Hugging Face's logo

TakoData
/
chart-reranker

Text Ranking
sentence-transformers
Safetensors
new
cross-encoder
reranker
Generated from Trainer
dataset_size:24588
loss:BinaryCrossEntropyLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
Community
noahjax commited on
Commit
7ffe7e9
·
verified ·
1 Parent(s): 40a79c5

Upload fine-tuned chart reranker model

Browse files
Files changed (4) hide show
  1. README.md +55 -64
  2. eval/CrossEncoderCorrelationEvaluator_validation_results.csv +5 -5
  3. model.safetensors +1 -1
  4. training_info.txt +1 -1
README.md CHANGED
@@ -4,7 +4,7 @@ tags:
4
  - cross-encoder
5
  - reranker
6
  - generated_from_trainer
7
- - dataset_size:28274
8
  - loss:BinaryCrossEntropyLoss
9
  base_model: Alibaba-NLP/gte-multilingual-reranker-base
10
  pipeline_tag: text-ranking
@@ -23,10 +23,10 @@ model-index:
23
  type: validation
24
  metrics:
25
  - type: pearson
26
- value: 0.871938379575355
27
  name: Pearson
28
  - type: spearman
29
- value: 0.8696556409896702
30
  name: Spearman
31
  ---
32
 
@@ -70,11 +70,11 @@ from sentence_transformers import CrossEncoder
70
  model = CrossEncoder("cross_encoder_model_id")
71
  # Get scores for pairs of texts
72
  pairs = [
73
- ['Which securities listed on ENXTAM were the top performers (highest returns) in the period shown?', 'Title: "Pedro\'s List Financials"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Financials"="Financials Overview", "Pedro\'s List"="Pedro\'s List, Inc."\nSources: S&P Global'],
74
- ['Best crypto platforms compliant with MiCA 2025, reliable, secure, and transparent.', 'Title: "Top Performing Crypto"\nCollections: Crypto Currencies\nDatasets: CryptoAssetMetrics\nChart Type: categorical_bar'],
75
- ['implications fiscales et sociales formes juridiques entreprises France 2025 apports industrie', 'Title: "Eagle Industries Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Eagle Industries"="Eagle Industry Co.,Ltd.", "Overview"="Stock Overview"\nSources: S&P Global'],
76
- ['Which US-listed stocks on the NYSE and NASDAQ are the top performers (highest returns)?', 'Title: "Nasdaq Inc. Financials"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Nasdaq Inc."="Nasdaq, Inc.", "Financials"="Financials Overview"\nSources: S&P Global'],
77
- ['How many children are recorded with wheezing, and has that number gone up or down over time?', 'Title: "Mani Number (Annual), Universal Music Number (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v3\nCanonical forms: "Number"="inventory"'],
78
  ]
79
  scores = model.predict(pairs)
80
  print(scores.shape)
@@ -82,13 +82,13 @@ print(scores.shape)
82
 
83
  # Or rank different texts based on similarity to a single text
84
  ranks = model.rank(
85
- 'Which securities listed on ENXTAM were the top performers (highest returns) in the period shown?',
86
  [
87
- 'Title: "Pedro\'s List Financials"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Financials"="Financials Overview", "Pedro\'s List"="Pedro\'s List, Inc."\nSources: S&P Global',
88
- 'Title: "Top Performing Crypto"\nCollections: Crypto Currencies\nDatasets: CryptoAssetMetrics\nChart Type: categorical_bar',
89
- 'Title: "Eagle Industries Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Eagle Industries"="Eagle Industry Co.,Ltd.", "Overview"="Stock Overview"\nSources: S&P Global',
90
- 'Title: "Nasdaq Inc. Financials"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Nasdaq Inc."="Nasdaq, Inc.", "Financials"="Financials Overview"\nSources: S&P Global',
91
- 'Title: "Mani Number (Annual), Universal Music Number (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v3\nCanonical forms: "Number"="inventory"',
92
  ]
93
  )
94
  # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
@@ -129,8 +129,8 @@ You can finetune this model on your own dataset.
129
 
130
  | Metric | Value |
131
  |:-------------|:-----------|
132
- | pearson | 0.8719 |
133
- | **spearman** | **0.8697** |
134
 
135
  <!--
136
  ## Bias, Risks and Limitations
@@ -150,19 +150,19 @@ You can finetune this model on your own dataset.
150
 
151
  #### Unnamed Dataset
152
 
153
- * Size: 28,274 training samples
154
  * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
155
  * Approximate statistics based on the first 1000 samples:
156
  | | sentence_0 | sentence_1 | label |
157
  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
158
  | type | string | string | float |
159
- | details | <ul><li>min: 3 characters</li><li>mean: 82.52 characters</li><li>max: 939 characters</li></ul> | <ul><li>min: 75 characters</li><li>mean: 171.07 characters</li><li>max: 436 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.43</li><li>max: 1.0</li></ul> |
160
  * Samples:
161
- | sentence_0 | sentence_1 | label |
162
- |:--------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
163
- | <code>Which securities listed on ENXTAM were the top performers (highest returns) in the period shown?</code> | <code>Title: "Pedro's List Financials"<br>Collections: Companies<br>Chart Type: company:finance<br>Canonical forms: "Financials"="Financials Overview", "Pedro's List"="Pedro's List, Inc."<br>Sources: S&P Global</code> | <code>0.0</code> |
164
- | <code>Best crypto platforms compliant with MiCA 2025, reliable, secure, and transparent.</code> | <code>Title: "Top Performing Crypto"<br>Collections: Crypto Currencies<br>Datasets: CryptoAssetMetrics<br>Chart Type: categorical_bar</code> | <code>0.25</code> |
165
- | <code>implications fiscales et sociales formes juridiques entreprises France 2025 apports industrie</code> | <code>Title: "Eagle Industries Overview"<br>Collections: Companies<br>Chart Type: company:finance<br>Canonical forms: "Eagle Industries"="Eagle Industry Co.,Ltd.", "Overview"="Stock Overview"<br>Sources: S&P Global</code> | <code>0.0</code> |
166
  * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
167
  ```json
168
  {
@@ -308,47 +308,38 @@ You can finetune this model on your own dataset.
308
  ### Training Logs
309
  | Epoch | Step | Training Loss | validation_spearman |
310
  |:------:|:----:|:-------------:|:-------------------:|
311
- | 0.1131 | 100 | - | 0.7659 |
312
- | 0.2262 | 200 | - | 0.7941 |
313
- | 0.3394 | 300 | - | 0.8119 |
314
- | 0.4525 | 400 | - | 0.8237 |
315
- | 0.5656 | 500 | 0.4773 | 0.8284 |
316
- | 0.6787 | 600 | - | 0.8304 |
317
- | 0.7919 | 700 | - | 0.8361 |
318
- | 0.9050 | 800 | - | 0.8454 |
319
- | 1.0 | 884 | - | 0.8382 |
320
- | 1.0181 | 900 | - | 0.8438 |
321
- | 1.1312 | 1000 | 0.4184 | 0.8469 |
322
- | 1.2443 | 1100 | - | 0.8458 |
323
- | 1.3575 | 1200 | - | 0.8492 |
324
- | 1.4706 | 1300 | - | 0.8514 |
325
- | 1.5837 | 1400 | - | 0.8567 |
326
- | 1.6968 | 1500 | 0.3897 | 0.8582 |
327
- | 1.8100 | 1600 | - | 0.8586 |
328
- | 1.9231 | 1700 | - | 0.8582 |
329
- | 2.0 | 1768 | - | 0.8587 |
330
- | 2.0362 | 1800 | - | 0.8583 |
331
- | 2.1493 | 1900 | - | 0.8597 |
332
- | 2.2624 | 2000 | 0.3709 | 0.8596 |
333
- | 2.3756 | 2100 | - | 0.8608 |
334
- | 2.4887 | 2200 | - | 0.8598 |
335
- | 2.6018 | 2300 | - | 0.8623 |
336
- | 2.7149 | 2400 | - | 0.8643 |
337
- | 2.8281 | 2500 | 0.3556 | 0.8661 |
338
- | 2.9412 | 2600 | - | 0.8672 |
339
- | 3.0 | 2652 | - | 0.8656 |
340
- | 3.0543 | 2700 | - | 0.8668 |
341
- | 3.1674 | 2800 | - | 0.8657 |
342
- | 3.2805 | 2900 | - | 0.8654 |
343
- | 3.3937 | 3000 | 0.3435 | 0.8656 |
344
- | 3.5068 | 3100 | - | 0.8665 |
345
- | 3.6199 | 3200 | - | 0.8661 |
346
- | 3.7330 | 3300 | - | 0.8660 |
347
- | 3.8462 | 3400 | - | 0.8666 |
348
- | 3.9593 | 3500 | 0.3364 | 0.8679 |
349
- | 4.0 | 3536 | - | 0.8674 |
350
- | 4.0724 | 3600 | - | 0.8670 |
351
- | 4.1855 | 3700 | - | 0.8697 |
352
 
353
 
354
  ### Framework Versions
 
4
  - cross-encoder
5
  - reranker
6
  - generated_from_trainer
7
+ - dataset_size:24504
8
  - loss:BinaryCrossEntropyLoss
9
  base_model: Alibaba-NLP/gte-multilingual-reranker-base
10
  pipeline_tag: text-ranking
 
23
  type: validation
24
  metrics:
25
  - type: pearson
26
+ value: 0.8721120209782917
27
  name: Pearson
28
  - type: spearman
29
+ value: 0.8685098375943734
30
  name: Spearman
31
  ---
32
 
 
70
  model = CrossEncoder("cross_encoder_model_id")
71
  # Get scores for pairs of texts
72
  pairs = [
73
+ ['include the popular publications as well', 'Title: "Americans\' Library use - past 3 months (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
74
+ ['Give it a good research topic', 'Title: "The most important issues facing the country (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
75
+ ['When and where are the Denver Broncos playing the Kansas City Chiefs?', 'Title: "Denver Broncos at Kansas City Chiefs"\nCollections: Football\nChart Type: game_score:football'],
76
+ ['49ers vs Seahawks', 'Title: "Seahawk Deep Ocean Technology, Inc. Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Overview"="Stock Overview"\nSources: S&P Global'],
77
+ ['Comparative review of JBL vs Marshall 2025 Bluetooth speakers', 'Title: "B&C Speakers Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "B&C Speakers"="B&C Speakers S.p.A.", "Overview"="Stock Overview"\nSources: S&P Global'],
78
  ]
79
  scores = model.predict(pairs)
80
  print(scores.shape)
 
82
 
83
  # Or rank different texts based on similarity to a single text
84
  ranks = model.rank(
85
+ 'include the popular publications as well',
86
  [
87
+ 'Title: "Americans\' Library use - past 3 months (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
88
+ 'Title: "The most important issues facing the country (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
89
+ 'Title: "Denver Broncos at Kansas City Chiefs"\nCollections: Football\nChart Type: game_score:football',
90
+ 'Title: "Seahawk Deep Ocean Technology, Inc. Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Overview"="Stock Overview"\nSources: S&P Global',
91
+ 'Title: "B&C Speakers Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "B&C Speakers"="B&C Speakers S.p.A.", "Overview"="Stock Overview"\nSources: S&P Global',
92
  ]
93
  )
94
  # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
 
129
 
130
  | Metric | Value |
131
  |:-------------|:-----------|
132
+ | pearson | 0.8721 |
133
+ | **spearman** | **0.8685** |
134
 
135
  <!--
136
  ## Bias, Risks and Limitations
 
150
 
151
  #### Unnamed Dataset
152
 
153
+ * Size: 24,504 training samples
154
  * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
155
  * Approximate statistics based on the first 1000 samples:
156
  | | sentence_0 | sentence_1 | label |
157
  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
158
  | type | string | string | float |
159
+ | details | <ul><li>min: 2 characters</li><li>mean: 86.83 characters</li><li>max: 993 characters</li></ul> | <ul><li>min: 77 characters</li><li>mean: 169.16 characters</li><li>max: 360 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
160
  * Samples:
161
+ | sentence_0 | sentence_1 | label |
162
+ |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
163
+ | <code>include the popular publications as well</code> | <code>Title: "Americans' Library use - past 3 months (United States)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>0.5</code> |
164
+ | <code>Give it a good research topic</code> | <code>Title: "The most important issues facing the country (United Kingdom)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>1.0</code> |
165
+ | <code>When and where are the Denver Broncos playing the Kansas City Chiefs?</code> | <code>Title: "Denver Broncos at Kansas City Chiefs"<br>Collections: Football<br>Chart Type: game_score:football</code> | <code>1.0</code> |
166
  * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
167
  ```json
168
  {
 
308
  ### Training Logs
309
  | Epoch | Step | Training Loss | validation_spearman |
310
  |:------:|:----:|:-------------:|:-------------------:|
311
+ | 0.1305 | 100 | - | 0.7594 |
312
+ | 0.2611 | 200 | - | 0.7951 |
313
+ | 0.3916 | 300 | - | 0.8050 |
314
+ | 0.5222 | 400 | - | 0.8200 |
315
+ | 0.6527 | 500 | 0.468 | 0.8290 |
316
+ | 0.7833 | 600 | - | 0.8331 |
317
+ | 0.9138 | 700 | - | 0.8347 |
318
+ | 1.0 | 766 | - | 0.8434 |
319
+ | 1.0444 | 800 | - | 0.8432 |
320
+ | 1.1749 | 900 | - | 0.8467 |
321
+ | 1.3055 | 1000 | 0.4135 | 0.8473 |
322
+ | 1.4360 | 1100 | - | 0.8475 |
323
+ | 1.5666 | 1200 | - | 0.8535 |
324
+ | 1.6971 | 1300 | - | 0.8518 |
325
+ | 1.8277 | 1400 | - | 0.8571 |
326
+ | 1.9582 | 1500 | 0.3747 | 0.8577 |
327
+ | 2.0 | 1532 | - | 0.8556 |
328
+ | 2.0888 | 1600 | - | 0.8587 |
329
+ | 2.2193 | 1700 | - | 0.8609 |
330
+ | 2.3499 | 1800 | - | 0.8612 |
331
+ | 2.4804 | 1900 | - | 0.8619 |
332
+ | 2.6110 | 2000 | 0.3515 | 0.8626 |
333
+ | 2.7415 | 2100 | - | 0.8622 |
334
+ | 2.8721 | 2200 | - | 0.8653 |
335
+ | 3.0 | 2298 | - | 0.8656 |
336
+ | 3.0026 | 2300 | - | 0.8656 |
337
+ | 3.1332 | 2400 | - | 0.8643 |
338
+ | 3.2637 | 2500 | 0.3421 | 0.8646 |
339
+ | 3.3943 | 2600 | - | 0.8654 |
340
+ | 3.5248 | 2700 | - | 0.8666 |
341
+ | 3.6554 | 2800 | - | 0.8640 |
342
+ | 3.7859 | 2900 | - | 0.8685 |
 
 
 
 
 
 
 
 
 
343
 
344
 
345
  ### Framework Versions
eval/CrossEncoderCorrelationEvaluator_validation_results.csv CHANGED
@@ -1,6 +1,6 @@
1
  epoch,steps,Pearson_Correlation,Spearman_Correlation
2
- 1.0,884,0.8401566336608757,0.8382300998214652
3
- 2.0,1768,0.8615358688421247,0.8587285555844401
4
- 3.0,2652,0.8681246217823901,0.8655791469764533
5
- 4.0,3536,0.8692562599529016,0.8674157368971115
6
- 5.0,4420,0.8707395217729429,0.8684691714077699
 
1
  epoch,steps,Pearson_Correlation,Spearman_Correlation
2
+ 1.0,766,0.8453028536443531,0.8434351098924865
3
+ 2.0,1532,0.8574271674817566,0.8556349102862147
4
+ 3.0,2298,0.8687755325286843,0.865637110569002
5
+ 4.0,3064,0.8698030506575616,0.8669249926545327
6
+ 5.0,3830,0.8701775404822807,0.8675087793394471
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:922f11cc3ffaaa5abc058dadce6044e126ff6b5b5c6595408217073cd2d86548
3
  size 1223854204
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:06035d5c262912d8a1e0fd97e71fc51f0e84c66ed6a5f7e14862da0e88600252
3
  size 1223854204
training_info.txt CHANGED
@@ -1,5 +1,5 @@
1
  Base Model: Alibaba-NLP/gte-multilingual-reranker-base
2
- Training Samples: 28274
3
  Epochs: 5
4
  Batch Size: 32
5
  Learning Rate: 2e-05
 
1
  Base Model: Alibaba-NLP/gte-multilingual-reranker-base
2
+ Training Samples: 24504
3
  Epochs: 5
4
  Batch Size: 32
5
  Learning Rate: 2e-05