Add new SentenceTransformer model
Browse files- README.md +358 -425
- config.json +1 -1
- config_sentence_transformers.json +4 -4
- model.safetensors +1 -1
README.md
CHANGED
|
@@ -1,5 +1,123 @@
|
|
| 1 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
base_model: BAAI/bge-small-en-v1.5
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
library_name: sentence-transformers
|
| 4 |
metrics:
|
| 5 |
- cosine_accuracy@1
|
|
@@ -18,152 +136,6 @@ metrics:
|
|
| 18 |
- cosine_mrr@10
|
| 19 |
- cosine_mrr@100
|
| 20 |
- cosine_map@100
|
| 21 |
-
- dot_accuracy@1
|
| 22 |
-
- dot_accuracy@5
|
| 23 |
-
- dot_accuracy@10
|
| 24 |
-
- dot_precision@1
|
| 25 |
-
- dot_precision@5
|
| 26 |
-
- dot_precision@10
|
| 27 |
-
- dot_recall@1
|
| 28 |
-
- dot_recall@5
|
| 29 |
-
- dot_recall@10
|
| 30 |
-
- dot_ndcg@5
|
| 31 |
-
- dot_ndcg@10
|
| 32 |
-
- dot_ndcg@100
|
| 33 |
-
- dot_mrr@5
|
| 34 |
-
- dot_mrr@10
|
| 35 |
-
- dot_mrr@100
|
| 36 |
-
- dot_map@100
|
| 37 |
-
pipeline_tag: sentence-similarity
|
| 38 |
-
tags:
|
| 39 |
-
- sentence-transformers
|
| 40 |
-
- sentence-similarity
|
| 41 |
-
- feature-extraction
|
| 42 |
-
- generated_from_trainer
|
| 43 |
-
- dataset_size:1606
|
| 44 |
-
- loss:GISTEmbedLoss
|
| 45 |
-
widget:
|
| 46 |
-
- source_sentence: Do the tours include visits to all the major ghats and Akhara camps?
|
| 47 |
-
sentences:
|
| 48 |
-
- Yes, many tours do cover all major ghats such as Sangam, Ram Ghat, and Dashashwamedh
|
| 49 |
-
Ghat, along with visits to some of the most significant Akhara camps. These tours
|
| 50 |
-
offer pilgrims a unique opportunity to witness the religious and cultural significance
|
| 51 |
-
of these locations. However, we recommend reviewing the specific itinerary of
|
| 52 |
-
your chosen tour for precise details.
|
| 53 |
-
- Yes, many tours do cover all major ghats such as Sangam, Ram Ghat, and Dashashwamedh
|
| 54 |
-
Ghat, along with visits to some of the most significant Akhara camps. These tours
|
| 55 |
-
offer pilgrims a unique opportunity to witness the religious and cultural significance
|
| 56 |
-
of these locations. However, we recommend reviewing the specific itinerary of
|
| 57 |
-
your chosen tour for precise details.
|
| 58 |
-
- The orchestra rehearsed late into the night, perfecting their performance for
|
| 59 |
-
the upcoming concert. Each musician contributed their unique sound, creating a
|
| 60 |
-
harmonious blend of instruments. The conductor insisted on precision and emotion,
|
| 61 |
-
ensuring every note resonated with the audience's heart. Attendees can expect
|
| 62 |
-
a captivating experience, filled with dynamic melodies and intricate crescendos
|
| 63 |
-
that highlight the orchestra's talent and dedication. For a firsthand experience,
|
| 64 |
-
consider arriving early to enjoy the pre-concert discussions.
|
| 65 |
-
- source_sentence: What is the significance of the Naga Sadhus in the Shahi Snan?
|
| 66 |
-
sentences:
|
| 67 |
-
- The Naga Sadhus hold a significant place in the Shahi Snan during the Kumbh Mela
|
| 68 |
-
as they are considered the guardians of faith and ancient traditions within Hinduism.
|
| 69 |
-
Known for their ash-covered, unclothed bodies, long matted hair, and intense spiritual
|
| 70 |
-
practices, the Naga Sadhus are the first to take the holy dip during the Shahi
|
| 71 |
-
Snan, symbolizing purity, renunciation, and spiritual strength. Their participation
|
| 72 |
-
is believed to purify the waters of the sacred rivers, making them spiritually
|
| 73 |
-
potent for the millions of pilgrims who follow. The Naga Sadhus’ procession to
|
| 74 |
-
the river, marked by their vibrant chants, tridents, and fearless demeanor, is
|
| 75 |
-
one of the most awe-inspiring spectacles of the Kumbh Mela. Their presence represents
|
| 76 |
-
the commitment to asceticism, devotion, and the protection of religious traditions,
|
| 77 |
-
adding a deeper layer of spiritual intensity and significance to the Shahi Snan
|
| 78 |
-
ritual.
|
| 79 |
-
- During the processions of Peshwai and Shahi Snaans at the Maha Kumbh Mela, Mahamandaleshwaras
|
| 80 |
-
play a unique and central role as the spiritual leaders of their Akharas. They
|
| 81 |
-
lead their followers in grand, royal processions to the riverbanks for the Shahi
|
| 82 |
-
Snan (royal bath), symbolizing the beginning of the holy ritual. Riding on beautifully
|
| 83 |
-
decorated chariots, elephants, or horses, they lead the march with great reverence
|
| 84 |
-
and authority, followed by their disciples, saints, and devotees. The presence
|
| 85 |
-
of Mahamandaleshwaras in these processions signifies the spiritual sanctity and
|
| 86 |
-
importance of the ritual, inspiring pilgrims to partake in the spiritual energy
|
| 87 |
-
and blessings of the holy dip. Their leadership adds a sense of grandeur and divine
|
| 88 |
-
significance to the Shahi Snaans, making them the focal point of the Kumbh Mela.
|
| 89 |
-
- The vibrant world of reptiles is fascinating to explore, particularly focusing
|
| 90 |
-
on the unique adaptations they possess for survival. Snakes, for instance, exhibit
|
| 91 |
-
remarkable methods of locomotion, allowing them to navigate diverse terrains with
|
| 92 |
-
ease. Some species are known for their ability to blend into their surroundings,
|
| 93 |
-
employing camouflage techniques that render them nearly invisible to both predators
|
| 94 |
-
and prey. Additionally, many reptiles display fascinating reproductive behaviors,
|
| 95 |
-
with some laying eggs in protected environments while others give birth to live
|
| 96 |
-
young. The intricate ecosystems that support these creatures highlight the interdependence
|
| 97 |
-
between various species, illustrating the delicate balance of nature. Understanding
|
| 98 |
-
these dynamics can enhance our appreciation for the biodiversity that exists in
|
| 99 |
-
our world and the intricate roles each species plays within its habitat.
|
| 100 |
-
- source_sentence: Are there any carpool or ride-sharing options to travel to Prayagraj?
|
| 101 |
-
sentences:
|
| 102 |
-
- 'In the realm of culinary experiences, exploring the myriad flavors of Italian
|
| 103 |
-
cuisine can be quite delightful. One might consider the following aspects:<br><br>1.
|
| 104 |
-
Pasta Varieties: There are numerous types of pasta, from spaghetti to fettuccine,
|
| 105 |
-
each offering a distinct texture and taste in dishes.<br>2. Regional Sauces: Different
|
| 106 |
-
areas of Italy are known for unique sauces, such as marinara, pesto, and Alfredo,
|
| 107 |
-
which can transform a simple meal into a feast. Additionally, using fresh, local
|
| 108 |
-
ingredients enhances the flavors.<br>3. Dining Etiquette: Understanding Italian
|
| 109 |
-
dining customs, such as the significance of antipasti, can enrich one''s experience
|
| 110 |
-
while enjoying meals with family and friends.'
|
| 111 |
-
- 'Yes, there are multiple carpooling and ride-sharing options you can use to travel
|
| 112 |
-
to Prayagraj. These include:<br><br>1. BlaBlaCar: This is a trusted community
|
| 113 |
-
carpooling app where you can connect with people who are traveling in the same
|
| 114 |
-
direction.<br>2. Uber and Ola Share: Both Uber and Ola offer ride-sharing options
|
| 115 |
-
where you can share your ride with other passengers. Please note this might depend
|
| 116 |
-
on the city you are traveling from.<br>3. Local Carpooling groups: There may be
|
| 117 |
-
local carpooling groups on social media platforms like Facebook and WhatsApp where
|
| 118 |
-
people share their travel plans.'
|
| 119 |
-
- The Kumbh Mela hosts a diverse array of spiritual gurus, each representing different
|
| 120 |
-
spiritual traditions and philosophies within Hinduism. Prominent among them are
|
| 121 |
-
the Mahamandaleshwaras of the various Akharas, who are highly respected for their
|
| 122 |
-
deep knowledge of scriptures and spiritual leadership. Then there are the Naga
|
| 123 |
-
Sadhus, known for their ascetic lifestyle and unique appearance, who represent
|
| 124 |
-
intense spiritual discipline and renunciation. \n \n The Acharyas and Prayagwals
|
| 125 |
-
serve as guides and teachers for pilgrims, offering religious services and performing
|
| 126 |
-
important rituals like Pind Daan and Shraadh. Additionally, there are Dandi Sanyasis
|
| 127 |
-
who follow the path of austerity and renunciation, emphasizing self-discipline
|
| 128 |
-
and simplicity.
|
| 129 |
-
- source_sentence: What is the best train route to Prayagraj from Varanasi?
|
| 130 |
-
sentences:
|
| 131 |
-
- The best train route from Varanasi to Prayagraj is via the Indian Railways. There
|
| 132 |
-
are multiple trains that operate on this route daily. <br><br>1. VBS BSB Express
|
| 133 |
-
(14235)<br>2. Shiv Ganga Express (12559)<br>3. Mahanagri Express (11093)<br>4.
|
| 134 |
-
Kashi Vishwanath Express (14257)<br>5. Vande Bharat <br><br>For the most accurate
|
| 135 |
-
and up-to-date information on train timings to Prayagraj, please visit the IRCTC
|
| 136 |
-
website <<u><a target='_blank' href='https://www.irctc.co.in/nget/'>https://www.irctc.co.in/nget/</a></u>>
|
| 137 |
-
- Yes, towing services are available if your vehicle breaks down in the parking
|
| 138 |
-
lot.
|
| 139 |
-
- A delightful assortment of pastries can significantly enhance any gathering. Chocolate
|
| 140 |
-
eclairs, fruit tarts, and macarons are popular choices among guests. <br><br>1.
|
| 141 |
-
Lemon meringue tart<br>2. Almond croissant<br>3. Raspberry mille-feuille<br>4.
|
| 142 |
-
Vanilla cream puff<br>5. Caramel flan <br><br>For an exquisite culinary experience,
|
| 143 |
-
consider attending a pastry-making workshop for hands-on learning and tips from
|
| 144 |
-
skilled bakers.
|
| 145 |
-
- source_sentence: What does Deep Daan symbolize?
|
| 146 |
-
sentences:
|
| 147 |
-
- In the quiet corners of a bustling city, the sound of a distant siren punctuates
|
| 148 |
-
the air, hinting at life’s unpredictability. A lone musician sets up his stand,
|
| 149 |
-
strings resonating softly as pedestrians pass by, each lost in their own thoughts.
|
| 150 |
-
The warmth of the sun flows over the pavement, while children chase after colorful
|
| 151 |
-
kites soaring high above. Nearby, a group gathers for laughter and stories, each
|
| 152 |
-
voice woven into a tapestry of community and connection. As day turns to dusk,
|
| 153 |
-
the sky transforms into a palette of vibrant colors, inviting dreams and possibilities
|
| 154 |
-
under the expansive canvas of the universe.
|
| 155 |
-
- Deep Daan involves the ritual of lighting oil lamps (diyas) and floating them
|
| 156 |
-
on the river as an offering to the divine. This act symbolizes the removal of
|
| 157 |
-
darkness and ignorance, representing the soul’s journey towards enlightenment
|
| 158 |
-
and spiritual awakening. The flickering lamps also signify hope, devotion, and
|
| 159 |
-
a wish for divine blessings. During the Kumbh Mela, Deep Daan is considered a
|
| 160 |
-
powerful ritual that purifies the mind and soul, bringing peace and fulfillment
|
| 161 |
-
to the devotees performing it.
|
| 162 |
-
- The duration of the tours typically ranges from 1-day to 3-day packages. Start
|
| 163 |
-
times for the tours are usually early in the morning to ensure participants make
|
| 164 |
-
the most of the day’s activities, which may include attending religious rituals,
|
| 165 |
-
visiting temples, and sightseeing. Exact timings will be communicated to you once
|
| 166 |
-
your booking is confirmed.
|
| 167 |
model-index:
|
| 168 |
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
|
| 169 |
results:
|
|
@@ -175,101 +147,53 @@ model-index:
|
|
| 175 |
type: val_evaluator
|
| 176 |
metrics:
|
| 177 |
- type: cosine_accuracy@1
|
| 178 |
-
value: 0.
|
| 179 |
name: Cosine Accuracy@1
|
| 180 |
- type: cosine_accuracy@5
|
| 181 |
-
value: 0.
|
| 182 |
name: Cosine Accuracy@5
|
| 183 |
- type: cosine_accuracy@10
|
| 184 |
-
value: 0.
|
| 185 |
name: Cosine Accuracy@10
|
| 186 |
- type: cosine_precision@1
|
| 187 |
-
value: 0.
|
| 188 |
name: Cosine Precision@1
|
| 189 |
- type: cosine_precision@5
|
| 190 |
-
value: 0.
|
| 191 |
name: Cosine Precision@5
|
| 192 |
- type: cosine_precision@10
|
| 193 |
-
value: 0.
|
| 194 |
name: Cosine Precision@10
|
| 195 |
- type: cosine_recall@1
|
| 196 |
-
value: 0.
|
| 197 |
name: Cosine Recall@1
|
| 198 |
- type: cosine_recall@5
|
| 199 |
-
value: 0.
|
| 200 |
name: Cosine Recall@5
|
| 201 |
- type: cosine_recall@10
|
| 202 |
-
value: 0.
|
| 203 |
name: Cosine Recall@10
|
| 204 |
- type: cosine_ndcg@5
|
| 205 |
-
value: 0.
|
| 206 |
name: Cosine Ndcg@5
|
| 207 |
- type: cosine_ndcg@10
|
| 208 |
-
value: 0.
|
| 209 |
name: Cosine Ndcg@10
|
| 210 |
- type: cosine_ndcg@100
|
| 211 |
-
value: 0.
|
| 212 |
name: Cosine Ndcg@100
|
| 213 |
- type: cosine_mrr@5
|
| 214 |
-
value: 0.
|
| 215 |
name: Cosine Mrr@5
|
| 216 |
- type: cosine_mrr@10
|
| 217 |
-
value: 0.
|
| 218 |
name: Cosine Mrr@10
|
| 219 |
- type: cosine_mrr@100
|
| 220 |
-
value: 0.
|
| 221 |
name: Cosine Mrr@100
|
| 222 |
- type: cosine_map@100
|
| 223 |
-
value: 0.
|
| 224 |
name: Cosine Map@100
|
| 225 |
-
- type: dot_accuracy@1
|
| 226 |
-
value: 0.5621890547263682
|
| 227 |
-
name: Dot Accuracy@1
|
| 228 |
-
- type: dot_accuracy@5
|
| 229 |
-
value: 0.9353233830845771
|
| 230 |
-
name: Dot Accuracy@5
|
| 231 |
-
- type: dot_accuracy@10
|
| 232 |
-
value: 0.9676616915422885
|
| 233 |
-
name: Dot Accuracy@10
|
| 234 |
-
- type: dot_precision@1
|
| 235 |
-
value: 0.5621890547263682
|
| 236 |
-
name: Dot Precision@1
|
| 237 |
-
- type: dot_precision@5
|
| 238 |
-
value: 0.1870646766169154
|
| 239 |
-
name: Dot Precision@5
|
| 240 |
-
- type: dot_precision@10
|
| 241 |
-
value: 0.09676616915422885
|
| 242 |
-
name: Dot Precision@10
|
| 243 |
-
- type: dot_recall@1
|
| 244 |
-
value: 0.5621890547263682
|
| 245 |
-
name: Dot Recall@1
|
| 246 |
-
- type: dot_recall@5
|
| 247 |
-
value: 0.9353233830845771
|
| 248 |
-
name: Dot Recall@5
|
| 249 |
-
- type: dot_recall@10
|
| 250 |
-
value: 0.9676616915422885
|
| 251 |
-
name: Dot Recall@10
|
| 252 |
-
- type: dot_ndcg@5
|
| 253 |
-
value: 0.776654033153749
|
| 254 |
-
name: Dot Ndcg@5
|
| 255 |
-
- type: dot_ndcg@10
|
| 256 |
-
value: 0.7875252591924246
|
| 257 |
-
name: Dot Ndcg@10
|
| 258 |
-
- type: dot_ndcg@100
|
| 259 |
-
value: 0.795208625109
|
| 260 |
-
name: Dot Ndcg@100
|
| 261 |
-
- type: dot_mrr@5
|
| 262 |
-
value: 0.7223880597014923
|
| 263 |
-
name: Dot Mrr@5
|
| 264 |
-
- type: dot_mrr@10
|
| 265 |
-
value: 0.7271164021164023
|
| 266 |
-
name: Dot Mrr@10
|
| 267 |
-
- type: dot_mrr@100
|
| 268 |
-
value: 0.7290074495782858
|
| 269 |
-
name: Dot Mrr@100
|
| 270 |
-
- type: dot_map@100
|
| 271 |
-
value: 0.7290074495782857
|
| 272 |
-
name: Dot Map@100
|
| 273 |
---
|
| 274 |
|
| 275 |
# SentenceTransformer based on BAAI/bge-small-en-v1.5
|
|
@@ -282,7 +206,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [B
|
|
| 282 |
- **Model Type:** Sentence Transformer
|
| 283 |
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
|
| 284 |
- **Maximum Sequence Length:** 512 tokens
|
| 285 |
-
- **Output Dimensionality:** 384
|
| 286 |
- **Similarity Function:** Cosine Similarity
|
| 287 |
<!-- - **Training Dataset:** Unknown -->
|
| 288 |
<!-- - **Language:** Unknown -->
|
|
@@ -322,9 +246,9 @@ from sentence_transformers import SentenceTransformer
|
|
| 322 |
model = SentenceTransformer("himanshu23099/bge_embedding_finetune1")
|
| 323 |
# Run inference
|
| 324 |
sentences = [
|
| 325 |
-
'
|
| 326 |
-
'
|
| 327 |
-
'In
|
| 328 |
]
|
| 329 |
embeddings = model.encode(sentences)
|
| 330 |
print(embeddings.shape)
|
|
@@ -365,43 +289,28 @@ You can finetune this model on your own dataset.
|
|
| 365 |
### Metrics
|
| 366 |
|
| 367 |
#### Information Retrieval
|
|
|
|
| 368 |
* Dataset: `val_evaluator`
|
| 369 |
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 370 |
|
| 371 |
-
| Metric | Value
|
| 372 |
-
|
| 373 |
-
| cosine_accuracy@1 | 0.
|
| 374 |
-
| cosine_accuracy@5 | 0.
|
| 375 |
-
| cosine_accuracy@10 | 0.
|
| 376 |
-
| cosine_precision@1 | 0.
|
| 377 |
-
| cosine_precision@5 | 0.
|
| 378 |
-
| cosine_precision@10 | 0.
|
| 379 |
-
| cosine_recall@1 | 0.
|
| 380 |
-
| cosine_recall@5 | 0.
|
| 381 |
-
| cosine_recall@10 | 0.
|
| 382 |
-
| cosine_ndcg@5 | 0.
|
| 383 |
-
| cosine_ndcg@10 | 0.
|
| 384 |
-
| cosine_ndcg@100
|
| 385 |
-
| cosine_mrr@5 | 0.
|
| 386 |
-
| cosine_mrr@10 | 0.
|
| 387 |
-
| cosine_mrr@100 | 0.
|
| 388 |
-
| cosine_map@100 | 0.
|
| 389 |
-
| dot_accuracy@1 | 0.5622 |
|
| 390 |
-
| dot_accuracy@5 | 0.9353 |
|
| 391 |
-
| dot_accuracy@10 | 0.9677 |
|
| 392 |
-
| dot_precision@1 | 0.5622 |
|
| 393 |
-
| dot_precision@5 | 0.1871 |
|
| 394 |
-
| dot_precision@10 | 0.0968 |
|
| 395 |
-
| dot_recall@1 | 0.5622 |
|
| 396 |
-
| dot_recall@5 | 0.9353 |
|
| 397 |
-
| dot_recall@10 | 0.9677 |
|
| 398 |
-
| dot_ndcg@5 | 0.7767 |
|
| 399 |
-
| dot_ndcg@10 | 0.7875 |
|
| 400 |
-
| dot_ndcg@100 | 0.7952 |
|
| 401 |
-
| dot_mrr@5 | 0.7224 |
|
| 402 |
-
| dot_mrr@10 | 0.7271 |
|
| 403 |
-
| dot_mrr@100 | 0.729 |
|
| 404 |
-
| **dot_map@100** | **0.729** |
|
| 405 |
|
| 406 |
<!--
|
| 407 |
## Bias, Risks and Limitations
|
|
@@ -422,19 +331,19 @@ You can finetune this model on your own dataset.
|
|
| 422 |
#### Unnamed Dataset
|
| 423 |
|
| 424 |
|
| 425 |
-
* Size:
|
| 426 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 427 |
* Approximate statistics based on the first 1000 samples:
|
| 428 |
| | anchor | positive | negative |
|
| 429 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
|
| 430 |
| type | string | string | string |
|
| 431 |
-
| details | <ul><li>min:
|
| 432 |
* Samples:
|
| 433 |
-
| anchor
|
| 434 |
-
|
| 435 |
-
| <code>
|
| 436 |
-
| <code>
|
| 437 |
-
| <code>
|
| 438 |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 439 |
```json
|
| 440 |
{'guide': SentenceTransformer(
|
|
@@ -449,19 +358,19 @@ You can finetune this model on your own dataset.
|
|
| 449 |
#### Unnamed Dataset
|
| 450 |
|
| 451 |
|
| 452 |
-
* Size:
|
| 453 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 454 |
-
* Approximate statistics based on the first
|
| 455 |
-
| | anchor | positive | negative
|
| 456 |
-
|
| 457 |
-
| type | string | string | string
|
| 458 |
-
| details | <ul><li>min:
|
| 459 |
* Samples:
|
| 460 |
-
| anchor
|
| 461 |
-
|
| 462 |
-
| <code>
|
| 463 |
-
| <code>
|
| 464 |
-
| <code>
|
| 465 |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 466 |
```json
|
| 467 |
{'guide': SentenceTransformer(
|
|
@@ -573,6 +482,7 @@ You can finetune this model on your own dataset.
|
|
| 573 |
- `gradient_checkpointing`: False
|
| 574 |
- `gradient_checkpointing_kwargs`: None
|
| 575 |
- `include_inputs_for_metrics`: False
|
|
|
|
| 576 |
- `eval_do_concat_batches`: True
|
| 577 |
- `fp16_backend`: auto
|
| 578 |
- `push_to_hub_model_id`: None
|
|
@@ -594,7 +504,10 @@ You can finetune this model on your own dataset.
|
|
| 594 |
- `optim_target_modules`: None
|
| 595 |
- `batch_eval_metrics`: False
|
| 596 |
- `eval_on_start`: False
|
|
|
|
| 597 |
- `eval_use_gather_object`: False
|
|
|
|
|
|
|
| 598 |
- `batch_sampler`: batch_sampler
|
| 599 |
- `multi_dataset_batch_sampler`: proportional
|
| 600 |
|
|
@@ -603,170 +516,190 @@ You can finetune this model on your own dataset.
|
|
| 603 |
### Training Logs
|
| 604 |
<details><summary>Click to expand</summary>
|
| 605 |
|
| 606 |
-
| Epoch
|
| 607 |
-
|
| 608 |
-
| 0.
|
| 609 |
-
| 0.
|
| 610 |
-
| 0.
|
| 611 |
-
| 0.
|
| 612 |
-
| 0.
|
| 613 |
-
|
|
| 614 |
-
|
|
| 615 |
-
|
|
| 616 |
-
|
|
| 617 |
-
|
|
| 618 |
-
|
|
| 619 |
-
|
|
| 620 |
-
|
|
| 621 |
-
|
|
| 622 |
-
|
|
| 623 |
-
|
|
| 624 |
-
|
|
| 625 |
-
|
|
| 626 |
-
|
|
| 627 |
-
|
|
| 628 |
-
|
|
| 629 |
-
|
|
| 630 |
-
|
|
| 631 |
-
|
|
| 632 |
-
|
|
| 633 |
-
|
|
| 634 |
-
|
|
| 635 |
-
|
|
| 636 |
-
|
|
| 637 |
-
|
|
| 638 |
-
|
|
| 639 |
-
|
|
| 640 |
-
|
|
| 641 |
-
|
|
| 642 |
-
|
|
| 643 |
-
|
|
| 644 |
-
|
|
| 645 |
-
|
|
| 646 |
-
|
|
| 647 |
-
|
|
| 648 |
-
|
|
| 649 |
-
|
|
| 650 |
-
|
|
| 651 |
-
|
|
| 652 |
-
|
|
| 653 |
-
|
|
| 654 |
-
|
|
| 655 |
-
|
|
| 656 |
-
|
|
| 657 |
-
|
|
| 658 |
-
|
|
| 659 |
-
|
|
| 660 |
-
|
|
| 661 |
-
|
|
| 662 |
-
|
|
| 663 |
-
|
|
| 664 |
-
|
|
| 665 |
-
|
|
| 666 |
-
|
|
| 667 |
-
|
|
| 668 |
-
|
|
| 669 |
-
|
|
| 670 |
-
|
|
| 671 |
-
|
|
| 672 |
-
|
|
| 673 |
-
|
|
| 674 |
-
|
|
| 675 |
-
|
|
| 676 |
-
|
|
| 677 |
-
|
|
| 678 |
-
|
|
| 679 |
-
|
|
| 680 |
-
|
|
| 681 |
-
|
|
| 682 |
-
|
|
| 683 |
-
|
|
| 684 |
-
|
|
| 685 |
-
|
|
| 686 |
-
|
|
| 687 |
-
|
|
| 688 |
-
|
|
| 689 |
-
|
|
| 690 |
-
|
|
| 691 |
-
|
|
| 692 |
-
|
|
| 693 |
-
|
|
| 694 |
-
|
|
| 695 |
-
|
|
| 696 |
-
|
|
| 697 |
-
|
|
| 698 |
-
|
|
| 699 |
-
|
|
| 700 |
-
|
|
| 701 |
-
|
|
| 702 |
-
|
|
| 703 |
-
|
|
| 704 |
-
|
|
| 705 |
-
|
|
| 706 |
-
|
|
| 707 |
-
|
|
| 708 |
-
|
|
| 709 |
-
|
|
| 710 |
-
|
|
| 711 |
-
|
|
| 712 |
-
|
|
| 713 |
-
|
|
| 714 |
-
|
|
| 715 |
-
|
|
| 716 |
-
|
|
| 717 |
-
|
|
| 718 |
-
|
|
| 719 |
-
|
|
| 720 |
-
|
|
| 721 |
-
|
|
| 722 |
-
|
|
| 723 |
-
|
|
| 724 |
-
|
|
| 725 |
-
|
|
| 726 |
-
|
|
| 727 |
-
|
|
| 728 |
-
|
|
| 729 |
-
|
|
| 730 |
-
|
|
| 731 |
-
|
|
| 732 |
-
|
|
| 733 |
-
|
|
| 734 |
-
|
|
| 735 |
-
|
|
| 736 |
-
|
|
| 737 |
-
|
|
| 738 |
-
|
|
| 739 |
-
|
|
| 740 |
-
|
|
| 741 |
-
|
|
| 742 |
-
|
|
| 743 |
-
|
|
| 744 |
-
|
|
| 745 |
-
|
|
| 746 |
-
|
|
| 747 |
-
|
|
| 748 |
-
|
|
| 749 |
-
|
|
| 750 |
-
|
|
| 751 |
-
|
|
| 752 |
-
|
|
| 753 |
-
|
|
| 754 |
-
|
|
| 755 |
-
|
|
| 756 |
-
|
|
| 757 |
-
|
|
| 758 |
-
|
| 759 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 760 |
</details>
|
| 761 |
|
| 762 |
### Framework Versions
|
| 763 |
- Python: 3.10.12
|
| 764 |
-
- Sentence Transformers: 3.
|
| 765 |
-
- Transformers: 4.
|
| 766 |
-
- PyTorch: 2.5.
|
| 767 |
-
- Accelerate:
|
| 768 |
- Datasets: 3.1.0
|
| 769 |
-
- Tokenizers: 0.
|
| 770 |
|
| 771 |
## Citation
|
| 772 |
|
|
|
|
| 1 |
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:3507
|
| 8 |
+
- loss:GISTEmbedLoss
|
| 9 |
base_model: BAAI/bge-small-en-v1.5
|
| 10 |
+
widget:
|
| 11 |
+
- source_sentence: What skills and traditions do the Akharas display?
|
| 12 |
+
sentences:
|
| 13 |
+
- "Are there specific vendors recommended for tent city booking?\n Yes, there are\
|
| 14 |
+
\ 7 approved vendors for setting up bookings in the Tent City for Kumbh Mela including\
|
| 15 |
+
\ : UP Tourism Tent Colony; Rishikul Kumbh Cottages; Aagman Maha Kumbh; Kumbh\
|
| 16 |
+
\ Village; Kumbh Camp India; Shivadya Kumbh Canvas. For more information about\
|
| 17 |
+
\ these vendors and their services, please click here"
|
| 18 |
+
- The Akharas display a wide range of skills and traditions that reflect their deep
|
| 19 |
+
spiritual heritage and ascetic practices. These include martial arts training,
|
| 20 |
+
such as wrestling, sword fighting, and the use of traditional weapons like tridents
|
| 21 |
+
(trishuls), maces (gada), and spears. Such skills symbolize their readiness to
|
| 22 |
+
protect Dharma and their spiritual communities. Additionally, Akharas emphasize
|
| 23 |
+
the tradition of Yoga and meditation, teaching various asanas and techniques for
|
| 24 |
+
self-discipline and spiritual growth. They also focus on Vedic rituals, chanting,
|
| 25 |
+
and sacred ceremonies to maintain their connection with the divine. Akharas uphold
|
| 26 |
+
the practice of 'Vairagya' or renunciation, where sadhus detach from worldly desires
|
| 27 |
+
to pursue a path of spiritual enlightenment. These traditions are on full display
|
| 28 |
+
during the Kumbh Mela, especially during the Shahi Snan, where the Naga Sadhus
|
| 29 |
+
lead the processions with their unique practices and skills.
|
| 30 |
+
- On a bright summer afternoon, the children gathered at the edge of the park, their
|
| 31 |
+
laughter echoing through the trees. They played games, running around with colorful
|
| 32 |
+
kites soaring high against the azure sky. Some kids chose to ride their bicycles
|
| 33 |
+
along the winding paths, while others set up a picnic with sandwiches and juice
|
| 34 |
+
boxes spread out on a checkered blanket. Nearby, a couple of dogs chased each
|
| 35 |
+
other joyfully, their tails wagging with uncontainable excitement as the scent
|
| 36 |
+
of fresh grass filled the air. The sun slowly dipped toward the horizon, casting
|
| 37 |
+
a warm golden glow, and everyone paused to watch the beauty of the sunset while
|
| 38 |
+
sharing stories, bonding over the simple joys of life. The day shimmered with
|
| 39 |
+
happiness, creating memories that would last long after the sun had set.
|
| 40 |
+
- source_sentence: Refund kab milega
|
| 41 |
+
sentences:
|
| 42 |
+
- "How late can I make changes to my booking before the tour date?\n Refunds and\
|
| 43 |
+
\ changes to bookings are subject to the following cancellation policy:\n \n 15\
|
| 44 |
+
\ days or more in advance: 90% of the booking amount will be refunded\n 10-15\
|
| 45 |
+
\ days in advance: 75% of the booking amount will be refunded\n 3-10 days in advance:\
|
| 46 |
+
\ 50% of the booking amount will be refunded\n Less than 3 days in advance: No\
|
| 47 |
+
\ refund\n \n Please make any changes or cancellations well in advance to avoid\
|
| 48 |
+
\ forfeiting your booking amount."
|
| 49 |
+
- "Is there any provision for women-only E-Rickshaws for added safety and comfort?\n\
|
| 50 |
+
\ No, there is no provision for women-only E-Rickshaws"
|
| 51 |
+
- 'Can I pay for the tour in installments?
|
| 52 |
+
|
| 53 |
+
No, the tour fee must be paid in full at the time of booking. Unfortunately, installment
|
| 54 |
+
plans are not available. Ensure that full payment is made to secure your booking
|
| 55 |
+
well in advance.'
|
| 56 |
+
- source_sentence: Are there any dedicated helpdesks or kiosks at the Airport for
|
| 57 |
+
information about transport to the Mela?
|
| 58 |
+
sentences:
|
| 59 |
+
- The forest is alive with the sounds of rustling leaves and chirping birds. As
|
| 60 |
+
the sun rises, a golden light filters through the trees, creating a magical atmosphere.
|
| 61 |
+
Walkers often find solace in nature, where the peaceful surroundings can soothe
|
| 62 |
+
the mind and inspire creativity. Each path taken may lead to a hidden waterfall
|
| 63 |
+
or a scenic overlook, inviting exploration and adventure.
|
| 64 |
+
- "What is Aarti\n In India, since ancient times, rivers are worshipped due to their\
|
| 65 |
+
\ importance to the human life. \n \n Likewise, in Tirathraj Prayagraj, Aartis’\
|
| 66 |
+
\ are performed on the banks of Ganga, Yamuna and at Sangam with great admiration,\
|
| 67 |
+
\ deep-rooted honor and devotion. In Prayagraj, Prayagraj Mela Authority and various\
|
| 68 |
+
\ other communities make grand arrangements for these Aartis.\n \n The Aartis\
|
| 69 |
+
\ are performed in the mornings and evenings, in which priests (Batuks), normally\
|
| 70 |
+
\ 5 to 7 in number, chant hymns with great fervor, holding meticulously designed\
|
| 71 |
+
\ lamps and worship the rivers with utmost devotion. \n \n The lamps held by the\
|
| 72 |
+
\ batuks represent the importance of panchtatva. On one hand, flames of the lamps\
|
| 73 |
+
\ signify bowing to the waters of the sacred rivers and on the other, the holy\
|
| 74 |
+
\ fumes emanating from the lamps appear to play the mystic of heaven on earth.\
|
| 75 |
+
\ \n List of Aliases: [['Prayag', 'Sangam'], ['Allahabad', 'PYG', 'Prayagraj'],\
|
| 76 |
+
\ ['Batuks', 'priests']]"
|
| 77 |
+
- Yes, there are people available to help you with transport information at the
|
| 78 |
+
airport. Tourist information centers would also be available across the city to
|
| 79 |
+
guide pilgrims to the Mela.
|
| 80 |
+
- source_sentence: Peeshwai Akhara time
|
| 81 |
+
sentences:
|
| 82 |
+
- "What is the connection between Akharas and Shahi Snan?\nAkharas are the central\
|
| 83 |
+
\ focus of the Shahi Snan during the Mahakumbh Mela. \U0001F549️\n \n The Akharas\
|
| 84 |
+
\ lead this ritual bath, with their Mahamandaleshwar taking the first dip in the\
|
| 85 |
+
\ sacred waters of the Sangam.\n \n The Akharas enter the bathing ghats in a grand\
|
| 86 |
+
\ procession, which includes chariots, elephants, horses, bands, and chanting\
|
| 87 |
+
\ saints and their followers."
|
| 88 |
+
- "When does Peshwai take place?\n The Peshwai of the Akharas is the first major\
|
| 89 |
+
\ attraction of the Mahakumbh. When the Akharas enter the Kumbh city with full\
|
| 90 |
+
\ grandeur, this is called the Peshwai. The Peshwai of each Akhara is conducted\
|
| 91 |
+
\ with proper rituals before the fair officially begins. \n List of Aliases:\
|
| 92 |
+
\ [['Peshwai', 'entry of Akharas with full grandeur', 'event', 'first major attraction\
|
| 93 |
+
\ of the Mahakumbh'], ['Akhada Darshan', 'Akharas'], , ['Akhand', 'Akhara', 'Kalpwasi\
|
| 94 |
+
\ Camp', 'Naga', 'Nagas', 'Sadhu', 'sadhus']]"
|
| 95 |
+
- Yes, towing services are available if your vehicle breaks down in the parking
|
| 96 |
+
lot.
|
| 97 |
+
- source_sentence: How long does it typically take to enter or exit the parking area
|
| 98 |
+
during peak times?
|
| 99 |
+
sentences:
|
| 100 |
+
- In a remote village, the annual kite festival attracts many visitors who come
|
| 101 |
+
to see the vibrant displays. The event showcases dozens of kites soaring high,
|
| 102 |
+
each crafted with unique designs. Local artisans prepare for months, selecting
|
| 103 |
+
colors and materials to make the best creations. Everyone enjoys the lively atmosphere
|
| 104 |
+
filled with music and laughter.
|
| 105 |
+
- 'What is the history and significance of the University of Allahabad?
|
| 106 |
+
|
| 107 |
+
Established in 1887, University of Allahabad is a prestigious educational institution.
|
| 108 |
+
It has a grand campus with prominent architectural structures:
|
| 109 |
+
|
| 110 |
+
The Science Faculty, formerly known as Muir Central College, is a notable building
|
| 111 |
+
showcasing Indo-Saracenic architecture. The structure includes a central 200 ft.
|
| 112 |
+
tower, and the interiors are adorned with marble and mosaic from Mirzapur.
|
| 113 |
+
|
| 114 |
+
The Arts Faculty and other buildings, constructed between 1910 and 1915, are renowned
|
| 115 |
+
for their architectural significance. It’s also historically significant as Rudyard
|
| 116 |
+
Kipling stayed here during 1888-89.'
|
| 117 |
+
- The time to enter or exit the parking area during peak times can vary based on
|
| 118 |
+
crowd density, time of day, and traffic management. Generally, it takes about
|
| 119 |
+
2 to 10 minutes.
|
| 120 |
+
pipeline_tag: sentence-similarity
|
| 121 |
library_name: sentence-transformers
|
| 122 |
metrics:
|
| 123 |
- cosine_accuracy@1
|
|
|
|
| 136 |
- cosine_mrr@10
|
| 137 |
- cosine_mrr@100
|
| 138 |
- cosine_map@100
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 139 |
model-index:
|
| 140 |
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
|
| 141 |
results:
|
|
|
|
| 147 |
type: val_evaluator
|
| 148 |
metrics:
|
| 149 |
- type: cosine_accuracy@1
|
| 150 |
+
value: 0.3443557582668187
|
| 151 |
name: Cosine Accuracy@1
|
| 152 |
- type: cosine_accuracy@5
|
| 153 |
+
value: 0.7229190421892816
|
| 154 |
name: Cosine Accuracy@5
|
| 155 |
- type: cosine_accuracy@10
|
| 156 |
+
value: 0.8038768529076397
|
| 157 |
name: Cosine Accuracy@10
|
| 158 |
- type: cosine_precision@1
|
| 159 |
+
value: 0.3443557582668187
|
| 160 |
name: Cosine Precision@1
|
| 161 |
- type: cosine_precision@5
|
| 162 |
+
value: 0.14458380843785631
|
| 163 |
name: Cosine Precision@5
|
| 164 |
- type: cosine_precision@10
|
| 165 |
+
value: 0.08038768529076395
|
| 166 |
name: Cosine Precision@10
|
| 167 |
- type: cosine_recall@1
|
| 168 |
+
value: 0.3443557582668187
|
| 169 |
name: Cosine Recall@1
|
| 170 |
- type: cosine_recall@5
|
| 171 |
+
value: 0.7229190421892816
|
| 172 |
name: Cosine Recall@5
|
| 173 |
- type: cosine_recall@10
|
| 174 |
+
value: 0.8038768529076397
|
| 175 |
name: Cosine Recall@10
|
| 176 |
- type: cosine_ndcg@5
|
| 177 |
+
value: 0.5504290811876199
|
| 178 |
name: Cosine Ndcg@5
|
| 179 |
- type: cosine_ndcg@10
|
| 180 |
+
value: 0.5765613499697346
|
| 181 |
name: Cosine Ndcg@10
|
| 182 |
- type: cosine_ndcg@100
|
| 183 |
+
value: 0.614171229811746
|
| 184 |
name: Cosine Ndcg@100
|
| 185 |
- type: cosine_mrr@5
|
| 186 |
+
value: 0.4926263778031162
|
| 187 |
name: Cosine Mrr@5
|
| 188 |
- type: cosine_mrr@10
|
| 189 |
+
value: 0.5033795768402376
|
| 190 |
name: Cosine Mrr@10
|
| 191 |
- type: cosine_mrr@100
|
| 192 |
+
value: 0.5113051664568566
|
| 193 |
name: Cosine Mrr@100
|
| 194 |
- type: cosine_map@100
|
| 195 |
+
value: 0.5113051664568576
|
| 196 |
name: Cosine Map@100
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 197 |
---
|
| 198 |
|
| 199 |
# SentenceTransformer based on BAAI/bge-small-en-v1.5
|
|
|
|
| 206 |
- **Model Type:** Sentence Transformer
|
| 207 |
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
|
| 208 |
- **Maximum Sequence Length:** 512 tokens
|
| 209 |
+
- **Output Dimensionality:** 384 dimensions
|
| 210 |
- **Similarity Function:** Cosine Similarity
|
| 211 |
<!-- - **Training Dataset:** Unknown -->
|
| 212 |
<!-- - **Language:** Unknown -->
|
|
|
|
| 246 |
model = SentenceTransformer("himanshu23099/bge_embedding_finetune1")
|
| 247 |
# Run inference
|
| 248 |
sentences = [
|
| 249 |
+
'How long does it typically take to enter or exit the parking area during peak times?',
|
| 250 |
+
'The time to enter or exit the parking area during peak times can vary based on crowd density, time of day, and traffic management. Generally, it takes about 2 to 10 minutes.',
|
| 251 |
+
'In a remote village, the annual kite festival attracts many visitors who come to see the vibrant displays. The event showcases dozens of kites soaring high, each crafted with unique designs. Local artisans prepare for months, selecting colors and materials to make the best creations. Everyone enjoys the lively atmosphere filled with music and laughter.',
|
| 252 |
]
|
| 253 |
embeddings = model.encode(sentences)
|
| 254 |
print(embeddings.shape)
|
|
|
|
| 289 |
### Metrics
|
| 290 |
|
| 291 |
#### Information Retrieval
|
| 292 |
+
|
| 293 |
* Dataset: `val_evaluator`
|
| 294 |
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 295 |
|
| 296 |
+
| Metric | Value |
|
| 297 |
+
|:--------------------|:-----------|
|
| 298 |
+
| cosine_accuracy@1 | 0.3444 |
|
| 299 |
+
| cosine_accuracy@5 | 0.7229 |
|
| 300 |
+
| cosine_accuracy@10 | 0.8039 |
|
| 301 |
+
| cosine_precision@1 | 0.3444 |
|
| 302 |
+
| cosine_precision@5 | 0.1446 |
|
| 303 |
+
| cosine_precision@10 | 0.0804 |
|
| 304 |
+
| cosine_recall@1 | 0.3444 |
|
| 305 |
+
| cosine_recall@5 | 0.7229 |
|
| 306 |
+
| cosine_recall@10 | 0.8039 |
|
| 307 |
+
| cosine_ndcg@5 | 0.5504 |
|
| 308 |
+
| cosine_ndcg@10 | 0.5766 |
|
| 309 |
+
| **cosine_ndcg@100** | **0.6142** |
|
| 310 |
+
| cosine_mrr@5 | 0.4926 |
|
| 311 |
+
| cosine_mrr@10 | 0.5034 |
|
| 312 |
+
| cosine_mrr@100 | 0.5113 |
|
| 313 |
+
| cosine_map@100 | 0.5113 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 314 |
|
| 315 |
<!--
|
| 316 |
## Bias, Risks and Limitations
|
|
|
|
| 331 |
#### Unnamed Dataset
|
| 332 |
|
| 333 |
|
| 334 |
+
* Size: 3,507 training samples
|
| 335 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 336 |
* Approximate statistics based on the first 1000 samples:
|
| 337 |
| | anchor | positive | negative |
|
| 338 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
|
| 339 |
| type | string | string | string |
|
| 340 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 12.02 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 117.69 tokens</li><li>max: 504 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 119.62 tokens</li><li>max: 422 tokens</li></ul> |
|
| 341 |
* Samples:
|
| 342 |
+
| anchor | positive | negative |
|
| 343 |
+
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 344 |
+
| <code>Tour departs how city</code> | <code>What is the itinerary for 1-day Maihar tour?<br> Maihar tour departs from Hotel Ilawart, Prayagraj at 7:00 AM and includes visit to Maa Sharda Devi Temple located atop Trikoota Hill. For more details and booking, click here: https://bit.ly/3YBcbI6 <br> List of Aliases: [['Allahabad', 'PYG', 'Prayagraj']]</code> | <code>What one-day outstation tours are available from Prayagraj?<br>The one-day outstation tours from Prayagraj include destinations such as Ayodhya, Varanasi, Maihar, and Chitrakoot. These tours offer a quick yet enriching journey to some of the most significant spiritual and cultural sites near Prayagraj.<br><br>For more details, visit : https://bit.ly/4eWFRoH</code> |
|
| 345 |
+
| <code>How train for Prayag reach</code> | <code>Which airlines operate flights to Prayagraj?<br> Several airlines operate flights to Prayagraj, India. However, availability may depend on your location and the time of travel. Some of the airlines that typically operate flights to Prayagraj include:<br> <br> 1. Air India<br> 2. IndiGo<br> 3. SpiceJet<br> <br> For the most accurate and up-to-date information on train timings to Prayagraj, please visit the IRCTC website <https://www.irctc.co.in/nget/> <br> List of Aliases: [['Allahabad', 'PYG', 'Prayagraj']]</code> | <code>What is the best train route to Prayagraj from Ayodhya?<br>To travel by train from Ayodhya to Prayagraj, you can use the Indian Railways' services. Here is a general guide for the route:<br><br>1. Ayodhya Cantt (AY) to Prayagraj Junction (PRYJ) via Train No. 14203: This is one of the direct trains to Prayagraj from Ayodhya. It generally runs on Tuesday and Friday.<br><br>2. Ayodhya Cantt (AY) to Prayagraj Rambag (PRRB) via Train No. 14205: This train runs regularly and is another direct route to Prayagraj.<br><br>For the most accurate and up-to-date information on train timings to Prayagraj, please visit the IRCTC website <https://www.irctc.co.in/nget/></code> |
|
| 346 |
+
| <code>Why should one do the Prayagraj Panchkoshi Parikrama?</code> | <code>The Prayagraj Panchkoshi Parikrama is a deeply revered spiritual journey that offers multiple benefits to devotees. It is believed to grant blessings equivalent to visiting all sacred pilgrimage sites in India, providing divine grace and spiritual merit. The Parikrama route covers significant temples like the Dwadash Madhav temples, Akshayavat, and Mankameshwar, which are steeped in Hindu mythology and history, allowing pilgrims to connect with the spiritual and cultural heritage of Prayagraj. This circumambulation around sacred sites is also seen as a way to cleanse one's sins and progress towards Moksha (liberation from the cycle of birth and rebirth), making it a path of introspection and spiritual growth. The pilgrimage fosters unity among people from diverse backgrounds, offering a unique cultural exchange and shared spiritual experience. By participating, devotees also help revive an ancient tradition integral to the Kumbh Mela for centuries, reconnecting with age-old practices t...</code> | <code>Elevators are remarkable inventions that revolutionized how we navigate tall buildings. They provide a swift, efficient means of transportation between floors, making urban life more accessible. These mechanical wonders operate on a system of pulleys and counterweights, enabling them to carry heavy loads effortlessly. Safety features like emergency brakes and backup power systems ensure that passengers remain secure during their journey. Various designs and styles can be seen in buildings around the world, from sleek modern glass models to vintage models that evoke nostalgia. Elevators also highlight the advancement of engineering and technology over time, evolving from rudimentary designs to sophisticated machines with smart technology. They are essential in various settings, including residential, commercial, and industrial spaces, offering convenience and practicality. Their presence also allows for the efficient use of vertical space, fostering creativity in architectural designs a...</code> |
|
| 347 |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 348 |
```json
|
| 349 |
{'guide': SentenceTransformer(
|
|
|
|
| 358 |
#### Unnamed Dataset
|
| 359 |
|
| 360 |
|
| 361 |
+
* Size: 877 evaluation samples
|
| 362 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 363 |
+
* Approximate statistics based on the first 877 samples:
|
| 364 |
+
| | anchor | positive | negative |
|
| 365 |
+
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
| 366 |
+
| type | string | string | string |
|
| 367 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 12.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 117.82 tokens</li><li>max: 504 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 117.68 tokens</li><li>max: 422 tokens</li></ul> |
|
| 368 |
* Samples:
|
| 369 |
+
| anchor | positive | negative |
|
| 370 |
+
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 371 |
+
| <code>Akhara means what</code> | <code>Is the word Akhara related to Akhand?<br> Many scholars believe that the word 'Akhara' originated from the word 'Akhand.' Initially, a group of armed ascetics was referred to as 'Akhand.' Over time, when these 'Akhand' groups evolved into centers for training in weaponry and martial arts, they came to be known as 'Akhara.' <br> List of Aliases: [['Akhand', 'Akhara', 'Kalpwasi Camp', 'Naga', 'Nagas', 'Sadhu', 'sadhus']]</code> | <code>Why did Adi Shankaracharya organize the Akharas?<br>According to the evidence available in the Akharas and the descriptions mentioned in their history, centuries ago, Adi Shankaracharya established these Akharas with the purpose of protecting Hindu temples and monasteries from foreign and non-believer invaders, as well as safeguarding the followers of Hinduism.<br> <br> Adi Shankaracharya believed that young saints should not only be proficient in scriptures (Shastra) but also in the art of weaponry (Shastra), so they could fulfill the duty of protecting the monasteries, temples, and their followers when necessary.</code> |
|
| 372 |
+
| <code>Why do so many people gather for this?</code> | <code>Millions gather for the Kumbh Mela due to its profound spiritual, cultural, and social significance. Rooted in ancient Hindu mythology, the Mela is believed to be an auspicious time when bathing in the sacred rivers—Ganga, Yamuna, and Saraswati—can cleanse sins and lead to spiritual liberation (Moksha). The event, occurring during rare celestial alignments, amplifies these spiritual benefits. It is a unique confluence of faith, where people from diverse backgrounds come together, creating a “mini-India” that fosters unity in diversity. \n The Mela also offers opportunities for spiritual learning through discourses by saints, religious rituals like Kalpvas, Deep Daan, and cultural performances. Moreover, the Kumbh Mela is a rare platform for connecting with spiritual leaders, experiencing religious tolerance, and participating in one of the world's largest peaceful gatherings, making it a must-attend event for millions seeking spiritual growth, community, and divine blessings.</code> | <code>In the bustling world of urban development, architects and city planners often seek innovative solutions to optimize living spaces. The integration of green spaces within urban environments not only enhances aesthetic appeal but also significantly improves residents' quality of life. Vertical gardens, rooftops, and community parks play a crucial role in providing habitats for local wildlife while promoting biodiversity in densely populated areas. <br><br>Furthermore, advancements in sustainable technology, such as solar panels and rainwater harvesting systems, are being incorporated into these designs, offering environmentally friendly alternatives that reduce utility costs for residents. Public art installations also contribute to community identity, fostering a sense of belonging among citizens. <br><br>Collaborative efforts between various stakeholders—governments, private sectors, and local communities—are essential to ensure these projects reflect the needs and desires of the people. The succ...</code> |
|
| 373 |
+
| <code>Do parking charges vary between different parking zones or proximity to the Mela grounds?</code> | <code>No, the parking charges are standardized and remain the same throughout, regardless of the parking zone or proximity to the Mela grounds. Charges are fixed at ₹5 for cycles, ₹15 for two-wheelers, ₹65 for 3-4 wheelers, and ₹260 for buses and heavy vehicles for 24 hours.</code> | <code>The ancient art of pottery involves molding clay into various shapes before firing it in a kiln. Traditionally, artisans use hand tools and techniques passed down through generations. Each region often has its own distinctive styles, resulting in a rich diversity of forms, glazes, and colors. Pottery can serve practical purposes, such as in cooking and storage, while also being a medium for artistic expression and cultural storytelling.</code> |
|
| 374 |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 375 |
```json
|
| 376 |
{'guide': SentenceTransformer(
|
|
|
|
| 482 |
- `gradient_checkpointing`: False
|
| 483 |
- `gradient_checkpointing_kwargs`: None
|
| 484 |
- `include_inputs_for_metrics`: False
|
| 485 |
+
- `include_for_metrics`: []
|
| 486 |
- `eval_do_concat_batches`: True
|
| 487 |
- `fp16_backend`: auto
|
| 488 |
- `push_to_hub_model_id`: None
|
|
|
|
| 504 |
- `optim_target_modules`: None
|
| 505 |
- `batch_eval_metrics`: False
|
| 506 |
- `eval_on_start`: False
|
| 507 |
+
- `use_liger_kernel`: False
|
| 508 |
- `eval_use_gather_object`: False
|
| 509 |
+
- `average_tokens_across_devices`: False
|
| 510 |
+
- `prompts`: None
|
| 511 |
- `batch_sampler`: batch_sampler
|
| 512 |
- `multi_dataset_batch_sampler`: proportional
|
| 513 |
|
|
|
|
| 516 |
### Training Logs
|
| 517 |
<details><summary>Click to expand</summary>
|
| 518 |
|
| 519 |
+
| Epoch | Step | Training Loss | Validation Loss | val_evaluator_cosine_ndcg@100 |
|
| 520 |
+
|:-------:|:----:|:-------------:|:---------------:|:-----------------------------:|
|
| 521 |
+
| 0.0909 | 10 | 1.9717 | 1.2192 | 0.4285 |
|
| 522 |
+
| 0.1818 | 20 | 1.8228 | 1.1896 | 0.4307 |
|
| 523 |
+
| 0.2727 | 30 | 1.9999 | 1.1429 | 0.4310 |
|
| 524 |
+
| 0.3636 | 40 | 1.6463 | 1.0845 | 0.4311 |
|
| 525 |
+
| 0.4545 | 50 | 1.9207 | 1.0205 | 0.4334 |
|
| 526 |
+
| 0.5455 | 60 | 1.5777 | 0.9509 | 0.4338 |
|
| 527 |
+
| 0.6364 | 70 | 1.4277 | 0.8810 | 0.4376 |
|
| 528 |
+
| 0.7273 | 80 | 1.408 | 0.8130 | 0.4432 |
|
| 529 |
+
| 0.8182 | 90 | 1.3565 | 0.7535 | 0.4436 |
|
| 530 |
+
| 0.9091 | 100 | 1.3322 | 0.6935 | 0.4495 |
|
| 531 |
+
| 1.0 | 110 | 0.8344 | 0.6420 | 0.4518 |
|
| 532 |
+
| 1.0909 | 120 | 1.1696 | 0.5956 | 0.4515 |
|
| 533 |
+
| 1.1818 | 130 | 0.9622 | 0.5524 | 0.4565 |
|
| 534 |
+
| 1.2727 | 140 | 0.9005 | 0.5173 | 0.4616 |
|
| 535 |
+
| 1.3636 | 150 | 0.962 | 0.4802 | 0.4662 |
|
| 536 |
+
| 1.4545 | 160 | 0.7924 | 0.4497 | 0.4693 |
|
| 537 |
+
| 1.5455 | 170 | 0.8955 | 0.4262 | 0.4711 |
|
| 538 |
+
| 1.6364 | 180 | 0.7652 | 0.4031 | 0.4736 |
|
| 539 |
+
| 1.7273 | 190 | 0.7517 | 0.3804 | 0.4773 |
|
| 540 |
+
| 1.8182 | 200 | 0.5669 | 0.3636 | 0.4784 |
|
| 541 |
+
| 1.9091 | 210 | 0.6641 | 0.3469 | 0.4813 |
|
| 542 |
+
| 2.0 | 220 | 0.5227 | 0.3267 | 0.4820 |
|
| 543 |
+
| 2.0909 | 230 | 0.6146 | 0.3075 | 0.4843 |
|
| 544 |
+
| 2.1818 | 240 | 0.4709 | 0.2908 | 0.4882 |
|
| 545 |
+
| 2.2727 | 250 | 0.5963 | 0.2780 | 0.4955 |
|
| 546 |
+
| 2.3636 | 260 | 0.5103 | 0.2668 | 0.4977 |
|
| 547 |
+
| 2.4545 | 270 | 0.4833 | 0.2566 | 0.5027 |
|
| 548 |
+
| 2.5455 | 280 | 0.4389 | 0.2431 | 0.5045 |
|
| 549 |
+
| 2.6364 | 290 | 0.4653 | 0.2317 | 0.5059 |
|
| 550 |
+
| 2.7273 | 300 | 0.3559 | 0.2263 | 0.5086 |
|
| 551 |
+
| 2.8182 | 310 | 0.4623 | 0.2197 | 0.5127 |
|
| 552 |
+
| 2.9091 | 320 | 0.3889 | 0.2103 | 0.5183 |
|
| 553 |
+
| 3.0 | 330 | 0.4014 | 0.2037 | 0.5206 |
|
| 554 |
+
| 3.0909 | 340 | 0.2977 | 0.1999 | 0.5228 |
|
| 555 |
+
| 3.1818 | 350 | 0.4656 | 0.1956 | 0.5266 |
|
| 556 |
+
| 3.2727 | 360 | 0.436 | 0.1873 | 0.5288 |
|
| 557 |
+
| 3.3636 | 370 | 0.3111 | 0.1803 | 0.5311 |
|
| 558 |
+
| 3.4545 | 380 | 0.333 | 0.1759 | 0.5325 |
|
| 559 |
+
| 3.5455 | 390 | 0.2899 | 0.1717 | 0.5381 |
|
| 560 |
+
| 3.6364 | 400 | 0.4245 | 0.1663 | 0.5419 |
|
| 561 |
+
| 3.7273 | 410 | 0.4247 | 0.1658 | 0.5421 |
|
| 562 |
+
| 3.8182 | 420 | 0.2251 | 0.1646 | 0.5442 |
|
| 563 |
+
| 3.9091 | 430 | 0.2784 | 0.1635 | 0.5448 |
|
| 564 |
+
| 4.0 | 440 | 0.2503 | 0.1613 | 0.5490 |
|
| 565 |
+
| 4.0909 | 450 | 0.2342 | 0.1588 | 0.5501 |
|
| 566 |
+
| 4.1818 | 460 | 0.3139 | 0.1584 | 0.5527 |
|
| 567 |
+
| 4.2727 | 470 | 0.2356 | 0.1552 | 0.5498 |
|
| 568 |
+
| 4.3636 | 480 | 0.3147 | 0.1496 | 0.5518 |
|
| 569 |
+
| 4.4545 | 490 | 0.2691 | 0.1469 | 0.5508 |
|
| 570 |
+
| 4.5455 | 500 | 0.2639 | 0.1466 | 0.5561 |
|
| 571 |
+
| 4.6364 | 510 | 0.1581 | 0.1432 | 0.5625 |
|
| 572 |
+
| 4.7273 | 520 | 0.1922 | 0.1406 | 0.5663 |
|
| 573 |
+
| 4.8182 | 530 | 0.2453 | 0.1406 | 0.5688 |
|
| 574 |
+
| 4.9091 | 540 | 0.2631 | 0.1399 | 0.5705 |
|
| 575 |
+
| 5.0 | 550 | 0.3324 | 0.1402 | 0.5681 |
|
| 576 |
+
| 5.0909 | 560 | 0.1801 | 0.1389 | 0.5715 |
|
| 577 |
+
| 5.1818 | 570 | 0.2096 | 0.1371 | 0.5736 |
|
| 578 |
+
| 5.2727 | 580 | 0.2167 | 0.1344 | 0.5743 |
|
| 579 |
+
| 5.3636 | 590 | 0.1553 | 0.1297 | 0.5791 |
|
| 580 |
+
| 5.4545 | 600 | 0.1903 | 0.1263 | 0.5790 |
|
| 581 |
+
| 5.5455 | 610 | 0.1388 | 0.1241 | 0.5816 |
|
| 582 |
+
| 5.6364 | 620 | 0.2642 | 0.1231 | 0.5809 |
|
| 583 |
+
| 5.7273 | 630 | 0.2119 | 0.1238 | 0.5792 |
|
| 584 |
+
| 5.8182 | 640 | 0.1767 | 0.1216 | 0.5809 |
|
| 585 |
+
| 5.9091 | 650 | 0.2167 | 0.1218 | 0.5810 |
|
| 586 |
+
| 6.0 | 660 | 0.26 | 0.1232 | 0.5793 |
|
| 587 |
+
| 6.0909 | 670 | 0.1603 | 0.1222 | 0.5807 |
|
| 588 |
+
| 6.1818 | 680 | 0.1534 | 0.1209 | 0.5794 |
|
| 589 |
+
| 6.2727 | 690 | 0.1742 | 0.1165 | 0.5821 |
|
| 590 |
+
| 6.3636 | 700 | 0.1133 | 0.1120 | 0.5824 |
|
| 591 |
+
| 6.4545 | 710 | 0.1198 | 0.1106 | 0.5817 |
|
| 592 |
+
| 6.5455 | 720 | 0.2019 | 0.1114 | 0.5832 |
|
| 593 |
+
| 6.6364 | 730 | 0.2268 | 0.1116 | 0.5823 |
|
| 594 |
+
| 6.7273 | 740 | 0.1779 | 0.1077 | 0.5887 |
|
| 595 |
+
| 6.8182 | 750 | 0.1586 | 0.1048 | 0.5892 |
|
| 596 |
+
| 6.9091 | 760 | 0.2074 | 0.1057 | 0.5872 |
|
| 597 |
+
| 7.0 | 770 | 0.1625 | 0.1091 | 0.5881 |
|
| 598 |
+
| 7.0909 | 780 | 0.2266 | 0.1079 | 0.5900 |
|
| 599 |
+
| 7.1818 | 790 | 0.148 | 0.1054 | 0.5895 |
|
| 600 |
+
| 7.2727 | 800 | 0.1248 | 0.1048 | 0.5916 |
|
| 601 |
+
| 7.3636 | 810 | 0.1753 | 0.1047 | 0.5956 |
|
| 602 |
+
| 7.4545 | 820 | 0.109 | 0.1045 | 0.5981 |
|
| 603 |
+
| 7.5455 | 830 | 0.1369 | 0.1056 | 0.5953 |
|
| 604 |
+
| 7.6364 | 840 | 0.1209 | 0.1068 | 0.5946 |
|
| 605 |
+
| 7.7273 | 850 | 0.182 | 0.1079 | 0.5952 |
|
| 606 |
+
| 7.8182 | 860 | 0.1116 | 0.1083 | 0.5978 |
|
| 607 |
+
| 7.9091 | 870 | 0.1813 | 0.1033 | 0.5985 |
|
| 608 |
+
| 8.0 | 880 | 0.1559 | 0.1010 | 0.6027 |
|
| 609 |
+
| 8.0909 | 890 | 0.1384 | 0.1019 | 0.6017 |
|
| 610 |
+
| 8.1818 | 900 | 0.1057 | 0.1034 | 0.6004 |
|
| 611 |
+
| 8.2727 | 910 | 0.1359 | 0.1033 | 0.5994 |
|
| 612 |
+
| 8.3636 | 920 | 0.0909 | 0.1008 | 0.6011 |
|
| 613 |
+
| 8.4545 | 930 | 0.0995 | 0.0986 | 0.6030 |
|
| 614 |
+
| 8.5455 | 940 | 0.1261 | 0.0973 | 0.6046 |
|
| 615 |
+
| 8.6364 | 950 | 0.1031 | 0.0955 | 0.6013 |
|
| 616 |
+
| 8.7273 | 960 | 0.1163 | 0.0949 | 0.6018 |
|
| 617 |
+
| 8.8182 | 970 | 0.1493 | 0.0963 | 0.6041 |
|
| 618 |
+
| 8.9091 | 980 | 0.13 | 0.0967 | 0.6044 |
|
| 619 |
+
| 9.0 | 990 | 0.1059 | 0.0937 | 0.6044 |
|
| 620 |
+
| 9.0909 | 1000 | 0.1287 | 0.0923 | 0.6045 |
|
| 621 |
+
| 9.1818 | 1010 | 0.1019 | 0.0924 | 0.6086 |
|
| 622 |
+
| 9.2727 | 1020 | 0.1645 | 0.0921 | 0.6086 |
|
| 623 |
+
| 9.3636 | 1030 | 0.1395 | 0.0931 | 0.6075 |
|
| 624 |
+
| 9.4545 | 1040 | 0.1067 | 0.0935 | 0.6051 |
|
| 625 |
+
| 9.5455 | 1050 | 0.1334 | 0.0930 | 0.6058 |
|
| 626 |
+
| 9.6364 | 1060 | 0.136 | 0.0919 | 0.6069 |
|
| 627 |
+
| 9.7273 | 1070 | 0.0968 | 0.0930 | 0.6052 |
|
| 628 |
+
| 9.8182 | 1080 | 0.1447 | 0.0946 | 0.6077 |
|
| 629 |
+
| 9.9091 | 1090 | 0.1288 | 0.0967 | 0.6049 |
|
| 630 |
+
| 10.0 | 1100 | 0.1001 | 0.0960 | 0.6034 |
|
| 631 |
+
| 10.0909 | 1110 | 0.1642 | 0.0952 | 0.6000 |
|
| 632 |
+
| 10.1818 | 1120 | 0.1737 | 0.0926 | 0.6028 |
|
| 633 |
+
| 10.2727 | 1130 | 0.1283 | 0.0906 | 0.6023 |
|
| 634 |
+
| 10.3636 | 1140 | 0.0959 | 0.0906 | 0.6073 |
|
| 635 |
+
| 10.4545 | 1150 | 0.0875 | 0.0927 | 0.6065 |
|
| 636 |
+
| 10.5455 | 1160 | 0.1284 | 0.0934 | 0.6058 |
|
| 637 |
+
| 10.6364 | 1170 | 0.1482 | 0.0937 | 0.6049 |
|
| 638 |
+
| 10.7273 | 1180 | 0.1089 | 0.0925 | 0.6018 |
|
| 639 |
+
| 10.8182 | 1190 | 0.0876 | 0.0896 | 0.6068 |
|
| 640 |
+
| 10.9091 | 1200 | 0.0849 | 0.0897 | 0.6062 |
|
| 641 |
+
| 11.0 | 1210 | 0.1041 | 0.0897 | 0.6073 |
|
| 642 |
+
| 11.0909 | 1220 | 0.107 | 0.0889 | 0.6043 |
|
| 643 |
+
| 11.1818 | 1230 | 0.1018 | 0.0868 | 0.6059 |
|
| 644 |
+
| 11.2727 | 1240 | 0.0835 | 0.0846 | 0.6106 |
|
| 645 |
+
| 11.3636 | 1250 | 0.1455 | 0.0831 | 0.6069 |
|
| 646 |
+
| 11.4545 | 1260 | 0.1071 | 0.0832 | 0.6051 |
|
| 647 |
+
| 11.5455 | 1270 | 0.0777 | 0.0839 | 0.6054 |
|
| 648 |
+
| 11.6364 | 1280 | 0.1218 | 0.0855 | 0.6051 |
|
| 649 |
+
| 11.7273 | 1290 | 0.0702 | 0.0862 | 0.6048 |
|
| 650 |
+
| 11.8182 | 1300 | 0.1017 | 0.0865 | 0.6068 |
|
| 651 |
+
| 11.9091 | 1310 | 0.1452 | 0.0860 | 0.6074 |
|
| 652 |
+
| 12.0 | 1320 | 0.1563 | 0.0855 | 0.6073 |
|
| 653 |
+
| 12.0909 | 1330 | 0.1026 | 0.0858 | 0.6102 |
|
| 654 |
+
| 12.1818 | 1340 | 0.108 | 0.0861 | 0.6062 |
|
| 655 |
+
| 12.2727 | 1350 | 0.078 | 0.0854 | 0.6055 |
|
| 656 |
+
| 12.3636 | 1360 | 0.0655 | 0.0847 | 0.6082 |
|
| 657 |
+
| 12.4545 | 1370 | 0.1075 | 0.0836 | 0.6085 |
|
| 658 |
+
| 12.5455 | 1380 | 0.0875 | 0.0846 | 0.6049 |
|
| 659 |
+
| 12.6364 | 1390 | 0.1082 | 0.0828 | 0.6096 |
|
| 660 |
+
| 12.7273 | 1400 | 0.1133 | 0.0816 | 0.6077 |
|
| 661 |
+
| 12.8182 | 1410 | 0.0931 | 0.0814 | 0.6106 |
|
| 662 |
+
| 12.9091 | 1420 | 0.0728 | 0.0818 | 0.6085 |
|
| 663 |
+
| 13.0 | 1430 | 0.1338 | 0.0827 | 0.6082 |
|
| 664 |
+
| 13.0909 | 1440 | 0.1232 | 0.0813 | 0.6076 |
|
| 665 |
+
| 13.1818 | 1450 | 0.093 | 0.0796 | 0.6110 |
|
| 666 |
+
| 13.2727 | 1460 | 0.0994 | 0.0793 | 0.6090 |
|
| 667 |
+
| 13.3636 | 1470 | 0.0424 | 0.0806 | 0.6109 |
|
| 668 |
+
| 13.4545 | 1480 | 0.0598 | 0.0833 | 0.6086 |
|
| 669 |
+
| 13.5455 | 1490 | 0.0813 | 0.0841 | 0.6093 |
|
| 670 |
+
| 13.6364 | 1500 | 0.0913 | 0.0817 | 0.6125 |
|
| 671 |
+
| 13.7273 | 1510 | 0.1048 | 0.0801 | 0.6133 |
|
| 672 |
+
| 13.8182 | 1520 | 0.0503 | 0.0800 | 0.6110 |
|
| 673 |
+
| 13.9091 | 1530 | 0.0954 | 0.0800 | 0.6111 |
|
| 674 |
+
| 14.0 | 1540 | 0.067 | 0.0791 | 0.6099 |
|
| 675 |
+
| 14.0909 | 1550 | 0.0808 | 0.0779 | 0.6111 |
|
| 676 |
+
| 14.1818 | 1560 | 0.1047 | 0.0783 | 0.6110 |
|
| 677 |
+
| 14.2727 | 1570 | 0.0685 | 0.0791 | 0.6125 |
|
| 678 |
+
| 14.3636 | 1580 | 0.1215 | 0.0793 | 0.6120 |
|
| 679 |
+
| 14.4545 | 1590 | 0.0761 | 0.0794 | 0.6157 |
|
| 680 |
+
| 14.5455 | 1600 | 0.0705 | 0.0790 | 0.6136 |
|
| 681 |
+
| 14.6364 | 1610 | 0.0722 | 0.0785 | 0.6098 |
|
| 682 |
+
| 14.7273 | 1620 | 0.0881 | 0.0785 | 0.6120 |
|
| 683 |
+
| 14.8182 | 1630 | 0.0668 | 0.0791 | 0.6122 |
|
| 684 |
+
| 14.9091 | 1640 | 0.1261 | 0.0787 | 0.6152 |
|
| 685 |
+
| 15.0 | 1650 | 0.0601 | 0.0784 | 0.6148 |
|
| 686 |
+
| 15.0909 | 1660 | 0.0701 | 0.0799 | 0.6167 |
|
| 687 |
+
| 15.1818 | 1670 | 0.1244 | 0.0794 | 0.6160 |
|
| 688 |
+
| 15.2727 | 1680 | 0.0531 | 0.0788 | 0.6174 |
|
| 689 |
+
| 15.3636 | 1690 | 0.0518 | 0.0780 | 0.6154 |
|
| 690 |
+
| 15.4545 | 1700 | 0.0961 | 0.0784 | 0.6142 |
|
| 691 |
+
| 15.5455 | 1710 | 0.1041 | - | - |
|
| 692 |
+
|
| 693 |
</details>
|
| 694 |
|
| 695 |
### Framework Versions
|
| 696 |
- Python: 3.10.12
|
| 697 |
+
- Sentence Transformers: 3.3.0
|
| 698 |
+
- Transformers: 4.46.2
|
| 699 |
+
- PyTorch: 2.5.1+cu121
|
| 700 |
+
- Accelerate: 1.1.1
|
| 701 |
- Datasets: 3.1.0
|
| 702 |
+
- Tokenizers: 0.20.3
|
| 703 |
|
| 704 |
## Citation
|
| 705 |
|
config.json
CHANGED
|
@@ -24,7 +24,7 @@
|
|
| 24 |
"pad_token_id": 0,
|
| 25 |
"position_embedding_type": "absolute",
|
| 26 |
"torch_dtype": "float32",
|
| 27 |
-
"transformers_version": "4.
|
| 28 |
"type_vocab_size": 2,
|
| 29 |
"use_cache": true,
|
| 30 |
"vocab_size": 30522
|
|
|
|
| 24 |
"pad_token_id": 0,
|
| 25 |
"position_embedding_type": "absolute",
|
| 26 |
"torch_dtype": "float32",
|
| 27 |
+
"transformers_version": "4.46.2",
|
| 28 |
"type_vocab_size": 2,
|
| 29 |
"use_cache": true,
|
| 30 |
"vocab_size": 30522
|
config_sentence_transformers.json
CHANGED
|
@@ -1,10 +1,10 @@
|
|
| 1 |
{
|
| 2 |
"__version__": {
|
| 3 |
-
"sentence_transformers": "3.
|
| 4 |
-
"transformers": "4.
|
| 5 |
-
"pytorch": "2.5.
|
| 6 |
},
|
| 7 |
"prompts": {},
|
| 8 |
"default_prompt_name": null,
|
| 9 |
-
"similarity_fn_name":
|
| 10 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.3.0",
|
| 4 |
+
"transformers": "4.46.2",
|
| 5 |
+
"pytorch": "2.5.1+cu121"
|
| 6 |
},
|
| 7 |
"prompts": {},
|
| 8 |
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 133462128
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:94adacda2ea8c6ad6837c2c1636afacb8ae8f0ad0661fe6d28fcd9526ce9f191
|
| 3 |
size 133462128
|