File size: 38,881 Bytes
5e1186f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 |
---
base_model: Snowflake/snowflake-arctic-embed-xs
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Represent this sentence for searching relevant passages: what
is spider silk made of'
sentences:
- Arachnid Pictures. Spider's silk is made up of chains of amino acids. In other
words, it is simply a protein (see How Food Works for details on amino acids and
proteins). The two primary amino acids are glycine and alanine. Spider silk is
extremely strong -- it is about five times stronger than steel and twice as strong
as Kevlar of the same weight.
- Spider silk is made of several kinds of proteins. These proteins are processed
and stored within the spider's abdomen. Spiders typically have six or eight spinnerets,
organs at the rear of their abdomen, through which they extrude the silk proteins.
- Neon is the second lightest noble gas, after helium, and it has an atomic number
of 10. On the periodic table, it is identified with the symbol Ne. The noble gases
were recognized in the late 1800s, when scientists realized that an entire class
of gases was missing from the periodic table of elements.
- source_sentence: 'Represent this sentence for searching relevant passages: what
is a caring community of learners'
sentences:
- 'A couple of my friends and I made hot ice for our school science fair. We used
sodium acetate. This project won first place at my schools science fair!!! Materials:
Stove. Pot. A spoon. A glass cup. and Sodium acetate (you can find it online or
in certain heat packs). How to do it: You heat water in a pot and put the sodium
acetate in the water.'
- Caring Community of Learners. A group or classroom in which children and adults
engage in warm, positive relationships; treat each other with respect; and learn
from and with each other. Self-concept. Children's stable perceptions about themselves
despite variations in their behavior.
- 'Transcript of Creating a Caring Community of Caring Learners: - Caring Community
of Learners: Group or classroom in which children and adults have positive, respectful
relationships and learn from each other. - attachment theory: children''s ability
to learn depends on trusting relationships with caregivers.'
- source_sentence: 'Represent this sentence for searching relevant passages: what
does dark circles around deep set eyes mean'
sentences:
- Production Planner, Manufacturing Salary. (United States). The average salary
for a Production Planner, Manufacturing is $51,962 per year. A skill in SAP Enterprise
Resource Planning (ERP) is associated with high pay for this job. People in this
job generally don't have more than 20 years' experience.
- Symptoms & Signs. Dark circles under the eyes are a common complaint of both men
and women, although they can occasionally be seen in children. As people age,
the skin becomes thinner and collagen is lost, sometimes enhancing the appearance
of blood vessels beneath the eyes and making the area appear darker.
- What are dark circles under the eyes? Dark circles under the eyes, sometimes called
shadows or dark rings under the eyes, are the appearance of dark skin between
the lower eyelid and the top of the cheek. Dark circles under the eyes can occur
in infants, children, adolescents and adults, and to men and women alike. It is
commonly assumed that dark circles under the eyes are caused by a lack of sleep,
and poor quality sleep and insomnia can certainly cause this condition.
- source_sentence: 'Represent this sentence for searching relevant passages: how big
is rv access'
sentences:
- The average length of bigger RVs is between 7.6 meters to 12 meters or 25 feet
to 40 feet. These vehicles are usually packed with different interesting features,
most of which are intended to offer luxury and convenience.
- Murder, My Sweet (released as Farewell, My Lovely in the United Kingdom) is a
1944 American film noir, directed by Edward Dmytryk and starring Dick Powell,
Claire Trevor, and Anne Shirley. The film is based on Raymond Chandler 's 1940
novel Farewell, My Lovely. A second film adaptation of the novel was made in 1975
and released under Chandler's title. Murder, My Sweet turned out to be Anne Shirley's
final film. She retired from acting in 1944 at age 26.
- It should be wider then the rv.....lol sorry could not pass that one up. A standard
RV is normally around 96 inches wide at most, newer larger class A's are around
102 inches wide. This width does not include mirrors or other safety equipment.....
Last edited by rtandc; 10-22-2010 at 05:41 AM..
- source_sentence: 'Represent this sentence for searching relevant passages: how many
pitchers used per game'
sentences:
- Trackback - A method by which a blogger receives notification which other bloggers
link to his or her blog entry
- Accroding to the statistics in the Baseball Reference page showing 2014 Major
League Baseball Pitching Pitches, 745 pitchers threw 704,983 pitches in 2430 games
for an average of 290 pitches per game.
- In modern day baseball, teams generally have five starting pitchers, and they
take it in turn to start a game every fifth day (hence the phrase rotation). Sometimes,
if the schedule pans out, a team can get away with a four man rotation, and in
the distant past some teams managed a three man rotation.
model-index:
- name: Fine-tuned snowflake actic xs based on MS-Marco triplets
results:
- task:
type: triplet
name: Triplet
dataset:
name: xs msmarco triplet
type: xs-msmarco-triplet
metrics:
- type: cosine_accuracy
value: 0.571
name: Cosine Accuracy
- type: dot_accuracy
value: 0.4286
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.5728
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.571
name: Euclidean Accuracy
- type: max_accuracy
value: 0.5728
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: xs msmarco triplet train
type: xs-msmarco-triplet-train
metrics:
- type: cosine_accuracy
value: 0.5696
name: Cosine Accuracy
- type: dot_accuracy
value: 0.43
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.5674
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.5696
name: Euclidean Accuracy
- type: max_accuracy
value: 0.5696
name: Max Accuracy
---
# Fine-tuned snowflake actic xs based on MS-Marco triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) on the sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) <!-- at revision 236cea8bda4680896324c8058c67e97c135eeb95 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("krumeto/snowflake-arctic-embed-xs-ms-marco-triplet")
# Run inference
sentences = [
'Represent this sentence for searching relevant passages: how many pitchers used per game',
'In modern day baseball, teams generally have five starting pitchers, and they take it in turn to start a game every fifth day (hence the phrase rotation). Sometimes, if the schedule pans out, a team can get away with a four man rotation, and in the distant past some teams managed a three man rotation.',
'Accroding to the statistics in the Baseball Reference page showing 2014 Major League Baseball Pitching Pitches, 745 pitchers threw 704,983 pitches in 2430 games for an average of 290 pitches per game.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `xs-msmarco-triplet`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.571 |
| dot_accuracy | 0.4286 |
| manhattan_accuracy | 0.5728 |
| euclidean_accuracy | 0.571 |
| **max_accuracy** | **0.5728** |
#### Triplet
* Dataset: `xs-msmarco-triplet-train`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.5696 |
| dot_accuracy | 0.43 |
| manhattan_accuracy | 0.5674 |
| euclidean_accuracy | 0.5696 |
| **max_accuracy** | **0.5696** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
* Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
* Size: 100,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 17.05 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 78.68 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 73.73 tokens</li><li>max: 205 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Liberal Arts Defined. The liberal arts are a set of academic disciplines that include the sciences and the humanities. When you study a liberal arts curriculum, you don't have to have one specific career goal, although you might. Instead, you'll assemble a broad foundation of knowledge that can be used in a wide spectrum of careers.</code> |
| <code>Represent this sentence for searching relevant passages: what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code> | <code>Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway.</code> |
| <code>Represent this sentence for searching relevant passages: what is normal plat count</code> | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> | <code>In the context of blood work, PLT refers to the platelet count. Platelets are the cells that cause blood clotting and control bleeding. The normal range of platelets for adults is 3.5 to 10.5 billion cells per liter of blood, according to the Mayo Clinic. Continue Reading.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
* Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
* Size: 5,000 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 17.09 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 80.07 tokens</li><li>max: 250 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 76.73 tokens</li><li>max: 341 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: what is the name of the material harder than diamonds</code> | <code>Nano-material is harder than diamonds By Will Knight A material that is harder than diamond has been created in the lab, by packing together tiny ânanorodsâ of carbon. The new material, known as aggregated carbon nanorods (ACNR), was created by compressing and heating super-strong carbon molecules called buckyballs or carbon-60.</code> | <code>What material is stronger than diamond? Diamonds are famous for their hardness, but not particularly for strength,since they are easily cleaved to create the facets that make the different cuts of diamonds for setting in a ring or necklace. Many materials are stronger than diamonds, only a couple of synthesized materials are harder than diamonds.</code> |
| <code>Represent this sentence for searching relevant passages: is pink impression a perennial tulip?</code> | <code>Tulip Pink Impression. close video. VIDEO. Tulip Pink Impression. The rich pink blooms of this hybrid are bound to make an impression, whether used in the landscape or as a cut flower. Robust stems and giant blooms characterise the range, and this hybrid is no exception. âPink Impressionâ will continue to impress thanks to the perennial potential of this range.</code> | <code>Tulip Pink Impression. The lustrous petals are a deep, rich rose at the center, shading to a delicate pale pink at the edge, while doing amazing things in between that include shades of both melon and sunset. Tall, strong, long-lasting and reliable, like most Darwin hybrids. An absolutely first-class Tulip.</code> |
| <code>Represent this sentence for searching relevant passages: define: colonization</code> | <code>Colonization. the settlement and economic development of the uninhabited borderlands of a country (internal colonization) or the establishment of settlements (engaging primarily in agricultural activity) beyond the frontiers of a country (external colonization).</code> | <code>Colonization is a process by which a central system of power dominates the surrounding land and its components. The term is derived from the Latin word colere, which means to inhabit. Also, colonization refers strictly to migration, for example, to settler colonies in America or Australia, trading posts, and plantations, while colonialism deals with this, along with ruling the existing indigenous peoples of styled new territories. Colonization was linked to the spread of tens of millions fro</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | xs-msmarco-triplet-train_max_accuracy | xs-msmarco-triplet_max_accuracy |
|:-----:|:----:|:-------------:|:------:|:-------------------------------------:|:-------------------------------:|
| 0 | 0 | - | - | - | 0.3648 |
| 0.016 | 100 | 1.0213 | 1.0078 | - | 0.3672 |
| 0.032 | 200 | 0.9785 | 0.9630 | - | 0.3764 |
| 0.048 | 300 | 0.9591 | 0.9190 | - | 0.4014 |
| 0.064 | 400 | 0.9088 | 0.8906 | - | 0.4392 |
| 0.08 | 500 | 0.9215 | 0.8707 | - | 0.4598 |
| 0.096 | 600 | 0.8692 | 0.8681 | - | 0.4874 |
| 0.112 | 700 | 0.8806 | 0.8538 | - | 0.4964 |
| 0.128 | 800 | 0.8801 | 0.8477 | - | 0.5106 |
| 0.144 | 900 | 0.8692 | 0.8414 | - | 0.5228 |
| 0.16 | 1000 | 0.8624 | 0.8391 | - | 0.5194 |
| 0.176 | 1100 | 0.8737 | 0.8397 | - | 0.5264 |
| 0.192 | 1200 | 0.8505 | 0.8344 | - | 0.5214 |
| 0.208 | 1300 | 0.8818 | 0.8358 | - | 0.5164 |
| 0.224 | 1400 | 0.8464 | 0.8269 | - | 0.5326 |
| 0.24 | 1500 | 0.8623 | 0.8291 | - | 0.5232 |
| 0.256 | 1600 | 0.8203 | 0.8407 | - | 0.5328 |
| 0.272 | 1700 | 0.8566 | 0.8257 | - | 0.5302 |
| 0.288 | 1800 | 0.8386 | 0.8198 | - | 0.5364 |
| 0.304 | 1900 | 0.8587 | 0.8172 | - | 0.5388 |
| 0.32 | 2000 | 0.8472 | 0.8233 | - | 0.5568 |
| 0.336 | 2100 | 0.8466 | 0.8188 | - | 0.5468 |
| 0.352 | 2200 | 0.8273 | 0.8190 | - | 0.5386 |
| 0.368 | 2300 | 0.8356 | 0.8183 | - | 0.5396 |
| 0.384 | 2400 | 0.8294 | 0.8156 | - | 0.5538 |
| 0.4 | 2500 | 0.8274 | 0.8168 | - | 0.5448 |
| 0.416 | 2600 | 0.8392 | 0.8093 | - | 0.5422 |
| 0.432 | 2700 | 0.8541 | 0.8087 | - | 0.5426 |
| 0.448 | 2800 | 0.8218 | 0.8086 | - | 0.5474 |
| 0.464 | 2900 | 0.8446 | 0.8062 | - | 0.554 |
| 0.48 | 3000 | 0.8405 | 0.8076 | - | 0.548 |
| 0.496 | 3100 | 0.8447 | 0.8087 | - | 0.553 |
| 0.512 | 3200 | 0.8453 | 0.8073 | - | 0.5536 |
| 0.528 | 3300 | 0.8371 | 0.8089 | - | 0.5504 |
| 0.544 | 3400 | 0.8548 | 0.8005 | - | 0.5516 |
| 0.56 | 3500 | 0.8162 | 0.8026 | - | 0.5572 |
| 0.576 | 3600 | 0.8577 | 0.7994 | - | 0.5558 |
| 0.592 | 3700 | 0.8289 | 0.7990 | - | 0.5526 |
| 0.608 | 3800 | 0.824 | 0.7967 | - | 0.562 |
| 0.624 | 3900 | 0.833 | 0.7959 | - | 0.5608 |
| 0.64 | 4000 | 0.8362 | 0.7958 | - | 0.5554 |
| 0.656 | 4100 | 0.8057 | 0.7966 | - | 0.5578 |
| 0.672 | 4200 | 0.8001 | 0.7943 | - | 0.5646 |
| 0.688 | 4300 | 0.8215 | 0.7937 | - | 0.5602 |
| 0.704 | 4400 | 0.8257 | 0.7933 | - | 0.5614 |
| 0.72 | 4500 | 0.8173 | 0.7942 | - | 0.5648 |
| 0.736 | 4600 | 0.8002 | 0.7922 | - | 0.5698 |
| 0.752 | 4700 | 0.8445 | 0.7899 | - | 0.5626 |
| 0.768 | 4800 | 0.825 | 0.7897 | - | 0.5592 |
| 0.784 | 4900 | 0.8151 | 0.7870 | - | 0.5696 |
| 0.8 | 5000 | 0.8223 | 0.7895 | - | 0.5676 |
| 0.816 | 5100 | 0.8235 | 0.7877 | - | 0.5656 |
| 0.832 | 5200 | 0.8355 | 0.7866 | - | 0.5688 |
| 0.848 | 5300 | 0.8218 | 0.7864 | - | 0.5672 |
| 0.864 | 5400 | 0.8384 | 0.7866 | - | 0.5652 |
| 0.88 | 5500 | 0.7988 | 0.7860 | - | 0.569 |
| 0.896 | 5600 | 0.8117 | 0.7867 | - | 0.5684 |
| 0.912 | 5700 | 0.8113 | 0.7861 | - | 0.5734 |
| 0.928 | 5800 | 0.8129 | 0.7860 | - | 0.5698 |
| 0.944 | 5900 | 0.799 | 0.7863 | - | 0.5688 |
| 0.96 | 6000 | 0.8269 | 0.7858 | - | 0.5708 |
| 0.976 | 6100 | 0.8066 | 0.7857 | - | 0.572 |
| 0.992 | 6200 | 0.8302 | 0.7856 | - | 0.5728 |
| 1.0 | 6250 | - | - | 0.5696 | - |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |