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souvickdascmsa019
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initial-colbert-ir

Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
bert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:242513
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Model card Files
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  1. 1_Dense/config.json +1 -0
  2. 1_Dense/model.safetensors +3 -0
  3. README.md +1112 -0
  4. added_tokens.json +4 -0
  5. config.json +26 -0
  6. config_sentence_transformers.json +49 -0
  7. model.safetensors +3 -0
  8. modules.json +14 -0
  9. optimizer.pt +3 -0
  10. rng_state.pth +3 -0
  11. scheduler.pt +3 -0
  12. sentence_bert_config.json +4 -0
  13. special_tokens_map.json +31 -0
  14. tokenizer.json +0 -0
  15. tokenizer_config.json +72 -0
  16. trainer_state.json +0 -0
  17. training_args.bin +3 -0
  18. vocab.txt +0 -0
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1
+ ---
2
+ language:
3
+ - en
4
+ tags:
5
+ - ColBERT
6
+ - PyLate
7
+ - sentence-transformers
8
+ - sentence-similarity
9
+ - feature-extraction
10
+ - generated_from_trainer
11
+ - dataset_size:242513
12
+ - loss:Contrastive
13
+ base_model: colbert-ir/colbertv2.0
14
+ datasets:
15
+ - reasonir/reasonir-data
16
+ pipeline_tag: sentence-similarity
17
+ library_name: PyLate
18
+ metrics:
19
+ - accuracy
20
+ model-index:
21
+ - name: PyLate model based on colbert-ir/colbertv2.0
22
+ results:
23
+ - task:
24
+ type: col-berttriplet
25
+ name: Col BERTTriplet
26
+ dataset:
27
+ name: Unknown
28
+ type: unknown
29
+ metrics:
30
+ - type: accuracy
31
+ value: 0.92734694480896
32
+ name: Accuracy
33
+ ---
34
+
35
+ # PyLate model based on colbert-ir/colbertv2.0
36
+
37
+ This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) on the [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
38
+
39
+ ## Model Details
40
+
41
+ ### Model Description
42
+ - **Model Type:** PyLate model
43
+ - **Base model:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
44
+ - **Document Length:** 180 tokens
45
+ - **Query Length:** 32 tokens
46
+ - **Output Dimensionality:** 128 tokens
47
+ - **Similarity Function:** MaxSim
48
+ - **Training Dataset:**
49
+ - [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data)
50
+ - **Language:** en
51
+ <!-- - **License:** Unknown -->
52
+
53
+ ### Model Sources
54
+
55
+ - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
56
+ - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
57
+ - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
58
+
59
+ ### Full Model Architecture
60
+
61
+ ```
62
+ ColBERT(
63
+ (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
64
+ (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
65
+ )
66
+ ```
67
+
68
+ ## Usage
69
+ First install the PyLate library:
70
+
71
+ ```bash
72
+ pip install -U pylate
73
+ ```
74
+
75
+ ### Retrieval
76
+
77
+ 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.
78
+
79
+ #### Indexing documents
80
+
81
+ First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
82
+
83
+ ```python
84
+ from pylate import indexes, models, retrieve
85
+
86
+ # Step 1: Load the ColBERT model
87
+ model = models.ColBERT(
88
+ model_name_or_path=pylate_model_id,
89
+ )
90
+
91
+ # Step 2: Initialize the Voyager index
92
+ index = indexes.Voyager(
93
+ index_folder="pylate-index",
94
+ index_name="index",
95
+ override=True, # This overwrites the existing index if any
96
+ )
97
+
98
+ # Step 3: Encode the documents
99
+ documents_ids = ["1", "2", "3"]
100
+ documents = ["document 1 text", "document 2 text", "document 3 text"]
101
+
102
+ documents_embeddings = model.encode(
103
+ documents,
104
+ batch_size=32,
105
+ is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
106
+ show_progress_bar=True,
107
+ )
108
+
109
+ # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
110
+ index.add_documents(
111
+ documents_ids=documents_ids,
112
+ documents_embeddings=documents_embeddings,
113
+ )
114
+ ```
115
+
116
+ 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:
117
+
118
+ ```python
119
+ # To load an index, simply instantiate it with the correct folder/name and without overriding it
120
+ index = indexes.Voyager(
121
+ index_folder="pylate-index",
122
+ index_name="index",
123
+ )
124
+ ```
125
+
126
+ #### Retrieving top-k documents for queries
127
+
128
+ Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
129
+ 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:
130
+
131
+ ```python
132
+ # Step 1: Initialize the ColBERT retriever
133
+ retriever = retrieve.ColBERT(index=index)
134
+
135
+ # Step 2: Encode the queries
136
+ queries_embeddings = model.encode(
137
+ ["query for document 3", "query for document 1"],
138
+ batch_size=32,
139
+ is_query=True, # # Ensure that it is set to False to indicate that these are queries
140
+ show_progress_bar=True,
141
+ )
142
+
143
+ # Step 3: Retrieve top-k documents
144
+ scores = retriever.retrieve(
145
+ queries_embeddings=queries_embeddings,
146
+ k=10, # Retrieve the top 10 matches for each query
147
+ )
148
+ ```
149
+
150
+ ### Reranking
151
+ 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:
152
+
153
+ ```python
154
+ from pylate import rank, models
155
+
156
+ queries = [
157
+ "query A",
158
+ "query B",
159
+ ]
160
+
161
+ documents = [
162
+ ["document A", "document B"],
163
+ ["document 1", "document C", "document B"],
164
+ ]
165
+
166
+ documents_ids = [
167
+ [1, 2],
168
+ [1, 3, 2],
169
+ ]
170
+
171
+ model = models.ColBERT(
172
+ model_name_or_path=pylate_model_id,
173
+ )
174
+
175
+ queries_embeddings = model.encode(
176
+ queries,
177
+ is_query=True,
178
+ )
179
+
180
+ documents_embeddings = model.encode(
181
+ documents,
182
+ is_query=False,
183
+ )
184
+
185
+ reranked_documents = rank.rerank(
186
+ documents_ids=documents_ids,
187
+ queries_embeddings=queries_embeddings,
188
+ documents_embeddings=documents_embeddings,
189
+ )
190
+ ```
191
+
192
+ <!--
193
+ ### Direct Usage (Transformers)
194
+
195
+ <details><summary>Click to see the direct usage in Transformers</summary>
196
+
197
+ </details>
198
+ -->
199
+
200
+ <!--
201
+ ### Downstream Usage (Sentence Transformers)
202
+
203
+ You can finetune this model on your own dataset.
204
+
205
+ <details><summary>Click to expand</summary>
206
+
207
+ </details>
208
+ -->
209
+
210
+ <!--
211
+ ### Out-of-Scope Use
212
+
213
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
214
+ -->
215
+
216
+ ## Evaluation
217
+
218
+ ### Metrics
219
+
220
+ #### Col BERTTriplet
221
+
222
+ * Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
223
+
224
+ | Metric | Value |
225
+ |:-------------|:-----------|
226
+ | **accuracy** | **0.9273** |
227
+
228
+ <!--
229
+ ## Bias, Risks and Limitations
230
+
231
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
232
+ -->
233
+
234
+ <!--
235
+ ### Recommendations
236
+
237
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
238
+ -->
239
+
240
+ ## Training Details
241
+
242
+ ### Training Dataset
243
+
244
+ #### reasonir-data
245
+
246
+ * Dataset: [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) at [0275f82](https://huggingface.co/datasets/reasonir/reasonir-data/tree/0275f825929b206d4ead23d34b4f8a50d4eddbc8)
247
+ * Size: 242,513 training samples
248
+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
249
+ * Approximate statistics based on the first 1000 samples:
250
+ | | query | positive | negative |
251
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
252
+ | type | string | string | string |
253
+ | details | <ul><li>min: 6 tokens</li><li>mean: 26.76 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.89 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.32 tokens</li><li>max: 32 tokens</li></ul> |
254
+ * Samples:
255
+ | query | positive | negative |
256
+ |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
257
+ | <code>Market analysis suggests that the ongoing trend of increased adoption of renewable energy sources will continue to drive the demand for solar panels in the coming years. According to various reports, the global solar panel market is projected to witness significant growth over the next decade, with some analysts predicting a compound annual growth rate (CAGR) of up to 20%. This growth is expected to be fueled by declining costs of production, government incentives, and growing environmental concerns. However, some experts also caution that the market may experience fluctuations due to trade policies, technological advancements, and changes in government regulations. Despite these challenges, the overall outlook for the solar panel market remains optimistic, with many companies investing heavily in research and development to improve efficiency and reduce costs. As the demand for renewable energy continues to rise, it is likely that the solar panel market will play a significant role in...</code> | <code>Contrary to the market analysis suggesting a compound annual growth rate (CAGR) of up to 20% for the global solar panel market, our financial reports indicate a more modest growth rate of 12% over the next decade. While we agree that declining production costs, government incentives, and growing environmental concerns will drive demand for solar panels, we also believe that trade policies, technological advancements, and changes in government regulations will have a more significant impact on the market than previously anticipated. Our projections suggest that the market will experience fluctuations, with some years experiencing higher growth rates than others. However, we do not anticipate the market to experience the same level of growth as predicted by other analysts. Our research indicates that the market will reach a saturation point, beyond which growth will slow down. Additionally, we believe that the impact of advancements in energy storage technologies on the solar panel marke...</code> | <code>The demand for solar panels has been on the rise in recent years, driven by an increase in environmental awareness and the need for sustainable energy sources. One of the key factors contributing to this growth is the decline in production costs. As technology advances, the cost of manufacturing solar panels has decreased, making them more affordable for consumers. Additionally, governments around the world have implemented policies and incentives to encourage the adoption of renewable energy sources, which has further boosted demand for solar panels. However, the solar panel market is not without its challenges. Trade policies and technological advancements can impact the market, and changes in government regulations can create uncertainty. Despite these challenges, the outlook for the solar panel market remains positive, with many companies investing heavily in research and development to improve efficiency and reduce costs. The development of new technologies, such as bifacial panel...</code> |
258
+ | <code>As the sun set over the vast savannah, a sense of tranquility washed over the pride of lions. Their tawny coats glistened in the fading light, and the sound of crickets provided a soothing background hum. Nearby, a group of humans, armed with cameras and curiosity, observed the wild animals from a safe distance.</code> | <code>The lions lazed in the shade of a nearby tree, their tawny coats a blur as they basked in the warmth. The visitors watched in awe, clicking away at their cameras to capture the majesty of the wild animals. Crickets provided a constant, soothing background noise as the humans took care to keep a safe distance from the pride.</code> | <code>The city's tree planting initiative has been a huge success, providing a serene oasis in the midst of the bustling metropolis. The sounds of the city – car horns, chatter and crickets – blend together to create a symphony of noise. While many humans have been drawn to the tranquility of the park, others have raised concerns about the integration of urban wildlife.</code> |
259
+ | <code>Recent advancements in the field of artificial intelligence have led to significant breakthroughs in natural language processing. This has far-reaching implications for various industries, including education, where AI-powered chatbots can enhance student learning experiences by providing personalization and real-time feedback. Moreover, the integration of AI in educational settings can help address issues of accessibility and equity.</code> | <code>The rapid expansion of AI research has yielded substantial progress in natural language processing, allowing for the development of more sophisticated AI-powered tools. In the education sector, AI-driven chatbots can facilitate individualized learning and offer instantaneous feedback, thereby enriching the overall learning environment. However, it is crucial to address concerns surrounding the digital divide to ensure that these technological advancements are accessible to all.</code> | <code>One of the primary challenges facing archaeologists today is the authentication of ancient artifacts, which often involves meticulous analysis of relics and literary texts. The discovery of a previously unknown scroll, buried deep within the labyrinthine passages of an Egyptian tomb, shed new light on the role of language in ancient cultures. Interestingly, the sophisticated syntax and nuanced vocabulary of the ancient Egyptian language have some similarities with modern-day linguistic structures.</code> |
260
+ * Loss: <code>pylate.losses.contrastive.Contrastive</code>
261
+
262
+ ### Evaluation Dataset
263
+
264
+ #### reasonir-data
265
+
266
+ * Dataset: [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) at [0275f82](https://huggingface.co/datasets/reasonir/reasonir-data/tree/0275f825929b206d4ead23d34b4f8a50d4eddbc8)
267
+ * Size: 2,450 evaluation samples
268
+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
269
+ * Approximate statistics based on the first 1000 samples:
270
+ | | query | positive | negative |
271
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
272
+ | type | string | string | string |
273
+ | details | <ul><li>min: 6 tokens</li><li>mean: 26.92 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.98 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.5 tokens</li><li>max: 32 tokens</li></ul> |
274
+ * Samples:
275
+ | query | positive | negative |
276
+ |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
277
+ | <code>In the recent speech, the politician claimed that the current tax reform will benefit the middle class and lead to a significant increase in economic growth. The politician stated, 'Our plan is to cut taxes across the board, simplifying the tax code and making it fairer for all Americans. We're taking the money from the unfair and complex system and putting it back in the pockets of the hardworking people.' The politician also emphasized the effects of the cut taxes, 'When we cut taxes, we're putting more money in the hands of our small business owners and workers, who are the backbone of our economy. This means new jobs will be created, wages will rise, and the people who actually create the jobs, the entrepreneurs, will have the funds needed to invest more into their businesses.' Moreover, the politician asserted, 'Our country will witness a major boost in job creation and economic growth which in turn will positively affect local communities all around the country.' Furthermore, the...</code> | <code>According to reputable sources in economics research, the tax reform that the administration is trying to implement closely resembles that of the 2001 and the 2003 cuts under former President George Bush and that of 1981 under President Ronald Reagan who reduced tax rates 23% and 25% respectively. Research carried out by a major university indicated that these reforms only yielded an estimated 10% increase in tax revenue, since the decrease in tax income could result in compensated revenues through economic stimulation. Some studies were actually pointing to the idea that no trickle-down economics apply as more funds were being placed in the already wealthy communities. This change could shift the economical inequalities to an extreme and showed a direct relationship between a tax reduction and a large national deficit increase. Employer demand for the borderline employee may not actually increase from the creation of the new jobs, and economists believed. The variation of wages for jo...</code> | <code>The concept of a universal basic income has been a topic of discussion among economists and policymakers in recent years. While some see it as a viable solution to poverty and economic inequality, others argue that it is not feasible due to the financial constraints it would impose on governments. One of the main concerns is that implementing a universal basic income would require significant funding, which would likely come from increased taxes or redistribution of existing social welfare funds. Critics argue that this could lead to a decrease in economic growth, as people may be less incentivized to work if they are receiving a guaranteed income. On the other hand, proponents argue that a universal basic income would provide a safety net for the most vulnerable members of society and allow people to pursue meaningful work rather than just taking any job for the sake of a paycheck. Some countries have experimented with universal basic income pilots, but the results have been mixed. Fi...</code> |
278
+ | <code>Recent advances in super-resolution techniques have led to a greater understanding of many sub-cellular structures and have opened up new avenues for exploring cellular behavior at the nanoscale. Fluorescence imaging, in particular, has greatly benefited from these advances and has enabled researchers to visualize the distribution and dynamics of proteins in real time. However, further developments in fluorescence imaging rely on a better comprehension of the interactions between imaging probes and their molecular environment. A crucial factor in these interactions is the size and shape of the probes, which must be optimized to minimize disruption of the native dynamics of the system while also achieving high fluorescence yields. The DNA-based probes have emerged as a promising solution, offering the opportunity to tune the size and shape of the probes to optimize performance.</code> | <code>Microscopy</code> | <code>Biophysics</code> |
279
+ | <code>I recently purchased this top-of-the-line smartwatch for my birthday, and I must say that it has been a revelation in terms of keeping track of my vital signs and daily activity levels. The watch has an elegant design that doesn't clash with my other wearable accessories, and I love how the touchscreen display lets me access a wealth of health metrics at a glance. Although I've encountered several instances where the heart rate monitoring system was delayed in capturing accurate readings, this minor shortcoming hardly detracts from my overall satisfaction with the product. The value proposition it presents in terms of quality, accuracy, and ascendancy over competing offerings makes it a compelling option in this class of devices. Despite never having owned one before, I found the smartwatch straightforward to use, and the companion app did an excellent job of simplifying the tracking and analysis of my fitness journey. Nothing in particular distinguishes this product's methodology in c...</code> | <code>authentic</code> | <code>fake</code> |
280
+ * Loss: <code>pylate.losses.contrastive.Contrastive</code>
281
+
282
+ ### Training Hyperparameters
283
+ #### Non-Default Hyperparameters
284
+
285
+ - `eval_strategy`: steps
286
+ - `per_device_train_batch_size`: 16
287
+ - `per_device_eval_batch_size`: 32
288
+ - `gradient_accumulation_steps`: 2
289
+ - `learning_rate`: 2e-05
290
+ - `weight_decay`: 0.01
291
+ - `warmup_steps`: 100
292
+ - `fp16`: True
293
+ - `remove_unused_columns`: False
294
+
295
+ #### All Hyperparameters
296
+ <details><summary>Click to expand</summary>
297
+
298
+ - `overwrite_output_dir`: False
299
+ - `do_predict`: False
300
+ - `eval_strategy`: steps
301
+ - `prediction_loss_only`: True
302
+ - `per_device_train_batch_size`: 16
303
+ - `per_device_eval_batch_size`: 32
304
+ - `per_gpu_train_batch_size`: None
305
+ - `per_gpu_eval_batch_size`: None
306
+ - `gradient_accumulation_steps`: 2
307
+ - `eval_accumulation_steps`: None
308
+ - `torch_empty_cache_steps`: None
309
+ - `learning_rate`: 2e-05
310
+ - `weight_decay`: 0.01
311
+ - `adam_beta1`: 0.9
312
+ - `adam_beta2`: 0.999
313
+ - `adam_epsilon`: 1e-08
314
+ - `max_grad_norm`: 1.0
315
+ - `num_train_epochs`: 3
316
+ - `max_steps`: -1
317
+ - `lr_scheduler_type`: linear
318
+ - `lr_scheduler_kwargs`: {}
319
+ - `warmup_ratio`: 0.0
320
+ - `warmup_steps`: 100
321
+ - `log_level`: passive
322
+ - `log_level_replica`: warning
323
+ - `log_on_each_node`: True
324
+ - `logging_nan_inf_filter`: True
325
+ - `save_safetensors`: True
326
+ - `save_on_each_node`: False
327
+ - `save_only_model`: False
328
+ - `restore_callback_states_from_checkpoint`: False
329
+ - `no_cuda`: False
330
+ - `use_cpu`: False
331
+ - `use_mps_device`: False
332
+ - `seed`: 42
333
+ - `data_seed`: None
334
+ - `jit_mode_eval`: False
335
+ - `use_ipex`: False
336
+ - `bf16`: False
337
+ - `fp16`: True
338
+ - `fp16_opt_level`: O1
339
+ - `half_precision_backend`: auto
340
+ - `bf16_full_eval`: False
341
+ - `fp16_full_eval`: False
342
+ - `tf32`: None
343
+ - `local_rank`: 0
344
+ - `ddp_backend`: None
345
+ - `tpu_num_cores`: None
346
+ - `tpu_metrics_debug`: False
347
+ - `debug`: []
348
+ - `dataloader_drop_last`: False
349
+ - `dataloader_num_workers`: 0
350
+ - `dataloader_prefetch_factor`: None
351
+ - `past_index`: -1
352
+ - `disable_tqdm`: False
353
+ - `remove_unused_columns`: False
354
+ - `label_names`: None
355
+ - `load_best_model_at_end`: False
356
+ - `ignore_data_skip`: False
357
+ - `fsdp`: []
358
+ - `fsdp_min_num_params`: 0
359
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
360
+ - `fsdp_transformer_layer_cls_to_wrap`: None
361
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
362
+ - `deepspeed`: None
363
+ - `label_smoothing_factor`: 0.0
364
+ - `optim`: adamw_torch
365
+ - `optim_args`: None
366
+ - `adafactor`: False
367
+ - `group_by_length`: False
368
+ - `length_column_name`: length
369
+ - `ddp_find_unused_parameters`: None
370
+ - `ddp_bucket_cap_mb`: None
371
+ - `ddp_broadcast_buffers`: False
372
+ - `dataloader_pin_memory`: True
373
+ - `dataloader_persistent_workers`: False
374
+ - `skip_memory_metrics`: True
375
+ - `use_legacy_prediction_loop`: False
376
+ - `push_to_hub`: False
377
+ - `resume_from_checkpoint`: None
378
+ - `hub_model_id`: None
379
+ - `hub_strategy`: every_save
380
+ - `hub_private_repo`: None
381
+ - `hub_always_push`: False
382
+ - `gradient_checkpointing`: False
383
+ - `gradient_checkpointing_kwargs`: None
384
+ - `include_inputs_for_metrics`: False
385
+ - `include_for_metrics`: []
386
+ - `eval_do_concat_batches`: True
387
+ - `fp16_backend`: auto
388
+ - `push_to_hub_model_id`: None
389
+ - `push_to_hub_organization`: None
390
+ - `mp_parameters`:
391
+ - `auto_find_batch_size`: False
392
+ - `full_determinism`: False
393
+ - `torchdynamo`: None
394
+ - `ray_scope`: last
395
+ - `ddp_timeout`: 1800
396
+ - `torch_compile`: False
397
+ - `torch_compile_backend`: None
398
+ - `torch_compile_mode`: None
399
+ - `dispatch_batches`: None
400
+ - `split_batches`: None
401
+ - `include_tokens_per_second`: False
402
+ - `include_num_input_tokens_seen`: False
403
+ - `neftune_noise_alpha`: None
404
+ - `optim_target_modules`: None
405
+ - `batch_eval_metrics`: False
406
+ - `eval_on_start`: False
407
+ - `use_liger_kernel`: False
408
+ - `eval_use_gather_object`: False
409
+ - `average_tokens_across_devices`: False
410
+ - `prompts`: None
411
+ - `batch_sampler`: batch_sampler
412
+ - `multi_dataset_batch_sampler`: proportional
413
+
414
+ </details>
415
+
416
+ ### Training Logs
417
+ <details><summary>Click to expand</summary>
418
+
419
+ | Epoch | Step | Training Loss | Validation Loss | accuracy |
420
+ |:------:|:-----:|:-------------:|:---------------:|:--------:|
421
+ | 0.0066 | 50 | 3.9255 | - | - |
422
+ | 0.0132 | 100 | 1.7945 | - | - |
423
+ | 0.0198 | 150 | 1.5522 | - | - |
424
+ | 0.0264 | 200 | 1.6267 | - | - |
425
+ | 0.0330 | 250 | 1.5434 | - | - |
426
+ | 0 | 0 | - | - | 0.8714 |
427
+ | 0.0330 | 250 | - | 0.8547 | - |
428
+ | 0.0396 | 300 | 1.3113 | - | - |
429
+ | 0.0462 | 350 | 1.3674 | - | - |
430
+ | 0.0528 | 400 | 1.3417 | - | - |
431
+ | 0.0594 | 450 | 1.2831 | - | - |
432
+ | 0.0660 | 500 | 1.2243 | - | - |
433
+ | 0 | 0 | - | - | 0.8820 |
434
+ | 0.0660 | 500 | - | 0.7873 | - |
435
+ | 0.0726 | 550 | 1.2276 | - | - |
436
+ | 0.0792 | 600 | 1.2502 | - | - |
437
+ | 0.0858 | 650 | 1.2247 | - | - |
438
+ | 0.0924 | 700 | 1.178 | - | - |
439
+ | 0.0990 | 750 | 1.2379 | - | - |
440
+ | 0 | 0 | - | - | 0.8931 |
441
+ | 0.0990 | 750 | - | 0.7503 | - |
442
+ | 0.1056 | 800 | 1.3893 | - | - |
443
+ | 0.1122 | 850 | 1.1852 | - | - |
444
+ | 0.1187 | 900 | 1.1082 | - | - |
445
+ | 0.1253 | 950 | 0.9946 | - | - |
446
+ | 0.1319 | 1000 | 1.1834 | - | - |
447
+ | 0 | 0 | - | - | 0.8996 |
448
+ | 0.1319 | 1000 | - | 0.7309 | - |
449
+ | 0.1385 | 1050 | 1.1556 | - | - |
450
+ | 0.1451 | 1100 | 1.0251 | - | - |
451
+ | 0.1517 | 1150 | 1.1943 | - | - |
452
+ | 0.1583 | 1200 | 1.086 | - | - |
453
+ | 0.1649 | 1250 | 1.1236 | - | - |
454
+ | 0 | 0 | - | - | 0.9008 |
455
+ | 0.1649 | 1250 | - | 0.6946 | - |
456
+ | 0.1715 | 1300 | 1.0485 | - | - |
457
+ | 0.1781 | 1350 | 0.9481 | - | - |
458
+ | 0.1847 | 1400 | 1.0898 | - | - |
459
+ | 0.1913 | 1450 | 1.0863 | - | - |
460
+ | 0.1979 | 1500 | 1.0756 | - | - |
461
+ | 0 | 0 | - | - | 0.9037 |
462
+ | 0.1979 | 1500 | - | 0.6747 | - |
463
+ | 0.2045 | 1550 | 0.9973 | - | - |
464
+ | 0.2111 | 1600 | 1.1098 | - | - |
465
+ | 0.2177 | 1650 | 1.1745 | - | - |
466
+ | 0.2243 | 1700 | 0.9654 | - | - |
467
+ | 0.2309 | 1750 | 1.0919 | - | - |
468
+ | 0 | 0 | - | - | 0.9094 |
469
+ | 0.2309 | 1750 | - | 0.6499 | - |
470
+ | 0.2375 | 1800 | 1.0249 | - | - |
471
+ | 0.2441 | 1850 | 0.9863 | - | - |
472
+ | 0.2507 | 1900 | 1.1091 | - | - |
473
+ | 0.2573 | 1950 | 1.0989 | - | - |
474
+ | 0.2639 | 2000 | 1.0662 | - | - |
475
+ | 0 | 0 | - | - | 0.9065 |
476
+ | 0.2639 | 2000 | - | 0.6661 | - |
477
+ | 0.2705 | 2050 | 1.0456 | - | - |
478
+ | 0.2771 | 2100 | 1.1349 | - | - |
479
+ | 0.2837 | 2150 | 1.0111 | - | - |
480
+ | 0.2903 | 2200 | 1.026 | - | - |
481
+ | 0.2969 | 2250 | 0.9415 | - | - |
482
+ | 0 | 0 | - | - | 0.9073 |
483
+ | 0.2969 | 2250 | - | 0.6390 | - |
484
+ | 0.3035 | 2300 | 0.9761 | - | - |
485
+ | 0.3101 | 2350 | 0.9748 | - | - |
486
+ | 0.3167 | 2400 | 1.0238 | - | - |
487
+ | 0.3233 | 2450 | 1.0456 | - | - |
488
+ | 0.3299 | 2500 | 0.9895 | - | - |
489
+ | 0 | 0 | - | - | 0.9110 |
490
+ | 0.3299 | 2500 | - | 0.6435 | - |
491
+ | 0.3365 | 2550 | 0.8796 | - | - |
492
+ | 0.3431 | 2600 | 1.0172 | - | - |
493
+ | 0.3497 | 2650 | 1.014 | - | - |
494
+ | 0.3562 | 2700 | 0.9748 | - | - |
495
+ | 0.3628 | 2750 | 0.9273 | - | - |
496
+ | 0 | 0 | - | - | 0.9082 |
497
+ | 0.3628 | 2750 | - | 0.6303 | - |
498
+ | 0.3694 | 2800 | 1.0122 | - | - |
499
+ | 0.3760 | 2850 | 1.0054 | - | - |
500
+ | 0.3826 | 2900 | 0.8974 | - | - |
501
+ | 0.3892 | 2950 | 0.9396 | - | - |
502
+ | 0.3958 | 3000 | 0.8734 | - | - |
503
+ | 0 | 0 | - | - | 0.9049 |
504
+ | 0.3958 | 3000 | - | 0.6238 | - |
505
+ | 0.4024 | 3050 | 1.0048 | - | - |
506
+ | 0.4090 | 3100 | 0.9701 | - | - |
507
+ | 0.4156 | 3150 | 0.9924 | - | - |
508
+ | 0.4222 | 3200 | 0.9349 | - | - |
509
+ | 0.4288 | 3250 | 0.974 | - | - |
510
+ | 0 | 0 | - | - | 0.9118 |
511
+ | 0.4288 | 3250 | - | 0.6216 | - |
512
+ | 0.4354 | 3300 | 1.0539 | - | - |
513
+ | 0.4420 | 3350 | 0.9389 | - | - |
514
+ | 0.4486 | 3400 | 0.9171 | - | - |
515
+ | 0.4552 | 3450 | 0.9706 | - | - |
516
+ | 0.4618 | 3500 | 1.0124 | - | - |
517
+ | 0 | 0 | - | - | 0.9065 |
518
+ | 0.4618 | 3500 | - | 0.6126 | - |
519
+ | 0.4684 | 3550 | 0.9215 | - | - |
520
+ | 0.4750 | 3600 | 0.8563 | - | - |
521
+ | 0.4816 | 3650 | 0.8249 | - | - |
522
+ | 0.4882 | 3700 | 0.8794 | - | - |
523
+ | 0.4948 | 3750 | 1.0013 | - | - |
524
+ | 0 | 0 | - | - | 0.9078 |
525
+ | 0.4948 | 3750 | - | 0.6022 | - |
526
+ | 0.5014 | 3800 | 0.922 | - | - |
527
+ | 0.5080 | 3850 | 0.9168 | - | - |
528
+ | 0.5146 | 3900 | 0.9315 | - | - |
529
+ | 0.5212 | 3950 | 0.9265 | - | - |
530
+ | 0.5278 | 4000 | 0.9453 | - | - |
531
+ | 0 | 0 | - | - | 0.9078 |
532
+ | 0.5278 | 4000 | - | 0.6083 | - |
533
+ | 0.5344 | 4050 | 0.9585 | - | - |
534
+ | 0.5410 | 4100 | 0.9886 | - | - |
535
+ | 0.5476 | 4150 | 0.9081 | - | - |
536
+ | 0.5542 | 4200 | 0.8181 | - | - |
537
+ | 0.5608 | 4250 | 0.8806 | - | - |
538
+ | 0 | 0 | - | - | 0.9118 |
539
+ | 0.5608 | 4250 | - | 0.5918 | - |
540
+ | 0.5674 | 4300 | 0.858 | - | - |
541
+ | 0.5740 | 4350 | 0.8781 | - | - |
542
+ | 0.5806 | 4400 | 0.9059 | - | - |
543
+ | 0.5871 | 4450 | 0.8475 | - | - |
544
+ | 0.5937 | 4500 | 0.9649 | - | - |
545
+ | 0 | 0 | - | - | 0.9057 |
546
+ | 0.5937 | 4500 | - | 0.5951 | - |
547
+ | 0.6003 | 4550 | 0.969 | - | - |
548
+ | 0.6069 | 4600 | 0.8685 | - | - |
549
+ | 0.6135 | 4650 | 0.9555 | - | - |
550
+ | 0.6201 | 4700 | 0.9166 | - | - |
551
+ | 0.6267 | 4750 | 0.877 | - | - |
552
+ | 0 | 0 | - | - | 0.9073 |
553
+ | 0.6267 | 4750 | - | 0.5858 | - |
554
+ | 0.6333 | 4800 | 0.938 | - | - |
555
+ | 0.6399 | 4850 | 0.9211 | - | - |
556
+ | 0.6465 | 4900 | 0.9699 | - | - |
557
+ | 0.6531 | 4950 | 0.8818 | - | - |
558
+ | 0.6597 | 5000 | 0.9814 | - | - |
559
+ | 0 | 0 | - | - | 0.9176 |
560
+ | 0.6597 | 5000 | - | 0.5705 | - |
561
+ | 0.6663 | 5050 | 0.8588 | - | - |
562
+ | 0.6729 | 5100 | 0.8922 | - | - |
563
+ | 0.6795 | 5150 | 1.0096 | - | - |
564
+ | 0.6861 | 5200 | 0.9217 | - | - |
565
+ | 0.6927 | 5250 | 0.9446 | - | - |
566
+ | 0 | 0 | - | - | 0.9147 |
567
+ | 0.6927 | 5250 | - | 0.5740 | - |
568
+ | 0.6993 | 5300 | 0.9301 | - | - |
569
+ | 0.7059 | 5350 | 0.8436 | - | - |
570
+ | 0.7125 | 5400 | 0.8547 | - | - |
571
+ | 0.7191 | 5450 | 0.9552 | - | - |
572
+ | 0.7257 | 5500 | 0.9227 | - | - |
573
+ | 0 | 0 | - | - | 0.9135 |
574
+ | 0.7257 | 5500 | - | 0.5913 | - |
575
+ | 0.7323 | 5550 | 0.8813 | - | - |
576
+ | 0.7389 | 5600 | 0.8519 | - | - |
577
+ | 0.7455 | 5650 | 0.8223 | - | - |
578
+ | 0.7521 | 5700 | 0.8603 | - | - |
579
+ | 0.7587 | 5750 | 0.8208 | - | - |
580
+ | 0 | 0 | - | - | 0.9151 |
581
+ | 0.7587 | 5750 | - | 0.5698 | - |
582
+ | 0.7653 | 5800 | 0.8542 | - | - |
583
+ | 0.7719 | 5850 | 0.7924 | - | - |
584
+ | 0.7785 | 5900 | 0.9238 | - | - |
585
+ | 0.7851 | 5950 | 0.8303 | - | - |
586
+ | 0.7917 | 6000 | 0.8254 | - | - |
587
+ | 0 | 0 | - | - | 0.9159 |
588
+ | 0.7917 | 6000 | - | 0.5643 | - |
589
+ | 0.7983 | 6050 | 0.8556 | - | - |
590
+ | 0.8049 | 6100 | 0.9286 | - | - |
591
+ | 0.8115 | 6150 | 0.8776 | - | - |
592
+ | 0.8180 | 6200 | 0.8146 | - | - |
593
+ | 0.8246 | 6250 | 0.8469 | - | - |
594
+ | 0 | 0 | - | - | 0.9127 |
595
+ | 0.8246 | 6250 | - | 0.5627 | - |
596
+ | 0.8312 | 6300 | 0.9719 | - | - |
597
+ | 0.8378 | 6350 | 0.9297 | - | - |
598
+ | 0.8444 | 6400 | 0.896 | - | - |
599
+ | 0.8510 | 6450 | 0.8709 | - | - |
600
+ | 0.8576 | 6500 | 0.9436 | - | - |
601
+ | 0 | 0 | - | - | 0.9159 |
602
+ | 0.8576 | 6500 | - | 0.5638 | - |
603
+ | 0.8642 | 6550 | 0.8938 | - | - |
604
+ | 0.8708 | 6600 | 0.8065 | - | - |
605
+ | 0.8774 | 6650 | 0.8281 | - | - |
606
+ | 0.8840 | 6700 | 0.8449 | - | - |
607
+ | 0.8906 | 6750 | 0.813 | - | - |
608
+ | 0 | 0 | - | - | 0.9167 |
609
+ | 0.8906 | 6750 | - | 0.5694 | - |
610
+ | 0.8972 | 6800 | 0.9052 | - | - |
611
+ | 0.9038 | 6850 | 0.9501 | - | - |
612
+ | 0.9104 | 6900 | 0.9612 | - | - |
613
+ | 0.9170 | 6950 | 0.8649 | - | - |
614
+ | 0.9236 | 7000 | 0.7366 | - | - |
615
+ | 0 | 0 | - | - | 0.9188 |
616
+ | 0.9236 | 7000 | - | 0.5691 | - |
617
+ | 0.9302 | 7050 | 0.9621 | - | - |
618
+ | 0.9368 | 7100 | 0.9154 | - | - |
619
+ | 0.9434 | 7150 | 0.8617 | - | - |
620
+ | 0.9500 | 7200 | 0.8629 | - | - |
621
+ | 0.9566 | 7250 | 0.899 | - | - |
622
+ | 0 | 0 | - | - | 0.9159 |
623
+ | 0.9566 | 7250 | - | 0.5559 | - |
624
+ | 0.9632 | 7300 | 0.7766 | - | - |
625
+ | 0.9698 | 7350 | 0.8968 | - | - |
626
+ | 0.9764 | 7400 | 0.8462 | - | - |
627
+ | 0.9830 | 7450 | 0.8478 | - | - |
628
+ | 0.9896 | 7500 | 0.8184 | - | - |
629
+ | 0 | 0 | - | - | 0.9163 |
630
+ | 0.9896 | 7500 | - | 0.5564 | - |
631
+ | 0.9962 | 7550 | 0.8445 | - | - |
632
+ | 1.0028 | 7600 | 0.7305 | - | - |
633
+ | 1.0094 | 7650 | 0.695 | - | - |
634
+ | 1.0160 | 7700 | 0.779 | - | - |
635
+ | 1.0226 | 7750 | 0.5876 | - | - |
636
+ | 0 | 0 | - | - | 0.9184 |
637
+ | 1.0226 | 7750 | - | 0.5776 | - |
638
+ | 1.0292 | 7800 | 0.6372 | - | - |
639
+ | 1.0358 | 7850 | 0.7066 | - | - |
640
+ | 1.0424 | 7900 | 0.6561 | - | - |
641
+ | 1.0490 | 7950 | 0.6854 | - | - |
642
+ | 1.0555 | 8000 | 0.7083 | - | - |
643
+ | 0 | 0 | - | - | 0.9212 |
644
+ | 1.0555 | 8000 | - | 0.5645 | - |
645
+ | 1.0621 | 8050 | 0.6618 | - | - |
646
+ | 1.0687 | 8100 | 0.6602 | - | - |
647
+ | 1.0753 | 8150 | 0.7141 | - | - |
648
+ | 1.0819 | 8200 | 0.7599 | - | - |
649
+ | 1.0885 | 8250 | 0.6307 | - | - |
650
+ | 0 | 0 | - | - | 0.9159 |
651
+ | 1.0885 | 8250 | - | 0.5608 | - |
652
+ | 1.0951 | 8300 | 0.6611 | - | - |
653
+ | 1.1017 | 8350 | 0.6308 | - | - |
654
+ | 1.1083 | 8400 | 0.7035 | - | - |
655
+ | 1.1149 | 8450 | 0.683 | - | - |
656
+ | 1.1215 | 8500 | 0.7077 | - | - |
657
+ | 0 | 0 | - | - | 0.9184 |
658
+ | 1.1215 | 8500 | - | 0.5556 | - |
659
+ | 1.1281 | 8550 | 0.7153 | - | - |
660
+ | 1.1347 | 8600 | 0.6186 | - | - |
661
+ | 1.1413 | 8650 | 0.6289 | - | - |
662
+ | 1.1479 | 8700 | 0.5718 | - | - |
663
+ | 1.1545 | 8750 | 0.5749 | - | - |
664
+ | 0 | 0 | - | - | 0.9167 |
665
+ | 1.1545 | 8750 | - | 0.5695 | - |
666
+ | 1.1611 | 8800 | 0.6788 | - | - |
667
+ | 1.1677 | 8850 | 0.7731 | - | - |
668
+ | 1.1743 | 8900 | 0.6954 | - | - |
669
+ | 1.1809 | 8950 | 0.7404 | - | - |
670
+ | 1.1875 | 9000 | 0.6871 | - | - |
671
+ | 0 | 0 | - | - | 0.9208 |
672
+ | 1.1875 | 9000 | - | 0.5666 | - |
673
+ | 1.1941 | 9050 | 0.6415 | - | - |
674
+ | 1.2007 | 9100 | 0.6517 | - | - |
675
+ | 1.2073 | 9150 | 0.7354 | - | - |
676
+ | 1.2139 | 9200 | 0.7325 | - | - |
677
+ | 1.2205 | 9250 | 0.6272 | - | - |
678
+ | 0 | 0 | - | - | 0.9147 |
679
+ | 1.2205 | 9250 | - | 0.5714 | - |
680
+ | 1.2271 | 9300 | 0.7292 | - | - |
681
+ | 1.2337 | 9350 | 0.6325 | - | - |
682
+ | 1.2403 | 9400 | 0.6344 | - | - |
683
+ | 1.2469 | 9450 | 0.7218 | - | - |
684
+ | 1.2535 | 9500 | 0.6815 | - | - |
685
+ | 0 | 0 | - | - | 0.9176 |
686
+ | 1.2535 | 9500 | - | 0.5651 | - |
687
+ | 1.2601 | 9550 | 0.7186 | - | - |
688
+ | 1.2667 | 9600 | 0.6145 | - | - |
689
+ | 1.2733 | 9650 | 0.7095 | - | - |
690
+ | 1.2799 | 9700 | 0.674 | - | - |
691
+ | 1.2864 | 9750 | 0.7405 | - | - |
692
+ | 0 | 0 | - | - | 0.9200 |
693
+ | 1.2864 | 9750 | - | 0.5666 | - |
694
+ | 1.2930 | 9800 | 0.7186 | - | - |
695
+ | 1.2996 | 9850 | 0.6352 | - | - |
696
+ | 1.3062 | 9900 | 0.7077 | - | - |
697
+ | 1.3128 | 9950 | 0.6873 | - | - |
698
+ | 1.3194 | 10000 | 0.5939 | - | - |
699
+ | 0 | 0 | - | - | 0.9204 |
700
+ | 1.3194 | 10000 | - | 0.5752 | - |
701
+ | 1.3260 | 10050 | 0.7171 | - | - |
702
+ | 1.3326 | 10100 | 0.6592 | - | - |
703
+ | 1.3392 | 10150 | 0.6631 | - | - |
704
+ | 1.3458 | 10200 | 0.7658 | - | - |
705
+ | 1.3524 | 10250 | 0.6213 | - | - |
706
+ | 0 | 0 | - | - | 0.9180 |
707
+ | 1.3524 | 10250 | - | 0.5678 | - |
708
+ | 1.3590 | 10300 | 0.6486 | - | - |
709
+ | 1.3656 | 10350 | 0.662 | - | - |
710
+ | 1.3722 | 10400 | 0.6924 | - | - |
711
+ | 1.3788 | 10450 | 0.7106 | - | - |
712
+ | 1.3854 | 10500 | 0.7239 | - | - |
713
+ | 0 | 0 | - | - | 0.9184 |
714
+ | 1.3854 | 10500 | - | 0.5687 | - |
715
+ | 1.3920 | 10550 | 0.735 | - | - |
716
+ | 1.3986 | 10600 | 0.6784 | - | - |
717
+ | 1.4052 | 10650 | 0.6886 | - | - |
718
+ | 1.4118 | 10700 | 0.649 | - | - |
719
+ | 1.4184 | 10750 | 0.6133 | - | - |
720
+ | 0 | 0 | - | - | 0.9200 |
721
+ | 1.4184 | 10750 | - | 0.5683 | - |
722
+ | 1.4250 | 10800 | 0.6635 | - | - |
723
+ | 1.4316 | 10850 | 0.6803 | - | - |
724
+ | 1.4382 | 10900 | 0.6497 | - | - |
725
+ | 1.4448 | 10950 | 0.6812 | - | - |
726
+ | 1.4514 | 11000 | 0.7493 | - | - |
727
+ | 0 | 0 | - | - | 0.9220 |
728
+ | 1.4514 | 11000 | - | 0.5587 | - |
729
+ | 1.4580 | 11050 | 0.6694 | - | - |
730
+ | 1.4646 | 11100 | 0.6782 | - | - |
731
+ | 1.4712 | 11150 | 0.6839 | - | - |
732
+ | 1.4778 | 11200 | 0.671 | - | - |
733
+ | 1.4844 | 11250 | 0.7648 | - | - |
734
+ | 0 | 0 | - | - | 0.9208 |
735
+ | 1.4844 | 11250 | - | 0.5466 | - |
736
+ | 1.4910 | 11300 | 0.7448 | - | - |
737
+ | 1.4976 | 11350 | 0.6811 | - | - |
738
+ | 1.5042 | 11400 | 0.6984 | - | - |
739
+ | 1.5108 | 11450 | 0.6676 | - | - |
740
+ | 1.5174 | 11500 | 0.7054 | - | - |
741
+ | 0 | 0 | - | - | 0.9204 |
742
+ | 1.5174 | 11500 | - | 0.5569 | - |
743
+ | 1.5239 | 11550 | 0.6109 | - | - |
744
+ | 1.5305 | 11600 | 0.7581 | - | - |
745
+ | 1.5371 | 11650 | 0.7035 | - | - |
746
+ | 1.5437 | 11700 | 0.6943 | - | - |
747
+ | 1.5503 | 11750 | 0.6225 | - | - |
748
+ | 0 | 0 | - | - | 0.9224 |
749
+ | 1.5503 | 11750 | - | 0.5571 | - |
750
+ | 1.5569 | 11800 | 0.661 | - | - |
751
+ | 1.5635 | 11850 | 0.635 | - | - |
752
+ | 1.5701 | 11900 | 0.613 | - | - |
753
+ | 1.5767 | 11950 | 0.6502 | - | - |
754
+ | 1.5833 | 12000 | 0.6935 | - | - |
755
+ | 0 | 0 | - | - | 0.9200 |
756
+ | 1.5833 | 12000 | - | 0.5579 | - |
757
+ | 1.5899 | 12050 | 0.6147 | - | - |
758
+ | 1.5965 | 12100 | 0.6575 | - | - |
759
+ | 1.6031 | 12150 | 0.6837 | - | - |
760
+ | 1.6097 | 12200 | 0.7437 | - | - |
761
+ | 1.6163 | 12250 | 0.6808 | - | - |
762
+ | 0 | 0 | - | - | 0.9204 |
763
+ | 1.6163 | 12250 | - | 0.5507 | - |
764
+ | 1.6229 | 12300 | 0.6698 | - | - |
765
+ | 1.6295 | 12350 | 0.6803 | - | - |
766
+ | 1.6361 | 12400 | 0.676 | - | - |
767
+ | 1.6427 | 12450 | 0.6418 | - | - |
768
+ | 1.6493 | 12500 | 0.6042 | - | - |
769
+ | 0 | 0 | - | - | 0.9188 |
770
+ | 1.6493 | 12500 | - | 0.5563 | - |
771
+ | 1.6559 | 12550 | 0.7139 | - | - |
772
+ | 1.6625 | 12600 | 0.6995 | - | - |
773
+ | 1.6691 | 12650 | 0.6097 | - | - |
774
+ | 1.6757 | 12700 | 0.6407 | - | - |
775
+ | 1.6823 | 12750 | 0.5994 | - | - |
776
+ | 0 | 0 | - | - | 0.9249 |
777
+ | 1.6823 | 12750 | - | 0.5621 | - |
778
+ | 1.6889 | 12800 | 0.6642 | - | - |
779
+ | 1.6955 | 12850 | 0.6198 | - | - |
780
+ | 1.7021 | 12900 | 0.6648 | - | - |
781
+ | 1.7087 | 12950 | 0.5644 | - | - |
782
+ | 1.7153 | 13000 | 0.6531 | - | - |
783
+ | 0 | 0 | - | - | 0.9241 |
784
+ | 1.7153 | 13000 | - | 0.5617 | - |
785
+ | 1.7219 | 13050 | 0.6159 | - | - |
786
+ | 1.7285 | 13100 | 0.7855 | - | - |
787
+ | 1.7351 | 13150 | 0.6307 | - | - |
788
+ | 1.7417 | 13200 | 0.61 | - | - |
789
+ | 1.7483 | 13250 | 0.6672 | - | - |
790
+ | 0 | 0 | - | - | 0.9237 |
791
+ | 1.7483 | 13250 | - | 0.5589 | - |
792
+ | 1.7548 | 13300 | 0.6002 | - | - |
793
+ | 1.7614 | 13350 | 0.6638 | - | - |
794
+ | 1.7680 | 13400 | 0.6112 | - | - |
795
+ | 1.7746 | 13450 | 0.6236 | - | - |
796
+ | 1.7812 | 13500 | 0.6245 | - | - |
797
+ | 0 | 0 | - | - | 0.9220 |
798
+ | 1.7812 | 13500 | - | 0.5580 | - |
799
+ | 1.7878 | 13550 | 0.7146 | - | - |
800
+ | 1.7944 | 13600 | 0.5969 | - | - |
801
+ | 1.8010 | 13650 | 0.7246 | - | - |
802
+ | 1.8076 | 13700 | 0.65 | - | - |
803
+ | 1.8142 | 13750 | 0.7136 | - | - |
804
+ | 0 | 0 | - | - | 0.9204 |
805
+ | 1.8142 | 13750 | - | 0.5533 | - |
806
+ | 1.8208 | 13800 | 0.7062 | - | - |
807
+ | 1.8274 | 13850 | 0.6987 | - | - |
808
+ | 1.8340 | 13900 | 0.6642 | - | - |
809
+ | 1.8406 | 13950 | 0.6761 | - | - |
810
+ | 1.8472 | 14000 | 0.6766 | - | - |
811
+ | 0 | 0 | - | - | 0.9212 |
812
+ | 1.8472 | 14000 | - | 0.5655 | - |
813
+ | 1.8538 | 14050 | 0.5758 | - | - |
814
+ | 1.8604 | 14100 | 0.6594 | - | - |
815
+ | 1.8670 | 14150 | 0.7866 | - | - |
816
+ | 1.8736 | 14200 | 0.5798 | - | - |
817
+ | 1.8802 | 14250 | 0.6472 | - | - |
818
+ | 0 | 0 | - | - | 0.9212 |
819
+ | 1.8802 | 14250 | - | 0.5509 | - |
820
+ | 1.8868 | 14300 | 0.7387 | - | - |
821
+ | 1.8934 | 14350 | 0.6677 | - | - |
822
+ | 1.9000 | 14400 | 0.6697 | - | - |
823
+ | 1.9066 | 14450 | 0.6711 | - | - |
824
+ | 1.9132 | 14500 | 0.6988 | - | - |
825
+ | 0 | 0 | - | - | 0.9229 |
826
+ | 1.9132 | 14500 | - | 0.5528 | - |
827
+ | 1.9198 | 14550 | 0.6301 | - | - |
828
+ | 1.9264 | 14600 | 0.6259 | - | - |
829
+ | 1.9330 | 14650 | 0.6223 | - | - |
830
+ | 1.9396 | 14700 | 0.5702 | - | - |
831
+ | 1.9462 | 14750 | 0.6324 | - | - |
832
+ | 0 | 0 | - | - | 0.9253 |
833
+ | 1.9462 | 14750 | - | 0.5508 | - |
834
+ | 1.9528 | 14800 | 0.6409 | - | - |
835
+ | 1.9594 | 14850 | 0.6609 | - | - |
836
+ | 1.9660 | 14900 | 0.6581 | - | - |
837
+ | 1.9726 | 14950 | 0.6313 | - | - |
838
+ | 1.9792 | 15000 | 0.6191 | - | - |
839
+ | 0 | 0 | - | - | 0.9216 |
840
+ | 1.9792 | 15000 | - | 0.5452 | - |
841
+ | 1.9858 | 15050 | 0.6665 | - | - |
842
+ | 1.9923 | 15100 | 0.5907 | - | - |
843
+ | 1.9989 | 15150 | 0.6586 | - | - |
844
+ | 2.0055 | 15200 | 0.5673 | - | - |
845
+ | 2.0121 | 15250 | 0.5516 | - | - |
846
+ | 0 | 0 | - | - | 0.9233 |
847
+ | 2.0121 | 15250 | - | 0.5589 | - |
848
+ | 2.0187 | 15300 | 0.5012 | - | - |
849
+ | 2.0253 | 15350 | 0.5227 | - | - |
850
+ | 2.0319 | 15400 | 0.4449 | - | - |
851
+ | 2.0385 | 15450 | 0.4862 | - | - |
852
+ | 2.0451 | 15500 | 0.5413 | - | - |
853
+ | 0 | 0 | - | - | 0.9233 |
854
+ | 2.0451 | 15500 | - | 0.5642 | - |
855
+ | 2.0517 | 15550 | 0.5462 | - | - |
856
+ | 2.0583 | 15600 | 0.5318 | - | - |
857
+ | 2.0649 | 15650 | 0.5706 | - | - |
858
+ | 2.0715 | 15700 | 0.5055 | - | - |
859
+ | 2.0781 | 15750 | 0.6141 | - | - |
860
+ | 0 | 0 | - | - | 0.9233 |
861
+ | 2.0781 | 15750 | - | 0.5611 | - |
862
+ | 2.0847 | 15800 | 0.5247 | - | - |
863
+ | 2.0913 | 15850 | 0.4817 | - | - |
864
+ | 2.0979 | 15900 | 0.4599 | - | - |
865
+ | 2.1045 | 15950 | 0.5676 | - | - |
866
+ | 2.1111 | 16000 | 0.3992 | - | - |
867
+ | 0 | 0 | - | - | 0.9237 |
868
+ | 2.1111 | 16000 | - | 0.5720 | - |
869
+ | 2.1177 | 16050 | 0.5337 | - | - |
870
+ | 2.1243 | 16100 | 0.4641 | - | - |
871
+ | 2.1309 | 16150 | 0.5636 | - | - |
872
+ | 2.1375 | 16200 | 0.4811 | - | - |
873
+ | 2.1441 | 16250 | 0.499 | - | - |
874
+ | 0 | 0 | - | - | 0.9216 |
875
+ | 2.1441 | 16250 | - | 0.5673 | - |
876
+ | 2.1507 | 16300 | 0.5822 | - | - |
877
+ | 2.1573 | 16350 | 0.5935 | - | - |
878
+ | 2.1639 | 16400 | 0.5028 | - | - |
879
+ | 2.1705 | 16450 | 0.5118 | - | - |
880
+ | 2.1771 | 16500 | 0.5623 | - | - |
881
+ | 0 | 0 | - | - | 0.9261 |
882
+ | 2.1771 | 16500 | - | 0.5656 | - |
883
+ | 2.1837 | 16550 | 0.481 | - | - |
884
+ | 2.1903 | 16600 | 0.5461 | - | - |
885
+ | 2.1969 | 16650 | 0.5802 | - | - |
886
+ | 2.2035 | 16700 | 0.5269 | - | - |
887
+ | 2.2101 | 16750 | 0.5022 | - | - |
888
+ | 0 | 0 | - | - | 0.9220 |
889
+ | 2.2101 | 16750 | - | 0.5671 | - |
890
+ | 2.2167 | 16800 | 0.5203 | - | - |
891
+ | 2.2232 | 16850 | 0.5461 | - | - |
892
+ | 2.2298 | 16900 | 0.5711 | - | - |
893
+ | 2.2364 | 16950 | 0.5615 | - | - |
894
+ | 2.2430 | 17000 | 0.5748 | - | - |
895
+ | 0 | 0 | - | - | 0.9257 |
896
+ | 2.2430 | 17000 | - | 0.5605 | - |
897
+ | 2.2496 | 17050 | 0.5272 | - | - |
898
+ | 2.2562 | 17100 | 0.4401 | - | - |
899
+ | 2.2628 | 17150 | 0.5158 | - | - |
900
+ | 2.2694 | 17200 | 0.5163 | - | - |
901
+ | 2.2760 | 17250 | 0.5195 | - | - |
902
+ | 0 | 0 | - | - | 0.9237 |
903
+ | 2.2760 | 17250 | - | 0.5647 | - |
904
+ | 2.2826 | 17300 | 0.5235 | - | - |
905
+ | 2.2892 | 17350 | 0.5335 | - | - |
906
+ | 2.2958 | 17400 | 0.4915 | - | - |
907
+ | 2.3024 | 17450 | 0.4915 | - | - |
908
+ | 2.3090 | 17500 | 0.4959 | - | - |
909
+ | 0 | 0 | - | - | 0.9233 |
910
+ | 2.3090 | 17500 | - | 0.5675 | - |
911
+ | 2.3156 | 17550 | 0.5161 | - | - |
912
+ | 2.3222 | 17600 | 0.4944 | - | - |
913
+ | 2.3288 | 17650 | 0.5052 | - | - |
914
+ | 2.3354 | 17700 | 0.4937 | - | - |
915
+ | 2.3420 | 17750 | 0.4695 | - | - |
916
+ | 0 | 0 | - | - | 0.9253 |
917
+ | 2.3420 | 17750 | - | 0.5615 | - |
918
+ | 2.3486 | 17800 | 0.5159 | - | - |
919
+ | 2.3552 | 17850 | 0.4992 | - | - |
920
+ | 2.3618 | 17900 | 0.5288 | - | - |
921
+ | 2.3684 | 17950 | 0.5247 | - | - |
922
+ | 2.3750 | 18000 | 0.5491 | - | - |
923
+ | 0 | 0 | - | - | 0.9257 |
924
+ | 2.3750 | 18000 | - | 0.5594 | - |
925
+ | 2.3816 | 18050 | 0.5332 | - | - |
926
+ | 2.3882 | 18100 | 0.529 | - | - |
927
+ | 2.3948 | 18150 | 0.5534 | - | - |
928
+ | 2.4014 | 18200 | 0.5595 | - | - |
929
+ | 2.4080 | 18250 | 0.573 | - | - |
930
+ | 0 | 0 | - | - | 0.9261 |
931
+ | 2.4080 | 18250 | - | 0.5610 | - |
932
+ | 2.4146 | 18300 | 0.4859 | - | - |
933
+ | 2.4212 | 18350 | 0.5019 | - | - |
934
+ | 2.4278 | 18400 | 0.4771 | - | - |
935
+ | 2.4344 | 18450 | 0.5062 | - | - |
936
+ | 2.4410 | 18500 | 0.5342 | - | - |
937
+ | 0 | 0 | - | - | 0.9229 |
938
+ | 2.4410 | 18500 | - | 0.5617 | - |
939
+ | 2.4476 | 18550 | 0.5275 | - | - |
940
+ | 2.4541 | 18600 | 0.576 | - | - |
941
+ | 2.4607 | 18650 | 0.5172 | - | - |
942
+ | 2.4673 | 18700 | 0.5127 | - | - |
943
+ | 2.4739 | 18750 | 0.4728 | - | - |
944
+ | 0 | 0 | - | - | 0.9249 |
945
+ | 2.4739 | 18750 | - | 0.5651 | - |
946
+ | 2.4805 | 18800 | 0.4256 | - | - |
947
+ | 2.4871 | 18850 | 0.4493 | - | - |
948
+ | 2.4937 | 18900 | 0.4881 | - | - |
949
+ | 2.5003 | 18950 | 0.4843 | - | - |
950
+ | 2.5069 | 19000 | 0.517 | - | - |
951
+ | 0 | 0 | - | - | 0.9249 |
952
+ | 2.5069 | 19000 | - | 0.5626 | - |
953
+ | 2.5135 | 19050 | 0.5927 | - | - |
954
+ | 2.5201 | 19100 | 0.5687 | - | - |
955
+ | 2.5267 | 19150 | 0.5261 | - | - |
956
+ | 2.5333 | 19200 | 0.5698 | - | - |
957
+ | 2.5399 | 19250 | 0.5593 | - | - |
958
+ | 0 | 0 | - | - | 0.9269 |
959
+ | 2.5399 | 19250 | - | 0.5581 | - |
960
+ | 2.5465 | 19300 | 0.571 | - | - |
961
+ | 2.5531 | 19350 | 0.5606 | - | - |
962
+ | 2.5597 | 19400 | 0.4912 | - | - |
963
+ | 2.5663 | 19450 | 0.4805 | - | - |
964
+ | 2.5729 | 19500 | 0.5324 | - | - |
965
+ | 0 | 0 | - | - | 0.9282 |
966
+ | 2.5729 | 19500 | - | 0.5537 | - |
967
+ | 2.5795 | 19550 | 0.5584 | - | - |
968
+ | 2.5861 | 19600 | 0.508 | - | - |
969
+ | 2.5927 | 19650 | 0.5231 | - | - |
970
+ | 2.5993 | 19700 | 0.557 | - | - |
971
+ | 2.6059 | 19750 | 0.5338 | - | - |
972
+ | 0 | 0 | - | - | 0.9257 |
973
+ | 2.6059 | 19750 | - | 0.5518 | - |
974
+ | 2.6125 | 19800 | 0.5037 | - | - |
975
+ | 2.6191 | 19850 | 0.6057 | - | - |
976
+ | 2.6257 | 19900 | 0.5571 | - | - |
977
+ | 2.6323 | 19950 | 0.5177 | - | - |
978
+ | 2.6389 | 20000 | 0.4946 | - | - |
979
+ | 0 | 0 | - | - | 0.9253 |
980
+ | 2.6389 | 20000 | - | 0.5548 | - |
981
+ | 2.6455 | 20050 | 0.5256 | - | - |
982
+ | 2.6521 | 20100 | 0.5107 | - | - |
983
+ | 2.6587 | 20150 | 0.5988 | - | - |
984
+ | 2.6653 | 20200 | 0.4907 | - | - |
985
+ | 2.6719 | 20250 | 0.4697 | - | - |
986
+ | 0 | 0 | - | - | 0.9269 |
987
+ | 2.6719 | 20250 | - | 0.5566 | - |
988
+ | 2.6785 | 20300 | 0.4897 | - | - |
989
+ | 2.6851 | 20350 | 0.5088 | - | - |
990
+ | 2.6916 | 20400 | 0.5442 | - | - |
991
+ | 2.6982 | 20450 | 0.536 | - | - |
992
+ | 2.7048 | 20500 | 0.551 | - | - |
993
+ | 0 | 0 | - | - | 0.9269 |
994
+ | 2.7048 | 20500 | - | 0.5562 | - |
995
+ | 2.7114 | 20550 | 0.5038 | - | - |
996
+ | 2.7180 | 20600 | 0.502 | - | - |
997
+ | 2.7246 | 20650 | 0.5021 | - | - |
998
+ | 2.7312 | 20700 | 0.5441 | - | - |
999
+ | 2.7378 | 20750 | 0.4818 | - | - |
1000
+ | 0 | 0 | - | - | 0.9286 |
1001
+ | 2.7378 | 20750 | - | 0.5548 | - |
1002
+ | 2.7444 | 20800 | 0.5012 | - | - |
1003
+ | 2.7510 | 20850 | 0.5294 | - | - |
1004
+ | 2.7576 | 20900 | 0.4674 | - | - |
1005
+ | 2.7642 | 20950 | 0.5436 | - | - |
1006
+ | 2.7708 | 21000 | 0.4609 | - | - |
1007
+ | 0 | 0 | - | - | 0.9269 |
1008
+ | 2.7708 | 21000 | - | 0.5538 | - |
1009
+ | 2.7774 | 21050 | 0.5015 | - | - |
1010
+ | 2.7840 | 21100 | 0.5299 | - | - |
1011
+ | 2.7906 | 21150 | 0.4363 | - | - |
1012
+ | 2.7972 | 21200 | 0.5018 | - | - |
1013
+ | 2.8038 | 21250 | 0.5079 | - | - |
1014
+ | 0 | 0 | - | - | 0.9265 |
1015
+ | 2.8038 | 21250 | - | 0.5549 | - |
1016
+ | 2.8104 | 21300 | 0.4467 | - | - |
1017
+ | 2.8170 | 21350 | 0.5769 | - | - |
1018
+ | 2.8236 | 21400 | 0.5323 | - | - |
1019
+ | 2.8302 | 21450 | 0.4714 | - | - |
1020
+ | 2.8368 | 21500 | 0.4491 | - | - |
1021
+ | 0 | 0 | - | - | 0.9257 |
1022
+ | 2.8368 | 21500 | - | 0.5538 | - |
1023
+ | 2.8434 | 21550 | 0.4801 | - | - |
1024
+ | 2.8500 | 21600 | 0.5132 | - | - |
1025
+ | 2.8566 | 21650 | 0.4542 | - | - |
1026
+ | 2.8632 | 21700 | 0.5015 | - | - |
1027
+ | 2.8698 | 21750 | 0.4818 | - | - |
1028
+ | 0 | 0 | - | - | 0.9278 |
1029
+ | 2.8698 | 21750 | - | 0.5554 | - |
1030
+ | 2.8764 | 21800 | 0.5078 | - | - |
1031
+ | 2.8830 | 21850 | 0.508 | - | - |
1032
+ | 2.8896 | 21900 | 0.5331 | - | - |
1033
+ | 2.8962 | 21950 | 0.5185 | - | - |
1034
+ | 2.9028 | 22000 | 0.4469 | - | - |
1035
+ | 0 | 0 | - | - | 0.9265 |
1036
+ | 2.9028 | 22000 | - | 0.5551 | - |
1037
+ | 2.9094 | 22050 | 0.4762 | - | - |
1038
+ | 2.9160 | 22100 | 0.5799 | - | - |
1039
+ | 2.9225 | 22150 | 0.4978 | - | - |
1040
+ | 2.9291 | 22200 | 0.566 | - | - |
1041
+ | 2.9357 | 22250 | 0.5837 | - | - |
1042
+ | 0 | 0 | - | - | 0.9269 |
1043
+ | 2.9357 | 22250 | - | 0.5532 | - |
1044
+ | 2.9423 | 22300 | 0.5401 | - | - |
1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
+ | 2.9885 | 22650 | 0.4917 | - | - |
1054
+ | 2.9951 | 22700 | 0.5128 | - | - |
1055
+
1056
+ </details>
1057
+
1058
+ ### Framework Versions
1059
+ - Python: 3.12.4
1060
+ - Sentence Transformers: 4.0.2
1061
+ - PyLate: 1.2.0
1062
+ - Transformers: 4.48.2
1063
+ - PyTorch: 2.6.0+cu124
1064
+ - Accelerate: 1.7.0
1065
+ - Datasets: 3.6.0
1066
+ - Tokenizers: 0.21.1
1067
+
1068
+
1069
+ ## Citation
1070
+
1071
+ ### BibTeX
1072
+
1073
+ #### Sentence Transformers
1074
+ ```bibtex
1075
+ @inproceedings{reimers-2019-sentence-bert,
1076
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1077
+ author = "Reimers, Nils and Gurevych, Iryna",
1078
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1079
+ month = "11",
1080
+ year = "2019",
1081
+ publisher = "Association for Computational Linguistics",
1082
+ url = "https://arxiv.org/abs/1908.10084"
1083
+ }
1084
+ ```
1085
+
1086
+ #### PyLate
1087
+ ```bibtex
1088
+ @misc{PyLate,
1089
+ title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
1090
+ author={Chaffin, Antoine and Sourty, Raphaël},
1091
+ url={https://github.com/lightonai/pylate},
1092
+ year={2024}
1093
+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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