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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
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noahjax commited on
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1 Parent(s): b90c7e1

Upload fine-tuned chart reranker model

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Files changed (4) hide show
  1. README.md +48 -38
  2. eval/CrossEncoderCorrelationEvaluator_validation_results.csv +3 -1
  3. model.safetensors +1 -1
  4. training_info.txt +3 -3
README.md CHANGED
@@ -4,7 +4,7 @@ tags:
4
  - cross-encoder
5
  - reranker
6
  - generated_from_trainer
7
- - dataset_size:6851
8
  - loss:BinaryCrossEntropyLoss
9
  base_model: cross-encoder/ms-marco-MiniLM-L6-v2
10
  pipeline_tag: text-ranking
@@ -23,10 +23,10 @@ model-index:
23
  type: validation
24
  metrics:
25
  - type: pearson
26
- value: 0.6742730018723011
27
  name: Pearson
28
  - type: spearman
29
- value: 0.5158175772359095
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
- ['According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?', 'Title: "Life satisfaction, measured weekly (United Kingdom)"\n Collections: YouGov Trackers\n Datasets: YouGovTrackerValueV2\n Chart Type: survey:timeseries\n Sources: YouGov'],
74
- ['Scoreline for Al‑Bayraq W vs Al‑Riyadh W (WFDL)', 'Title: "Grainger Overview, CBSE:IAM Overview"\n Collections: Companies\n Datasets: InstrumentClosePrice1Day\n Chart Type: timeseries'],
75
- ["According to the article 'Top 3 Higher Education Trends to Watch in 2025' by Hanover Research, what percentage of prospective college students in the U.S. report feeling 'not at all familiar' or only 'slightly familiar' with the application process?", 'Title: "AirTanker Services Limited Percentage"\n Collections: Companies\n Chart Type: company_card\n Company: name=ATS Corporation, aliases=[\'ATS Automation Tooling Systems Inc.\', \'Ats Corp\', \'ATS\']\n Sources: S&P Global'],
76
- ["When did RetailMeNot launch the '5 to Buy' event?", 'Title: "Art - past 3 months (United States)"\n Collections: YouGov Trackers\n Datasets: YouGovTrackerValueV2\n Chart Type: survey:timeseries\n Sources: YouGov]'],
77
- ["When was the article '5 Key Trends To Shape Your Business Strategy For 2025' by IESE Business School published on Forbes?", 'Title: "Business Coach Overview"\n Collections: Companies\n Chart Type: company_card\n Company: name=Business Coach Inc., aliases=[\'Business Coach\']\n Sources: S&P Global'],
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
- 'According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?',
86
  [
87
- 'Title: "Life satisfaction, measured weekly (United Kingdom)"\n Collections: YouGov Trackers\n Datasets: YouGovTrackerValueV2\n Chart Type: survey:timeseries\n Sources: YouGov',
88
- 'Title: "Grainger Overview, CBSE:IAM Overview"\n Collections: Companies\n Datasets: InstrumentClosePrice1Day\n Chart Type: timeseries',
89
- 'Title: "AirTanker Services Limited Percentage"\n Collections: Companies\n Chart Type: company_card\n Company: name=ATS Corporation, aliases=[\'ATS Automation Tooling Systems Inc.\', \'Ats Corp\', \'ATS\']\n Sources: S&P Global',
90
- 'Title: "Art - past 3 months (United States)"\n Collections: YouGov Trackers\n Datasets: YouGovTrackerValueV2\n Chart Type: survey:timeseries\n Sources: YouGov]',
91
- 'Title: "Business Coach Overview"\n Collections: Companies\n Chart Type: company_card\n Company: name=Business Coach Inc., aliases=[\'Business Coach\']\n Sources: S&P Global',
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 | 0.6743 |
133
- | **spearman** | **0.5158** |
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: 6,851 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: 7 characters</li><li>mean: 99.0 characters</li><li>max: 2253 characters</li></ul> | <ul><li>min: 79 characters</li><li>mean: 184.27 characters</li><li>max: 716 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> |
160
  * Samples:
161
- | sentence_0 | sentence_1 | label |
162
- |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
163
- | <code>According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?</code> | <code>Title: "Life satisfaction, measured weekly (United Kingdom)"<br> Collections: YouGov Trackers<br> Datasets: YouGovTrackerValueV2<br> Chart Type: survey:timeseries<br> Sources: YouGov</code> | <code>0.0</code> |
164
- | <code>Scoreline for Al‑Bayraq W vs Al‑Riyadh W (WFDL)</code> | <code>Title: "Grainger Overview, CBSE:IAM Overview"<br> Collections: Companies<br> Datasets: InstrumentClosePrice1Day<br> Chart Type: timeseries</code> | <code>0.0</code> |
165
- | <code>According to the article 'Top 3 Higher Education Trends to Watch in 2025' by Hanover Research, what percentage of prospective college students in the U.S. report feeling 'not at all familiar' or only 'slightly familiar' with the application process?</code> | <code>Title: "AirTanker Services Limited Percentage"<br> Collections: Companies<br> Chart Type: company_card<br> Company: name=ATS Corporation, aliases=['ATS Automation Tooling Systems Inc.', 'Ats Corp', 'ATS']<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
  {
@@ -175,9 +175,8 @@ You can finetune this model on your own dataset.
175
  #### Non-Default Hyperparameters
176
 
177
  - `eval_strategy`: steps
178
- - `per_device_train_batch_size`: 32
179
- - `per_device_eval_batch_size`: 32
180
- - `num_train_epochs`: 1
181
 
182
  #### All Hyperparameters
183
  <details><summary>Click to expand</summary>
@@ -186,8 +185,8 @@ You can finetune this model on your own dataset.
186
  - `do_predict`: False
187
  - `eval_strategy`: steps
188
  - `prediction_loss_only`: True
189
- - `per_device_train_batch_size`: 32
190
- - `per_device_eval_batch_size`: 32
191
  - `per_gpu_train_batch_size`: None
192
  - `per_gpu_eval_batch_size`: None
193
  - `gradient_accumulation_steps`: 1
@@ -199,7 +198,7 @@ You can finetune this model on your own dataset.
199
  - `adam_beta2`: 0.999
200
  - `adam_epsilon`: 1e-08
201
  - `max_grad_norm`: 1
202
- - `num_train_epochs`: 1
203
  - `max_steps`: -1
204
  - `lr_scheduler_type`: linear
205
  - `lr_scheduler_kwargs`: {}
@@ -305,12 +304,23 @@ You can finetune this model on your own dataset.
305
  </details>
306
 
307
  ### Training Logs
308
- | Epoch | Step | validation_spearman |
309
- |:------:|:----:|:-------------------:|
310
- | 0.2326 | 50 | 0.3960 |
311
- | 0.4651 | 100 | 0.4804 |
312
- | 0.6977 | 150 | 0.5031 |
313
- | 0.9302 | 200 | 0.5158 |
 
 
 
 
 
 
 
 
 
 
 
314
 
315
 
316
  ### Framework Versions
 
4
  - cross-encoder
5
  - reranker
6
  - generated_from_trainer
7
+ - dataset_size:8000
8
  - loss:BinaryCrossEntropyLoss
9
  base_model: cross-encoder/ms-marco-MiniLM-L6-v2
10
  pipeline_tag: text-ranking
 
23
  type: validation
24
  metrics:
25
  - type: pearson
26
+ value: 0.8481096700155641
27
  name: Pearson
28
  - type: spearman
29
+ value: 0.8528646396544212
30
  name: Spearman
31
  ---
32
 
 
70
  model = CrossEncoder("cross_encoder_model_id")
71
  # Get scores for pairs of texts
72
  pairs = [
73
+ ['prix blé tendre bio Indre et Loire 2025', 'Chart Title: "Wheat (US Soft Red Winter) Spot Price", Collections: Commodity Prices'],
74
+ ['oil prices', 'Chart Title: "West Texas Intermediate Crude Oil - Price in United States", Collections: Commodities::EIAEnergyIndicators::TimeseriesManager'],
75
+ ['Nvidia earnings AI chip demand', 'Chart Title: "Nvidia Quarterly Price to Earnings", Collections: Companies::CompanyComputedRatiosV2::TimeseriesManager'],
76
+ ['show me tesla stock performance 2020 to 2025', 'Title: "Manakoa Services Corporation Stock Performance"\n Collections: Companies\n Chart Type: company:private\n Sources: S&P Global'],
77
+ ['Samsung A56 5G mémoire', 'Chart Title: "Samsung Publishing Co., Ltd Stock Prices", Info: Stock details for company Samsung Publishing Co., Ltd, Collections: Company Card, Chart Type: company:finance'],
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
+ 'prix blé tendre bio Indre et Loire 2025',
86
  [
87
+ 'Chart Title: "Wheat (US Soft Red Winter) Spot Price", Collections: Commodity Prices',
88
+ 'Chart Title: "West Texas Intermediate Crude Oil - Price in United States", Collections: Commodities::EIAEnergyIndicators::TimeseriesManager',
89
+ 'Chart Title: "Nvidia Quarterly Price to Earnings", Collections: Companies::CompanyComputedRatiosV2::TimeseriesManager',
90
+ 'Title: "Manakoa Services Corporation Stock Performance"\n Collections: Companies\n Chart Type: company:private\n Sources: S&P Global',
91
+ 'Chart Title: "Samsung Publishing Co., Ltd Stock Prices", Info: Stock details for company Samsung Publishing Co., Ltd, Collections: Company Card, Chart Type: company:finance',
92
  ]
93
  )
94
  # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
 
129
 
130
  | Metric | Value |
131
  |:-------------|:-----------|
132
+ | pearson | 0.8481 |
133
+ | **spearman** | **0.8529** |
134
 
135
  <!--
136
  ## Bias, Risks and Limitations
 
150
 
151
  #### Unnamed Dataset
152
 
153
+ * Size: 8,000 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: 51.78 characters</li><li>max: 1024 characters</li></ul> | <ul><li>min: 49 characters</li><li>mean: 136.27 characters</li><li>max: 716 characters</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
160
  * Samples:
161
+ | sentence_0 | sentence_1 | label |
162
+ |:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
163
+ | <code>prix blé tendre bio Indre et Loire 2025</code> | <code>Chart Title: "Wheat (US Soft Red Winter) Spot Price", Collections: Commodity Prices</code> | <code>0.4</code> |
164
+ | <code>oil prices</code> | <code>Chart Title: "West Texas Intermediate Crude Oil - Price in United States", Collections: Commodities::EIAEnergyIndicators::TimeseriesManager</code> | <code>0.8</code> |
165
+ | <code>Nvidia earnings AI chip demand</code> | <code>Chart Title: "Nvidia Quarterly Price to Earnings", Collections: Companies::CompanyComputedRatiosV2::TimeseriesManager</code> | <code>0.4</code> |
166
  * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
167
  ```json
168
  {
 
175
  #### Non-Default Hyperparameters
176
 
177
  - `eval_strategy`: steps
178
+ - `per_device_train_batch_size`: 16
179
+ - `per_device_eval_batch_size`: 16
 
180
 
181
  #### All Hyperparameters
182
  <details><summary>Click to expand</summary>
 
185
  - `do_predict`: False
186
  - `eval_strategy`: steps
187
  - `prediction_loss_only`: True
188
+ - `per_device_train_batch_size`: 16
189
+ - `per_device_eval_batch_size`: 16
190
  - `per_gpu_train_batch_size`: None
191
  - `per_gpu_eval_batch_size`: None
192
  - `gradient_accumulation_steps`: 1
 
198
  - `adam_beta2`: 0.999
199
  - `adam_epsilon`: 1e-08
200
  - `max_grad_norm`: 1
201
+ - `num_train_epochs`: 3
202
  - `max_steps`: -1
203
  - `lr_scheduler_type`: linear
204
  - `lr_scheduler_kwargs`: {}
 
304
  </details>
305
 
306
  ### Training Logs
307
+ | Epoch | Step | Training Loss | validation_spearman |
308
+ |:-----:|:----:|:-------------:|:-------------------:|
309
+ | 0.2 | 100 | - | 0.7038 |
310
+ | 0.4 | 200 | - | 0.7816 |
311
+ | 0.6 | 300 | - | 0.8134 |
312
+ | 0.8 | 400 | - | 0.8216 |
313
+ | 1.0 | 500 | 0.8021 | 0.8296 |
314
+ | 1.2 | 600 | - | 0.8358 |
315
+ | 1.4 | 700 | - | 0.8418 |
316
+ | 1.6 | 800 | - | 0.8418 |
317
+ | 1.8 | 900 | - | 0.8478 |
318
+ | 2.0 | 1000 | 0.5726 | 0.8471 |
319
+ | 2.2 | 1100 | - | 0.8487 |
320
+ | 2.4 | 1200 | - | 0.8497 |
321
+ | 2.6 | 1300 | - | 0.8522 |
322
+ | 2.8 | 1400 | - | 0.8523 |
323
+ | 3.0 | 1500 | 0.5616 | 0.8529 |
324
 
325
 
326
  ### Framework Versions
eval/CrossEncoderCorrelationEvaluator_validation_results.csv CHANGED
@@ -1,2 +1,4 @@
1
  epoch,steps,Pearson_Correlation,Spearman_Correlation
2
- 1.0,215,0.6765057885942694,0.5160340950125839
 
 
 
1
  epoch,steps,Pearson_Correlation,Spearman_Correlation
2
+ 1.0,500,0.8334498280984426,0.8296374514172629
3
+ 2.0,1000,0.8444343598056561,0.8471494664684638
4
+ 3.0,1500,0.8481096700155641,0.8528646396544212
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:04a6402495d00a3b98e802a2e1ce50fada050156fa8ac9906d5c561e9fd2aec2
3
  size 90866412
 
1
  version https://git-lfs.github.com/spec/v1
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+ oid sha256:93357cfe857f758d0ab0429d2076e1599cd7661ab2cc03f999bede0267e1167c
3
  size 90866412
training_info.txt CHANGED
@@ -1,6 +1,6 @@
1
  Base Model: cross-encoder/ms-marco-MiniLM-L6-v2
2
- Training Samples: 6851
3
- Epochs: 1
4
- Batch Size: 32
5
  Learning Rate: 2e-05
6
  Max Length: 512
 
1
  Base Model: cross-encoder/ms-marco-MiniLM-L6-v2
2
+ Training Samples: 8000
3
+ Epochs: 3
4
+ Batch Size: 16
5
  Learning Rate: 2e-05
6
  Max Length: 512