Hugging Face's logo

yahyaabd
/
allstats-semantic-base-v1-2

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:123637
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Model card Files Community
yahyaabd commited on
Commit
032378a
·
verified ·
1 Parent(s): 15de366

Add new SentenceTransformer model

Browse files
Files changed (11) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +520 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +62 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,520 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:123637
8
+ - loss:CosineSimilarityLoss
9
+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
10
+ widget:
11
+ - source_sentence: Analisis biaya hidup di tiga kota Banten thn 2018
12
+ sentences:
13
+ - Indikator Konstruksi Triwulan I-2007
14
+ - Survei Biaya Hidup (SBH) 2018 Bengkulu
15
+ - Indikator Ekonomi Februari 2002
16
+ - source_sentence: Grafik ekspor hasil minyak Indonesia ke berbagai negara dari tahun
17
+ 2000 hingga 2023.
18
+ sentences:
19
+ - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65)
20
+ - Harga Produsen Gabah dan Beras Januari 2020
21
+ - Profil Usaha Konstruksi Perorangan Provinsi Papua 2016
22
+ - source_sentence: Tren konstruksi Indonesia tahun 2007 Q4
23
+ sentences:
24
+ - Laporan Bulanan Data Sosial Ekonomi Desember 2018
25
+ - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2023
26
+ - Inflasi Februari 2008 sebesar 0,5 persen
27
+ - source_sentence: Informasi tentang kepemilikan dan penggunaan AC di rumah tangga
28
+ Indonesia tahun 2013?
29
+ sentences:
30
+ - Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2014
31
+ - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
32
+ dan Jenis Pekerjaan, 2022-2023
33
+ - Indikator Konstruksi, Triwulan II-2022
34
+ - source_sentence: Statistik harga Ternate 2012
35
+ sentences:
36
+ - Statistik Perhubungan 2005
37
+ - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019
38
+ - Indikator Ekonomi Agustus 2002
39
+ datasets:
40
+ - yahyaabd/allstats-semantic-synthetic-dataset-v1
41
+ pipeline_tag: sentence-similarity
42
+ library_name: sentence-transformers
43
+ metrics:
44
+ - pearson_cosine
45
+ - spearman_cosine
46
+ model-index:
47
+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
48
+ results:
49
+ - task:
50
+ type: semantic-similarity
51
+ name: Semantic Similarity
52
+ dataset:
53
+ name: allstats semantic base v1 eval
54
+ type: allstats-semantic-base-v1-eval
55
+ metrics:
56
+ - type: pearson_cosine
57
+ value: 0.9868927327091045
58
+ name: Pearson Cosine
59
+ - type: spearman_cosine
60
+ value: 0.9277441071536588
61
+ name: Spearman Cosine
62
+ - task:
63
+ type: semantic-similarity
64
+ name: Semantic Similarity
65
+ dataset:
66
+ name: allstat semantic base v1 test
67
+ type: allstat-semantic-base-v1-test
68
+ metrics:
69
+ - type: pearson_cosine
70
+ value: 0.9867639981224826
71
+ name: Pearson Cosine
72
+ - type: spearman_cosine
73
+ value: 0.9256998894451143
74
+ name: Spearman Cosine
75
+ ---
76
+
77
+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
78
+
79
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
80
+
81
+ ## Model Details
82
+
83
+ ### Model Description
84
+ - **Model Type:** Sentence Transformer
85
+ - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
86
+ - **Maximum Sequence Length:** 128 tokens
87
+ - **Output Dimensionality:** 768 dimensions
88
+ - **Similarity Function:** Cosine Similarity
89
+ - **Training Dataset:**
90
+ - [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1)
91
+ <!-- - **Language:** Unknown -->
92
+ <!-- - **License:** Unknown -->
93
+
94
+ ### Model Sources
95
+
96
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
97
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
98
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
99
+
100
+ ### Full Model Architecture
101
+
102
+ ```
103
+ SentenceTransformer(
104
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
105
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
106
+ )
107
+ ```
108
+
109
+ ## Usage
110
+
111
+ ### Direct Usage (Sentence Transformers)
112
+
113
+ First install the Sentence Transformers library:
114
+
115
+ ```bash
116
+ pip install -U sentence-transformers
117
+ ```
118
+
119
+ Then you can load this model and run inference.
120
+ ```python
121
+ from sentence_transformers import SentenceTransformer
122
+
123
+ # Download from the 🤗 Hub
124
+ model = SentenceTransformer("yahyaabd/allstats-semantic-base-v1-2")
125
+ # Run inference
126
+ sentences = [
127
+ 'Statistik harga Ternate 2012',
128
+ 'Indikator Ekonomi Agustus 2002',
129
+ 'Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019',
130
+ ]
131
+ embeddings = model.encode(sentences)
132
+ print(embeddings.shape)
133
+ # [3, 768]
134
+
135
+ # Get the similarity scores for the embeddings
136
+ similarities = model.similarity(embeddings, embeddings)
137
+ print(similarities.shape)
138
+ # [3, 3]
139
+ ```
140
+
141
+ <!--
142
+ ### Direct Usage (Transformers)
143
+
144
+ <details><summary>Click to see the direct usage in Transformers</summary>
145
+
146
+ </details>
147
+ -->
148
+
149
+ <!--
150
+ ### Downstream Usage (Sentence Transformers)
151
+
152
+ You can finetune this model on your own dataset.
153
+
154
+ <details><summary>Click to expand</summary>
155
+
156
+ </details>
157
+ -->
158
+
159
+ <!--
160
+ ### Out-of-Scope Use
161
+
162
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
163
+ -->
164
+
165
+ ## Evaluation
166
+
167
+ ### Metrics
168
+
169
+ #### Semantic Similarity
170
+
171
+ * Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test`
172
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
173
+
174
+ | Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test |
175
+ |:--------------------|:-------------------------------|:------------------------------|
176
+ | pearson_cosine | 0.9869 | 0.9868 |
177
+ | **spearman_cosine** | **0.9277** | **0.9257** |
178
+
179
+ <!--
180
+ ## Bias, Risks and Limitations
181
+
182
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
183
+ -->
184
+
185
+ <!--
186
+ ### Recommendations
187
+
188
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
189
+ -->
190
+
191
+ ## Training Details
192
+
193
+ ### Training Dataset
194
+
195
+ #### allstats-semantic-synthetic-dataset-v1
196
+
197
+ * Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
198
+ * Size: 123,637 training samples
199
+ * Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
200
+ * Approximate statistics based on the first 1000 samples:
201
+ | | query | doc | label |
202
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
203
+ | type | string | string | float |
204
+ | details | <ul><li>min: 5 tokens</li><li>mean: 10.59 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.29 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
205
+ * Samples:
206
+ | query | doc | label |
207
+ |:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:------------------|
208
+ | <code>Analisis upah tenaga kerja ekonomi kreatif</code> | <code>Upah Tenaga Kerja Ekonomi Kreatif 2011-2016</code> | <code>0.88</code> |
209
+ | <code>cari data persentase rumah tangga yang menggunakan listrik pln menurut provinsi dari 1993 sampai 2022.</code> | <code>Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022</code> | <code>0.93</code> |
210
+ | <code>apakah ada tabel yang menunjukkan ekspor minyak mentah ke negara tujuan utama tahun 2000-2023?</code> | <code>IHK dan Rata-rata Upah per Bulan Buruh Peternakan dan Perikanan di Bawah Mandor (Supervisor), 2012-2014 (2012=100)</code> | <code>0.13</code> |
211
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
212
+ ```json
213
+ {
214
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
215
+ }
216
+ ```
217
+
218
+ ### Evaluation Dataset
219
+
220
+ #### allstats-semantic-synthetic-dataset-v1
221
+
222
+ * Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
223
+ * Size: 26,494 evaluation samples
224
+ * Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
225
+ * Approximate statistics based on the first 1000 samples:
226
+ | | query | doc | label |
227
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
228
+ | type | string | string | float |
229
+ | details | <ul><li>min: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.94 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
230
+ * Samples:
231
+ | query | doc | label |
232
+ |:--------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------|
233
+ | <code>SBH Aceh 2018: Meulaboh, Banda Aceh, Lhokseumawe</code> | <code>Survei Biaya Hidup (SBH) 2018 Meulaboh, Banda Aceh, dan Lhokseumawe</code> | <code>0.9</code> |
234
+ | <code>ekspor produk indonesia juli 2018 per negara</code> | <code>Direktori Perusahaan Pertambangan Besar 2013</code> | <code>0.07</code> |
235
+ | <code>peternakan sapi di jawa tengah 2011</code> | <code>Laporan Bulanan Data Sosial Ekonomi Juli 2024</code> | <code>0.07</code> |
236
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
237
+ ```json
238
+ {
239
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
240
+ }
241
+ ```
242
+
243
+ ### Training Hyperparameters
244
+ #### Non-Default Hyperparameters
245
+
246
+ - `eval_strategy`: steps
247
+ - `per_device_train_batch_size`: 64
248
+ - `per_device_eval_batch_size`: 64
249
+ - `num_train_epochs`: 24
250
+ - `warmup_ratio`: 0.1
251
+ - `fp16`: True
252
+ - `dataloader_num_workers`: 4
253
+ - `load_best_model_at_end`: True
254
+ - `label_smoothing_factor`: 0.1
255
+ - `eval_on_start`: True
256
+
257
+ #### All Hyperparameters
258
+ <details><summary>Click to expand</summary>
259
+
260
+ - `overwrite_output_dir`: False
261
+ - `do_predict`: False
262
+ - `eval_strategy`: steps
263
+ - `prediction_loss_only`: True
264
+ - `per_device_train_batch_size`: 64
265
+ - `per_device_eval_batch_size`: 64
266
+ - `per_gpu_train_batch_size`: None
267
+ - `per_gpu_eval_batch_size`: None
268
+ - `gradient_accumulation_steps`: 1
269
+ - `eval_accumulation_steps`: None
270
+ - `torch_empty_cache_steps`: None
271
+ - `learning_rate`: 5e-05
272
+ - `weight_decay`: 0.0
273
+ - `adam_beta1`: 0.9
274
+ - `adam_beta2`: 0.999
275
+ - `adam_epsilon`: 1e-08
276
+ - `max_grad_norm`: 1.0
277
+ - `num_train_epochs`: 24
278
+ - `max_steps`: -1
279
+ - `lr_scheduler_type`: linear
280
+ - `lr_scheduler_kwargs`: {}
281
+ - `warmup_ratio`: 0.1
282
+ - `warmup_steps`: 0
283
+ - `log_level`: passive
284
+ - `log_level_replica`: warning
285
+ - `log_on_each_node`: True
286
+ - `logging_nan_inf_filter`: True
287
+ - `save_safetensors`: True
288
+ - `save_on_each_node`: False
289
+ - `save_only_model`: False
290
+ - `restore_callback_states_from_checkpoint`: False
291
+ - `no_cuda`: False
292
+ - `use_cpu`: False
293
+ - `use_mps_device`: False
294
+ - `seed`: 42
295
+ - `data_seed`: None
296
+ - `jit_mode_eval`: False
297
+ - `use_ipex`: False
298
+ - `bf16`: False
299
+ - `fp16`: True
300
+ - `fp16_opt_level`: O1
301
+ - `half_precision_backend`: auto
302
+ - `bf16_full_eval`: False
303
+ - `fp16_full_eval`: False
304
+ - `tf32`: None
305
+ - `local_rank`: 0
306
+ - `ddp_backend`: None
307
+ - `tpu_num_cores`: None
308
+ - `tpu_metrics_debug`: False
309
+ - `debug`: []
310
+ - `dataloader_drop_last`: False
311
+ - `dataloader_num_workers`: 4
312
+ - `dataloader_prefetch_factor`: None
313
+ - `past_index`: -1
314
+ - `disable_tqdm`: False
315
+ - `remove_unused_columns`: True
316
+ - `label_names`: None
317
+ - `load_best_model_at_end`: True
318
+ - `ignore_data_skip`: False
319
+ - `fsdp`: []
320
+ - `fsdp_min_num_params`: 0
321
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
322
+ - `fsdp_transformer_layer_cls_to_wrap`: None
323
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
324
+ - `deepspeed`: None
325
+ - `label_smoothing_factor`: 0.1
326
+ - `optim`: adamw_torch
327
+ - `optim_args`: None
328
+ - `adafactor`: False
329
+ - `group_by_length`: False
330
+ - `length_column_name`: length
331
+ - `ddp_find_unused_parameters`: None
332
+ - `ddp_bucket_cap_mb`: None
333
+ - `ddp_broadcast_buffers`: False
334
+ - `dataloader_pin_memory`: True
335
+ - `dataloader_persistent_workers`: False
336
+ - `skip_memory_metrics`: True
337
+ - `use_legacy_prediction_loop`: False
338
+ - `push_to_hub`: False
339
+ - `resume_from_checkpoint`: None
340
+ - `hub_model_id`: None
341
+ - `hub_strategy`: every_save
342
+ - `hub_private_repo`: None
343
+ - `hub_always_push`: False
344
+ - `gradient_checkpointing`: False
345
+ - `gradient_checkpointing_kwargs`: None
346
+ - `include_inputs_for_metrics`: False
347
+ - `include_for_metrics`: []
348
+ - `eval_do_concat_batches`: True
349
+ - `fp16_backend`: auto
350
+ - `push_to_hub_model_id`: None
351
+ - `push_to_hub_organization`: None
352
+ - `mp_parameters`:
353
+ - `auto_find_batch_size`: False
354
+ - `full_determinism`: False
355
+ - `torchdynamo`: None
356
+ - `ray_scope`: last
357
+ - `ddp_timeout`: 1800
358
+ - `torch_compile`: False
359
+ - `torch_compile_backend`: None
360
+ - `torch_compile_mode`: None
361
+ - `dispatch_batches`: None
362
+ - `split_batches`: None
363
+ - `include_tokens_per_second`: False
364
+ - `include_num_input_tokens_seen`: False
365
+ - `neftune_noise_alpha`: None
366
+ - `optim_target_modules`: None
367
+ - `batch_eval_metrics`: False
368
+ - `eval_on_start`: True
369
+ - `use_liger_kernel`: False
370
+ - `eval_use_gather_object`: False
371
+ - `average_tokens_across_devices`: False
372
+ - `prompts`: None
373
+ - `batch_sampler`: batch_sampler
374
+ - `multi_dataset_batch_sampler`: proportional
375
+
376
+ </details>
377
+
378
+ ### Training Logs
379
+ | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
380
+ |:-----------:|:---------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:|
381
+ | 0 | 0 | - | 0.0942 | 0.6574 | - |
382
+ | 0.2588 | 500 | 0.0449 | 0.0262 | 0.7353 | - |
383
+ | 0.5176 | 1000 | 0.0232 | 0.0185 | 0.7592 | - |
384
+ | 0.7764 | 1500 | 0.0172 | 0.0154 | 0.7760 | - |
385
+ | 1.0352 | 2000 | 0.0153 | 0.0137 | 0.7905 | - |
386
+ | 1.2940 | 2500 | 0.0124 | 0.0130 | 0.7920 | - |
387
+ | 1.5528 | 3000 | 0.0119 | 0.0120 | 0.8048 | - |
388
+ | 1.8116 | 3500 | 0.0121 | 0.0121 | 0.8021 | - |
389
+ | 2.0704 | 4000 | 0.0114 | 0.0112 | 0.8018 | - |
390
+ | 2.3292 | 4500 | 0.0093 | 0.0117 | 0.7996 | - |
391
+ | 2.5880 | 5000 | 0.0097 | 0.0105 | 0.8133 | - |
392
+ | 2.8468 | 5500 | 0.0092 | 0.0103 | 0.8137 | - |
393
+ | 3.1056 | 6000 | 0.0085 | 0.0094 | 0.8247 | - |
394
+ | 3.3644 | 6500 | 0.0068 | 0.0090 | 0.8326 | - |
395
+ | 3.6232 | 7000 | 0.0073 | 0.0092 | 0.8273 | - |
396
+ | 3.8820 | 7500 | 0.007 | 0.0084 | 0.8404 | - |
397
+ | 4.1408 | 8000 | 0.0061 | 0.0083 | 0.8381 | - |
398
+ | 4.3996 | 8500 | 0.0057 | 0.0082 | 0.8382 | - |
399
+ | 4.6584 | 9000 | 0.0056 | 0.0074 | 0.8458 | - |
400
+ | 4.9172 | 9500 | 0.0057 | 0.0073 | 0.8468 | - |
401
+ | 5.1760 | 10000 | 0.0045 | 0.0071 | 0.8508 | - |
402
+ | 5.4348 | 10500 | 0.0041 | 0.0069 | 0.8579 | - |
403
+ | 5.6936 | 11000 | 0.0047 | 0.0069 | 0.8471 | - |
404
+ | 5.9524 | 11500 | 0.0046 | 0.0067 | 0.8554 | - |
405
+ | 6.2112 | 12000 | 0.0034 | 0.0062 | 0.8616 | - |
406
+ | 6.4700 | 12500 | 0.0034 | 0.0063 | 0.8636 | - |
407
+ | 6.7288 | 13000 | 0.0036 | 0.0062 | 0.8649 | - |
408
+ | 6.9876 | 13500 | 0.0037 | 0.0063 | 0.8641 | - |
409
+ | 7.2464 | 14000 | 0.0027 | 0.0059 | 0.8691 | - |
410
+ | 7.5052 | 14500 | 0.0027 | 0.0060 | 0.8733 | - |
411
+ | 7.7640 | 15000 | 0.0031 | 0.0060 | 0.8748 | - |
412
+ | 8.0228 | 15500 | 0.0028 | 0.0058 | 0.8736 | - |
413
+ | 8.2816 | 16000 | 0.0023 | 0.0055 | 0.8785 | - |
414
+ | 8.5404 | 16500 | 0.0025 | 0.0054 | 0.8801 | - |
415
+ | 8.7992 | 17000 | 0.0024 | 0.0058 | 0.8809 | - |
416
+ | 9.0580 | 17500 | 0.0026 | 0.0058 | 0.8811 | - |
417
+ | 9.3168 | 18000 | 0.002 | 0.0055 | 0.8824 | - |
418
+ | 9.5756 | 18500 | 0.002 | 0.0053 | 0.8859 | - |
419
+ | 9.8344 | 19000 | 0.0021 | 0.0053 | 0.8851 | - |
420
+ | 10.0932 | 19500 | 0.0019 | 0.0055 | 0.8904 | - |
421
+ | 10.3520 | 20000 | 0.0016 | 0.0052 | 0.8946 | - |
422
+ | 10.6108 | 20500 | 0.0017 | 0.0057 | 0.8884 | - |
423
+ | 10.8696 | 21000 | 0.0019 | 0.0055 | 0.8889 | - |
424
+ | 11.1284 | 21500 | 0.0016 | 0.0052 | 0.8942 | - |
425
+ | 11.3872 | 22000 | 0.0014 | 0.0053 | 0.8961 | - |
426
+ | 11.6460 | 22500 | 0.0016 | 0.0053 | 0.8928 | - |
427
+ | 11.9048 | 23000 | 0.0017 | 0.0051 | 0.8947 | - |
428
+ | 12.1636 | 23500 | 0.0013 | 0.0050 | 0.9015 | - |
429
+ | 12.4224 | 24000 | 0.0012 | 0.0059 | 0.8886 | - |
430
+ | 12.6812 | 24500 | 0.0014 | 0.0051 | 0.9030 | - |
431
+ | 12.9400 | 25000 | 0.0014 | 0.0051 | 0.9012 | - |
432
+ | 13.1988 | 25500 | 0.0011 | 0.0050 | 0.9037 | - |
433
+ | 13.4576 | 26000 | 0.0011 | 0.0050 | 0.9053 | - |
434
+ | 13.7164 | 26500 | 0.0011 | 0.0049 | 0.9060 | - |
435
+ | 13.9752 | 27000 | 0.0011 | 0.0049 | 0.9086 | - |
436
+ | 14.2340 | 27500 | 0.001 | 0.0048 | 0.9063 | - |
437
+ | 14.4928 | 28000 | 0.001 | 0.0051 | 0.9056 | - |
438
+ | 14.7516 | 28500 | 0.001 | 0.0051 | 0.9079 | - |
439
+ | 15.0104 | 29000 | 0.0011 | 0.0049 | 0.9080 | - |
440
+ | 15.2692 | 29500 | 0.0008 | 0.0048 | 0.9126 | - |
441
+ | 15.5280 | 30000 | 0.0008 | 0.0049 | 0.9112 | - |
442
+ | 15.7867 | 30500 | 0.0008 | 0.0049 | 0.9123 | - |
443
+ | 16.0455 | 31000 | 0.0008 | 0.0048 | 0.9133 | - |
444
+ | 16.3043 | 31500 | 0.0006 | 0.0048 | 0.9103 | - |
445
+ | 16.5631 | 32000 | 0.0007 | 0.0049 | 0.9144 | - |
446
+ | 16.8219 | 32500 | 0.0008 | 0.0048 | 0.9143 | - |
447
+ | 17.0807 | 33000 | 0.0007 | 0.0048 | 0.9159 | - |
448
+ | 17.3395 | 33500 | 0.0007 | 0.0047 | 0.9174 | - |
449
+ | 17.5983 | 34000 | 0.0006 | 0.0048 | 0.9175 | - |
450
+ | 17.8571 | 34500 | 0.0007 | 0.0047 | 0.9163 | - |
451
+ | 18.1159 | 35000 | 0.0006 | 0.0046 | 0.9195 | - |
452
+ | 18.3747 | 35500 | 0.0006 | 0.0047 | 0.9190 | - |
453
+ | 18.6335 | 36000 | 0.0006 | 0.0047 | 0.9192 | - |
454
+ | 18.8923 | 36500 | 0.0006 | 0.0047 | 0.9204 | - |
455
+ | 19.1511 | 37000 | 0.0005 | 0.0047 | 0.9219 | - |
456
+ | 19.4099 | 37500 | 0.0004 | 0.0046 | 0.9218 | - |
457
+ | 19.6687 | 38000 | 0.0005 | 0.0047 | 0.9221 | - |
458
+ | 19.9275 | 38500 | 0.0005 | 0.0046 | 0.9230 | - |
459
+ | 20.1863 | 39000 | 0.0005 | 0.0046 | 0.9233 | - |
460
+ | 20.4451 | 39500 | 0.0004 | 0.0046 | 0.9240 | - |
461
+ | 20.7039 | 40000 | 0.0005 | 0.0047 | 0.9234 | - |
462
+ | 20.9627 | 40500 | 0.0004 | 0.0047 | 0.9241 | - |
463
+ | 21.2215 | 41000 | 0.0004 | 0.0046 | 0.9253 | - |
464
+ | 21.4803 | 41500 | 0.0004 | 0.0046 | 0.9259 | - |
465
+ | 21.7391 | 42000 | 0.0004 | 0.0046 | 0.9262 | - |
466
+ | **21.9979** | **42500** | **0.0004** | **0.0046** | **0.9263** | **-** |
467
+ | 22.2567 | 43000 | 0.0003 | 0.0046 | 0.9266 | - |
468
+ | 22.5155 | 43500 | 0.0003 | 0.0046 | 0.9266 | - |
469
+ | 22.7743 | 44000 | 0.0003 | 0.0046 | 0.9273 | - |
470
+ | 23.0331 | 44500 | 0.0003 | 0.0046 | 0.9273 | - |
471
+ | 23.2919 | 45000 | 0.0003 | 0.0046 | 0.9274 | - |
472
+ | 23.5507 | 45500 | 0.0003 | 0.0046 | 0.9277 | - |
473
+ | 23.8095 | 46000 | 0.0003 | 0.0046 | 0.9277 | - |
474
+ | 24.0 | 46368 | - | - | - | 0.9257 |
475
+
476
+ * The bold row denotes the saved checkpoint.
477
+
478
+ ### Framework Versions
479
+ - Python: 3.10.12
480
+ - Sentence Transformers: 3.3.1
481
+ - Transformers: 4.47.1
482
+ - PyTorch: 2.5.1+cu124
483
+ - Accelerate: 1.2.1
484
+ - Datasets: 3.2.0
485
+ - Tokenizers: 0.21.0
486
+
487
+ ## Citation
488
+
489
+ ### BibTeX
490
+
491
+ #### Sentence Transformers
492
+ ```bibtex
493
+ @inproceedings{reimers-2019-sentence-bert,
494
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
495
+ author = "Reimers, Nils and Gurevych, Iryna",
496
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
497
+ month = "11",
498
+ year = "2019",
499
+ publisher = "Association for Computational Linguistics",
500
+ url = "https://arxiv.org/abs/1908.10084",
501
+ }
502
+ ```
503
+
504
+ <!--
505
+ ## Glossary
506
+
507
+ *Clearly define terms in order to be accessible across audiences.*
508
+ -->
509
+
510
+ <!--
511
+ ## Model Card Authors
512
+
513
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
514
+ -->
515
+
516
+ <!--
517
+ ## Model Card Contact
518
+
519
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
520
+ -->
config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
3
+ "architectures": [
4
+ "XLMRobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "gradient_checkpointing": false,
11
+ "hidden_act": "gelu",
12
+ "hidden_dropout_prob": 0.1,
13
+ "hidden_size": 768,
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
16
+ "layer_norm_eps": 1e-05,
17
+ "max_position_embeddings": 514,
18
+ "model_type": "xlm-roberta",
19
+ "num_attention_heads": 12,
20
+ "num_hidden_layers": 12,
21
+ "output_past": true,
22
+ "pad_token_id": 1,
23
+ "position_embedding_type": "absolute",
24
+ "torch_dtype": "float32",
25
+ "transformers_version": "4.47.1",
26
+ "type_vocab_size": 1,
27
+ "use_cache": true,
28
+ "vocab_size": 250002
29
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.47.1",
5
+ "pytorch": "2.5.1+cu124"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f9e16b93c06bdc8c85c0027cf48bf29a91a5568cd1e80d29761cb5e35d9f8b98
3
+ size 1112197096
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 128,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "<unk>",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
3
+ size 17082987
tokenizer_config.json ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": false,
46
+ "cls_token": "<s>",
47
+ "eos_token": "</s>",
48
+ "extra_special_tokens": {},
49
+ "mask_token": "<mask>",
50
+ "max_length": 128,
51
+ "model_max_length": 128,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "<pad>",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "</s>",
57
+ "stride": 0,
58
+ "tokenizer_class": "XLMRobertaTokenizer",
59
+ "truncation_side": "right",
60
+ "truncation_strategy": "longest_first",
61
+ "unk_token": "<unk>"
62
+ }