 | 0.8546 | - |
569
+ | 4.8184 | 84000 | 0.1794 | 0.2138 | 0.8543 | - |
570
+ | 4.8471 | 84500 | 0.1827 | 0.2147 | 0.8529 | - |
571
+ | 4.8758 | 85000 | 0.1773 | 0.2126 | 0.8535 | - |
572
+ | 4.9045 | 85500 | 0.1806 | 0.2124 | 0.8539 | - |
573
+ | 4.9332 | 86000 | 0.1843 | 0.2131 | 0.8490 | - |
574
+ | 4.9619 | 86500 | 0.1738 | 0.2120 | 0.8548 | - |
575
+ | 4.9905 | 87000 | 0.174 | 0.2120 | 0.8541 | - |
576
+ | 5.0192 | 87500 | 0.1644 | 0.2143 | 0.8533 | - |
577
+ | 5.0479 | 88000 | 0.1607 | 0.2132 | 0.8529 | - |
578
+ | 5.0766 | 88500 | 0.1539 | 0.2170 | 0.8504 | - |
579
+ | 5.1053 | 89000 | 0.1581 | 0.2123 | 0.8496 | - |
580
+ | 5.1339 | 89500 | 0.1563 | 0.2114 | 0.8519 | - |
581
+ | 5.1626 | 90000 | 0.158 | 0.2123 | 0.8494 | - |
582
+ | 5.1913 | 90500 | 0.1653 | 0.2147 | 0.8513 | - |
583
+ | 5.2200 | 91000 | 0.158 | 0.2170 | 0.8488 | - |
584
+ | 5.2487 | 91500 | 0.1559 | 0.2144 | 0.8489 | - |
585
+ | 5.2773 | 92000 | 0.165 | 0.2123 | 0.8498 | - |
586
+ | 5.3060 | 92500 | 0.15 | 0.2128 | 0.8497 | - |
587
+ | 5.3347 | 93000 | 0.1603 | 0.2126 | 0.8501 | - |
588
+ | 5.3634 | 93500 | 0.1584 | 0.2119 | 0.8490 | - |
589
+ | 5.3921 | 94000 | 0.1626 | 0.2121 | 0.8513 | - |
590
+ | 5.4208 | 94500 | 0.1585 | 0.2128 | 0.8505 | - |
591
+ | 5.4494 | 95000 | 0.1593 | 0.2099 | 0.8518 | - |
592
+ | 5.4781 | 95500 | 0.1546 | 0.2109 | 0.8482 | - |
593
+ | 5.5068 | 96000 | 0.1554 | 0.2115 | 0.8509 | - |
594
+ | 5.5355 | 96500 | 0.1628 | 0.2099 | 0.8507 | - |
595
+ | 5.5642 | 97000 | 0.1558 | 0.2124 | 0.8514 | - |
596
+ | 5.5928 | 97500 | 0.1556 | 0.2113 | 0.8510 | - |
597
+ | 5.6215 | 98000 | 0.1511 | 0.2091 | 0.8527 | - |
598
+ | 5.6502 | 98500 | 0.1545 | 0.2110 | 0.8518 | - |
599
+ | 5.6789 | 99000 | 0.1548 | 0.2113 | 0.8513 | - |
600
+ | 5.7076 | 99500 | 0.1556 | 0.2119 | 0.8516 | - |
601
+ | 5.7362 | 100000 | 0.1638 | 0.2113 | 0.8497 | - |
602
+ | 5.7649 | 100500 | 0.1516 | 0.2116 | 0.8500 | - |
603
+ | 5.7936 | 101000 | 0.1518 | 0.2124 | 0.8487 | - |
604
+ | 5.8223 | 101500 | 0.1562 | 0.2136 | 0.8485 | - |
605
+ | 5.8510 | 102000 | 0.1566 | 0.2132 | 0.8484 | - |
606
+ | 5.8797 | 102500 | 0.1517 | 0.2123 | 0.8487 | - |
607
+ | 5.9083 | 103000 | 0.1618 | 0.2114 | 0.8478 | - |
608
+ | 5.9370 | 103500 | 0.1531 | 0.2105 | 0.8487 | - |
609
+ | 5.9657 | 104000 | 0.1588 | 0.2106 | 0.8492 | - |
610
+ | 5.9944 | 104500 | 0.1563 | 0.2115 | 0.8486 | - |
611
+ | 6.0231 | 105000 | 0.151 | 0.2115 | 0.8505 | - |
612
+ | 6.0517 | 105500 | 0.1414 | 0.2111 | 0.8503 | - |
613
+ | 6.0804 | 106000 | 0.1316 | 0.2119 | 0.8491 | - |
614
+ | 6.1091 | 106500 | 0.1431 | 0.2108 | 0.8501 | - |
615
+ | 6.1378 | 107000 | 0.1336 | 0.2121 | 0.8508 | - |
616
+ | 6.1665 | 107500 | 0.1367 | 0.2111 | 0.8500 | - |
617
+ | 6.1951 | 108000 | 0.1397 | 0.2134 | 0.8496 | - |
618
+ | 6.2238 | 108500 | 0.1463 | 0.2128 | 0.8508 | - |
619
+ | 6.2525 | 109000 | 0.1416 | 0.2133 | 0.8525 | - |
620
+ | 6.2812 | 109500 | 0.147 | 0.2153 | 0.8504 | - |
621
+ | 6.3099 | 110000 | 0.1398 | 0.2133 | 0.8509 | - |
622
+ | 6.3386 | 110500 | 0.1451 | 0.2117 | 0.8534 | - |
623
+ | 6.3672 | 111000 | 0.1279 | 0.2129 | 0.8500 | - |
624
+ | 6.3959 | 111500 | 0.1309 | 0.2152 | 0.8484 | - |
625
+ | 6.4246 | 112000 | 0.1353 | 0.2128 | 0.8514 | - |
626
+ | 6.4533 | 112500 | 0.1355 | 0.2124 | 0.8501 | - |
627
+ | 6.4820 | 113000 | 0.1377 | 0.2141 | 0.8498 | - |
628
+ | 6.5106 | 113500 | 0.1376 | 0.2154 | 0.8522 | - |
629
+ | 6.5393 | 114000 | 0.1375 | 0.2148 | 0.8518 | - |
630
+ | 6.5680 | 114500 | 0.1348 | 0.2160 | 0.8507 | - |
631
+ | 6.5967 | 115000 | 0.1416 | 0.2139 | 0.8511 | - |
632
+ | 6.6254 | 115500 | 0.1396 | 0.2136 | 0.8503 | - |
633
+ | 6.6540 | 116000 | 0.1419 | 0.2130 | 0.8519 | - |
634
+ | 6.6827 | 116500 | 0.1435 | 0.2131 | 0.8527 | - |
635
+ | 6.7114 | 117000 | 0.1318 | 0.2128 | 0.8526 | - |
636
+ | 6.7401 | 117500 | 0.1362 | 0.2136 | 0.8530 | - |
637
+ | 6.7688 | 118000 | 0.1374 | 0.2125 | 0.8515 | - |
638
+ | 6.7975 | 118500 | 0.1384 | 0.2116 | 0.8528 | - |
639
+ | 6.8261 | 119000 | 0.1419 | 0.2122 | 0.8514 | - |
640
+ | 6.8548 | 119500 | 0.1303 | 0.2134 | 0.8506 | - |
641
+ | 6.8835 | 120000 | 0.1341 | 0.2125 | 0.8531 | - |
642
+ | 6.9122 | 120500 | 0.1342 | 0.2118 | 0.8512 | - |
643
+ | 6.9409 | 121000 | 0.1419 | 0.2107 | 0.8504 | - |
644
+ | 6.9695 | 121500 | 0.138 | 0.2108 | 0.8510 | - |
645
+ | 6.9982 | 122000 | 0.1318 | 0.2126 | 0.8525 | - |
646
+ | 7.0269 | 122500 | 0.1294 | 0.2138 | 0.8491 | - |
647
+ | 7.0556 | 123000 | 0.1355 | 0.2137 | 0.8504 | - |
648
+ | 7.0843 | 123500 | 0.1222 | 0.2148 | 0.8506 | - |
649
+ | 7.1129 | 124000 | 0.1231 | 0.2137 | 0.8530 | - |
650
+ | 7.1416 | 124500 | 0.1303 | 0.2142 | 0.8530 | - |
651
+ | 7.1703 | 125000 | 0.1281 | 0.2158 | 0.8515 | - |
652
+ | 7.1990 | 125500 | 0.1222 | 0.2134 | 0.8508 | - |
653
+ | 7.2277 | 126000 | 0.1316 | 0.2157 | 0.8512 | - |
654
+ | 7.2564 | 126500 | 0.1222 | 0.2151 | 0.8512 | - |
655
+ | 7.2850 | 127000 | 0.125 | 0.2147 | 0.8502 | - |
656
+ | 7.3137 | 127500 | 0.1271 | 0.2146 | 0.8508 | - |
657
+ | 7.3424 | 128000 | 0.1259 | 0.2167 | 0.8508 | - |
658
+ | 7.3711 | 128500 | 0.1318 | 0.2151 | 0.8525 | - |
659
+ | 7.3998 | 129000 | 0.1259 | 0.2182 | 0.8511 | - |
660
+ | 7.4284 | 129500 | 0.1249 | 0.2137 | 0.8541 | - |
661
+ | 7.4571 | 130000 | 0.1278 | 0.2177 | 0.8500 | - |
662
+ | 7.4858 | 130500 | 0.1246 | 0.2130 | 0.8507 | - |
663
+ | 7.5145 | 131000 | 0.1308 | 0.2107 | 0.8542 | - |
664
+ | 7.5432 | 131500 | 0.124 | 0.2107 | 0.8527 | - |
665
+ | 7.5718 | 132000 | 0.1196 | 0.2141 | 0.8505 | - |
666
+ | 7.6005 | 132500 | 0.1246 | 0.2116 | 0.8516 | - |
667
+ | 7.6292 | 133000 | 0.1269 | 0.2101 | 0.8534 | - |
668
+ | 7.6579 | 133500 | 0.1239 | 0.2110 | 0.8522 | - |
669
+ | 7.6866 | 134000 | 0.1344 | 0.2117 | 0.8512 | - |
670
+ | 7.7153 | 134500 | 0.129 | 0.2110 | 0.8515 | - |
671
+ | 7.7439 | 135000 | 0.1248 | 0.2113 | 0.8503 | - |
672
+ | 7.7726 | 135500 | 0.1261 | 0.2121 | 0.8501 | - |
673
+ | 7.8013 | 136000 | 0.1223 | 0.2110 | 0.8486 | - |
674
+ | 7.8300 | 136500 | 0.1236 | 0.2091 | 0.8494 | - |
675
+ | 7.8587 | 137000 | 0.1211 | 0.2084 | 0.8498 | - |
676
+ | 7.8873 | 137500 | 0.1187 | 0.2112 | 0.8473 | - |
677
+ | 7.9160 | 138000 | 0.1242 | 0.2090 | 0.8510 | - |
678
+ | 7.9447 | 138500 | 0.1206 | 0.2096 | 0.8503 | - |
679
+ | 7.9734 | 139000 | 0.1187 | 0.2125 | 0.8523 | - |
680
+ | 8.0021 | 139500 | 0.1242 | 0.2105 | 0.8497 | - |
681
+ | 8.0307 | 140000 | 0.1128 | 0.2134 | 0.8518 | - |
682
+ | 8.0594 | 140500 | 0.1188 | 0.2121 | 0.8509 | - |
683
+ | 8.0881 | 141000 | 0.1151 | 0.2133 | 0.8510 | - |
684
+ | 8.1168 | 141500 | 0.1213 | 0.2113 | 0.8508 | - |
685
+ | 8.1455 | 142000 | 0.1149 | 0.2126 | 0.8502 | - |
686
+ | 8.1742 | 142500 | 0.1126 | 0.2132 | 0.8528 | - |
687
+ | 8.2028 | 143000 | 0.1158 | 0.2129 | 0.8516 | - |
688
+ | 8.2315 | 143500 | 0.1188 | 0.2118 | 0.8510 | - |
689
+ | 8.2602 | 144000 | 0.1219 | 0.2115 | 0.8519 | - |
690
+ | 8.2889 | 144500 | 0.1184 | 0.2106 | 0.8522 | - |
691
+ | 8.3176 | 145000 | 0.1156 | 0.2102 | 0.8518 | - |
692
+ | 8.3462 | 145500 | 0.1125 | 0.2118 | 0.8513 | - |
693
+ | 8.3749 | 146000 | 0.1128 | 0.2103 | 0.8510 | - |
694
+ | 8.4036 | 146500 | 0.1118 | 0.2096 | 0.8502 | - |
695
+ | 8.4323 | 147000 | 0.1158 | 0.2080 | 0.8493 | - |
696
+ | 8.4610 | 147500 | 0.1172 | 0.2100 | 0.8477 | - |
697
+ | 8.4896 | 148000 | 0.1182 | 0.2153 | 0.8465 | - |
698
+ | 8.5183 | 148500 | 0.1173 | 0.2124 | 0.8483 | - |
699
+ | 8.5470 | 149000 | 0.1153 | 0.2118 | 0.8496 | - |
700
+ | 8.5757 | 149500 | 0.1172 | 0.2116 | 0.8487 | - |
701
+ | 8.6044 | 150000 | 0.114 | 0.2094 | 0.8516 | - |
702
+ | 8.6331 | 150500 | 0.1188 | 0.2117 | 0.8497 | - |
703
+ | 8.6617 | 151000 | 0.116 | 0.2128 | 0.8503 | - |
704
+ | 8.6904 | 151500 | 0.1152 | 0.2118 | 0.8505 | - |
705
+ | 8.7191 | 152000 | 0.1148 | 0.2147 | 0.8511 | - |
706
+ | 8.7478 | 152500 | 0.1136 | 0.2109 | 0.8514 | - |
707
+ | 8.7765 | 153000 | 0.1096 | 0.2104 | 0.8503 | - |
708
+ | 8.8051 | 153500 | 0.1151 | 0.2102 | 0.8509 | - |
709
+ | 8.8338 | 154000 | 0.1184 | 0.2129 | 0.8504 | - |
710
+ | 8.8625 | 154500 | 0.1156 | 0.2143 | 0.8503 | - |
711
+ | 8.8912 | 155000 | 0.1138 | 0.2121 | 0.8530 | - |
712
+ | 8.9199 | 155500 | 0.1144 | 0.2124 | 0.8527 | - |
713
+ | 8.9485 | 156000 | 0.1189 | 0.2136 | 0.8511 | - |
714
+ | 8.9772 | 156500 | 0.116 | 0.2139 | 0.8501 | - |
715
+ | 9.0059 | 157000 | 0.1132 | 0.2139 | 0.8495 | - |
716
+ | 9.0346 | 157500 | 0.1063 | 0.2135 | 0.8515 | - |
717
+ | 9.0633 | 158000 | 0.1069 | 0.2142 | 0.8501 | - |
718
+ | 9.0920 | 158500 | 0.0992 | 0.2163 | 0.8497 | - |
719
+ | 9.1206 | 159000 | 0.0995 | 0.2133 | 0.8534 | - |
720
+ | 9.1493 | 159500 | 0.1047 | 0.2144 | 0.8509 | - |
721
+ | 9.1780 | 160000 | 0.1074 | 0.2137 | 0.8516 | - |
722
+ | 9.2067 | 160500 | 0.1084 | 0.2169 | 0.8498 | - |
723
+ | 9.2354 | 161000 | 0.1081 | 0.2143 | 0.8505 | - |
724
+ | 9.2640 | 161500 | 0.1048 | 0.2141 | 0.8520 | - |
725
+ | 9.2927 | 162000 | 0.1055 | 0.2162 | 0.8489 | - |
726
+ | 9.3214 | 162500 | 0.101 | 0.2163 | 0.8485 | - |
727
+ | 9.3501 | 163000 | 0.1036 | 0.2153 | 0.8481 | - |
728
+ | 9.3788 | 163500 | 0.1057 | 0.2153 | 0.8489 | - |
729
+ | 9.4074 | 164000 | 0.1075 | 0.2150 | 0.8493 | - |
730
+ | 9.4361 | 164500 | 0.1055 | 0.2159 | 0.8500 | - |
731
+ | 9.4648 | 165000 | 0.1043 | 0.2152 | 0.8503 | - |
732
+ | 9.4935 | 165500 | 0.1072 | 0.2161 | 0.8513 | - |
733
+ | 9.5222 | 166000 | 0.1041 | 0.2154 | 0.8505 | - |
734
+ | 9.5509 | 166500 | 0.1064 | 0.2165 | 0.8505 | - |
735
+ | 9.5795 | 167000 | 0.1062 | 0.2168 | 0.8511 | - |
736
+ | 9.6082 | 167500 | 0.1046 | 0.2163 | 0.8494 | - |
737
+ | 9.6369 | 168000 | 0.1064 | 0.2167 | 0.8492 | - |
738
+ | 9.6656 | 168500 | 0.1017 | 0.2167 | 0.8499 | - |
739
+ | 9.6943 | 169000 | 0.1015 | 0.2143 | 0.8485 | - |
740
+ | 9.7229 | 169500 | 0.1075 | 0.2163 | 0.8477 | - |
741
+ | 9.7516 | 170000 | 0.1032 | 0.2176 | 0.8463 | - |
742
+ | 9.7803 | 170500 | 0.1115 | 0.2158 | 0.8471 | - |
743
+ | 9.8090 | 171000 | 0.1073 | 0.2127 | 0.8475 | - |
744
+ | 9.8377 | 171500 | 0.1056 | 0.2145 | 0.8481 | - |
745
+ | 9.8663 | 172000 | 0.108 | 0.2150 | 0.8488 | - |
746
+ | 9.8950 | 172500 | 0.1089 | 0.2142 | 0.8482 | - |
747
+ | 9.9237 | 173000 | 0.1037 | 0.2152 | 0.8475 | - |
748
+ | 9.9524 | 173500 | 0.1062 | 0.2137 | 0.8474 | - |
749
+ | 9.9811 | 174000 | 0.1058 | 0.2159 | 0.8482 | - |
750
+ | 10.0098 | 174500 | 0.101 | 0.2132 | 0.8481 | - |
751
+ | 10.0384 | 175000 | 0.1005 | 0.2152 | 0.8505 | - |
752
+ | 10.0671 | 175500 | 0.0963 | 0.2166 | 0.8475 | - |
753
+ | 10.0958 | 176000 | 0.1014 | 0.2150 | 0.8471 | - |
754
+ | 10.1245 | 176500 | 0.1012 | 0.2165 | 0.8467 | - |
755
+ | 10.1532 | 177000 | 0.1041 | 0.2144 | 0.8475 | - |
756
+ | 10.1818 | 177500 | 0.0982 | 0.2129 | 0.8483 | - |
757
+ | 10.2105 | 178000 | 0.098 | 0.2175 | 0.8463 | - |
758
+ | 10.2392 | 178500 | 0.0984 | 0.2127 | 0.8492 | - |
759
+ | 10.2679 | 179000 | 0.1 | 0.2148 | 0.8464 | - |
760
+ | 10.2966 | 179500 | 0.0985 | 0.2125 | 0.8468 | - |
761
+ | 10.3252 | 180000 | 0.1032 | 0.2102 | 0.8480 | - |
762
+ | 10.3539 | 180500 | 0.1019 | 0.2155 | 0.8455 | - |
763
+ | 10.3826 | 181000 | 0.1025 | 0.2105 | 0.8485 | - |
764
+ | 10.4113 | 181500 | 0.0987 | 0.2144 | 0.8471 | - |
765
+ | 10.4400 | 182000 | 0.1016 | 0.2142 | 0.8453 | - |
766
+ | 10.4687 | 182500 | 0.0981 | 0.2154 | 0.8466 | - |
767
+ | 10.4973 | 183000 | 0.0971 | 0.2150 | 0.8463 | - |
768
+ | 10.5260 | 183500 | 0.098 | 0.2136 | 0.8467 | - |
769
+ | 10.5547 | 184000 | 0.0995 | 0.2150 | 0.8469 | - |
770
+ | 10.5834 | 184500 | 0.0993 | 0.2134 | 0.8491 | - |
771
+ | 10.6121 | 185000 | 0.0983 | 0.2128 | 0.8483 | - |
772
+ | 10.6407 | 185500 | 0.1033 | 0.2143 | 0.8475 | - |
773
+ | 10.6694 | 186000 | 0.094 | 0.2138 | 0.8484 | - |
774
+ | 10.6981 | 186500 | 0.1026 | 0.2136 | 0.8477 | - |
775
+ | 10.7268 | 187000 | 0.1012 | 0.2140 | 0.8486 | - |
776
+ | 10.7555 | 187500 | 0.0926 | 0.2161 | 0.8481 | - |
777
+ | 10.7841 | 188000 | 0.1041 | 0.2134 | 0.8482 | - |
778
+ | 10.8128 | 188500 | 0.094 | 0.2150 | 0.8471 | - |
779
+ | 10.8415 | 189000 | 0.104 | 0.2157 | 0.8467 | - |
780
+ | 10.8702 | 189500 | 0.1015 | 0.2139 | 0.8472 | - |
781
+ | 10.8989 | 190000 | 0.0942 | 0.2173 | 0.8473 | - |
782
+ | 10.9276 | 190500 | 0.1002 | 0.2168 | 0.8471 | - |
783
+ | 10.9562 | 191000 | 0.1038 | 0.2169 | 0.8472 | - |
784
+ | 10.9849 | 191500 | 0.1026 | 0.2157 | 0.8463 | - |
785
+ | 11.0136 | 192000 | 0.0975 | 0.2161 | 0.8471 | - |
786
+ | 11.0423 | 192500 | 0.0918 | 0.2146 | 0.8476 | - |
787
+ | 11.0710 | 193000 | 0.0962 | 0.2172 | 0.8469 | - |
788
+ | 11.0996 | 193500 | 0.0928 | 0.2172 | 0.8472 | - |
789
+ | 11.1283 | 194000 | 0.0936 | 0.2165 | 0.8478 | - |
790
+ | 11.1570 | 194500 | 0.0875 | 0.2191 | 0.8472 | - |
791
+ | 11.1857 | 195000 | 0.0997 | 0.2190 | 0.8478 | - |
792
+ | 11.2144 | 195500 | 0.0937 | 0.2215 | 0.8455 | - |
793
+ | 11.2430 | 196000 | 0.0971 | 0.2168 | 0.8458 | - |
794
+ | 11.2717 | 196500 | 0.0963 | 0.2170 | 0.8456 | - |
795
+ | 11.3004 | 197000 | 0.0922 | 0.2183 | 0.8463 | - |
796
+ | 11.3291 | 197500 | 0.0946 | 0.2175 | 0.8448 | - |
797
+ | 11.3578 | 198000 | 0.0976 | 0.2172 | 0.8445 | - |
798
+ | 11.3865 | 198500 | 0.0918 | 0.2171 | 0.8457 | - |
799
+ | 11.4151 | 199000 | 0.1029 | 0.2165 | 0.8459 | - |
800
+ | 11.4438 | 199500 | 0.0949 | 0.2154 | 0.8475 | - |
801
+ | 11.4725 | 200000 | 0.0937 | 0.2172 | 0.8446 | - |
802
+ | 11.5012 | 200500 | 0.096 | 0.2181 | 0.8459 | - |
803
+ | 11.5299 | 201000 | 0.0957 | 0.2190 | 0.8451 | - |
804
+ | 11.5585 | 201500 | 0.0988 | 0.2164 | 0.8455 | - |
805
+ | 11.5872 | 202000 | 0.0966 | 0.2166 | 0.8443 | - |
806
+ | 11.6159 | 202500 | 0.0922 | 0.2168 | 0.8440 | - |
807
+ | 11.6446 | 203000 | 0.0914 | 0.2167 | 0.8452 | - |
808
+ | 11.6733 | 203500 | 0.0935 | 0.2153 | 0.8455 | - |
809
+ | 11.7019 | 204000 | 0.0946 | 0.2161 | 0.8455 | - |
810
+ | 11.7306 | 204500 | 0.0969 | 0.2159 | 0.8465 | - |
811
+ | 11.7593 | 205000 | 0.0956 | 0.2166 | 0.8448 | - |
812
+ | 11.7880 | 205500 | 0.0892 | 0.2150 | 0.8455 | - |
813
+ | 11.8167 | 206000 | 0.0919 | 0.2162 | 0.8459 | - |
814
+ | 11.8454 | 206500 | 0.0975 | 0.2162 | 0.8464 | - |
815
+ | 11.8740 | 207000 | 0.0925 | 0.2169 | 0.8454 | - |
816
+ | 11.9027 | 207500 | 0.0883 | 0.2169 | 0.8459 | - |
817
+ | 11.9314 | 208000 | 0.0957 | 0.2160 | 0.8468 | - |
818
+ | 11.9601 | 208500 | 0.0941 | 0.2162 | 0.8471 | - |
819
+ | 11.9888 | 209000 | 0.0924 | 0.2175 | 0.8465 | - |
820
+ | 12.0174 | 209500 | 0.0895 | 0.2159 | 0.8469 | - |
821
+ | 12.0461 | 210000 | 0.0877 | 0.2168 | 0.8457 | - |
822
+ | 12.0748 | 210500 | 0.0908 | 0.2162 | 0.8460 | - |
823
+ | 12.1035 | 211000 | 0.0907 | 0.2174 | 0.8461 | - |
824
+ | 12.1322 | 211500 | 0.0893 | 0.2179 | 0.8450 | - |
825
+ | 12.1608 | 212000 | 0.0868 | 0.2180 | 0.8452 | - |
826
+ | 12.1895 | 212500 | 0.0924 | 0.2180 | 0.8466 | - |
827
+ | 12.2182 | 213000 | 0.0888 | 0.2167 | 0.8462 | - |
828
+ | 12.2469 | 213500 | 0.0846 | 0.2167 | 0.8452 | - |
829
+ | 12.2756 | 214000 | 0.0921 | 0.2166 | 0.8458 | - |
830
+ | 12.3043 | 214500 | 0.0854 | 0.2176 | 0.8456 | - |
831
+ | 12.3329 | 215000 | 0.0877 | 0.2154 | 0.8452 | - |
832
+ | 12.3616 | 215500 | 0.0933 | 0.2162 | 0.8455 | - |
833
+ | 12.3903 | 216000 | 0.0849 | 0.2191 | 0.8449 | - |
834
+ | 12.4190 | 216500 | 0.0889 | 0.2195 | 0.8437 | - |
835
+ | 12.4477 | 217000 | 0.0886 | 0.2183 | 0.8449 | - |
836
+ | 12.4763 | 217500 | 0.0907 | 0.2175 | 0.8464 | - |
837
+ | 12.5050 | 218000 | 0.0915 | 0.2171 | 0.8458 | - |
838
+ | 12.5337 | 218500 | 0.0908 | 0.2180 | 0.8465 | - |
839
+ | 12.5624 | 219000 | 0.0863 | 0.2192 | 0.8450 | - |
840
+ | 12.5911 | 219500 | 0.086 | 0.2190 | 0.8460 | - |
841
+ | 12.6197 | 220000 | 0.0944 | 0.2190 | 0.8462 | - |
842
+ | 12.6484 | 220500 | 0.0858 | 0.2186 | 0.8459 | - |
843
+ | 12.6771 | 221000 | 0.0918 | 0.2176 | 0.8466 | - |
844
+ | 12.7058 | 221500 | 0.0934 | 0.2185 | 0.8468 | - |
845
+ | 12.7345 | 222000 | 0.0903 | 0.2182 | 0.8472 | - |
846
+ | 12.7632 | 222500 | 0.0858 | 0.2179 | 0.8467 | - |
847
+ | 12.7918 | 223000 | 0.0941 | 0.2188 | 0.8461 | - |
848
+ | 12.8205 | 223500 | 0.0867 | 0.2170 | 0.8464 | - |
849
+ | 12.8492 | 224000 | 0.0881 | 0.2173 | 0.8471 | - |
850
+ | 12.8779 | 224500 | 0.0869 | 0.2185 | 0.8464 | - |
851
+ | 12.9066 | 225000 | 0.0933 | 0.2181 | 0.8467 | - |
852
+ | 12.9352 | 225500 | 0.0923 | 0.2177 | 0.8464 | - |
853
+ | 12.9639 | 226000 | 0.0887 | 0.2175 | 0.8471 | - |
854
+ | 12.9926 | 226500 | 0.0958 | 0.2180 | 0.8472 | - |
855
+ | 13.0213 | 227000 | 0.085 | 0.2181 | 0.8463 | - |
856
+ | 13.0500 | 227500 | 0.0818 | 0.2169 | 0.8467 | - |
857
+ | 13.0786 | 228000 | 0.0876 | 0.2186 | 0.8459 | - |
858
+ | 13.1073 | 228500 | 0.0913 | 0.2190 | 0.8453 | - |
859
+ | 13.1360 | 229000 | 0.0853 | 0.2193 | 0.8454 | - |
860
+ | 13.1647 | 229500 | 0.0886 | 0.2199 | 0.8463 | - |
861
+ | 13.1934 | 230000 | 0.085 | 0.2202 | 0.8465 | - |
862
+ | 13.2221 | 230500 | 0.0879 | 0.2222 | 0.8453 | - |
863
+ | 13.2507 | 231000 | 0.0853 | 0.2208 | 0.8457 | - |
864
+ | 13.2794 | 231500 | 0.0831 | 0.2192 | 0.8459 | - |
865
+ | 13.3081 | 232000 | 0.0865 | 0.2202 | 0.8455 | - |
866
+ | 13.3368 | 232500 | 0.091 | 0.2209 | 0.8449 | - |
867
+ | 13.3655 | 233000 | 0.0848 | 0.2193 | 0.8454 | - |
868
+ | 13.3941 | 233500 | 0.0873 | 0.2192 | 0.8453 | - |
869
+ | 13.4228 | 234000 | 0.0813 | 0.2197 | 0.8449 | - |
870
+ | 13.4515 | 234500 | 0.0883 | 0.2205 | 0.8447 | - |
871
+ | 13.4802 | 235000 | 0.0858 | 0.2193 | 0.8463 | - |
872
+ | 13.5089 | 235500 | 0.0902 | 0.2197 | 0.8466 | - |
873
+ | 13.5375 | 236000 | 0.0837 | 0.2185 | 0.8469 | - |
874
+ | 13.5662 | 236500 | 0.0922 | 0.2201 | 0.8462 | - |
875
+ | 13.5949 | 237000 | 0.0876 | 0.2197 | 0.8463 | - |
876
+ | 13.6236 | 237500 | 0.0839 | 0.2191 | 0.8458 | - |
877
+ | 13.6523 | 238000 | 0.0878 | 0.2197 | 0.8454 | - |
878
+ | 13.6809 | 238500 | 0.0874 | 0.2197 | 0.8451 | - |
879
+ | 13.7096 | 239000 | 0.0848 | 0.2198 | 0.8457 | - |
880
+ | 13.7383 | 239500 | 0.0842 | 0.2185 | 0.8459 | - |
881
+ | 13.7670 | 240000 | 0.0827 | 0.2184 | 0.8463 | - |
882
+ | 13.7957 | 240500 | 0.0885 | 0.2176 | 0.8458 | - |
883
+ | 13.8244 | 241000 | 0.0872 | 0.2180 | 0.8462 | - |
884
+ | 13.8530 | 241500 | 0.0856 | 0.2180 | 0.8468 | - |
885
+ | 13.8817 | 242000 | 0.0887 | 0.2184 | 0.8459 | - |
886
+ | 13.9104 | 242500 | 0.0875 | 0.2187 | 0.8461 | - |
887
+ | 13.9391 | 243000 | 0.0857 | 0.2195 | 0.8460 | - |
888
+ | 13.9678 | 243500 | 0.0845 | 0.2188 | 0.8467 | - |
889
+ | 13.9964 | 244000 | 0.0896 | 0.2184 | 0.8463 | - |
890
+ | 14.0251 | 244500 | 0.0818 | 0.2189 | 0.8467 | - |
891
+ | 14.0538 | 245000 | 0.09 | 0.2194 | 0.8460 | - |
892
+ | 14.0825 | 245500 | 0.0842 | 0.2190 | 0.8456 | - |
893
+ | 14.1112 | 246000 | 0.0878 | 0.2190 | 0.8460 | - |
894
+ | 14.1398 | 246500 | 0.0838 | 0.2195 | 0.8462 | - |
895
+ | 14.1685 | 247000 | 0.0781 | 0.2201 | 0.8460 | - |
896
+ | 14.1972 | 247500 | 0.0847 | 0.2193 | 0.8466 | - |
897
+ | 14.2259 | 248000 | 0.0881 | 0.2188 | 0.8470 | - |
898
+ | 14.2546 | 248500 | 0.082 | 0.2184 | 0.8473 | - |
899
+ | 14.2833 | 249000 | 0.0886 | 0.2191 | 0.8469 | - |
900
+ | 14.3119 | 249500 | 0.0874 | 0.2195 | 0.8470 | - |
901
+ | 14.3406 | 250000 | 0.0833 | 0.2197 | 0.8465 | - |
902
+ | 14.3693 | 250500 | 0.0856 | 0.2197 | 0.8461 | - |
903
+ | 14.3980 | 251000 | 0.0834 | 0.2198 | 0.8464 | - |
904
+ | 14.4267 | 251500 | 0.0852 | 0.2199 | 0.8461 | - |
905
+ | 14.4553 | 252000 | 0.0853 | 0.2201 | 0.8456 | - |
906
+ | 14.4840 | 252500 | 0.0811 | 0.2197 | 0.8461 | - |
907
+ | 14.5127 | 253000 | 0.0778 | 0.2195 | 0.8464 | - |
908
+ | 14.5414 | 253500 | 0.0837 | 0.2200 | 0.8462 | - |
909
+ | 14.5701 | 254000 | 0.0835 | 0.2203 | 0.8459 | - |
910
+ | 14.5987 | 254500 | 0.0854 | 0.2199 | 0.8462 | - |
911
+ | 14.6274 | 255000 | 0.0877 | 0.2196 | 0.8464 | - |
912
+ | 14.6561 | 255500 | 0.0826 | 0.2198 | 0.8463 | - |
913
+ | 14.6848 | 256000 | 0.0894 | 0.2197 | 0.8463 | - |
914
+ | 14.7135 | 256500 | 0.0873 | 0.2199 | 0.8462 | - |
915
+ | 14.7422 | 257000 | 0.0818 | 0.2197 | 0.8462 | - |
916
+ | 14.7708 | 257500 | 0.0854 | 0.2196 | 0.8464 | - |
917
+ | 14.7995 | 258000 | 0.0823 | 0.2195 | 0.8464 | - |
918
+ | 14.8282 | 258500 | 0.0769 | 0.2194 | 0.8465 | - |
919
+ | 14.8569 | 259000 | 0.0842 | 0.2195 | 0.8465 | - |
920
+ | 14.8856 | 259500 | 0.0848 | 0.2195 | 0.8464 | - |
921
+ | 14.9142 | 260000 | 0.0839 | 0.2196 | 0.8464 | - |
922
+ | 14.9429 | 260500 | 0.089 | 0.2196 | 0.8464 | - |
923
+ | 14.9716 | 261000 | 0.0881 | 0.2197 | 0.8464 | - |
924
+ | -1 | -1 | - | - | - | 0.8431 |
925
+
926
+ </details>
927
+
928
+ ### Framework Versions
929
+ - Python: 3.13.0
930
+ - Sentence Transformers: 5.1.2
931
+ - Transformers: 4.57.1
932
+ - PyTorch: 2.9.1+cu128
933
+ - Accelerate: 1.11.0
934
+ - Datasets: 4.4.1
935
+ - Tokenizers: 0.22.1
936
+
937
+ ## Citation
938
+
939
+ ### BibTeX
940
+
941
+ #### Sentence Transformers
942
+ ```bibtex
943
+ @inproceedings{reimers-2019-sentence-bert,
944
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
945
+ author = "Reimers, Nils and Gurevych, Iryna",
946
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
947
+ month = "11",
948
+ year = "2019",
949
+ publisher = "Association for Computational Linguistics",
950
+ url = "https://arxiv.org/abs/1908.10084",
951
+ }
952
+ ```
953
+
954
+ #### MatryoshkaLoss
955
+ ```bibtex
956
+ @misc{kusupati2024matryoshka,
957
+ title={Matryoshka Representation Learning},
958
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
959
+ year={2024},
960
+ eprint={2205.13147},
961
+ archivePrefix={arXiv},
962
+ primaryClass={cs.LG}
963
+ }
964
+ ```
965
+
966
+ #### MultipleNegativesRankingLoss
967
+ ```bibtex
968
+ @misc{henderson2017efficient,
969
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
970
+ 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},
971
+ year={2017},
972
+ eprint={1705.00652},
973
+ archivePrefix={arXiv},
974
+ primaryClass={cs.CL}
975
+ }
976
+ ```
977
+
978
+ <!--
979
+ ## Glossary
980
+
981
+ *Clearly define terms in order to be accessible across audiences.*
982
+ -->
983
+
984
+ <!--
985
+ ## Model Card Authors
986
+
987
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
988
+ -->
989
+
990
+ <!--
991
+ ## Model Card Contact
992
+
993
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
994
+ -->
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+ "tokenizer_class": "T5Tokenizer",
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+ "unk_token": "<unk>"
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+ }