Upload fine-tuned chart reranker model
Browse files- README.md +44 -37
- 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-reranker-modernbert-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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@@ -70,11 +70,11 @@ from sentence_transformers import CrossEncoder
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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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[
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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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'Title: "
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'Title: "
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'Title: "
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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
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| type | 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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</details>
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### Training Logs
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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:7779
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- loss:BinaryCrossEntropyLoss
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base_model: Alibaba-NLP/gte-reranker-modernbert-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: 0.8888985992978667
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name: Pearson
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- type: spearman
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value: 0.8845425048973017
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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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['Cohere funding history: amounts raised by round', 'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global'],
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['villes sympa à voir entre turin et come', 'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2'],
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['Current housing inventory in Chattanooga, TN', 'Title: "Tusculum, TN Inventory - House"\nCollections: Residential Real Estate\nDatasets: RegionalRealEstateIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Inventory"="inventory_seasonally_unadjusted"\nSources: Redfin'],
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["What's Tesla's raw material inventory?", 'Title: "Tesla Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Tesla"="Tesla, Inc.", "Overview"="Stock Overview"\nSources: S&P Global'],
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['current weather in hong kong', 'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather'],
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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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'Cohere funding history: amounts raised by round',
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[
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'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global',
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'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2',
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'Title: "Tusculum, TN Inventory - House"\nCollections: Residential Real Estate\nDatasets: RegionalRealEstateIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Inventory"="inventory_seasonally_unadjusted"\nSources: Redfin',
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'Title: "Tesla Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Tesla"="Tesla, Inc.", "Overview"="Stock Overview"\nSources: S&P Global',
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'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather',
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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 | 0.8889 |
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| **spearman** | **0.8845** |
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<!--
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## Bias, Risks and Limitations
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#### Unnamed Dataset
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* Size: 7,779 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: 4 characters</li><li>mean: 44.22 characters</li><li>max: 116 characters</li></ul> | <ul><li>min: 75 characters</li><li>mean: 184.59 characters</li><li>max: 383 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>Cohere funding history: amounts raised by round</code> | <code>Title: "Cohere Overview"<br>Collections: Companies<br>Chart Type: company:private<br>Sources: S&P Global</code> | <code>0.75</code> |
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| <code>villes sympa à voir entre turin et come</code> | <code>Title: "Turin F.C. Schedule"<br>Collections: Soccer<br>Chart Type: schedule:soccer_team_v2</code> | <code>0.0</code> |
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| <code>Current housing inventory in Chattanooga, TN</code> | <code>Title: "Tusculum, TN Inventory - House"<br>Collections: Residential Real Estate<br>Datasets: RegionalRealEstateIndicators<br>Chart Type: timeseries:eav_v2<br>Canonical forms: "Inventory"="inventory_seasonally_unadjusted"<br>Sources: Redfin</code> | <code>0.25</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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</details>
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### Training Logs
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| Epoch | Step | Training Loss | validation_spearman |
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|:------:|:----:|:-------------:|:-------------------:|
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| 0.4098 | 100 | - | 0.8203 |
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| 0.8197 | 200 | - | 0.8565 |
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| 1.0 | 244 | - | 0.8587 |
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| 1.2295 | 300 | - | 0.8632 |
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| 1.6393 | 400 | - | 0.8772 |
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| 2.0 | 488 | - | 0.8714 |
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| 2.0492 | 500 | 0.4207 | 0.8776 |
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| 2.4590 | 600 | - | 0.8786 |
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| 2.8689 | 700 | - | 0.8761 |
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| 3.0 | 732 | - | 0.8824 |
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| 3.2787 | 800 | - | 0.8817 |
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| 3.6885 | 900 | - | 0.8838 |
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| 4.0 | 976 | - | 0.8835 |
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| 4.0984 | 1000 | 0.3261 | 0.8836 |
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| 4.5082 | 1100 | - | 0.8843 |
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| 4.9180 | 1200 | - | 0.8845 |
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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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1.0,244,0.8620642924096914,0.8587166361363444
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2.0,488,0.8764832585164201,0.8713859435370955
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3.0,732,0.8867003524365638,0.8823857804088827
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 598436708
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version https://git-lfs.github.com/spec/v1
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size 598436708
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training_info.txt
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Base Model: Alibaba-NLP/gte-reranker-modernbert-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-reranker-modernbert-base
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Training Samples: 7779
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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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