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joe32140
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ColModernBERT-base-msmarco-en-bge

Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:808728
loss:Distillation
Eval Results
text-embeddings-inference
Model card Files
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Add new SentenceTransformer model

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  1. 1_Dense/config.json +1 -0
  2. 1_Dense/model.safetensors +3 -0
  3. README.md +934 -0
  4. config.json +46 -0
  5. config_sentence_transformers.json +49 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +31 -0
  10. tokenizer.json +0 -0
  11. tokenizer_config.json +968 -0
1_Dense/config.json ADDED
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+ {"in_features": 768, "out_features": 128, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}
1_Dense/model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:baed1eb195b645cff428228265bca73161d512c5ef977b45ca8121ab7bba1417
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+ size 393304
README.md ADDED
@@ -0,0 +1,934 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: answerdotai/ModernBERT-base
3
+ datasets:
4
+ - lightonai/ms-marco-en-bge
5
+ language:
6
+ - en
7
+ library_name: PyLate
8
+ pipeline_tag: sentence-similarity
9
+ tags:
10
+ - ColBERT
11
+ - PyLate
12
+ - sentence-transformers
13
+ - sentence-similarity
14
+ - feature-extraction
15
+ - generated_from_trainer
16
+ - dataset_size:808728
17
+ - loss:Distillation
18
+ ---
19
+
20
+ # PyLate model based on answerdotai/ModernBERT-base
21
+
22
+ This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
23
+
24
+ ## Model Details
25
+
26
+ ### Model Description
27
+ - **Model Type:** PyLate model
28
+ - **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
29
+ - **Document Length:** 180 tokens
30
+ - **Query Length:** 32 tokens
31
+ - **Output Dimensionality:** 128 tokens
32
+ - **Similarity Function:** MaxSim
33
+ - **Training Dataset:**
34
+ - [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge)
35
+ - **Language:** en
36
+ <!-- - **License:** Unknown -->
37
+
38
+ ### Model Sources
39
+
40
+ - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
41
+ - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
42
+ - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
43
+
44
+ ### Full Model Architecture
45
+
46
+ ```
47
+ ColBERT(
48
+ (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: ModernBertModel
49
+ (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
50
+ )
51
+ ```
52
+
53
+ ## Usage
54
+ First install the PyLate library:
55
+
56
+ ```bash
57
+ pip install -U pylate
58
+ ```
59
+
60
+ ### Retrieval
61
+
62
+ PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
63
+
64
+ #### Indexing documents
65
+
66
+ First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
67
+
68
+ ```python
69
+ from pylate import indexes, models, retrieve
70
+
71
+ # Step 1: Load the ColBERT model
72
+ model = models.ColBERT(
73
+ model_name_or_path=pylate_model_id,
74
+ )
75
+
76
+ # Step 2: Initialize the Voyager index
77
+ index = indexes.Voyager(
78
+ index_folder="pylate-index",
79
+ index_name="index",
80
+ override=True, # This overwrites the existing index if any
81
+ )
82
+
83
+ # Step 3: Encode the documents
84
+ documents_ids = ["1", "2", "3"]
85
+ documents = ["document 1 text", "document 2 text", "document 3 text"]
86
+
87
+ documents_embeddings = model.encode(
88
+ documents,
89
+ batch_size=32,
90
+ is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
91
+ show_progress_bar=True,
92
+ )
93
+
94
+ # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
95
+ index.add_documents(
96
+ documents_ids=documents_ids,
97
+ documents_embeddings=documents_embeddings,
98
+ )
99
+ ```
100
+
101
+ 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:
102
+
103
+ ```python
104
+ # To load an index, simply instantiate it with the correct folder/name and without overriding it
105
+ index = indexes.Voyager(
106
+ index_folder="pylate-index",
107
+ index_name="index",
108
+ )
109
+ ```
110
+
111
+ #### Retrieving top-k documents for queries
112
+
113
+ Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
114
+ 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:
115
+
116
+ ```python
117
+ # Step 1: Initialize the ColBERT retriever
118
+ retriever = retrieve.ColBERT(index=index)
119
+
120
+ # Step 2: Encode the queries
121
+ queries_embeddings = model.encode(
122
+ ["query for document 3", "query for document 1"],
123
+ batch_size=32,
124
+ is_query=True, # # Ensure that it is set to False to indicate that these are queries
125
+ show_progress_bar=True,
126
+ )
127
+
128
+ # Step 3: Retrieve top-k documents
129
+ scores = retriever.retrieve(
130
+ queries_embeddings=queries_embeddings,
131
+ k=10, # Retrieve the top 10 matches for each query
132
+ )
133
+ ```
134
+
135
+ ### Reranking
136
+ 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:
137
+
138
+ ```python
139
+ from pylate import rank, models
140
+
141
+ queries = [
142
+ "query A",
143
+ "query B",
144
+ ]
145
+
146
+ documents = [
147
+ ["document A", "document B"],
148
+ ["document 1", "document C", "document B"],
149
+ ]
150
+
151
+ documents_ids = [
152
+ [1, 2],
153
+ [1, 3, 2],
154
+ ]
155
+
156
+ model = models.ColBERT(
157
+ model_name_or_path=pylate_model_id,
158
+ )
159
+
160
+ queries_embeddings = model.encode(
161
+ queries,
162
+ is_query=True,
163
+ )
164
+
165
+ documents_embeddings = model.encode(
166
+ documents,
167
+ is_query=False,
168
+ )
169
+
170
+ reranked_documents = rank.rerank(
171
+ documents_ids=documents_ids,
172
+ queries_embeddings=queries_embeddings,
173
+ documents_embeddings=documents_embeddings,
174
+ )
175
+ ```
176
+
177
+ <!--
178
+ ### Direct Usage (Transformers)
179
+
180
+ <details><summary>Click to see the direct usage in Transformers</summary>
181
+
182
+ </details>
183
+ -->
184
+
185
+ <!--
186
+ ### Downstream Usage (Sentence Transformers)
187
+
188
+ You can finetune this model on your own dataset.
189
+
190
+ <details><summary>Click to expand</summary>
191
+
192
+ </details>
193
+ -->
194
+
195
+ <!--
196
+ ### Out-of-Scope Use
197
+
198
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
199
+ -->
200
+
201
+ <!--
202
+ ## Bias, Risks and Limitations
203
+
204
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
205
+ -->
206
+
207
+ <!--
208
+ ### Recommendations
209
+
210
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
211
+ -->
212
+
213
+ ## Training Details
214
+
215
+ ### Training Dataset
216
+
217
+ #### train
218
+
219
+ * Dataset: [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) at [11e6ffa](https://huggingface.co/datasets/lightonai/ms-marco-en-bge/tree/11e6ffa1d22f461579f451eb31bdc964244cb61f)
220
+ * Size: 808,728 training samples
221
+ * Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
222
+ * Approximate statistics based on the first 1000 samples:
223
+ | | query_id | document_ids | scores |
224
+ |:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
225
+ | type | string | list | list |
226
+ | details | <ul><li>min: 5 tokens</li><li>mean: 5.59 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
227
+ * Samples:
228
+ | query_id | document_ids | scores |
229
+ |:--------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
230
+ | <code>121352</code> | <code>['2259784', '4923159', '40211', '1545154', '8527175', ...]</code> | <code>[0.2343463897705078, 0.639204204082489, 0.3806908428668976, 0.5623092651367188, 0.8051995635032654, ...]</code> |
231
+ | <code>634306</code> | <code>['7723525', '1874779', '379307', '2738583', '7599583', ...]</code> | <code>[0.7124203443527222, 0.7379189729690552, 0.5786551237106323, 0.6142299175262451, 0.6755089163780212, ...]</code> |
232
+ | <code>920825</code> | <code>['5976297', '2866112', '3560294', '3285659', '4706740', ...]</code> | <code>[0.6462352871894836, 0.7880821228027344, 0.791019856929779, 0.7709633111953735, 0.8284491300582886, ...]</code> |
233
+ * Loss: <code>pylate.losses.distillation.Distillation</code>
234
+
235
+ ### Training Hyperparameters
236
+ #### Non-Default Hyperparameters
237
+
238
+ - `per_device_train_batch_size`: 4
239
+ - `gradient_accumulation_steps`: 4
240
+ - `learning_rate`: 8e-05
241
+ - `num_train_epochs`: 1
242
+ - `warmup_ratio`: 0.05
243
+ - `bf16`: True
244
+ - `tf32`: True
245
+
246
+ #### All Hyperparameters
247
+ <details><summary>Click to expand</summary>
248
+
249
+ - `overwrite_output_dir`: False
250
+ - `do_predict`: False
251
+ - `eval_strategy`: no
252
+ - `prediction_loss_only`: True
253
+ - `per_device_train_batch_size`: 4
254
+ - `per_device_eval_batch_size`: 8
255
+ - `per_gpu_train_batch_size`: None
256
+ - `per_gpu_eval_batch_size`: None
257
+ - `gradient_accumulation_steps`: 4
258
+ - `eval_accumulation_steps`: None
259
+ - `torch_empty_cache_steps`: None
260
+ - `learning_rate`: 8e-05
261
+ - `weight_decay`: 0.0
262
+ - `adam_beta1`: 0.9
263
+ - `adam_beta2`: 0.999
264
+ - `adam_epsilon`: 1e-08
265
+ - `max_grad_norm`: 1.0
266
+ - `num_train_epochs`: 1
267
+ - `max_steps`: -1
268
+ - `lr_scheduler_type`: linear
269
+ - `lr_scheduler_kwargs`: {}
270
+ - `warmup_ratio`: 0.05
271
+ - `warmup_steps`: 0
272
+ - `log_level`: passive
273
+ - `log_level_replica`: warning
274
+ - `log_on_each_node`: True
275
+ - `logging_nan_inf_filter`: True
276
+ - `save_safetensors`: True
277
+ - `save_on_each_node`: False
278
+ - `save_only_model`: False
279
+ - `restore_callback_states_from_checkpoint`: False
280
+ - `no_cuda`: False
281
+ - `use_cpu`: False
282
+ - `use_mps_device`: False
283
+ - `seed`: 42
284
+ - `data_seed`: None
285
+ - `jit_mode_eval`: False
286
+ - `use_ipex`: False
287
+ - `bf16`: True
288
+ - `fp16`: False
289
+ - `fp16_opt_level`: O1
290
+ - `half_precision_backend`: auto
291
+ - `bf16_full_eval`: False
292
+ - `fp16_full_eval`: False
293
+ - `tf32`: True
294
+ - `local_rank`: 0
295
+ - `ddp_backend`: None
296
+ - `tpu_num_cores`: None
297
+ - `tpu_metrics_debug`: False
298
+ - `debug`: []
299
+ - `dataloader_drop_last`: False
300
+ - `dataloader_num_workers`: 0
301
+ - `dataloader_prefetch_factor`: None
302
+ - `past_index`: -1
303
+ - `disable_tqdm`: False
304
+ - `remove_unused_columns`: True
305
+ - `label_names`: None
306
+ - `load_best_model_at_end`: False
307
+ - `ignore_data_skip`: False
308
+ - `fsdp`: []
309
+ - `fsdp_min_num_params`: 0
310
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
311
+ - `fsdp_transformer_layer_cls_to_wrap`: None
312
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
313
+ - `deepspeed`: None
314
+ - `label_smoothing_factor`: 0.0
315
+ - `optim`: adamw_torch
316
+ - `optim_args`: None
317
+ - `adafactor`: False
318
+ - `group_by_length`: False
319
+ - `length_column_name`: length
320
+ - `ddp_find_unused_parameters`: None
321
+ - `ddp_bucket_cap_mb`: None
322
+ - `ddp_broadcast_buffers`: False
323
+ - `dataloader_pin_memory`: True
324
+ - `dataloader_persistent_workers`: False
325
+ - `skip_memory_metrics`: True
326
+ - `use_legacy_prediction_loop`: False
327
+ - `push_to_hub`: False
328
+ - `resume_from_checkpoint`: None
329
+ - `hub_model_id`: None
330
+ - `hub_strategy`: every_save
331
+ - `hub_private_repo`: None
332
+ - `hub_always_push`: False
333
+ - `gradient_checkpointing`: False
334
+ - `gradient_checkpointing_kwargs`: None
335
+ - `include_inputs_for_metrics`: False
336
+ - `include_for_metrics`: []
337
+ - `eval_do_concat_batches`: True
338
+ - `fp16_backend`: auto
339
+ - `push_to_hub_model_id`: None
340
+ - `push_to_hub_organization`: None
341
+ - `mp_parameters`:
342
+ - `auto_find_batch_size`: False
343
+ - `full_determinism`: False
344
+ - `torchdynamo`: None
345
+ - `ray_scope`: last
346
+ - `ddp_timeout`: 1800
347
+ - `torch_compile`: False
348
+ - `torch_compile_backend`: None
349
+ - `torch_compile_mode`: None
350
+ - `dispatch_batches`: None
351
+ - `split_batches`: None
352
+ - `include_tokens_per_second`: False
353
+ - `include_num_input_tokens_seen`: False
354
+ - `neftune_noise_alpha`: None
355
+ - `optim_target_modules`: None
356
+ - `batch_eval_metrics`: False
357
+ - `eval_on_start`: False
358
+ - `use_liger_kernel`: False
359
+ - `eval_use_gather_object`: False
360
+ - `average_tokens_across_devices`: False
361
+ - `prompts`: None
362
+ - `batch_sampler`: batch_sampler
363
+ - `multi_dataset_batch_sampler`: proportional
364
+
365
+ </details>
366
+
367
+ ### Training Logs
368
+ <details><summary>Click to expand</summary>
369
+
370
+ | Epoch | Step | Training Loss |
371
+ |:------:|:-----:|:-------------:|
372
+ | 0.0020 | 100 | 0.0524 |
373
+ | 0.0040 | 200 | 0.0482 |
374
+ | 0.0059 | 300 | 0.0464 |
375
+ | 0.0079 | 400 | 0.043 |
376
+ | 0.0099 | 500 | 0.0387 |
377
+ | 0.0119 | 600 | 0.0383 |
378
+ | 0.0138 | 700 | 0.0345 |
379
+ | 0.0158 | 800 | 0.0307 |
380
+ | 0.0178 | 900 | 0.0294 |
381
+ | 0.0198 | 1000 | 0.0275 |
382
+ | 0.0218 | 1100 | 0.0271 |
383
+ | 0.0237 | 1200 | 0.0264 |
384
+ | 0.0257 | 1300 | 0.0258 |
385
+ | 0.0277 | 1400 | 0.0246 |
386
+ | 0.0297 | 1500 | 0.0239 |
387
+ | 0.0317 | 1600 | 0.023 |
388
+ | 0.0336 | 1700 | 0.0216 |
389
+ | 0.0356 | 1800 | 0.0282 |
390
+ | 0.0376 | 1900 | 0.0211 |
391
+ | 0.0396 | 2000 | 0.0205 |
392
+ | 0.0415 | 2100 | 0.0197 |
393
+ | 0.0435 | 2200 | 0.0187 |
394
+ | 0.0455 | 2300 | 0.0184 |
395
+ | 0.0475 | 2400 | 0.0177 |
396
+ | 0.0495 | 2500 | 0.0179 |
397
+ | 0.0514 | 2600 | 0.0173 |
398
+ | 0.0534 | 2700 | 0.0169 |
399
+ | 0.0554 | 2800 | 0.0163 |
400
+ | 0.0574 | 2900 | 0.016 |
401
+ | 0.0594 | 3000 | 0.016 |
402
+ | 0.0613 | 3100 | 0.0147 |
403
+ | 0.0633 | 3200 | 0.0148 |
404
+ | 0.0653 | 3300 | 0.0155 |
405
+ | 0.0673 | 3400 | 0.0149 |
406
+ | 0.0692 | 3500 | 0.0149 |
407
+ | 0.0712 | 3600 | 0.0141 |
408
+ | 0.0732 | 3700 | 0.0145 |
409
+ | 0.0752 | 3800 | 0.0142 |
410
+ | 0.0772 | 3900 | 0.0143 |
411
+ | 0.0791 | 4000 | 0.0137 |
412
+ | 0.0811 | 4100 | 0.0134 |
413
+ | 0.0831 | 4200 | 0.0129 |
414
+ | 0.0851 | 4300 | 0.0133 |
415
+ | 0.0871 | 4400 | 0.0135 |
416
+ | 0.0890 | 4500 | 0.0128 |
417
+ | 0.0910 | 4600 | 0.0126 |
418
+ | 0.0930 | 4700 | 0.0126 |
419
+ | 0.0950 | 4800 | 0.0129 |
420
+ | 0.0969 | 4900 | 0.0127 |
421
+ | 0.0989 | 5000 | 0.0127 |
422
+ | 0.1009 | 5100 | 0.0125 |
423
+ | 0.1029 | 5200 | 0.0119 |
424
+ | 0.1049 | 5300 | 0.0124 |
425
+ | 0.1068 | 5400 | 0.012 |
426
+ | 0.1088 | 5500 | 0.013 |
427
+ | 0.1108 | 5600 | 0.0119 |
428
+ | 0.1128 | 5700 | 0.0118 |
429
+ | 0.1147 | 5800 | 0.0121 |
430
+ | 0.1167 | 5900 | 0.0119 |
431
+ | 0.1187 | 6000 | 0.0116 |
432
+ | 0.1207 | 6100 | 0.0112 |
433
+ | 0.1227 | 6200 | 0.0116 |
434
+ | 0.1246 | 6300 | 0.0115 |
435
+ | 0.1266 | 6400 | 0.0119 |
436
+ | 0.1286 | 6500 | 0.0115 |
437
+ | 0.1306 | 6600 | 0.0109 |
438
+ | 0.1326 | 6700 | 0.0114 |
439
+ | 0.1345 | 6800 | 0.0114 |
440
+ | 0.1365 | 6900 | 0.0109 |
441
+ | 0.1385 | 7000 | 0.011 |
442
+ | 0.1405 | 7100 | 0.0111 |
443
+ | 0.1424 | 7200 | 0.0109 |
444
+ | 0.1444 | 7300 | 0.0108 |
445
+ | 0.1464 | 7400 | 0.0112 |
446
+ | 0.1484 | 7500 | 0.0106 |
447
+ | 0.1504 | 7600 | 0.011 |
448
+ | 0.1523 | 7700 | 0.0106 |
449
+ | 0.1543 | 7800 | 0.0107 |
450
+ | 0.1563 | 7900 | 0.0108 |
451
+ | 0.1583 | 8000 | 0.0106 |
452
+ | 0.1603 | 8100 | 0.0107 |
453
+ | 0.1622 | 8200 | 0.0108 |
454
+ | 0.1642 | 8300 | 0.0103 |
455
+ | 0.1662 | 8400 | 0.0107 |
456
+ | 0.1682 | 8500 | 0.0104 |
457
+ | 0.1701 | 8600 | 0.011 |
458
+ | 0.1721 | 8700 | 0.0105 |
459
+ | 0.1741 | 8800 | 0.0105 |
460
+ | 0.1761 | 8900 | 0.01 |
461
+ | 0.1781 | 9000 | 0.0106 |
462
+ | 0.1800 | 9100 | 0.0105 |
463
+ | 0.1820 | 9200 | 0.0104 |
464
+ | 0.1840 | 9300 | 0.0104 |
465
+ | 0.1860 | 9400 | 0.0107 |
466
+ | 0.1879 | 9500 | 0.0102 |
467
+ | 0.1899 | 9600 | 0.0103 |
468
+ | 0.1919 | 9700 | 0.0105 |
469
+ | 0.1939 | 9800 | 0.01 |
470
+ | 0.1959 | 9900 | 0.0098 |
471
+ | 0.1978 | 10000 | 0.0099 |
472
+ | 0.1998 | 10100 | 0.0099 |
473
+ | 0.2018 | 10200 | 0.0099 |
474
+ | 0.2038 | 10300 | 0.0098 |
475
+ | 0.2058 | 10400 | 0.01 |
476
+ | 0.2077 | 10500 | 0.0101 |
477
+ | 0.2097 | 10600 | 0.0098 |
478
+ | 0.2117 | 10700 | 0.0101 |
479
+ | 0.2137 | 10800 | 0.0098 |
480
+ | 0.2156 | 10900 | 0.0101 |
481
+ | 0.2176 | 11000 | 0.01 |
482
+ | 0.2196 | 11100 | 0.01 |
483
+ | 0.2216 | 11200 | 0.0096 |
484
+ | 0.2236 | 11300 | 0.0096 |
485
+ | 0.2255 | 11400 | 0.0096 |
486
+ | 0.2275 | 11500 | 0.0098 |
487
+ | 0.2295 | 11600 | 0.0099 |
488
+ | 0.2315 | 11700 | 0.0094 |
489
+ | 0.2335 | 11800 | 0.0096 |
490
+ | 0.2354 | 11900 | 0.0094 |
491
+ | 0.2374 | 12000 | 0.0098 |
492
+ | 0.2394 | 12100 | 0.0095 |
493
+ | 0.2414 | 12200 | 0.0095 |
494
+ | 0.2433 | 12300 | 0.0098 |
495
+ | 0.2453 | 12400 | 0.0097 |
496
+ | 0.2473 | 12500 | 0.0094 |
497
+ | 0.2493 | 12600 | 0.0093 |
498
+ | 0.2513 | 12700 | 0.0093 |
499
+ | 0.2532 | 12800 | 0.0092 |
500
+ | 0.2552 | 12900 | 0.0094 |
501
+ | 0.2572 | 13000 | 0.0095 |
502
+ | 0.2592 | 13100 | 0.0093 |
503
+ | 0.2612 | 13200 | 0.009 |
504
+ | 0.2631 | 13300 | 0.0087 |
505
+ | 0.2651 | 13400 | 0.0089 |
506
+ | 0.2671 | 13500 | 0.009 |
507
+ | 0.2691 | 13600 | 0.0091 |
508
+ | 0.2710 | 13700 | 0.0092 |
509
+ | 0.2730 | 13800 | 0.0089 |
510
+ | 0.2750 | 13900 | 0.0091 |
511
+ | 0.2770 | 14000 | 0.0092 |
512
+ | 0.2790 | 14100 | 0.0088 |
513
+ | 0.2809 | 14200 | 0.009 |
514
+ | 0.2829 | 14300 | 0.0091 |
515
+ | 0.2849 | 14400 | 0.0086 |
516
+ | 0.2869 | 14500 | 0.009 |
517
+ | 0.2888 | 14600 | 0.0088 |
518
+ | 0.2908 | 14700 | 0.0092 |
519
+ | 0.2928 | 14800 | 0.009 |
520
+ | 0.2948 | 14900 | 0.0088 |
521
+ | 0.2968 | 15000 | 0.0087 |
522
+ | 0.2987 | 15100 | 0.0085 |
523
+ | 0.3007 | 15200 | 0.009 |
524
+ | 0.3027 | 15300 | 0.0088 |
525
+ | 0.3047 | 15400 | 0.0086 |
526
+ | 0.3067 | 15500 | 0.0087 |
527
+ | 0.3086 | 15600 | 0.0088 |
528
+ | 0.3106 | 15700 | 0.0085 |
529
+ | 0.3126 | 15800 | 0.0088 |
530
+ | 0.3146 | 15900 | 0.0085 |
531
+ | 0.3165 | 16000 | 0.0086 |
532
+ | 0.3185 | 16100 | 0.0086 |
533
+ | 0.3205 | 16200 | 0.0087 |
534
+ | 0.3225 | 16300 | 0.0088 |
535
+ | 0.3245 | 16400 | 0.0087 |
536
+ | 0.3264 | 16500 | 0.0087 |
537
+ | 0.3284 | 16600 | 0.0086 |
538
+ | 0.3304 | 16700 | 0.0087 |
539
+ | 0.3324 | 16800 | 0.0092 |
540
+ | 0.3344 | 16900 | 0.0085 |
541
+ | 0.3363 | 17000 | 0.0088 |
542
+ | 0.3383 | 17100 | 0.0084 |
543
+ | 0.3403 | 17200 | 0.0088 |
544
+ | 0.3423 | 17300 | 0.0083 |
545
+ | 0.3442 | 17400 | 0.0085 |
546
+ | 0.3462 | 17500 | 0.0083 |
547
+ | 0.3482 | 17600 | 0.0084 |
548
+ | 0.3502 | 17700 | 0.0084 |
549
+ | 0.3522 | 17800 | 0.0083 |
550
+ | 0.3541 | 17900 | 0.0087 |
551
+ | 0.3561 | 18000 | 0.0083 |
552
+ | 0.3581 | 18100 | 0.0085 |
553
+ | 0.3601 | 18200 | 0.0082 |
554
+ | 0.3621 | 18300 | 0.0079 |
555
+ | 0.3640 | 18400 | 0.0085 |
556
+ | 0.3660 | 18500 | 0.0084 |
557
+ | 0.3680 | 18600 | 0.0082 |
558
+ | 0.3700 | 18700 | 0.0083 |
559
+ | 0.3719 | 18800 | 0.0082 |
560
+ | 0.3739 | 18900 | 0.0082 |
561
+ | 0.3759 | 19000 | 0.0083 |
562
+ | 0.3779 | 19100 | 0.0081 |
563
+ | 0.3799 | 19200 | 0.0083 |
564
+ | 0.3818 | 19300 | 0.0079 |
565
+ | 0.3838 | 19400 | 0.0083 |
566
+ | 0.3858 | 19500 | 0.0082 |
567
+ | 0.3878 | 19600 | 0.0084 |
568
+ | 0.3897 | 19700 | 0.0084 |
569
+ | 0.3917 | 19800 | 0.008 |
570
+ | 0.3937 | 19900 | 0.0081 |
571
+ | 0.3957 | 20000 | 0.0083 |
572
+ | 0.3977 | 20100 | 0.0082 |
573
+ | 0.3996 | 20200 | 0.0078 |
574
+ | 0.4016 | 20300 | 0.0079 |
575
+ | 0.4036 | 20400 | 0.0081 |
576
+ | 0.4056 | 20500 | 0.0085 |
577
+ | 0.4076 | 20600 | 0.0082 |
578
+ | 0.4095 | 20700 | 0.008 |
579
+ | 0.4115 | 20800 | 0.0079 |
580
+ | 0.4135 | 20900 | 0.0081 |
581
+ | 0.4155 | 21000 | 0.008 |
582
+ | 0.4174 | 21100 | 0.0079 |
583
+ | 0.4194 | 21200 | 0.0077 |
584
+ | 0.4214 | 21300 | 0.0078 |
585
+ | 0.4234 | 21400 | 0.0082 |
586
+ | 0.4254 | 21500 | 0.008 |
587
+ | 0.4273 | 21600 | 0.0076 |
588
+ | 0.4293 | 21700 | 0.0075 |
589
+ | 0.4313 | 21800 | 0.0078 |
590
+ | 0.4333 | 21900 | 0.0081 |
591
+ | 0.4353 | 22000 | 0.0077 |
592
+ | 0.4372 | 22100 | 0.0079 |
593
+ | 0.4392 | 22200 | 0.0078 |
594
+ | 0.4412 | 22300 | 0.0078 |
595
+ | 0.4432 | 22400 | 0.0077 |
596
+ | 0.4451 | 22500 | 0.0078 |
597
+ | 0.4471 | 22600 | 0.0079 |
598
+ | 0.4491 | 22700 | 0.0078 |
599
+ | 0.4511 | 22800 | 0.0079 |
600
+ | 0.4531 | 22900 | 0.0075 |
601
+ | 0.4550 | 23000 | 0.0077 |
602
+ | 0.4570 | 23100 | 0.0076 |
603
+ | 0.4590 | 23200 | 0.0078 |
604
+ | 0.4610 | 23300 | 0.0075 |
605
+ | 0.4629 | 23400 | 0.0075 |
606
+ | 0.4649 | 23500 | 0.0078 |
607
+ | 0.4669 | 23600 | 0.0075 |
608
+ | 0.4689 | 23700 | 0.0076 |
609
+ | 0.4709 | 23800 | 0.0075 |
610
+ | 0.4728 | 23900 | 0.0075 |
611
+ | 0.4748 | 24000 | 0.0075 |
612
+ | 0.4768 | 24100 | 0.0076 |
613
+ | 0.4788 | 24200 | 0.0079 |
614
+ | 0.4808 | 24300 | 0.0076 |
615
+ | 0.4827 | 24400 | 0.0077 |
616
+ | 0.4847 | 24500 | 0.0077 |
617
+ | 0.4867 | 24600 | 0.0073 |
618
+ | 0.4887 | 24700 | 0.0077 |
619
+ | 0.4906 | 24800 | 0.0076 |
620
+ | 0.4926 | 24900 | 0.0075 |
621
+ | 0.4946 | 25000 | 0.0076 |
622
+ | 0.4966 | 25100 | 0.0078 |
623
+ | 0.4986 | 25200 | 0.0077 |
624
+ | 0.5005 | 25300 | 0.0076 |
625
+ | 0.5025 | 25400 | 0.0076 |
626
+ | 0.5045 | 25500 | 0.0076 |
627
+ | 0.5065 | 25600 | 0.0073 |
628
+ | 0.5085 | 25700 | 0.0075 |
629
+ | 0.5104 | 25800 | 0.0072 |
630
+ | 0.5124 | 25900 | 0.0074 |
631
+ | 0.5144 | 26000 | 0.0075 |
632
+ | 0.5164 | 26100 | 0.0075 |
633
+ | 0.5183 | 26200 | 0.0072 |
634
+ | 0.5203 | 26300 | 0.0073 |
635
+ | 0.5223 | 26400 | 0.0073 |
636
+ | 0.5243 | 26500 | 0.0073 |
637
+ | 0.5263 | 26600 | 0.0076 |
638
+ | 0.5282 | 26700 | 0.0075 |
639
+ | 0.5302 | 26800 | 0.0075 |
640
+ | 0.5322 | 26900 | 0.0071 |
641
+ | 0.5342 | 27000 | 0.0074 |
642
+ | 0.5362 | 27100 | 0.0073 |
643
+ | 0.5381 | 27200 | 0.0072 |
644
+ | 0.5401 | 27300 | 0.0071 |
645
+ | 0.5421 | 27400 | 0.0073 |
646
+ | 0.5441 | 27500 | 0.0072 |
647
+ | 0.5460 | 27600 | 0.0076 |
648
+ | 0.5480 | 27700 | 0.0072 |
649
+ | 0.5500 | 27800 | 0.0074 |
650
+ | 0.5520 | 27900 | 0.0072 |
651
+ | 0.5540 | 28000 | 0.0072 |
652
+ | 0.5559 | 28100 | 0.0071 |
653
+ | 0.5579 | 28200 | 0.0069 |
654
+ | 0.5599 | 28300 | 0.0071 |
655
+ | 0.5619 | 28400 | 0.0075 |
656
+ | 0.5638 | 28500 | 0.0074 |
657
+ | 0.5658 | 28600 | 0.0072 |
658
+ | 0.5678 | 28700 | 0.0074 |
659
+ | 0.5698 | 28800 | 0.0072 |
660
+ | 0.5718 | 28900 | 0.0072 |
661
+ | 0.5737 | 29000 | 0.0073 |
662
+ | 0.5757 | 29100 | 0.0072 |
663
+ | 0.5777 | 29200 | 0.0069 |
664
+ | 0.5797 | 29300 | 0.0069 |
665
+ | 0.5817 | 29400 | 0.007 |
666
+ | 0.5836 | 29500 | 0.0071 |
667
+ | 0.5856 | 29600 | 0.007 |
668
+ | 0.5876 | 29700 | 0.0069 |
669
+ | 0.5896 | 29800 | 0.0072 |
670
+ | 0.5915 | 29900 | 0.007 |
671
+ | 0.5935 | 30000 | 0.007 |
672
+ | 0.5955 | 30100 | 0.007 |
673
+ | 0.5975 | 30200 | 0.0069 |
674
+ | 0.5995 | 30300 | 0.0068 |
675
+ | 0.6014 | 30400 | 0.0071 |
676
+ | 0.6034 | 30500 | 0.007 |
677
+ | 0.6054 | 30600 | 0.0071 |
678
+ | 0.6074 | 30700 | 0.007 |
679
+ | 0.6094 | 30800 | 0.0069 |
680
+ | 0.6113 | 30900 | 0.007 |
681
+ | 0.6133 | 31000 | 0.0071 |
682
+ | 0.6153 | 31100 | 0.0069 |
683
+ | 0.6173 | 31200 | 0.007 |
684
+ | 0.6192 | 31300 | 0.0068 |
685
+ | 0.6212 | 31400 | 0.0069 |
686
+ | 0.6232 | 31500 | 0.0068 |
687
+ | 0.6252 | 31600 | 0.0068 |
688
+ | 0.6272 | 31700 | 0.007 |
689
+ | 0.6291 | 31800 | 0.0068 |
690
+ | 0.6311 | 31900 | 0.0069 |
691
+ | 0.6331 | 32000 | 0.0068 |
692
+ | 0.6351 | 32100 | 0.0069 |
693
+ | 0.6370 | 32200 | 0.0066 |
694
+ | 0.6390 | 32300 | 0.0068 |
695
+ | 0.6410 | 32400 | 0.0067 |
696
+ | 0.6430 | 32500 | 0.0068 |
697
+ | 0.6450 | 32600 | 0.0069 |
698
+ | 0.6469 | 32700 | 0.0068 |
699
+ | 0.6489 | 32800 | 0.0065 |
700
+ | 0.6509 | 32900 | 0.0068 |
701
+ | 0.6529 | 33000 | 0.0067 |
702
+ | 0.6549 | 33100 | 0.0066 |
703
+ | 0.6568 | 33200 | 0.0069 |
704
+ | 0.6588 | 33300 | 0.0067 |
705
+ | 0.6608 | 33400 | 0.0067 |
706
+ | 0.6628 | 33500 | 0.0068 |
707
+ | 0.6647 | 33600 | 0.0066 |
708
+ | 0.6667 | 33700 | 0.0069 |
709
+ | 0.6687 | 33800 | 0.0069 |
710
+ | 0.6707 | 33900 | 0.0064 |
711
+ | 0.6727 | 34000 | 0.0065 |
712
+ | 0.6746 | 34100 | 0.0067 |
713
+ | 0.6766 | 34200 | 0.0063 |
714
+ | 0.6786 | 34300 | 0.0067 |
715
+ | 0.6806 | 34400 | 0.0066 |
716
+ | 0.6826 | 34500 | 0.0065 |
717
+ | 0.6845 | 34600 | 0.0064 |
718
+ | 0.6865 | 34700 | 0.0066 |
719
+ | 0.6885 | 34800 | 0.0065 |
720
+ | 0.6905 | 34900 | 0.0064 |
721
+ | 0.6924 | 35000 | 0.0066 |
722
+ | 0.6944 | 35100 | 0.0064 |
723
+ | 0.6964 | 35200 | 0.0064 |
724
+ | 0.6984 | 35300 | 0.0066 |
725
+ | 0.7004 | 35400 | 0.0065 |
726
+ | 0.7023 | 35500 | 0.0067 |
727
+ | 0.7043 | 35600 | 0.0065 |
728
+ | 0.7063 | 35700 | 0.0064 |
729
+ | 0.7083 | 35800 | 0.0066 |
730
+ | 0.7103 | 35900 | 0.0065 |
731
+ | 0.7122 | 36000 | 0.0067 |
732
+ | 0.7142 | 36100 | 0.0069 |
733
+ | 0.7162 | 36200 | 0.0065 |
734
+ | 0.7182 | 36300 | 0.0064 |
735
+ | 0.7201 | 36400 | 0.0064 |
736
+ | 0.7221 | 36500 | 0.0066 |
737
+ | 0.7241 | 36600 | 0.0065 |
738
+ | 0.7261 | 36700 | 0.0062 |
739
+ | 0.7281 | 36800 | 0.0068 |
740
+ | 0.7300 | 36900 | 0.0064 |
741
+ | 0.7320 | 37000 | 0.0067 |
742
+ | 0.7340 | 37100 | 0.0063 |
743
+ | 0.7360 | 37200 | 0.0063 |
744
+ | 0.7379 | 37300 | 0.0064 |
745
+ | 0.7399 | 37400 | 0.0066 |
746
+ | 0.7419 | 37500 | 0.0065 |
747
+ | 0.7439 | 37600 | 0.0064 |
748
+ | 0.7459 | 37700 | 0.0065 |
749
+ | 0.7478 | 37800 | 0.0064 |
750
+ | 0.7498 | 37900 | 0.0063 |
751
+ | 0.7518 | 38000 | 0.0062 |
752
+ | 0.7538 | 38100 | 0.0064 |
753
+ | 0.7558 | 38200 | 0.0062 |
754
+ | 0.7577 | 38300 | 0.0064 |
755
+ | 0.7597 | 38400 | 0.0063 |
756
+ | 0.7617 | 38500 | 0.0063 |
757
+ | 0.7637 | 38600 | 0.0065 |
758
+ | 0.7656 | 38700 | 0.0063 |
759
+ | 0.7676 | 38800 | 0.0064 |
760
+ | 0.7696 | 38900 | 0.0062 |
761
+ | 0.7716 | 39000 | 0.0062 |
762
+ | 0.7736 | 39100 | 0.0062 |
763
+ | 0.7755 | 39200 | 0.0063 |
764
+ | 0.7775 | 39300 | 0.0065 |
765
+ | 0.7795 | 39400 | 0.0061 |
766
+ | 0.7815 | 39500 | 0.0062 |
767
+ | 0.7835 | 39600 | 0.0063 |
768
+ | 0.7854 | 39700 | 0.0062 |
769
+ | 0.7874 | 39800 | 0.0062 |
770
+ | 0.7894 | 39900 | 0.0063 |
771
+ | 0.7914 | 40000 | 0.0059 |
772
+ | 0.7933 | 40100 | 0.0063 |
773
+ | 0.7953 | 40200 | 0.0064 |
774
+ | 0.7973 | 40300 | 0.006 |
775
+ | 0.7993 | 40400 | 0.0063 |
776
+ | 0.8013 | 40500 | 0.0061 |
777
+ | 0.8032 | 40600 | 0.0061 |
778
+ | 0.8052 | 40700 | 0.0062 |
779
+ | 0.8072 | 40800 | 0.0062 |
780
+ | 0.8092 | 40900 | 0.006 |
781
+ | 0.8112 | 41000 | 0.0061 |
782
+ | 0.8131 | 41100 | 0.0063 |
783
+ | 0.8151 | 41200 | 0.0059 |
784
+ | 0.8171 | 41300 | 0.0062 |
785
+ | 0.8191 | 41400 | 0.0062 |
786
+ | 0.8210 | 41500 | 0.0062 |
787
+ | 0.8230 | 41600 | 0.0062 |
788
+ | 0.8250 | 41700 | 0.0061 |
789
+ | 0.8270 | 41800 | 0.0061 |
790
+ | 0.8290 | 41900 | 0.0061 |
791
+ | 0.8309 | 42000 | 0.0063 |
792
+ | 0.8329 | 42100 | 0.0064 |
793
+ | 0.8349 | 42200 | 0.0063 |
794
+ | 0.8369 | 42300 | 0.0063 |
795
+ | 0.8388 | 42400 | 0.0061 |
796
+ | 0.8408 | 42500 | 0.0062 |
797
+ | 0.8428 | 42600 | 0.0062 |
798
+ | 0.8448 | 42700 | 0.0061 |
799
+ | 0.8468 | 42800 | 0.0059 |
800
+ | 0.8487 | 42900 | 0.006 |
801
+ | 0.8507 | 43000 | 0.0061 |
802
+ | 0.8527 | 43100 | 0.0062 |
803
+ | 0.8547 | 43200 | 0.0058 |
804
+ | 0.8567 | 43300 | 0.0065 |
805
+ | 0.8586 | 43400 | 0.0064 |
806
+ | 0.8606 | 43500 | 0.006 |
807
+ | 0.8626 | 43600 | 0.0061 |
808
+ | 0.8646 | 43700 | 0.0059 |
809
+ | 0.8665 | 43800 | 0.0063 |
810
+ | 0.8685 | 43900 | 0.0061 |
811
+ | 0.8705 | 44000 | 0.006 |
812
+ | 0.8725 | 44100 | 0.0061 |
813
+ | 0.8745 | 44200 | 0.0061 |
814
+ | 0.8764 | 44300 | 0.0059 |
815
+ | 0.8784 | 44400 | 0.006 |
816
+ | 0.8804 | 44500 | 0.006 |
817
+ | 0.8824 | 44600 | 0.0059 |
818
+ | 0.8844 | 44700 | 0.0062 |
819
+ | 0.8863 | 44800 | 0.006 |
820
+ | 0.8883 | 44900 | 0.006 |
821
+ | 0.8903 | 45000 | 0.0058 |
822
+ | 0.8923 | 45100 | 0.006 |
823
+ | 0.8942 | 45200 | 0.0061 |
824
+ | 0.8962 | 45300 | 0.006 |
825
+ | 0.8982 | 45400 | 0.0059 |
826
+ | 0.9002 | 45500 | 0.0059 |
827
+ | 0.9022 | 45600 | 0.006 |
828
+ | 0.9041 | 45700 | 0.0062 |
829
+ | 0.9061 | 45800 | 0.0056 |
830
+ | 0.9081 | 45900 | 0.0057 |
831
+ | 0.9101 | 46000 | 0.006 |
832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
+ | 0.9437 | 47700 | 0.0058 |
849
+ | 0.9457 | 47800 | 0.006 |
850
+ | 0.9477 | 47900 | 0.0059 |
851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
+
878
+ </details>
879
+
880
+ ### Framework Versions
881
+ - Python: 3.11.9
882
+ - Sentence Transformers: 3.3.0
883
+ - PyLate: 1.1.4
884
+ - Transformers: 4.48.0.dev0
885
+ - PyTorch: 2.4.0
886
+ - Accelerate: 1.2.1
887
+ - Datasets: 2.21.0
888
+ - Tokenizers: 0.21.0
889
+
890
+
891
+ ## Citation
892
+
893
+ ### BibTeX
894
+
895
+ #### Sentence Transformers
896
+ ```bibtex
897
+ @inproceedings{reimers-2019-sentence-bert,
898
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
899
+ author = "Reimers, Nils and Gurevych, Iryna",
900
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
901
+ month = "11",
902
+ year = "2019",
903
+ publisher = "Association for Computational Linguistics",
904
+ url = "https://arxiv.org/abs/1908.10084"
905
+ }
906
+ ```
907
+
908
+ #### PyLate
909
+ ```bibtex
910
+ @misc{PyLate,
911
+ title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
912
+ author={Chaffin, Antoine and Sourty, Raphaël},
913
+ url={https://github.com/lightonai/pylate},
914
+ year={2024}
915
+ }
916
+ ```
917
+
918
+ <!--
919
+ ## Glossary
920
+
921
+ *Clearly define terms in order to be accessible across audiences.*
922
+ -->
923
+
924
+ <!--
925
+ ## Model Card Authors
926
+
927
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
928
+ -->
929
+
930
+ <!--
931
+ ## Model Card Contact
932
+
933
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
934
+ -->
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