Upload fine-tuned chart reranker model
Browse files- README.md +49 -43
- eval/CrossEncoderCorrelationEvaluator_validation_results.csv +5 -5
- model.safetensors +1 -1
- training_info.txt +1 -1
README.md
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- cross-encoder
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- reranker
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- generated_from_trainer
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- dataset_size:
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- loss:BinaryCrossEntropyLoss
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base_model: Alibaba-NLP/gte-multilingual-reranker-base
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pipeline_tag: text-ranking
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type: validation
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metrics:
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- type: pearson
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value: 0.
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name: Pearson
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- type: spearman
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value: 0.
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name: Spearman
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---
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model = CrossEncoder("cross_encoder_model_id")
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# Get scores for pairs of texts
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pairs = [
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['
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'
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[
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'Title: "
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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| Metric | Value |
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|:-------------|:-----------|
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| pearson | 0.
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| **spearman** | **0.
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<!--
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## Bias, Risks and Limitations
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#### Unnamed Dataset
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* Size:
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence_0 | sentence_1
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| type | string | string
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| details | <ul><li>min:
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* Samples:
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| sentence_0
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| <code>
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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### Training Logs
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| Epoch | Step | Training Loss | validation_spearman |
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|:------:|:----:|:-------------:|:-------------------:|
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### Framework Versions
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- cross-encoder
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- reranker
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- generated_from_trainer
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- dataset_size:12349
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- loss:BinaryCrossEntropyLoss
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base_model: Alibaba-NLP/gte-multilingual-reranker-base
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pipeline_tag: text-ranking
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type: validation
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metrics:
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- type: pearson
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value: 0.8643473065020739
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name: Pearson
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- type: spearman
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value: 0.8620968090164374
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name: Spearman
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---
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model = CrossEncoder("cross_encoder_model_id")
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# Get scores for pairs of texts
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pairs = [
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['DJ mixers compatible with Apple Music 2025', 'Title: "Music devices - radio (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
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['current USD to PLN exchange rate', 'Title: "Conversion rate from PLN to USD"\nCollections: Foreign Exchange Rates\nDatasets: Forex\nChart Type: exchange:currency\nSources: Xignite'],
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['Aktuelle Investmenttrends 2025', 'Title: "Financial activity - next 12 months (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
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["What are Amazon's accrued liabilities?", 'Title: "Amazon Expenses Accrued (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v2\nCanonical forms: "Expenses Accrued"="accrued_expenses_total"\nSources: S&P Global'],
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["Costco's long-term lease obligations", 'Title: "Air Lease Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Air Lease"="Air Lease Corporation", "Overview"="Stock Overview"\nSources: S&P Global'],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'DJ mixers compatible with Apple Music 2025',
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[
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'Title: "Music devices - radio (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
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'Title: "Conversion rate from PLN to USD"\nCollections: Foreign Exchange Rates\nDatasets: Forex\nChart Type: exchange:currency\nSources: Xignite',
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'Title: "Financial activity - next 12 months (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
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'Title: "Amazon Expenses Accrued (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v2\nCanonical forms: "Expenses Accrued"="accrued_expenses_total"\nSources: S&P Global',
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'Title: "Air Lease Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Air Lease"="Air Lease Corporation", "Overview"="Stock Overview"\nSources: S&P Global',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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| Metric | Value |
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|:-------------|:-----------|
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| pearson | 0.8643 |
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| **spearman** | **0.8621** |
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<!--
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## Bias, Risks and Limitations
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#### Unnamed Dataset
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* Size: 12,349 training samples
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence_0 | sentence_1 | label |
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|:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 5 characters</li><li>mean: 46.81 characters</li><li>max: 123 characters</li></ul> | <ul><li>min: 77 characters</li><li>mean: 182.4 characters</li><li>max: 495 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence_0 | sentence_1 | label |
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|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
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| <code>DJ mixers compatible with Apple Music 2025</code> | <code>Title: "Music devices - radio (United States)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>0.25</code> |
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| <code>current USD to PLN exchange rate</code> | <code>Title: "Conversion rate from PLN to USD"<br>Collections: Foreign Exchange Rates<br>Datasets: Forex<br>Chart Type: exchange:currency<br>Sources: Xignite</code> | <code>0.75</code> |
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| <code>Aktuelle Investmenttrends 2025</code> | <code>Title: "Financial activity - next 12 months (United States)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>0.75</code> |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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### Training Logs
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| Epoch | Step | Training Loss | validation_spearman |
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|:------:|:----:|:-------------:|:-------------------:|
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| 0.2591 | 100 | - | 0.7835 |
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| 0.5181 | 200 | - | 0.8161 |
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| 0.7772 | 300 | - | 0.8369 |
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| 1.0 | 386 | - | 0.8392 |
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| 1.0363 | 400 | - | 0.8442 |
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| 1.2953 | 500 | 0.47 | 0.8475 |
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| 1.5544 | 600 | - | 0.8533 |
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| 1.8135 | 700 | - | 0.8544 |
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| 2.0 | 772 | - | 0.8579 |
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| 2.0725 | 800 | - | 0.8585 |
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### Framework Versions
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eval/CrossEncoderCorrelationEvaluator_validation_results.csv
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epoch,steps,Pearson_Correlation,Spearman_Correlation
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1.0,386,0.837652001216976,0.8392216545657446
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2.0,772,0.8587273733508869,0.8579410042452386
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3.0,1158,0.8640762414247022,0.8607296855959887
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1223854204
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version https://git-lfs.github.com/spec/v1
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size 1223854204
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training_info.txt
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Base Model: Alibaba-NLP/gte-multilingual-reranker-base
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Training Samples:
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Epochs: 5
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Batch Size: 32
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Learning Rate: 2e-05
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Base Model: Alibaba-NLP/gte-multilingual-reranker-base
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Training Samples: 12349
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Epochs: 5
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Batch Size: 32
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Learning Rate: 2e-05
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