Upload fine-tuned chart reranker model
Browse files- README.md +60 -49
- 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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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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# [{'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:20347
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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: 0.8381245620713855
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name: Pearson
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- type: spearman
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value: 0.8388188648567115
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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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['Thanks, now you have everything pick the most important item or 2 or three if you find it really appropriate from each group. Just simplify this list a bit, to make sure I have my micro nutrients, vitamins, whatever checked off.', 'Title: "Natural Grocers by Vitamin Cottage Overview"\nCollections: Companies\nDatasets: InstrumentClosePrice1Day\nChart Type: timeseries:eav_v3\nCanonical forms: "Natural Grocers by Vitamin Cottage"="closing_price"'],
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['How do people feel about Nicola Sturgeon?', 'Title: "Nicola Sturgeon fame & popularity tracker (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
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['Create a skit about hino. It should be a horror theme and humor in the end. Without the need of driving a truck. it can be about hino genuine spareparts or technician services', 'Title: "Hino Motors Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Hino Motors"="Hino Motors, Ltd.", "Overview"="Stock Overview"\nSources: S&P Global'],
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['no i mean talk about the trends in school', 'Title: "Should private schools be banned? (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
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['Exchange rate Moroccan dirham to euro 29 October 2025', 'Title: "Conversion rate from EUR to MAD"\nCollections: Foreign Exchange Rates\nDatasets: Forex\nChart Type: exchange:currency\nSources: Xignite'],
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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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'Thanks, now you have everything pick the most important item or 2 or three if you find it really appropriate from each group. Just simplify this list a bit, to make sure I have my micro nutrients, vitamins, whatever checked off.',
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[
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'Title: "Natural Grocers by Vitamin Cottage Overview"\nCollections: Companies\nDatasets: InstrumentClosePrice1Day\nChart Type: timeseries:eav_v3\nCanonical forms: "Natural Grocers by Vitamin Cottage"="closing_price"',
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'Title: "Nicola Sturgeon fame & popularity tracker (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
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'Title: "Hino Motors Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Hino Motors"="Hino Motors, Ltd.", "Overview"="Stock Overview"\nSources: S&P Global',
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'Title: "Should private schools be banned? (United Kingdom)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
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'Title: "Conversion rate from EUR to MAD"\nCollections: Foreign Exchange Rates\nDatasets: Forex\nChart Type: exchange:currency\nSources: Xignite',
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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 | 0.8381 |
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| **spearman** | **0.8388** |
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<!--
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## Bias, Risks and Limitations
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#### Unnamed Dataset
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* Size: 20,347 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: 1 characters</li><li>mean: 84.39 characters</li><li>max: 943 characters</li></ul> | <ul><li>min: 74 characters</li><li>mean: 180.44 characters</li><li>max: 396 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</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>Thanks, now you have everything pick the most important item or 2 or three if you find it really appropriate from each group. Just simplify this list a bit, to make sure I have my micro nutrients, vitamins, whatever checked off.</code> | <code>Title: "Natural Grocers by Vitamin Cottage Overview"<br>Collections: Companies<br>Datasets: InstrumentClosePrice1Day<br>Chart Type: timeseries:eav_v3<br>Canonical forms: "Natural Grocers by Vitamin Cottage"="closing_price"</code> | <code>0.0</code> |
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| <code>How do people feel about Nicola Sturgeon?</code> | <code>Title: "Nicola Sturgeon fame & popularity tracker (United Kingdom)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>1.0</code> |
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| <code>Create a skit about hino. It should be a horror theme and humor in the end. Without the need of driving a truck. it can be about hino genuine spareparts or technician services</code> | <code>Title: "Hino Motors Overview"<br>Collections: Companies<br>Chart Type: company:finance<br>Canonical forms: "Hino Motors"="Hino Motors, Ltd.", "Overview"="Stock Overview"<br>Sources: S&P Global</code> | <code>0.5</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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| 0.1572 | 100 | - | 0.7137 |
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| 0.3145 | 200 | - | 0.7573 |
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| 0.4717 | 300 | - | 0.7748 |
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| 0.6289 | 400 | - | 0.7888 |
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| 0.7862 | 500 | 0.5153 | 0.8000 |
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| 0.9434 | 600 | - | 0.8039 |
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| 1.0 | 636 | - | 0.8044 |
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| 3.1447 | 2000 | 0.3904 | 0.8336 |
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| 3.9308 | 2500 | 0.3741 | 0.8370 |
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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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epoch,steps,Pearson_Correlation,Spearman_Correlation
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1.0,636,0.8050961988795169,0.8044347672638916
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2.0,1272,0.8267567950795853,0.8284146931811501
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3.0,1908,0.8351882809975475,0.8355004054548
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4.0,2544,0.8381740944766652,0.8382614031363851
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5.0,3180,0.8368434817201468,0.8374989674723212
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1223854204
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version https://git-lfs.github.com/spec/v1
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oid sha256:f6dde1675c82135fb9296d9c990693ce3373c5982f7f01cd53a72fb674e86d82
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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: 20347
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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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