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daebakgazua
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250702_OhLoRA_interview_sbert_next_question

Text Classification
Safetensors
Korean
roberta
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  1. README.md +870 -13
  2. config.json +28 -0
  3. config_sentence_transformers.json +10 -0
  4. model.safetensors +3 -0
  5. modules.json +14 -0
  6. readme_ML_interview_output_answer.txt +1 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +51 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +60 -0
  11. vocab.txt +0 -0
README.md CHANGED
@@ -1,13 +1,870 @@
1
- ---
2
- license: mit
3
- language:
4
- - ko
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- base_model:
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- - klue/roberta-base
7
- pipeline_tag: text-classification
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- ---
9
-
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- S-BERT model for **Next Question Selection** for **Interview** function of [Oh-LoRA 👱‍♀️ (오로라) ML Tutor](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_07_02_OhLoRA_ML_Tutor).
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-
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- * This S-BERT model is a Fine-tuned version of ```klue/roberta-base```.
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- * [Detailed info (in Korean)](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_07_02_OhLoRA_ML_Tutor/ai_interview#1-2-%EB%8B%A4%EC%9D%8C-%EC%A7%88%EB%AC%B8-%EC%84%A0%ED%83%9D)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
7
+ - dataset_size:11664
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+ - loss:CosineSimilarityLoss
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+ base_model: klue/roberta-base
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+ widget:
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+ - source_sentence: Multi-Class, Multi-Label 중 BCE 가 좋은 task -> 이건 분명 멀티라벨이지.
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+ sentences:
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+ - 기본 경험
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+ - 면접 시작 인사
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+ - 좋아하는 아이돌
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+ - source_sentence: Loss Function 관련 실무 경험 -> [기본 경험] 확률 예측에서 MSE Loss, MAE Loss 써
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+ 봤어! 엄청 혼났다 ㅠㅠ
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+ sentences:
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+ - Loss Function 예시
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+ - Multi-Label 에서 CE + Softmax 적용 문제점
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+ - 용어 질문
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+ - source_sentence: Loss Function 관련 실무 경험 -> [상세 경험] 필수적인 Loss Term 인 Cross-Entropy
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+ Loss 가 빠졌더라! 그래서 그거 해결해서 성능 20% 개선했지!
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+ sentences:
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+ - LLM Fine-Tuning 의 PEFT
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+ - Loss Function 예시
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+ - 마지막 할 말
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+ - source_sentence: 거대 언어 모델 정의 -> 수백억 파라미터로 구성된 언어 모델!
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+ sentences:
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+ - BCE 가 좋은 task
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+ - LoRA 와 QLoRA 의 차이
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+ - 기본 경험
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+ - source_sentence: PEFT 방법 5가지 -> Adapter Layer 추가하는 거랑 음 그리고 PEFT! 그거 알지?
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+ sentences:
35
+ - 머신러닝
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+ - LoRA 와 QLoRA 의 차이
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+ - 용어 질문
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
41
+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on klue/roberta-base
45
+ results:
46
+ - task:
47
+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
50
+ name: valid evaluator
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+ type: valid_evaluator
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+ metrics:
53
+ - type: pearson_cosine
54
+ value: 0.9999519237820663
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.3303596809565949
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+ name: Spearman Cosine
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+ ---
60
+
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+ # SentenceTransformer based on klue/roberta-base
62
+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). 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.
64
+
65
+ ## Model Details
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+
67
+ ### Model Description
68
+ - **Model Type:** Sentence Transformer
69
+ - **Base model:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
70
+ - **Maximum Sequence Length:** 64 tokens
71
+ - **Output Dimensionality:** 768 dimensions
72
+ - **Similarity Function:** Cosine Similarity
73
+ <!-- - **Training Dataset:** Unknown -->
74
+ <!-- - **Language:** Unknown -->
75
+ <!-- - **License:** Unknown -->
76
+
77
+ ### Model Sources
78
+
79
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
80
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
81
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
82
+
83
+ ### Full Model Architecture
84
+
85
+ ```
86
+ SentenceTransformer(
87
+ (0): Transformer({'max_seq_length': 64, 'do_lower_case': True}) with Transformer model: RobertaModel
88
+ (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})
89
+ )
90
+ ```
91
+
92
+ ## Usage
93
+
94
+ ### Direct Usage (Sentence Transformers)
95
+
96
+ First install the Sentence Transformers library:
97
+
98
+ ```bash
99
+ pip install -U sentence-transformers
100
+ ```
101
+
102
+ Then you can load this model and run inference.
103
+ ```python
104
+ from sentence_transformers import SentenceTransformer
105
+
106
+ # Download from the 🤗 Hub
107
+ model = SentenceTransformer("sentence_transformers_model_id")
108
+ # Run inference
109
+ sentences = [
110
+ 'PEFT 방법 5가지 -> Adapter Layer 추가하는 거랑 음 그리고 PEFT! 그거 알지?',
111
+ 'LoRA 와 QLoRA 의 차이',
112
+ '용어 질문',
113
+ ]
114
+ embeddings = model.encode(sentences)
115
+ print(embeddings.shape)
116
+ # [3, 768]
117
+
118
+ # Get the similarity scores for the embeddings
119
+ similarities = model.similarity(embeddings, embeddings)
120
+ print(similarities.shape)
121
+ # [3, 3]
122
+ ```
123
+
124
+ <!--
125
+ ### Direct Usage (Transformers)
126
+
127
+ <details><summary>Click to see the direct usage in Transformers</summary>
128
+
129
+ </details>
130
+ -->
131
+
132
+ <!--
133
+ ### Downstream Usage (Sentence Transformers)
134
+
135
+ You can finetune this model on your own dataset.
136
+
137
+ <details><summary>Click to expand</summary>
138
+
139
+ </details>
140
+ -->
141
+
142
+ <!--
143
+ ### Out-of-Scope Use
144
+
145
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
146
+ -->
147
+
148
+ ## Evaluation
149
+
150
+ ### Metrics
151
+
152
+ #### Semantic Similarity
153
+
154
+ * Dataset: `valid_evaluator`
155
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
156
+
157
+ | Metric | Value |
158
+ |:--------------------|:-----------|
159
+ | pearson_cosine | 1.0 |
160
+ | **spearman_cosine** | **0.3304** |
161
+
162
+ <!--
163
+ ## Bias, Risks and Limitations
164
+
165
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
166
+ -->
167
+
168
+ <!--
169
+ ### Recommendations
170
+
171
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
172
+ -->
173
+
174
+ ## Training Details
175
+
176
+ ### Training Dataset
177
+
178
+ #### Unnamed Dataset
179
+
180
+ * Size: 11,664 training samples
181
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
182
+ * Approximate statistics based on the first 1000 samples:
183
+ | | sentence_0 | sentence_1 | label |
184
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
185
+ | type | string | string | float |
186
+ | details | <ul><li>min: 7 tokens</li><li>mean: 29.04 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.85 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.03</li><li>max: 1.0</li></ul> |
187
+ * Samples:
188
+ | sentence_0 | sentence_1 | label |
189
+ |:---------------------------------------------------------------------------|:-------------------------|:-----------------|
190
+ | <code>Loss Function 정의 -> 모델이 잘못 예측한 것에 대한 패널티를 수식으로 정의한 거 아니야? 맞지?</code> | <code>MSE Loss 설명</code> | <code>0.0</code> |
191
+ | <code>인공지능, 머신러닝, 딥러닝 차이 -> 딥러닝은 신경망이라는 걸 이용해서 머신러닝을 하는 거지!</code> | <code>좋아하는 아이돌</code> | <code>0.0</code> |
192
+ | <code>MSE Loss 설명 -> 각 데이터별로 오차를 구하고 그 제곱을 평균한 거야!</code> | <code>거대 언어 모델 정의</code> | <code>0.0</code> |
193
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
194
+ ```json
195
+ {
196
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
197
+ }
198
+ ```
199
+
200
+ ### Training Hyperparameters
201
+ #### Non-Default Hyperparameters
202
+
203
+ - `eval_strategy`: steps
204
+ - `per_device_train_batch_size`: 16
205
+ - `per_device_eval_batch_size`: 16
206
+ - `num_train_epochs`: 40
207
+ - `multi_dataset_batch_sampler`: round_robin
208
+
209
+ #### All Hyperparameters
210
+ <details><summary>Click to expand</summary>
211
+
212
+ - `overwrite_output_dir`: False
213
+ - `do_predict`: False
214
+ - `eval_strategy`: steps
215
+ - `prediction_loss_only`: True
216
+ - `per_device_train_batch_size`: 16
217
+ - `per_device_eval_batch_size`: 16
218
+ - `per_gpu_train_batch_size`: None
219
+ - `per_gpu_eval_batch_size`: None
220
+ - `gradient_accumulation_steps`: 1
221
+ - `eval_accumulation_steps`: None
222
+ - `torch_empty_cache_steps`: None
223
+ - `learning_rate`: 5e-05
224
+ - `weight_decay`: 0.0
225
+ - `adam_beta1`: 0.9
226
+ - `adam_beta2`: 0.999
227
+ - `adam_epsilon`: 1e-08
228
+ - `max_grad_norm`: 1
229
+ - `num_train_epochs`: 40
230
+ - `max_steps`: -1
231
+ - `lr_scheduler_type`: linear
232
+ - `lr_scheduler_kwargs`: {}
233
+ - `warmup_ratio`: 0.0
234
+ - `warmup_steps`: 0
235
+ - `log_level`: passive
236
+ - `log_level_replica`: warning
237
+ - `log_on_each_node`: True
238
+ - `logging_nan_inf_filter`: True
239
+ - `save_safetensors`: True
240
+ - `save_on_each_node`: False
241
+ - `save_only_model`: False
242
+ - `restore_callback_states_from_checkpoint`: False
243
+ - `no_cuda`: False
244
+ - `use_cpu`: False
245
+ - `use_mps_device`: False
246
+ - `seed`: 42
247
+ - `data_seed`: None
248
+ - `jit_mode_eval`: False
249
+ - `use_ipex`: False
250
+ - `bf16`: False
251
+ - `fp16`: False
252
+ - `fp16_opt_level`: O1
253
+ - `half_precision_backend`: auto
254
+ - `bf16_full_eval`: False
255
+ - `fp16_full_eval`: False
256
+ - `tf32`: None
257
+ - `local_rank`: 0
258
+ - `ddp_backend`: None
259
+ - `tpu_num_cores`: None
260
+ - `tpu_metrics_debug`: False
261
+ - `debug`: []
262
+ - `dataloader_drop_last`: False
263
+ - `dataloader_num_workers`: 0
264
+ - `dataloader_prefetch_factor`: None
265
+ - `past_index`: -1
266
+ - `disable_tqdm`: False
267
+ - `remove_unused_columns`: True
268
+ - `label_names`: None
269
+ - `load_best_model_at_end`: False
270
+ - `ignore_data_skip`: False
271
+ - `fsdp`: []
272
+ - `fsdp_min_num_params`: 0
273
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
274
+ - `tp_size`: 0
275
+ - `fsdp_transformer_layer_cls_to_wrap`: None
276
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
277
+ - `deepspeed`: None
278
+ - `label_smoothing_factor`: 0.0
279
+ - `optim`: adamw_torch
280
+ - `optim_args`: None
281
+ - `adafactor`: False
282
+ - `group_by_length`: False
283
+ - `length_column_name`: length
284
+ - `ddp_find_unused_parameters`: None
285
+ - `ddp_bucket_cap_mb`: None
286
+ - `ddp_broadcast_buffers`: False
287
+ - `dataloader_pin_memory`: True
288
+ - `dataloader_persistent_workers`: False
289
+ - `skip_memory_metrics`: True
290
+ - `use_legacy_prediction_loop`: False
291
+ - `push_to_hub`: False
292
+ - `resume_from_checkpoint`: None
293
+ - `hub_model_id`: None
294
+ - `hub_strategy`: every_save
295
+ - `hub_private_repo`: None
296
+ - `hub_always_push`: False
297
+ - `gradient_checkpointing`: False
298
+ - `gradient_checkpointing_kwargs`: None
299
+ - `include_inputs_for_metrics`: False
300
+ - `include_for_metrics`: []
301
+ - `eval_do_concat_batches`: True
302
+ - `fp16_backend`: auto
303
+ - `push_to_hub_model_id`: None
304
+ - `push_to_hub_organization`: None
305
+ - `mp_parameters`:
306
+ - `auto_find_batch_size`: False
307
+ - `full_determinism`: False
308
+ - `torchdynamo`: None
309
+ - `ray_scope`: last
310
+ - `ddp_timeout`: 1800
311
+ - `torch_compile`: False
312
+ - `torch_compile_backend`: None
313
+ - `torch_compile_mode`: None
314
+ - `include_tokens_per_second`: False
315
+ - `include_num_input_tokens_seen`: False
316
+ - `neftune_noise_alpha`: None
317
+ - `optim_target_modules`: None
318
+ - `batch_eval_metrics`: False
319
+ - `eval_on_start`: False
320
+ - `use_liger_kernel`: False
321
+ - `eval_use_gather_object`: False
322
+ - `average_tokens_across_devices`: False
323
+ - `prompts`: None
324
+ - `batch_sampler`: batch_sampler
325
+ - `multi_dataset_batch_sampler`: round_robin
326
+
327
+ </details>
328
+
329
+ ### Training Logs
330
+ <details><summary>Click to expand</summary>
331
+
332
+ | Epoch | Step | Training Loss | valid_evaluator_spearman_cosine |
333
+ |:-------:|:-----:|:-------------:|:-------------------------------:|
334
+ | 0.1001 | 73 | - | 0.0133 |
335
+ | 0.2003 | 146 | - | -0.0061 |
336
+ | 0.3004 | 219 | - | 0.0476 |
337
+ | 0.4005 | 292 | - | 0.1975 |
338
+ | 0.5007 | 365 | - | 0.2232 |
339
+ | 0.6008 | 438 | - | 0.2484 |
340
+ | 0.6859 | 500 | 0.0952 | - |
341
+ | 0.7010 | 511 | - | 0.2631 |
342
+ | 0.8011 | 584 | - | 0.2481 |
343
+ | 0.9012 | 657 | - | 0.2594 |
344
+ | 1.0 | 729 | - | 0.2798 |
345
+ | 1.0014 | 730 | - | 0.2792 |
346
+ | 1.1015 | 803 | - | 0.2875 |
347
+ | 1.2016 | 876 | - | 0.2941 |
348
+ | 1.3018 | 949 | - | 0.2897 |
349
+ | 1.3717 | 1000 | 0.0285 | - |
350
+ | 1.4019 | 1022 | - | 0.3089 |
351
+ | 1.5021 | 1095 | - | 0.3130 |
352
+ | 1.6022 | 1168 | - | 0.3121 |
353
+ | 1.7023 | 1241 | - | 0.3170 |
354
+ | 1.8025 | 1314 | - | 0.2639 |
355
+ | 1.9026 | 1387 | - | 0.3031 |
356
+ | 2.0 | 1458 | - | 0.3203 |
357
+ | 2.0027 | 1460 | - | 0.3200 |
358
+ | 2.0576 | 1500 | 0.0215 | - |
359
+ | 2.1029 | 1533 | - | 0.3205 |
360
+ | 2.2030 | 1606 | - | 0.3180 |
361
+ | 2.3032 | 1679 | - | 0.3009 |
362
+ | 2.4033 | 1752 | - | 0.2967 |
363
+ | 2.5034 | 1825 | - | 0.3215 |
364
+ | 2.6036 | 1898 | - | 0.3187 |
365
+ | 2.7037 | 1971 | - | 0.3230 |
366
+ | 2.7435 | 2000 | 0.0141 | - |
367
+ | 2.8038 | 2044 | - | 0.3216 |
368
+ | 2.9040 | 2117 | - | 0.3152 |
369
+ | 3.0 | 2187 | - | 0.3206 |
370
+ | 3.0041 | 2190 | - | 0.3202 |
371
+ | 3.1043 | 2263 | - | 0.3272 |
372
+ | 3.2044 | 2336 | - | 0.3270 |
373
+ | 3.3045 | 2409 | - | 0.3251 |
374
+ | 3.4047 | 2482 | - | 0.3291 |
375
+ | 3.4294 | 2500 | 0.0105 | - |
376
+ | 3.5048 | 2555 | - | 0.3267 |
377
+ | 3.6049 | 2628 | - | 0.3214 |
378
+ | 3.7051 | 2701 | - | 0.3275 |
379
+ | 3.8052 | 2774 | - | 0.3275 |
380
+ | 3.9053 | 2847 | - | 0.3295 |
381
+ | 4.0 | 2916 | - | 0.3288 |
382
+ | 4.0055 | 2920 | - | 0.3296 |
383
+ | 4.1056 | 2993 | - | 0.3293 |
384
+ | 4.1152 | 3000 | 0.0078 | - |
385
+ | 4.2058 | 3066 | - | 0.3280 |
386
+ | 4.3059 | 3139 | - | 0.3117 |
387
+ | 4.4060 | 3212 | - | 0.3250 |
388
+ | 4.5062 | 3285 | - | 0.3212 |
389
+ | 4.6063 | 3358 | - | 0.3277 |
390
+ | 4.7064 | 3431 | - | 0.3208 |
391
+ | 4.8011 | 3500 | 0.0033 | - |
392
+ | 4.8066 | 3504 | - | 0.3177 |
393
+ | 4.9067 | 3577 | - | 0.3260 |
394
+ | 5.0 | 3645 | - | 0.3246 |
395
+ | 5.0069 | 3650 | - | 0.3259 |
396
+ | 5.1070 | 3723 | - | 0.3298 |
397
+ | 5.2071 | 3796 | - | 0.3199 |
398
+ | 5.3073 | 3869 | - | 0.3297 |
399
+ | 5.4074 | 3942 | - | 0.3256 |
400
+ | 5.4870 | 4000 | 0.0035 | - |
401
+ | 5.5075 | 4015 | - | 0.3286 |
402
+ | 5.6077 | 4088 | - | 0.3251 |
403
+ | 5.7078 | 4161 | - | 0.3269 |
404
+ | 5.8080 | 4234 | - | 0.3298 |
405
+ | 5.9081 | 4307 | - | 0.3265 |
406
+ | 6.0 | 4374 | - | 0.3047 |
407
+ | 6.0082 | 4380 | - | 0.3181 |
408
+ | 6.1084 | 4453 | - | 0.3301 |
409
+ | 6.1728 | 4500 | 0.0023 | - |
410
+ | 6.2085 | 4526 | - | 0.3301 |
411
+ | 6.3086 | 4599 | - | 0.3296 |
412
+ | 6.4088 | 4672 | - | 0.3251 |
413
+ | 6.5089 | 4745 | - | 0.3291 |
414
+ | 6.6091 | 4818 | - | 0.3295 |
415
+ | 6.7092 | 4891 | - | 0.3289 |
416
+ | 6.8093 | 4964 | - | 0.3254 |
417
+ | 6.8587 | 5000 | 0.0011 | - |
418
+ | 6.9095 | 5037 | - | 0.3271 |
419
+ | 7.0 | 5103 | - | 0.3300 |
420
+ | 7.0096 | 5110 | - | 0.3300 |
421
+ | 7.1097 | 5183 | - | 0.3287 |
422
+ | 7.2099 | 5256 | - | 0.3285 |
423
+ | 7.3100 | 5329 | - | 0.3291 |
424
+ | 7.4102 | 5402 | - | 0.3289 |
425
+ | 7.5103 | 5475 | - | 0.3246 |
426
+ | 7.5446 | 5500 | 0.0008 | - |
427
+ | 7.6104 | 5548 | - | 0.3283 |
428
+ | 7.7106 | 5621 | - | 0.3287 |
429
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430
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431
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432
+ | 8.0110 | 5840 | - | 0.3280 |
433
+ | 8.1111 | 5913 | - | 0.3289 |
434
+ | 8.2112 | 5986 | - | 0.3250 |
435
+ | 8.2305 | 6000 | 0.0014 | - |
436
+ | 8.3114 | 6059 | - | 0.3225 |
437
+ | 8.4115 | 6132 | - | 0.3290 |
438
+ | 8.5117 | 6205 | - | 0.3260 |
439
+ | 8.6118 | 6278 | - | 0.3248 |
440
+ | 8.7119 | 6351 | - | 0.3285 |
441
+ | 8.8121 | 6424 | - | 0.3163 |
442
+ | 8.9122 | 6497 | - | 0.3295 |
443
+ | 8.9163 | 6500 | 0.0029 | - |
444
+ | 9.0 | 6561 | - | 0.3299 |
445
+ | 9.0123 | 6570 | - | 0.3299 |
446
+ | 9.1125 | 6643 | - | 0.3283 |
447
+ | 9.2126 | 6716 | - | 0.3115 |
448
+ | 9.3128 | 6789 | - | 0.3150 |
449
+ | 9.4129 | 6862 | - | 0.3281 |
450
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451
+ | 9.6022 | 7000 | 0.0021 | - |
452
+ | 9.6132 | 7008 | - | 0.3279 |
453
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454
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455
+ | 9.9136 | 7227 | - | 0.3301 |
456
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457
+ | 10.0137 | 7300 | - | 0.3286 |
458
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459
+ | 10.2140 | 7446 | - | 0.3292 |
460
+ | 10.2881 | 7500 | 0.0022 | - |
461
+ | 10.3141 | 7519 | - | 0.3302 |
462
+ | 10.4143 | 7592 | - | 0.3026 |
463
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464
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465
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466
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467
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468
+ | 10.9739 | 8000 | 0.0027 | - |
469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
+ | 13.0316 | 9500 | 0.0003 | - |
496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
+ | 19.2263 | 14016 | - | 0.3304 |
573
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574
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575
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576
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577
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578
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579
+ | 19.8903 | 14500 | 0.0001 | - |
580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
+ | 21.2620 | 15500 | 0.0001 | - |
598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
+ | 23.0316 | 16790 | - | 0.3304 |
620
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621
+ | 23.2318 | 16936 | - | 0.3304 |
622
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623
+ | 23.3320 | 17009 | - | 0.3304 |
624
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625
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626
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627
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628
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629
+ | 23.9328 | 17447 | - | 0.3304 |
630
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631
+ | 24.0055 | 17500 | 0.0001 | - |
632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
+ | 26.1358 | 19053 | - | 0.3303 |
659
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660
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661
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662
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663
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664
+ | 26.7366 | 19491 | - | 0.3304 |
665
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666
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667
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668
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669
+ | 27.0370 | 19710 | - | 0.3304 |
670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
+ | 30.8642 | 22500 | 0.0001 | - |
717
+ | 30.9424 | 22557 | - | 0.3304 |
718
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719
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720
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721
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722
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723
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724
+ | 31.5432 | 22995 | - | 0.3304 |
725
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726
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727
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728
+ | 31.8436 | 23214 | - | 0.3304 |
729
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730
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731
+ | 32.0439 | 23360 | - | 0.3304 |
732
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733
+ | 32.2359 | 23500 | 0.0001 | - |
734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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815
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818
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819
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821
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823
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824
+ | 39.5542 | 28835 | - | 0.3304 |
825
+
826
+ </details>
827
+
828
+ ### Framework Versions
829
+ - Python: 3.10.11
830
+ - Sentence Transformers: 4.1.0
831
+ - Transformers: 4.51.3
832
+ - PyTorch: 2.6.0+cu124
833
+ - Accelerate: 1.0.1
834
+ - Datasets: 3.5.0
835
+ - Tokenizers: 0.21.1
836
+
837
+ ## Citation
838
+
839
+ ### BibTeX
840
+
841
+ #### Sentence Transformers
842
+ ```bibtex
843
+ @inproceedings{reimers-2019-sentence-bert,
844
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
845
+ author = "Reimers, Nils and Gurevych, Iryna",
846
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
847
+ month = "11",
848
+ year = "2019",
849
+ publisher = "Association for Computational Linguistics",
850
+ url = "https://arxiv.org/abs/1908.10084",
851
+ }
852
+ ```
853
+
854
+ <!--
855
+ ## Glossary
856
+
857
+ *Clearly define terms in order to be accessible across audiences.*
858
+ -->
859
+
860
+ <!--
861
+ ## Model Card Authors
862
+
863
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
864
+ -->
865
+
866
+ <!--
867
+ ## Model Card Contact
868
+
869
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
870
+ -->
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