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
CHANGED
|
@@ -4,7 +4,7 @@ tags:
|
|
| 4 |
- cross-encoder
|
| 5 |
- reranker
|
| 6 |
- generated_from_trainer
|
| 7 |
-
- dataset_size:
|
| 8 |
- loss:BinaryCrossEntropyLoss
|
| 9 |
base_model: Alibaba-NLP/gte-reranker-modernbert-base
|
| 10 |
pipeline_tag: text-ranking
|
|
@@ -23,10 +23,10 @@ model-index:
|
|
| 23 |
type: validation
|
| 24 |
metrics:
|
| 25 |
- type: pearson
|
| 26 |
-
value: 0.
|
| 27 |
name: Pearson
|
| 28 |
- type: spearman
|
| 29 |
-
value: 0.
|
| 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 |
-
['
|
| 74 |
-
[
|
| 75 |
-
['
|
| 76 |
-
['
|
| 77 |
-
['
|
| 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 |
-
'
|
| 86 |
[
|
| 87 |
-
'Title: "
|
| 88 |
-
'Title: "
|
| 89 |
-
'Title: "
|
| 90 |
-
'Title: "
|
| 91 |
-
'Title: "
|
| 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.
|
| 133 |
-
| **spearman** | **0.
|
| 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:
|
| 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
|
| 157 |
-
|:--------|:----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| 158 |
-
| type | string
|
| 159 |
-
| details | <ul><li>min:
|
| 160 |
* Samples:
|
| 161 |
-
| sentence_0
|
| 162 |
-
|:-------------------------------------------------------------
|
| 163 |
-
| <code>
|
| 164 |
-
| <code>
|
| 165 |
-
| <code>
|
| 166 |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
|
| 167 |
```json
|
| 168 |
{
|
|
@@ -305,17 +305,24 @@ You can finetune this model on your own dataset.
|
|
| 305 |
</details>
|
| 306 |
|
| 307 |
### Training Logs
|
| 308 |
-
| Epoch
|
| 309 |
-
|:-----:|:----:|:-------------:|:-------------------:|
|
| 310 |
-
| 0.
|
| 311 |
-
|
|
| 312 |
-
| 1.
|
| 313 |
-
|
|
| 314 |
-
|
|
| 315 |
-
|
|
| 316 |
-
|
|
| 317 |
-
|
|
| 318 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 319 |
|
| 320 |
|
| 321 |
### Framework Versions
|
|
|
|
| 4 |
- cross-encoder
|
| 5 |
- reranker
|
| 6 |
- generated_from_trainer
|
| 7 |
+
- dataset_size:7779
|
| 8 |
- loss:BinaryCrossEntropyLoss
|
| 9 |
base_model: Alibaba-NLP/gte-reranker-modernbert-base
|
| 10 |
pipeline_tag: text-ranking
|
|
|
|
| 23 |
type: validation
|
| 24 |
metrics:
|
| 25 |
- type: pearson
|
| 26 |
+
value: 0.8888985992978667
|
| 27 |
name: Pearson
|
| 28 |
- type: spearman
|
| 29 |
+
value: 0.8845425048973017
|
| 30 |
name: Spearman
|
| 31 |
---
|
| 32 |
|
|
|
|
| 70 |
model = CrossEncoder("cross_encoder_model_id")
|
| 71 |
# Get scores for pairs of texts
|
| 72 |
pairs = [
|
| 73 |
+
['Cohere funding history: amounts raised by round', 'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global'],
|
| 74 |
+
['villes sympa à voir entre turin et come', 'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2'],
|
| 75 |
+
['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'],
|
| 76 |
+
["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'],
|
| 77 |
+
['current weather in hong kong', 'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather'],
|
| 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 |
+
'Cohere funding history: amounts raised by round',
|
| 86 |
[
|
| 87 |
+
'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global',
|
| 88 |
+
'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2',
|
| 89 |
+
'Title: "Tusculum, TN Inventory - House"\nCollections: Residential Real Estate\nDatasets: RegionalRealEstateIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Inventory"="inventory_seasonally_unadjusted"\nSources: Redfin',
|
| 90 |
+
'Title: "Tesla Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Tesla"="Tesla, Inc.", "Overview"="Stock Overview"\nSources: S&P Global',
|
| 91 |
+
'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather',
|
| 92 |
]
|
| 93 |
)
|
| 94 |
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
|
|
|
|
| 129 |
|
| 130 |
| Metric | Value |
|
| 131 |
|:-------------|:-----------|
|
| 132 |
+
| pearson | 0.8889 |
|
| 133 |
+
| **spearman** | **0.8845** |
|
| 134 |
|
| 135 |
<!--
|
| 136 |
## Bias, Risks and Limitations
|
|
|
|
| 150 |
|
| 151 |
#### Unnamed Dataset
|
| 152 |
|
| 153 |
+
* Size: 7,779 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: 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> |
|
| 160 |
* Samples:
|
| 161 |
+
| sentence_0 | sentence_1 | label |
|
| 162 |
+
|:-------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
|
| 163 |
+
| <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> |
|
| 164 |
+
| <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> |
|
| 165 |
+
| <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> |
|
| 166 |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
|
| 167 |
```json
|
| 168 |
{
|
|
|
|
| 305 |
</details>
|
| 306 |
|
| 307 |
### Training Logs
|
| 308 |
+
| Epoch | Step | Training Loss | validation_spearman |
|
| 309 |
+
|:------:|:----:|:-------------:|:-------------------:|
|
| 310 |
+
| 0.4098 | 100 | - | 0.8203 |
|
| 311 |
+
| 0.8197 | 200 | - | 0.8565 |
|
| 312 |
+
| 1.0 | 244 | - | 0.8587 |
|
| 313 |
+
| 1.2295 | 300 | - | 0.8632 |
|
| 314 |
+
| 1.6393 | 400 | - | 0.8772 |
|
| 315 |
+
| 2.0 | 488 | - | 0.8714 |
|
| 316 |
+
| 2.0492 | 500 | 0.4207 | 0.8776 |
|
| 317 |
+
| 2.4590 | 600 | - | 0.8786 |
|
| 318 |
+
| 2.8689 | 700 | - | 0.8761 |
|
| 319 |
+
| 3.0 | 732 | - | 0.8824 |
|
| 320 |
+
| 3.2787 | 800 | - | 0.8817 |
|
| 321 |
+
| 3.6885 | 900 | - | 0.8838 |
|
| 322 |
+
| 4.0 | 976 | - | 0.8835 |
|
| 323 |
+
| 4.0984 | 1000 | 0.3261 | 0.8836 |
|
| 324 |
+
| 4.5082 | 1100 | - | 0.8843 |
|
| 325 |
+
| 4.9180 | 1200 | - | 0.8845 |
|
| 326 |
|
| 327 |
|
| 328 |
### Framework Versions
|
eval/CrossEncoderCorrelationEvaluator_validation_results.csv
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
epoch,steps,Pearson_Correlation,Spearman_Correlation
|
| 2 |
-
1.0,
|
| 3 |
-
2.0,
|
| 4 |
-
3.0,
|
| 5 |
-
4.0,
|
| 6 |
-
5.0,
|
|
|
|
| 1 |
epoch,steps,Pearson_Correlation,Spearman_Correlation
|
| 2 |
+
1.0,244,0.8620642924096914,0.8587166361363444
|
| 3 |
+
2.0,488,0.8764832585164201,0.8713859435370955
|
| 4 |
+
3.0,732,0.8867003524365638,0.8823857804088827
|
| 5 |
+
4.0,976,0.8881431986959347,0.8835376105032559
|
| 6 |
+
5.0,1220,0.8889602207955667,0.8845866499868097
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 598436708
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:71ca08ed8176f01a71eaa842d8135564d04d405af3ad33d2ba4c1f91e581b05d
|
| 3 |
size 598436708
|
training_info.txt
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
Base Model: Alibaba-NLP/gte-reranker-modernbert-base
|
| 2 |
-
Training Samples:
|
| 3 |
Epochs: 5
|
| 4 |
Batch Size: 32
|
| 5 |
Learning Rate: 2e-05
|
|
|
|
| 1 |
Base Model: Alibaba-NLP/gte-reranker-modernbert-base
|
| 2 |
+
Training Samples: 7779
|
| 3 |
Epochs: 5
|
| 4 |
Batch Size: 32
|
| 5 |
Learning Rate: 2e-05
|