rztk-bohdanbilonoh
commited on
Commit
•
c1f514e
1
Parent(s):
b6f4a4f
Model save
Browse files- 1_Pooling/config.json +10 -0
- README.md +719 -0
- config_sentence_transformers.json +13 -0
- model.safetensors +1 -1
- modules.json +20 -0
- sentence_bert_config.json +4 -0
1_Pooling/config.json
ADDED
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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@@ -0,0 +1,719 @@
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+
---
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base_model: intfloat/multilingual-e5-base
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+
datasets:
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- rztk/rozetka_positive_pairs
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+
language: []
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library_name: sentence-transformers
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metrics:
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- dot_accuracy@1
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+
- dot_accuracy@3
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+
- dot_accuracy@5
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+
- dot_accuracy@10
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+
- dot_precision@1
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+
- dot_precision@3
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+
- dot_precision@5
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+
- dot_precision@10
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+
- dot_recall@1
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+
- dot_recall@3
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+
- dot_recall@5
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+
- dot_recall@10
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+
- dot_ndcg@10
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+
- dot_mrr@10
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+
- dot_map@100
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+
- dot_ndcg@1
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24 |
+
- dot_mrr@1
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+
pipeline_tag: sentence-similarity
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+
tags:
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+
- sentence-transformers
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+
- sentence-similarity
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+
- feature-extraction
|
30 |
+
- generated_from_trainer
|
31 |
+
- dataset_size:44800
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- loss:RZTKMatryoshka2dLoss
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+
widget:
|
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- source_sentence: папка планшет
|
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+
sentences:
|
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- <category>Сифони</category><brand>Alcaplast</brand><options><option_title>Гарантія</option_title><option_value>24
|
37 |
+
місяці офіційної гарантії від виробника</option_value><option_title>Кількість
|
38 |
+
вантажних місць</option_title><option_value>1</option_value><option_title>Країна-виробник
|
39 |
+
товару</option_title><option_value>Чехія</option_value><option_title>Призначення</option_title><option_value>Для
|
40 |
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душових піддонів</option_value><option_title>Матеріал</option_title><option_value>Пластик</option_value><option_title>Вид</option_title><option_value>Пляшковий</option_value><option_title>Під'єднані
|
41 |
+
до пральної машини</option_title><option_value>Немає</option_value><option_title>Колір</option_title><option_value>Білий
|
42 |
+
+ Хром</option_value><option_title>Тип</option_title><option_value>Сифон</option_value><option_title>Теги</option_title><option_value>недорогий
|
43 |
+
сифон</option_value><option_title>відкривання/перекриття зливних отворів</option_title><option_value>Неперекривний</option_value><option_title>Різновид
|
44 |
+
гідрозатвора</option_title><option_value>Мокрий (без мембрани)</option_value><option_title>Діаметр
|
45 |
+
під'єднання</option_title><option_value>90 мм</option_value><option_title>Діаметр
|
46 |
+
патрубка в каналізацію</option_title><option_value>40 мм</option_value><option_title>Переливання</option_title><option_value>Без
|
47 |
+
функції переливу</option_value><option_title>Тип гарантійного талона</option_title><option_value>Гарантія
|
48 |
+
по чеку</option_value><option_title>Доставка Premium</option_title><option_title>Доставка</option_title><option_value>Доставка
|
49 |
+
в магазини ROZETKA</option_value></options>
|
50 |
+
- Сифон для душевого поддона ALCA PLAST A49CR (8594045930627)
|
51 |
+
- <category>Папки-планшеты</category><brand>iTEM</brand><options><option_title>Формат</option_title><option_value>A4</option_value><option_title>Материал</option_title><option_value>Картон</option_value><option_title>Страна
|
52 |
+
регистрации бренда</option_title><option_value>Украина</option_value><option_title>Страна-производитель
|
53 |
+
товара</option_title><option_value>Украина</option_value></options>
|
54 |
+
- source_sentence: вино игристое
|
55 |
+
sentences:
|
56 |
+
- Женские резиновые сапоги Demar HAWAI LADY 0076V 36 (23.8 см) Черные (5901232011374)
|
57 |
+
- Верстак складной Ryobi RWB01
|
58 |
+
- Вино ігристе Adamanti біле напівсолодке 0.75 л 12.5% (4860004073259)
|
59 |
+
- source_sentence: елка искуственная
|
60 |
+
sentences:
|
61 |
+
- <category>Підставки та столики для ноутбуків</category><brand>UFT</brand><options><option_title>Вид</option_title><option_value>Столики</option_value><option_title>Охолодження</option_title><option_value>Активне</option_value><option_title>Максимальна
|
62 |
+
діагональ ноутбука</option_title><option_value>16"</option_value><option_title>Колір</option_title><option_value>Синій</option_value><option_title>Матеріал</option_title><option_value>Метал</option_value><option_title>Кількість
|
63 |
+
вантажних місць</option_title><option_value>1</option_value></options>
|
64 |
+
- Декоративная елка, 90см (122-F12)
|
65 |
+
- Конструктор LEGO Minecraft Гарбузова ферма 257 деталей (21248)
|
66 |
+
- source_sentence: переходник
|
67 |
+
sentences:
|
68 |
+
- Штучна ялинка «Ніка» 1.8 м
|
69 |
+
- Набір інструментів NEO торцевих головок 108 шт., 1, 4, 1/2 "CrV (08-666)
|
70 |
+
- <category>Кабели и адаптеры</category><brand>Protech</brand><options><option_title>Гарантия</option_title><option_value>6
|
71 |
+
месяцев</option_value><option_title>Длина</option_title><option_value>0.2 м</option_value><option_title>Тип</option_title><option_value>Адаптеры
|
72 |
+
(Переходники)</option_value><option_title>Количество грузовых мест</option_title><option_value>1</option_value><option_title>Страна
|
73 |
+
регистрации бренда</option_title><option_value>Китай</option_value><option_title>Страна-производитель
|
74 |
+
товара</option_title><option_value>Китай</option_value><option_title>Цвет</option_title><option_value>Серебристый</option_value><option_title>Тип
|
75 |
+
гарантийного талона</option_title><option_value>Гарантия по чеку</option_value><option_title>Доставка
|
76 |
+
Premium</option_title><option_title>Тип коннектора 1</option_title><option_value>USB
|
77 |
+
Type-C</option_value><option_title>Тип коннектора 2</option_title><option_value>USB</option_value></options>
|
78 |
+
- source_sentence: поилка для детей
|
79 |
+
sentences:
|
80 |
+
- Шафа розпашній Fenster Оксфорд Лагуна
|
81 |
+
- <category>Аксессуары для наушников</category><brand>ArmorStandart</brand><options><option_title>Гарантия</option_title><option_value>14
|
82 |
+
дней</option_value><option_title>Тип наушников</option_title><option_value>Вкладыши</option_value><option_title>Вид</option_title><option_value>Чехлы</option_value><option_title>Цвет</option_title><option_value>Dark
|
83 |
+
Green</option_value><option_title>Количество грузовых мест</option_title><option_value>1</option_value><option_title>Страна
|
84 |
+
регистрации бренда</option_title><option_value>Украина</option_value><option_title>Страна-производитель
|
85 |
+
товара</option_title><option_value>Китай</option_value><option_title>Тип гарантийного
|
86 |
+
талона</option_title><option_value>Гарантия по чеку</option_value><option_title>Материал</option_title><option_value>Силикон</option_value><option_title>Доставка
|
87 |
+
Premium</option_title><option_title>Совместимая серия</option_title><option_value>Apple
|
88 |
+
AirPods</option_value><option_title>Доставка</option_title><option_value>Доставка
|
89 |
+
в магазины ROZETKA</option_value></options>
|
90 |
+
- <category>Поїльники та непроливайки</category><brand>Nuk</brand><options><option_title>Стать
|
91 |
+
дитини</option_title><option_value>Хлопчик</option_value><option_title>Стать дитини</option_title><option_value>Дівчинка</option_value><option_title>Кількість
|
92 |
+
вантажних місць</option_title><option_value>1</option_value><option_title>Країна
|
93 |
+
реєстрації бренда</option_title><option_value>Німеччина</option_value><option_title>Країна-виробник
|
94 |
+
товару</option_title><option_value>Німеччина</option_value><option_title>Об'єм,
|
95 |
+
мл</option_title><option_value>300</option_value><option_title>Матеріал</option_title><option_value>Пластик</option_value><option_title>Колір</option_title><option_value>Блакитний</option_value><option_title>Тип</option_title><option_value>Поїльник</option_value><option_title>Тип
|
96 |
+
гарантійного талона</option_title><option_value>Гарантія по чеку</option_value><option_title>Доставка
|
97 |
+
Premium</option_title></options>
|
98 |
+
model-index:
|
99 |
+
- name: SentenceTransformer based on intfloat/multilingual-e5-base
|
100 |
+
results:
|
101 |
+
- task:
|
102 |
+
type: information-retrieval
|
103 |
+
name: Information Retrieval
|
104 |
+
dataset:
|
105 |
+
name: rusisms uk title
|
106 |
+
type: rusisms-uk-title
|
107 |
+
metrics:
|
108 |
+
- type: dot_accuracy@1
|
109 |
+
value: 0.5428571428571428
|
110 |
+
name: Dot Accuracy@1
|
111 |
+
- type: dot_accuracy@3
|
112 |
+
value: 0.6888888888888889
|
113 |
+
name: Dot Accuracy@3
|
114 |
+
- type: dot_accuracy@5
|
115 |
+
value: 0.7492063492063492
|
116 |
+
name: Dot Accuracy@5
|
117 |
+
- type: dot_accuracy@10
|
118 |
+
value: 0.8
|
119 |
+
name: Dot Accuracy@10
|
120 |
+
- type: dot_precision@1
|
121 |
+
value: 0.5428571428571428
|
122 |
+
name: Dot Precision@1
|
123 |
+
- type: dot_precision@3
|
124 |
+
value: 0.5216931216931217
|
125 |
+
name: Dot Precision@3
|
126 |
+
- type: dot_precision@5
|
127 |
+
value: 0.5034920634920634
|
128 |
+
name: Dot Precision@5
|
129 |
+
- type: dot_precision@10
|
130 |
+
value: 0.47682539682539676
|
131 |
+
name: Dot Precision@10
|
132 |
+
- type: dot_recall@1
|
133 |
+
value: 0.009248137199056617
|
134 |
+
name: Dot Recall@1
|
135 |
+
- type: dot_recall@3
|
136 |
+
value: 0.023803562659985587
|
137 |
+
name: Dot Recall@3
|
138 |
+
- type: dot_recall@5
|
139 |
+
value: 0.03509680885707945
|
140 |
+
name: Dot Recall@5
|
141 |
+
- type: dot_recall@10
|
142 |
+
value: 0.05987127144737185
|
143 |
+
name: Dot Recall@10
|
144 |
+
- type: dot_ndcg@10
|
145 |
+
value: 0.4936504584984999
|
146 |
+
name: Dot Ndcg@10
|
147 |
+
- type: dot_mrr@10
|
148 |
+
value: 0.6286608717561099
|
149 |
+
name: Dot Mrr@10
|
150 |
+
- type: dot_map@100
|
151 |
+
value: 0.14035920755466383
|
152 |
+
name: Dot Map@100
|
153 |
+
- task:
|
154 |
+
type: information-retrieval
|
155 |
+
name: Information Retrieval
|
156 |
+
dataset:
|
157 |
+
name: 'rusisms uk title matryoshka dim 768 '
|
158 |
+
type: rusisms-uk-title--matryoshka_dim-768--
|
159 |
+
metrics:
|
160 |
+
- type: dot_accuracy@1
|
161 |
+
value: 0.1619047619047619
|
162 |
+
name: Dot Accuracy@1
|
163 |
+
- type: dot_precision@1
|
164 |
+
value: 0.1619047619047619
|
165 |
+
name: Dot Precision@1
|
166 |
+
- type: dot_recall@1
|
167 |
+
value: 0.0020219082190057404
|
168 |
+
name: Dot Recall@1
|
169 |
+
- type: dot_ndcg@1
|
170 |
+
value: 0.1619047619047619
|
171 |
+
name: Dot Ndcg@1
|
172 |
+
- type: dot_mrr@1
|
173 |
+
value: 0.1619047619047619
|
174 |
+
name: Dot Mrr@1
|
175 |
+
- type: dot_map@100
|
176 |
+
value: 0.02128340409566104
|
177 |
+
name: Dot Map@100
|
178 |
+
- task:
|
179 |
+
type: information-retrieval
|
180 |
+
name: Information Retrieval
|
181 |
+
dataset:
|
182 |
+
name: 'rusisms uk title matryoshka dim 512 '
|
183 |
+
type: rusisms-uk-title--matryoshka_dim-512--
|
184 |
+
metrics:
|
185 |
+
- type: dot_accuracy@1
|
186 |
+
value: 0.14603174603174604
|
187 |
+
name: Dot Accuracy@1
|
188 |
+
- type: dot_precision@1
|
189 |
+
value: 0.14603174603174604
|
190 |
+
name: Dot Precision@1
|
191 |
+
- type: dot_recall@1
|
192 |
+
value: 0.0016964404522008209
|
193 |
+
name: Dot Recall@1
|
194 |
+
- type: dot_ndcg@1
|
195 |
+
value: 0.14603174603174604
|
196 |
+
name: Dot Ndcg@1
|
197 |
+
- type: dot_mrr@1
|
198 |
+
value: 0.14603174603174604
|
199 |
+
name: Dot Mrr@1
|
200 |
+
- type: dot_map@100
|
201 |
+
value: 0.015212846443877073
|
202 |
+
name: Dot Map@100
|
203 |
+
- task:
|
204 |
+
type: information-retrieval
|
205 |
+
name: Information Retrieval
|
206 |
+
dataset:
|
207 |
+
name: 'rusisms uk title matryoshka dim 256 '
|
208 |
+
type: rusisms-uk-title--matryoshka_dim-256--
|
209 |
+
metrics:
|
210 |
+
- type: dot_accuracy@1
|
211 |
+
value: 0.10158730158730159
|
212 |
+
name: Dot Accuracy@1
|
213 |
+
- type: dot_precision@1
|
214 |
+
value: 0.10158730158730159
|
215 |
+
name: Dot Precision@1
|
216 |
+
- type: dot_recall@1
|
217 |
+
value: 0.0012653450153450154
|
218 |
+
name: Dot Recall@1
|
219 |
+
- type: dot_ndcg@1
|
220 |
+
value: 0.10158730158730159
|
221 |
+
name: Dot Ndcg@1
|
222 |
+
- type: dot_mrr@1
|
223 |
+
value: 0.10158730158730159
|
224 |
+
name: Dot Mrr@1
|
225 |
+
- type: dot_map@100
|
226 |
+
value: 0.011952854173853285
|
227 |
+
name: Dot Map@100
|
228 |
+
- task:
|
229 |
+
type: information-retrieval
|
230 |
+
name: Information Retrieval
|
231 |
+
dataset:
|
232 |
+
name: 'rusisms uk title matryoshka dim 128 '
|
233 |
+
type: rusisms-uk-title--matryoshka_dim-128--
|
234 |
+
metrics:
|
235 |
+
- type: dot_accuracy@1
|
236 |
+
value: 0.05396825396825397
|
237 |
+
name: Dot Accuracy@1
|
238 |
+
- type: dot_precision@1
|
239 |
+
value: 0.05396825396825397
|
240 |
+
name: Dot Precision@1
|
241 |
+
- type: dot_recall@1
|
242 |
+
value: 0.0007494719994719994
|
243 |
+
name: Dot Recall@1
|
244 |
+
- type: dot_ndcg@1
|
245 |
+
value: 0.05396825396825397
|
246 |
+
name: Dot Ndcg@1
|
247 |
+
- type: dot_mrr@1
|
248 |
+
value: 0.05396825396825397
|
249 |
+
name: Dot Mrr@1
|
250 |
+
- type: dot_map@100
|
251 |
+
value: 0.0053781586003166125
|
252 |
+
name: Dot Map@100
|
253 |
+
---
|
254 |
+
|
255 |
+
# SentenceTransformer based on intfloat/multilingual-e5-base
|
256 |
+
|
257 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the [rztk/rozetka_positive_pairs](https://huggingface.co/datasets/rztk/rozetka_positive_pairs) 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.
|
258 |
+
|
259 |
+
## Model Details
|
260 |
+
|
261 |
+
### Model Description
|
262 |
+
- **Model Type:** Sentence Transformer
|
263 |
+
- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
|
264 |
+
- **Maximum Sequence Length:** 512 tokens
|
265 |
+
- **Output Dimensionality:** 768 tokens
|
266 |
+
- **Similarity Function:** Cosine Similarity
|
267 |
+
- **Training Dataset:**
|
268 |
+
- [rztk/rozetka_positive_pairs](https://huggingface.co/datasets/rztk/rozetka_positive_pairs)
|
269 |
+
<!-- - **Language:** Unknown -->
|
270 |
+
<!-- - **License:** Unknown -->
|
271 |
+
|
272 |
+
### Model Sources
|
273 |
+
|
274 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
275 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
276 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
277 |
+
|
278 |
+
### Full Model Architecture
|
279 |
+
|
280 |
+
```
|
281 |
+
SentenceTransformer(
|
282 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
|
283 |
+
(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})
|
284 |
+
(2): Normalize()
|
285 |
+
)
|
286 |
+
```
|
287 |
+
|
288 |
+
## Usage
|
289 |
+
|
290 |
+
### Direct Usage (Sentence Transformers)
|
291 |
+
|
292 |
+
First install the Sentence Transformers library:
|
293 |
+
|
294 |
+
```bash
|
295 |
+
pip install -U sentence-transformers
|
296 |
+
```
|
297 |
+
|
298 |
+
Then you can load this model and run inference.
|
299 |
+
```python
|
300 |
+
from sentence_transformers import SentenceTransformer
|
301 |
+
|
302 |
+
# Download from the 🤗 Hub
|
303 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
304 |
+
# Run inference
|
305 |
+
sentences = [
|
306 |
+
'поилка для детей',
|
307 |
+
"<category>Поїльники та непроливайки</category><brand>Nuk</brand><options><option_title>Стать дитини</option_title><option_value>Хлопчик</option_value><option_title>Стать дитини</option_title><option_value>Дівчинка</option_value><option_title>Кількість вантажних місць</option_title><option_value>1</option_value><option_title>Країна реєстрації бренда</option_title><option_value>Німеччина</option_value><option_title>Країна-виробник товару</option_title><option_value>Німеччина</option_value><option_title>Об'єм, мл</option_title><option_value>300</option_value><option_title>Матеріал</option_title><option_value>Пластик</option_value><option_title>Колір</option_title><option_value>Блакитний</option_value><option_title>Тип</option_title><option_value>Поїльник</option_value><option_title>Тип гарантійного талона</option_title><option_value>Гарантія по чеку</option_value><option_title>Доставка Premium</option_title></options>",
|
308 |
+
'Шафа розпашній Fenster Оксфорд Лагуна',
|
309 |
+
]
|
310 |
+
embeddings = model.encode(sentences)
|
311 |
+
print(embeddings.shape)
|
312 |
+
# [3, 768]
|
313 |
+
|
314 |
+
# Get the similarity scores for the embeddings
|
315 |
+
similarities = model.similarity(embeddings, embeddings)
|
316 |
+
print(similarities.shape)
|
317 |
+
# [3, 3]
|
318 |
+
```
|
319 |
+
|
320 |
+
<!--
|
321 |
+
### Direct Usage (Transformers)
|
322 |
+
|
323 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
324 |
+
|
325 |
+
</details>
|
326 |
+
-->
|
327 |
+
|
328 |
+
<!--
|
329 |
+
### Downstream Usage (Sentence Transformers)
|
330 |
+
|
331 |
+
You can finetune this model on your own dataset.
|
332 |
+
|
333 |
+
<details><summary>Click to expand</summary>
|
334 |
+
|
335 |
+
</details>
|
336 |
+
-->
|
337 |
+
|
338 |
+
<!--
|
339 |
+
### Out-of-Scope Use
|
340 |
+
|
341 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
342 |
+
-->
|
343 |
+
|
344 |
+
## Evaluation
|
345 |
+
|
346 |
+
### Metrics
|
347 |
+
|
348 |
+
#### Information Retrieval
|
349 |
+
* Dataset: `rusisms-uk-title`
|
350 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
351 |
+
|
352 |
+
| Metric | Value |
|
353 |
+
|:-----------------|:-----------|
|
354 |
+
| dot_accuracy@1 | 0.5429 |
|
355 |
+
| dot_accuracy@3 | 0.6889 |
|
356 |
+
| dot_accuracy@5 | 0.7492 |
|
357 |
+
| dot_accuracy@10 | 0.8 |
|
358 |
+
| dot_precision@1 | 0.5429 |
|
359 |
+
| dot_precision@3 | 0.5217 |
|
360 |
+
| dot_precision@5 | 0.5035 |
|
361 |
+
| dot_precision@10 | 0.4768 |
|
362 |
+
| dot_recall@1 | 0.0092 |
|
363 |
+
| dot_recall@3 | 0.0238 |
|
364 |
+
| dot_recall@5 | 0.0351 |
|
365 |
+
| dot_recall@10 | 0.0599 |
|
366 |
+
| dot_ndcg@10 | 0.4937 |
|
367 |
+
| dot_mrr@10 | 0.6287 |
|
368 |
+
| **dot_map@100** | **0.1404** |
|
369 |
+
|
370 |
+
#### Information Retrieval
|
371 |
+
* Dataset: `rusisms-uk-title--matryoshka_dim-768--`
|
372 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
373 |
+
|
374 |
+
| Metric | Value |
|
375 |
+
|:----------------|:-----------|
|
376 |
+
| dot_accuracy@1 | 0.1619 |
|
377 |
+
| dot_precision@1 | 0.1619 |
|
378 |
+
| dot_recall@1 | 0.002 |
|
379 |
+
| dot_ndcg@1 | 0.1619 |
|
380 |
+
| dot_mrr@1 | 0.1619 |
|
381 |
+
| **dot_map@100** | **0.0213** |
|
382 |
+
|
383 |
+
#### Information Retrieval
|
384 |
+
* Dataset: `rusisms-uk-title--matryoshka_dim-512--`
|
385 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
386 |
+
|
387 |
+
| Metric | Value |
|
388 |
+
|:----------------|:-----------|
|
389 |
+
| dot_accuracy@1 | 0.146 |
|
390 |
+
| dot_precision@1 | 0.146 |
|
391 |
+
| dot_recall@1 | 0.0017 |
|
392 |
+
| dot_ndcg@1 | 0.146 |
|
393 |
+
| dot_mrr@1 | 0.146 |
|
394 |
+
| **dot_map@100** | **0.0152** |
|
395 |
+
|
396 |
+
#### Information Retrieval
|
397 |
+
* Dataset: `rusisms-uk-title--matryoshka_dim-256--`
|
398 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
399 |
+
|
400 |
+
| Metric | Value |
|
401 |
+
|:----------------|:----------|
|
402 |
+
| dot_accuracy@1 | 0.1016 |
|
403 |
+
| dot_precision@1 | 0.1016 |
|
404 |
+
| dot_recall@1 | 0.0013 |
|
405 |
+
| dot_ndcg@1 | 0.1016 |
|
406 |
+
| dot_mrr@1 | 0.1016 |
|
407 |
+
| **dot_map@100** | **0.012** |
|
408 |
+
|
409 |
+
#### Information Retrieval
|
410 |
+
* Dataset: `rusisms-uk-title--matryoshka_dim-128--`
|
411 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
412 |
+
|
413 |
+
| Metric | Value |
|
414 |
+
|:----------------|:-----------|
|
415 |
+
| dot_accuracy@1 | 0.054 |
|
416 |
+
| dot_precision@1 | 0.054 |
|
417 |
+
| dot_recall@1 | 0.0007 |
|
418 |
+
| dot_ndcg@1 | 0.054 |
|
419 |
+
| dot_mrr@1 | 0.054 |
|
420 |
+
| **dot_map@100** | **0.0054** |
|
421 |
+
|
422 |
+
<!--
|
423 |
+
## Bias, Risks and Limitations
|
424 |
+
|
425 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
426 |
+
-->
|
427 |
+
|
428 |
+
<!--
|
429 |
+
### Recommendations
|
430 |
+
|
431 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
432 |
+
-->
|
433 |
+
|
434 |
+
## Training Details
|
435 |
+
|
436 |
+
### Training Dataset
|
437 |
+
|
438 |
+
#### rztk/rozetka_positive_pairs
|
439 |
+
|
440 |
+
* Dataset: [rztk/rozetka_positive_pairs](https://huggingface.co/datasets/rztk/rozetka_positive_pairs)
|
441 |
+
* Size: 44,800 training samples
|
442 |
+
* Columns: <code>query</code> and <code>text</code>
|
443 |
+
* Approximate statistics based on the first 1000 samples:
|
444 |
+
| | query | text |
|
445 |
+
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
446 |
+
| type | string | string |
|
447 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 7.18 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 158.88 tokens</li><li>max: 512 tokens</li></ul> |
|
448 |
+
* Samples:
|
449 |
+
| query | text |
|
450 |
+
|:-----------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
451 |
+
| <code>p smart z</code> | <code>TPU чехол Ultrathin Series 0,33 mm для Huawei P Smart Z Безбарвний (прозорий)</code> |
|
452 |
+
| <code>p smart z</code> | <code><category>Чохли для мобільних телефонів</category><options><option_title>Матеріал</option_title><option_value>Силікон</option_value><option_title>Колір</option_title><option_value>Transparent</option_value><option_title>Сумісна модель</option_title><option_value>P Smart Z</option_value></options></code> |
|
453 |
+
| <code>p smart z</code> | <code>TPU чехол Ultrathin Series 0,33mm для Huawei P Smart Z Бесцветный (прозрачный)</code> |
|
454 |
+
* Loss: <code>sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss</code> with these parameters:
|
455 |
+
```json
|
456 |
+
{
|
457 |
+
"loss": "RZTKMultipleNegativesRankingLoss",
|
458 |
+
"n_layers_per_step": 1,
|
459 |
+
"last_layer_weight": 1.0,
|
460 |
+
"prior_layers_weight": 1.0,
|
461 |
+
"kl_div_weight": 1.0,
|
462 |
+
"kl_temperature": 0.3,
|
463 |
+
"matryoshka_dims": [
|
464 |
+
768,
|
465 |
+
512,
|
466 |
+
256,
|
467 |
+
128
|
468 |
+
],
|
469 |
+
"matryoshka_weights": [
|
470 |
+
1,
|
471 |
+
1,
|
472 |
+
1,
|
473 |
+
1
|
474 |
+
],
|
475 |
+
"n_dims_per_step": 1
|
476 |
+
}
|
477 |
+
```
|
478 |
+
|
479 |
+
### Evaluation Dataset
|
480 |
+
|
481 |
+
#### rztk/rozetka_positive_pairs
|
482 |
+
|
483 |
+
* Dataset: [rztk/rozetka_positive_pairs](https://huggingface.co/datasets/rztk/rozetka_positive_pairs)
|
484 |
+
* Size: 4,480 evaluation samples
|
485 |
+
* Columns: <code>query</code> and <code>text</code>
|
486 |
+
* Approximate statistics based on the first 1000 samples:
|
487 |
+
| | query | text |
|
488 |
+
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
|
489 |
+
| type | string | string |
|
490 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 6.29 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 161.36 tokens</li><li>max: 512 tokens</li></ul> |
|
491 |
+
* Samples:
|
492 |
+
| query | text |
|
493 |
+
|:------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
494 |
+
| <code>кошелек женский</code> | <code>Портмоне BAELLERRY Forever N2345 Черный (020354)</code> |
|
495 |
+
| <code>кошелек женский</code> | <code><category>Гаманці</category><brand>Baellerry</brand><options><option_title>Для кого</option_title><option_value>Для жінок</option_value><option_title>Вид</option_title><option_value>Портмоне</option_value><option_title>Матеріал</option_title><option_value>Штучна шкіра</option_value><option_title>Країна-виробник товару</option_title><option_value>Китай</option_value></options></code> |
|
496 |
+
| <code>кошелек женский</code> | <code>Портмоне BAELLERRY Forever N2345 Черный (020354)</code> |
|
497 |
+
* Loss: <code>sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss</code> with these parameters:
|
498 |
+
```json
|
499 |
+
{
|
500 |
+
"loss": "RZTKMultipleNegativesRankingLoss",
|
501 |
+
"n_layers_per_step": 1,
|
502 |
+
"last_layer_weight": 1.0,
|
503 |
+
"prior_layers_weight": 1.0,
|
504 |
+
"kl_div_weight": 1.0,
|
505 |
+
"kl_temperature": 0.3,
|
506 |
+
"matryoshka_dims": [
|
507 |
+
768,
|
508 |
+
512,
|
509 |
+
256,
|
510 |
+
128
|
511 |
+
],
|
512 |
+
"matryoshka_weights": [
|
513 |
+
1,
|
514 |
+
1,
|
515 |
+
1,
|
516 |
+
1
|
517 |
+
],
|
518 |
+
"n_dims_per_step": 1
|
519 |
+
}
|
520 |
+
```
|
521 |
+
|
522 |
+
### Training Hyperparameters
|
523 |
+
#### Non-Default Hyperparameters
|
524 |
+
|
525 |
+
- `eval_strategy`: steps
|
526 |
+
- `per_device_train_batch_size`: 112
|
527 |
+
- `per_device_eval_batch_size`: 112
|
528 |
+
- `torch_empty_cache_steps`: 30
|
529 |
+
- `learning_rate`: 2e-05
|
530 |
+
- `num_train_epochs`: 1.0
|
531 |
+
- `warmup_ratio`: 0.1
|
532 |
+
- `bf16`: True
|
533 |
+
- `bf16_full_eval`: True
|
534 |
+
- `tf32`: True
|
535 |
+
- `dataloader_num_workers`: 2
|
536 |
+
- `load_best_model_at_end`: True
|
537 |
+
- `optim`: adafactor
|
538 |
+
- `push_to_hub`: True
|
539 |
+
|
540 |
+
#### All Hyperparameters
|
541 |
+
<details><summary>Click to expand</summary>
|
542 |
+
|
543 |
+
- `overwrite_output_dir`: False
|
544 |
+
- `do_predict`: False
|
545 |
+
- `eval_strategy`: steps
|
546 |
+
- `prediction_loss_only`: True
|
547 |
+
- `per_device_train_batch_size`: 112
|
548 |
+
- `per_device_eval_batch_size`: 112
|
549 |
+
- `per_gpu_train_batch_size`: None
|
550 |
+
- `per_gpu_eval_batch_size`: None
|
551 |
+
- `gradient_accumulation_steps`: 1
|
552 |
+
- `eval_accumulation_steps`: None
|
553 |
+
- `torch_empty_cache_steps`: 30
|
554 |
+
- `learning_rate`: 2e-05
|
555 |
+
- `weight_decay`: 0.0
|
556 |
+
- `adam_beta1`: 0.9
|
557 |
+
- `adam_beta2`: 0.999
|
558 |
+
- `adam_epsilon`: 1e-08
|
559 |
+
- `max_grad_norm`: 1.0
|
560 |
+
- `num_train_epochs`: 1.0
|
561 |
+
- `max_steps`: -1
|
562 |
+
- `lr_scheduler_type`: linear
|
563 |
+
- `lr_scheduler_kwargs`: {}
|
564 |
+
- `warmup_ratio`: 0.1
|
565 |
+
- `warmup_steps`: 0
|
566 |
+
- `log_level`: passive
|
567 |
+
- `log_level_replica`: warning
|
568 |
+
- `log_on_each_node`: True
|
569 |
+
- `logging_nan_inf_filter`: True
|
570 |
+
- `save_safetensors`: True
|
571 |
+
- `save_on_each_node`: False
|
572 |
+
- `save_only_model`: False
|
573 |
+
- `restore_callback_states_from_checkpoint`: False
|
574 |
+
- `no_cuda`: False
|
575 |
+
- `use_cpu`: False
|
576 |
+
- `use_mps_device`: False
|
577 |
+
- `seed`: 42
|
578 |
+
- `data_seed`: None
|
579 |
+
- `jit_mode_eval`: False
|
580 |
+
- `use_ipex`: False
|
581 |
+
- `bf16`: True
|
582 |
+
- `fp16`: False
|
583 |
+
- `fp16_opt_level`: O1
|
584 |
+
- `half_precision_backend`: auto
|
585 |
+
- `bf16_full_eval`: True
|
586 |
+
- `fp16_full_eval`: False
|
587 |
+
- `tf32`: True
|
588 |
+
- `local_rank`: 0
|
589 |
+
- `ddp_backend`: None
|
590 |
+
- `tpu_num_cores`: None
|
591 |
+
- `tpu_metrics_debug`: False
|
592 |
+
- `debug`: []
|
593 |
+
- `dataloader_drop_last`: True
|
594 |
+
- `dataloader_num_workers`: 2
|
595 |
+
- `dataloader_prefetch_factor`: None
|
596 |
+
- `past_index`: -1
|
597 |
+
- `disable_tqdm`: False
|
598 |
+
- `remove_unused_columns`: True
|
599 |
+
- `label_names`: None
|
600 |
+
- `load_best_model_at_end`: True
|
601 |
+
- `ignore_data_skip`: False
|
602 |
+
- `fsdp`: []
|
603 |
+
- `fsdp_min_num_params`: 0
|
604 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
605 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
606 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
607 |
+
- `deepspeed`: None
|
608 |
+
- `label_smoothing_factor`: 0.0
|
609 |
+
- `optim`: adafactor
|
610 |
+
- `optim_args`: None
|
611 |
+
- `adafactor`: False
|
612 |
+
- `group_by_length`: False
|
613 |
+
- `length_column_name`: length
|
614 |
+
- `ddp_find_unused_parameters`: None
|
615 |
+
- `ddp_bucket_cap_mb`: None
|
616 |
+
- `ddp_broadcast_buffers`: False
|
617 |
+
- `dataloader_pin_memory`: True
|
618 |
+
- `dataloader_persistent_workers`: False
|
619 |
+
- `skip_memory_metrics`: True
|
620 |
+
- `use_legacy_prediction_loop`: False
|
621 |
+
- `push_to_hub`: True
|
622 |
+
- `resume_from_checkpoint`: None
|
623 |
+
- `hub_model_id`: None
|
624 |
+
- `hub_strategy`: every_save
|
625 |
+
- `hub_private_repo`: False
|
626 |
+
- `hub_always_push`: False
|
627 |
+
- `gradient_checkpointing`: False
|
628 |
+
- `gradient_checkpointing_kwargs`: None
|
629 |
+
- `include_inputs_for_metrics`: False
|
630 |
+
- `eval_do_concat_batches`: True
|
631 |
+
- `fp16_backend`: auto
|
632 |
+
- `push_to_hub_model_id`: None
|
633 |
+
- `push_to_hub_organization`: None
|
634 |
+
- `mp_parameters`:
|
635 |
+
- `auto_find_batch_size`: False
|
636 |
+
- `full_determinism`: False
|
637 |
+
- `torchdynamo`: None
|
638 |
+
- `ray_scope`: last
|
639 |
+
- `ddp_timeout`: 1800
|
640 |
+
- `torch_compile`: False
|
641 |
+
- `torch_compile_backend`: None
|
642 |
+
- `torch_compile_mode`: None
|
643 |
+
- `dispatch_batches`: None
|
644 |
+
- `split_batches`: None
|
645 |
+
- `include_tokens_per_second`: False
|
646 |
+
- `include_num_input_tokens_seen`: False
|
647 |
+
- `neftune_noise_alpha`: None
|
648 |
+
- `optim_target_modules`: None
|
649 |
+
- `batch_eval_metrics`: False
|
650 |
+
- `eval_on_start`: False
|
651 |
+
- `use_liger_kernel`: False
|
652 |
+
- `eval_use_gather_object`: False
|
653 |
+
- `batch_sampler`: batch_sampler
|
654 |
+
- `multi_dataset_batch_sampler`: proportional
|
655 |
+
- `ddp_static_graph`: False
|
656 |
+
- `ddp_comm_hook`: bf16
|
657 |
+
- `gradient_as_bucket_view`: False
|
658 |
+
|
659 |
+
</details>
|
660 |
+
|
661 |
+
### Training Logs
|
662 |
+
| Epoch | Step | Training Loss | loss | rusisms-uk-title--matryoshka_dim-128--_dot_map@100 | rusisms-uk-title--matryoshka_dim-256--_dot_map@100 | rusisms-uk-title--matryoshka_dim-512--_dot_map@100 | rusisms-uk-title--matryoshka_dim-768--_dot_map@100 | rusisms-uk-title_dot_map@100 |
|
663 |
+
|:-------:|:------:|:-------------:|:----------:|:--------------------------------------------------:|:--------------------------------------------------:|:--------------------------------------------------:|:--------------------------------------------------:|:----------------------------:|
|
664 |
+
| 0.1 | 10 | 6.6103 | - | - | - | - | - | - |
|
665 |
+
| 0.2 | 20 | 5.524 | - | - | - | - | - | - |
|
666 |
+
| 0.3 | 30 | 4.759 | 3.6444 | - | - | - | - | - |
|
667 |
+
| 0.4 | 40 | 4.5195 | - | - | - | - | - | - |
|
668 |
+
| 0.5 | 50 | 3.6598 | - | - | - | - | - | - |
|
669 |
+
| 0.6 | 60 | 3.7912 | 2.8962 | - | - | - | - | - |
|
670 |
+
| 0.7 | 70 | 3.9935 | - | - | - | - | - | - |
|
671 |
+
| 0.8 | 80 | 3.3929 | - | - | - | - | - | - |
|
672 |
+
| **0.9** | **90** | **3.6101** | **2.6889** | **-** | **-** | **-** | **-** | **-** |
|
673 |
+
| 1.0 | 100 | 3.8753 | - | 0.0054 | 0.0120 | 0.0152 | 0.0213 | 0.1404 |
|
674 |
+
|
675 |
+
* The bold row denotes the saved checkpoint.
|
676 |
+
|
677 |
+
### Framework Versions
|
678 |
+
- Python: 3.12.6
|
679 |
+
- Sentence Transformers: 3.0.1
|
680 |
+
- Transformers: 4.45.1
|
681 |
+
- PyTorch: 2.4.1
|
682 |
+
- Accelerate: 0.34.2
|
683 |
+
- Datasets: 3.0.0
|
684 |
+
- Tokenizers: 0.20.0
|
685 |
+
|
686 |
+
## Citation
|
687 |
+
|
688 |
+
### BibTeX
|
689 |
+
|
690 |
+
#### Sentence Transformers
|
691 |
+
```bibtex
|
692 |
+
@inproceedings{reimers-2019-sentence-bert,
|
693 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
694 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
695 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
696 |
+
month = "11",
|
697 |
+
year = "2019",
|
698 |
+
publisher = "Association for Computational Linguistics",
|
699 |
+
url = "https://arxiv.org/abs/1908.10084",
|
700 |
+
}
|
701 |
+
```
|
702 |
+
|
703 |
+
<!--
|
704 |
+
## Glossary
|
705 |
+
|
706 |
+
*Clearly define terms in order to be accessible across audiences.*
|
707 |
+
-->
|
708 |
+
|
709 |
+
<!--
|
710 |
+
## Model Card Authors
|
711 |
+
|
712 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
713 |
+
-->
|
714 |
+
|
715 |
+
<!--
|
716 |
+
## Model Card Contact
|
717 |
+
|
718 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
719 |
+
-->
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.0.1",
|
4 |
+
"transformers": "4.45.1",
|
5 |
+
"pytorch": "2.4.1"
|
6 |
+
},
|
7 |
+
"prompts": {
|
8 |
+
"query": "query: ",
|
9 |
+
"passage": "passage: "
|
10 |
+
},
|
11 |
+
"default_prompt_name": null,
|
12 |
+
"similarity_fn_name": null
|
13 |
+
}
|
model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 556109872
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:48b0bc6d823b415718596e69b55f4a07d986360bb1bbec1b008e0f665ba8dbd7
|
3 |
size 556109872
|
modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|