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ColeH0415
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comp90042-crossencoder-factcheck

Text Ranking
sentence-transformers
Safetensors
deberta-v2
cross-encoder
reranker
Generated from Trainer
dataset_size:7419
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
ColeH0415 commited on
Commit
b978496
·
verified ·
1 Parent(s): 1b5ab9a

CE fine-tuned epoch 3/3

Browse files
Files changed (3) hide show
  1. README.md +17 -17
  2. eval/CrossEncoderClassificationEvaluator_ce-val_results.csv +1 -0
  3. model.safetensors +1 -1
README.md CHANGED
@@ -28,25 +28,25 @@ model-index:
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  type: ce-val
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  metrics:
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  - type: accuracy
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- value: 0.8299065420560747
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  name: Accuracy
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  - type: accuracy_threshold
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- value: -0.3896324336528778
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  name: Accuracy Threshold
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  - type: f1
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- value: 0.8914285714285715
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  name: F1
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  - type: f1_threshold
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- value: -1.0047539472579956
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  name: F1 Threshold
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  - type: precision
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- value: 0.8351177730192719
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  name: Precision
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  - type: recall
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- value: 0.9558823529411765
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  name: Recall
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  - type: average_precision
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- value: 0.9535681876950206
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  name: Average Precision
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  ---
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@@ -107,7 +107,7 @@ pairs = [
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  ]
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  scores = model.predict(pairs)
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  print(scores)
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- # [ 3.6842 2.0192 1.9189 -0.9618 0.3676]
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  # Or rank different texts based on similarity to a single text
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  ranks = model.rank(
@@ -158,13 +158,13 @@ You can finetune this model on your own dataset.
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  | Metric | Value |
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  |:----------------------|:-----------|
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- | accuracy | 0.8299 |
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- | accuracy_threshold | -0.3896 |
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- | f1 | 0.8914 |
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- | f1_threshold | -1.0048 |
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- | precision | 0.8351 |
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- | recall | 0.9559 |
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- | **average_precision** | **0.9536** |
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  <!--
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  ## Bias, Risks and Limitations
@@ -317,11 +317,11 @@ You can finetune this model on your own dataset.
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  ### Training Logs
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  | Epoch | Step | ce-val_average_precision |
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  |:-----:|:----:|:------------------------:|
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- | 1.0 | 301 | 0.9536 |
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  ### Training Time
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- - **Training**: 27.9 seconds
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  ### Framework Versions
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  - Python: 3.12.13
 
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  type: ce-val
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  metrics:
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  - type: accuracy
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+ value: 0.8411214953271028
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  name: Accuracy
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  - type: accuracy_threshold
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+ value: -0.21951499581336975
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  name: Accuracy Threshold
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  - type: f1
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+ value: 0.8989547038327527
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  name: F1
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  - type: f1_threshold
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+ value: -1.483269453048706
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  name: F1 Threshold
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  - type: precision
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+ value: 0.8543046357615894
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  name: Precision
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  - type: recall
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+ value: 0.9485294117647058
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  name: Recall
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  - type: average_precision
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+ value: 0.9597205150279606
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  name: Average Precision
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  ---
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107
  ]
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  scores = model.predict(pairs)
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  print(scores)
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+ # [ 5.2169 2.2205 2.9421 -1.5924 -1.1596]
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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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  | Metric | Value |
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  |:----------------------|:-----------|
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+ | accuracy | 0.8411 |
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+ | accuracy_threshold | -0.2195 |
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+ | f1 | 0.899 |
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+ | f1_threshold | -1.4833 |
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+ | precision | 0.8543 |
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+ | recall | 0.9485 |
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+ | **average_precision** | **0.9597** |
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  <!--
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  ## Bias, Risks and Limitations
 
317
  ### Training Logs
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  | Epoch | Step | ce-val_average_precision |
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  |:-----:|:----:|:------------------------:|
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+ | 1.0 | 301 | 0.9597 |
321
 
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  ### Training Time
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+ - **Training**: 23.5 seconds
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  ### Framework Versions
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  - Python: 3.12.13
eval/CrossEncoderClassificationEvaluator_ce-val_results.csv CHANGED
@@ -1,3 +1,4 @@
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  epoch,steps,Accuracy,Accuracy_Threshold,F1,F1_Threshold,Precision,Recall,Average_Precision
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  1.0,301,0.8130841121495327,-0.25794858,0.8845265588914549,-0.43757033,0.8362445414847162,0.9387254901960784,0.9335939639352934
3
  1.0,301,0.8299065420560747,-0.38963243,0.8914285714285715,-1.004754,0.8351177730192719,0.9558823529411765,0.9535681876950206
 
 
1
  epoch,steps,Accuracy,Accuracy_Threshold,F1,F1_Threshold,Precision,Recall,Average_Precision
2
  1.0,301,0.8130841121495327,-0.25794858,0.8845265588914549,-0.43757033,0.8362445414847162,0.9387254901960784,0.9335939639352934
3
  1.0,301,0.8299065420560747,-0.38963243,0.8914285714285715,-1.004754,0.8351177730192719,0.9558823529411765,0.9535681876950206
4
+ 1.0,301,0.8411214953271028,-0.219515,0.8989547038327527,-1.4832695,0.8543046357615894,0.9485294117647058,0.9597205150279606
model.safetensors CHANGED
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