File size: 43,863 Bytes
f086e27 12bf4bc f086e27 57da0cc f086e27 12bf4bc f086e27 12bf4bc f086e27 57da0cc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 12bf4bc f086e27 57da0cc f086e27 | 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 | ---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5191
- loss:Contrastive
base_model: LiquidAI/LFM2-ColBERT-350M
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on LiquidAI/LFM2-ColBERT-350M
results:
- task:
type: col-berttriplet
name: Col BERTTriplet
dataset:
name: Unknown
type: unknown
metrics:
- type: accuracy
value: 0.9757365584373474
name: Accuracy
---
# PyLate model based on LiquidAI/LFM2-ColBERT-350M
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [LiquidAI/LFM2-ColBERT-350M](https://huggingface.co/LiquidAI/LFM2-ColBERT-350M). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [LiquidAI/LFM2-ColBERT-350M](https://huggingface.co/LiquidAI/LFM2-ColBERT-350M) <!-- at revision 3cccd22d874924de3d56640167f1aa056c0c6809 -->
- **Document Length:** 8192 tokens
- **Query Length:** 64 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 8191, 'do_lower_case': False, 'architecture': 'Lfm2Model'})
(1): Dense({'in_features': 1024, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="yNilay/colbert-personal-context",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="yNilay/colbert-personal-context",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### 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
#### Col BERTTriplet
* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
| Metric | Value |
|:-------------|:-----------|
| **accuracy** | **0.9757** |
<!--
## 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
#### Unnamed Dataset
* Size: 5,191 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: 23 tokens</li><li>mean: 54.59 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 63.91 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 39.07 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Should I tell Sultan to accept Tom Barrack's inauguration invitation? I know Tom's the closest Trump confidant outside the family now, but from what I remember discussing with Michael about going public versus keeping my head down, I'm worried about the optics. Is it even worth it given what I told Sultan about the crowds? I need to decide tonight.</code> | <code>Subject: Re: Presidential inauguration<br><br><br>---<br><br>From: Sultan Bin Sulayem<br>Date: Fri, Jan 6, 2017 at 4:08 AM<br><br>Should I accept the invitation sent by Tom barrack<br><br>---<br><br>From: jeffrey E. [jeevacation@gmail.com]<br>To: Sultan Bin Sulayem<br>Date: 1/6/2017 2:49:57 PM<br><br>http://www.cnn.com/2017/01/06/politics/tom-barrack-donald-trump-inauguration/index.html</code> | <code>Your reservation at The Four Seasons New York is confirmed for Friday.</code> |
| <code>What's the latest on my inroads to the Trump administration? Last I checked, Thiel was getting close to them and there was talk about him getting a role. Where do things stand now and who's my best access point at this point?</code> | <code>Subject: Fwd: fact checking questions for New York Magazine story<br><br><br>---<br><br>From: Yablon, Alex<br>To: jeevacation@gmail.com<br>Date: Tue, Mar 31, 2015 at 11:23 AM<br><br>Hi Jeffrey,<br><br>Sorry for the phone tag. In case it's easier to respond by email, I have put my questions below. I'm at my desk and should be here until 5, and should be free on my cell phone after 6:30.<br><br>Best,<br><br>Alex<br><br>-what is the square footage of your Manhattan home?<br><br>-do you work on a laptop from your dining room, with a large white board for notes and several pairs of reading glasses close at hand? Is the dining room windowless?<br><br>-do paparazzi often camp outside your home?<br><br>-did Michael once visit on the same day as a head of state who'd had a police escort?<br><br>-did you tell Michael about a dinner you'd hosted for six tech entrepreneurs who had a combined worth of several hundred billion dollars? When was this meal?<br><br>-Do you believe that there are now more people who possess Roosevelt or Carnegie-levels of wealth, the sort that can ri...</code> | <code>Subject: Re:<br><br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>Date: Sep 20, 2018, at 10:40 AM<br><br>The Guardian has learned that Rubenfeld is currently the subject of an internal investigation at Yale. The investigation is focused on Rubenfeld's conduct, particularly with female law students. Students have also raised related concerns to Yale authorities about Chua's powerful influence in the clerkships process. The investigation was initiated before Kavanaugh was nominated by Donald Trump to serve on the high court. Rubenfeld said in a statement to the Guardian: "In June, Yale University informed me that it would conduct what it terms an 'informal review' of certain allegations, but that to preserve anonymity, I was not entitled to know any specifics. As a result, I do not know what I am alleged to have said or done. I was further advised that the allegations were not of the kind that would jeopardize my position as a long-tenured member of the faculty."<br><br>---<br><br>From: Lawrence Krauss<br>To: jef...</code> |
| <code>What's the current status with Lawrence and those harassment allegations from BuzzFeed? Did he ever get around to writing that point by point refutation we talked about? And how's he doing with the Bulletin now that he took that leave?</code> | <code>Subject: URGENT: BuzzFeed News inquiry re allegations of sexual harassment<br><br><br>---<br><br><br><br>case, that the University would remove the allegation from my record after 5 years, which makes me surprised that someone violated<br>that written agreement with you.<br><br>Re item 6: You report on ASU’s response to item #6 , without including the fact that the University specifically stated there were never<br>any allegations of sexual misconduct or harassment by me at the University, and the outside complaints were in fact related specifically<br>to your item #6. Further you neglect to mention that this complaint was by an anonymous third party, not the individual who was<br>allegedly harassed, who never lodged a complain, and that no specific evidence was provided of the alleged transgression. \| was<br>surprised and dismayed that both ASU and ANU launched investigations on the basis of this but was told by both Universities that<br>because of my high profile even such unsubstantiated third party complaints at private event...</code> | <code>Subject: Re:<br><br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>To: Joi Ito<br>Date: Nov 22, 2017, at 10:44<br><br>all good?<br><br>---<br><br>From: Joi Ito<br>To: jeffrey E. <jeevacation@gmail.com><br>Date: Wed, Nov 22, 2017 at 10:51 AM<br><br>Pretty good. Had a pinched nerve in my neck that screwed me up for awhile. How about you? Any plans to come to Boston?<br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>To: Joi Ito<br>Date: Nov 22, 2017, at 10:57<br><br>maybe week of 3rd not seeing anything that exciting ornew ? you? with all these guys getting busted for harassment , 1 have moved slightly up on the repuation ladder and have been asked everday for advice etc. this morning I have Ken Starr coming to point out how if clinton cigar lewinsky were to be outed today the world would be a different place<br><br>---<br><br>From: Joi<br>To: jeffrey E. [jeevacation@gmail.com]<br>Date: 11/22/2017 6:14:38 PM<br><br>Lots of stuff going on that week but I’m in town.<br><br>#metoo is quite amazing...<br><br>Madars is doing well. His PhD paper won an award and he found a vulner...</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Evaluation Dataset
#### Unnamed Dataset
* Size: 577 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 577 samples:
| | query | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 23 tokens</li><li>mean: 54.08 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 63.95 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.6 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A reporter from BuzzFeed just reached out about those old allegations and they're publishing soon. What advice did I give that physicist friend Lawrence a few years back when he was dealing with something similar? I remember telling him something about how these reporters operate and what they do with your responses.</code> | <code>Subject: Re:<br><br><br>---<br><br>From: R. Couri Hay<br>To: jeevacation@gmail.com<br>Date: Friday, March 4 2011 07:03 PM<br><br>Good morning Jeffery, <br>The Newsweek story is being written without your input as you read this. Lloyd Grove is not writing the story, he made a few calls but he <br>was taken off the project. This is for Newsweek, the magazine that is on the stands, not the website. It's still 1200 to 1500 words. <br>Alexandra Wolfe needs to turn in a draft of the story by Monday. I've been subtly guiding Alexandra on your behalf, but would really like <br>to formalize this job with your attorneys and then I would really need to give names and numbers of pro Jeffery power brokers for <br>Alexandra to call. She has already called Donald Trump, Leon Black, and Les Wexner among others. I've given her Jonathans number, <br>please let me focus on this before its too late to spin this in your direction. I'm in all afternoon my office number is My cell <br>is: My email i _________ <br>Cheers, <br>Couri<br><br>---<br><br>From: jeffrey epstein <j...</code> | <code>Subject: Re: <br><br><br>---<br><br>From: J [jeevacation@gmail.com]<br>To: Michael Wolff<br>Date: On Thu, May 30, 2019 at 5:29 PM<br><br>is it a coincidence that the russian that bought the house in palm beach and knows all , is the same guy <br>that sold a painting last year to mbs for 450 million dollars. that was only worth 1. 5m?<br><br>---<br><br>From: Michael Wolff<br>To: J <jeevacation@gmail.com><br>Date: On Thu, May 30, 2019 at 5:33 PM<br><br>So MBS was paying him off? Why? Ideas?<br><br>---<br><br>From: J <jeevacation@gmail.com><br>To: Michael Wolff<br>Date: On Thu, May 30, 2019 at 5:35 PM<br><br>reminder trump overuled congress on yemen.<br><br>---<br><br>From: Michael Wolff<br>To: J <jeevacation@gmail.com><br>Date: On Thu, May 30, 2019 at 5:37 PM<br><br>Starting to smell sweet, the way you put it!<br><br>---<br><br>From: J <jeevacation@gmail.com><br>To: Michael Wolff<br>Date: On Thu, May 30, 2019 at 5:40 PM<br><br>In addition my art guyd said the painting wasn't very good<br><br>---<br><br>From: Michael Wolff<br>To: J <jeevacation@gmail.com><br>Date: On Thu, May 30, 2019 at 5:41 PM<br><br>You have an art guy?<br><br>---<br><br>From: ...</code> |
| <code>Michael Wolff wants me to do something with him again - he mentioned some big TV interview opportunity. Can you remind me what advice he gave me last time about going public and dealing with the media? I think it was something about going on Charlie Rose and becoming an anti-Trump voice to get political cover. And should I reach out to Kathy about this since she's been helping me understand Washington?</code> | <code>Subject: Re:<br><br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>To: Thorbjørn Jagland<br>Date: Feb 17, 2017 9:27 PM<br><br>Im in paris until thurs? you?<br><br>---<br><br>From: Thorbjørn Jagland<br>To: jeffrey E. <jeevacation@gmail.com><br>Date: Feb 19, 2017 8:54 AM<br><br>Is It possible for you to pass by Strasbourg, it would be great. I really need to understand more about Trump<br>and what's going on in the American society.<br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>To: Thorbjørn Jagland<br>Date: Feb 19, 2017 11:14<br><br>yes, that should be possible. remind me how long is the fast train? otherwise ill fly. what days are good<br>for you<br><br>---<br><br>From: Thorbjørn Jagland<br>To: jeffrey E. [jeevacation@gmail.com]<br>Date: 2/19/2017 9:11:12 PM<br><br>Train, 1h45, I'll pick you up at the train station. Tuesday afternoon is ok. Also Wednesday, but only after 6</code> | <code>Please find attached the Q3 board meeting minutes for your review.</code> |
| <code>Where do things stand with the media and legal situation now? I have that meeting next week and need to refresh my memory on what we've covered with reporters, our current search results status, and where Michael Wolff left off.</code> | <code>Subject: Re: Shears Update<br><br><br>---<br><br>From: Tyler Shears<br>To: jeffrey E. [jeevacation@gmail.com]<br>Date: 7/16/2014 5:56:14 PM<br><br>yes agree<br>result got worse with recent negative press, clinton, as we discussed<br>we were down to 1 negative (forbes) before that - Christina can confirm this.<br>It will get back to 1 and then 0 so long as negative things stop coming out. if new negative keeps coming out it<br>really dismantles much of our effort... especially when it involves an ex-president<br>no excuses here i'm not pleased with where it is at and am still working to make it happen<br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>Date: Wed, Jul 16, 2014 at 1:25 PM<br><br>Results still very bad<br><br>---<br><br>From: Christina Galbraith<br>To: Tyler Shears<br>Date: Wednesday, July 16, 2014<br><br>Hi Tyler,<br>The social media sites are constantly updated: LinkedIn, Facebook, Twitter, google +.<br>I'll be in touch later today re: feature article.<br>Could you put a site map into the Net site.? This helps rankings. Also could you add it to google ana...</code> | <code>Subject: Re:<br><br><br>---<br><br>From: jeffrey E. [jeevacation@gmail.com]<br>To: Jonathan Farkas<br>Date: 12/7/2016 12:25:22 P.M. Eastern Standard Time<br><br>plenty left<br><br>---<br><br>From: Jonathan Farkas<br>Date: Wed, Dec 7, 2016 at 1:01 PM<br><br>Hi jeffrey hope all is well \| think you are going to have a winderful life from now on in your opinion how much is left in<br>this market it's been a trump triumph \| gave him some money through woody best jonathan<br><br>---<br><br>From: jeffrey E. [jeevacation@gmail.com]<br>To: Jonathan Farkas<br>Date: 12/7/2016 5:30:18 PM<br><br>oy</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 2
- `gradient_accumulation_steps`: 32
- `learning_rate`: 3e-06
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `gradient_checkpointing`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `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`: True
- `fp16`: False
- `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`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | accuracy |
|:-------:|:-------:|:-------------:|:---------------:|:--------:|
| 0.1233 | 10 | 3.3802 | - | - |
| 0.2465 | 20 | 1.1614 | - | - |
| 0.3698 | 30 | 0.5549 | - | - |
| 0.4931 | 40 | 0.4642 | - | - |
| 0.6163 | 50 | 0.3749 | - | - |
| 0.7396 | 60 | 0.3587 | - | - |
| 0.8629 | 70 | 0.349 | - | - |
| 0.9861 | 80 | 0.367 | - | - |
| 0 | 0 | - | - | 0.9723 |
| 1.0 | 82 | - | 1.0230 | - |
| 1.0986 | 90 | 0.2812 | - | - |
| 1.2219 | 100 | 0.3016 | - | - |
| 1.3451 | 110 | 0.2187 | - | - |
| 1.4684 | 120 | 0.2104 | - | - |
| 1.5917 | 130 | 0.2656 | - | - |
| 1.7149 | 140 | 0.2199 | - | - |
| 1.8382 | 150 | 0.2356 | - | - |
| 1.9615 | 160 | 0.2607 | - | - |
| 0 | 0 | - | - | 0.9757 |
| 2.0 | 164 | - | 0.9950 | - |
| 2.0740 | 170 | 0.1836 | - | - |
| 2.1972 | 180 | 0.1789 | - | - |
| 2.3205 | 190 | 0.2342 | - | - |
| 2.4438 | 200 | 0.1904 | - | - |
| 2.5670 | 210 | 0.1312 | - | - |
| 2.6903 | 220 | 0.1813 | - | - |
| 2.8136 | 230 | 0.1826 | - | - |
| 2.9368 | 240 | 0.1456 | - | - |
| 0 | 0 | - | - | 0.9757 |
| **3.0** | **246** | **-** | **0.873** | **-** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 5.1.1
- PyLate: 1.4.0
- Transformers: 4.56.2
- PyTorch: 2.9.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
## 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"
}
```
#### PyLate
```bibtex
@inproceedings{DBLP:conf/cikm/ChaffinS25,
author = {Antoine Chaffin and
Rapha{"{e}}l Sourty},
editor = {Meeyoung Cha and
Chanyoung Park and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
booktitle = {Proceedings of the 34th {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
10-14, 2025},
pages = {6334--6339},
publisher = {{ACM}},
year = {2025},
url = {https://github.com/lightonai/pylate},
doi = {10.1145/3746252.3761608},
}
```
<!--
## 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.*
--> |