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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
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 Community
ColeH0415 commited on
Commit
4db7167
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verified ·
1 Parent(s): 69d8e59

CE fine-tuned epoch 3/3 best_val=0.8448

Browse files
Files changed (2) hide show
  1. README.md +17 -17
  2. 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: 0.8424242424242424
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  name: Accuracy
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  - type: accuracy_threshold
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- value: -1.4543638229370117
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  name: Accuracy Threshold
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  - type: f1
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- value: 0.8487584650112867
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  name: F1
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  - type: f1_threshold
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- value: -1.8693041801452637
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  name: F1 Threshold
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  - type: precision
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- value: 0.7949260042283298
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  name: Precision
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  - type: recall
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- value: 0.910411622276029
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  name: Recall
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  - type: average_precision
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- value: 0.91111876065531
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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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- # [-6.7653 1.1449 2.2433 5.7045 -7.9876]
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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 | 0.8424 |
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- | accuracy_threshold | -1.4544 |
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- | f1 | 0.8488 |
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- | f1_threshold | -1.8693 |
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- | precision | 0.7949 |
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- | recall | 0.9104 |
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- | **average_precision** | **0.9111** |
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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 | -1 | 0.9111 |
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  ### Training Time
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- - **Training**: 32.6 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: 0.8448484848484848
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  name: Accuracy
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  - type: accuracy_threshold
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+ value: -2.50508451461792
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  name: Accuracy Threshold
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  - type: f1
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+ value: 0.8505214368482039
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  name: F1
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  - type: f1_threshold
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+ value: -2.667440176010132
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  name: F1 Threshold
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  - type: precision
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+ value: 0.8155555555555556
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  name: Precision
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  - type: recall
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+ value: 0.8886198547215496
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  name: Recall
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  - type: average_precision
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+ value: 0.9152025768340708
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  name: Average Precision
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  ---
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  ]
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  scores = model.predict(pairs)
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  print(scores)
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+ # [ -9.1708 2.7075 3.8609 8.2862 -10.5857]
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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 | 0.8448 |
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+ | accuracy_threshold | -2.5051 |
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+ | f1 | 0.8505 |
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+ | f1_threshold | -2.6674 |
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+ | precision | 0.8156 |
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+ | recall | 0.8886 |
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+ | **average_precision** | **0.9152** |
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  <!--
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  ## Bias, Risks and Limitations
 
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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 | -1 | 0.9152 |
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  ### Training Time
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+ - **Training**: 33.2 seconds
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  ### Framework Versions
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  - Python: 3.12.13
model.safetensors CHANGED
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