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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
2307357
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verified ·
1 Parent(s): 98fca8d

CE fine-tuned epoch 3/3 best_val=0.6897

Browse files
Files changed (2) hide show
  1. README.md +19 -15
  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: 0.6351515151515151
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  name: Accuracy
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  - type: accuracy_threshold
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- value: 0.5755879878997803
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  name: Accuracy Threshold
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  - type: f1
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- value: 0.6981132075471698
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  name: F1
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  - type: f1_threshold
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- value: 0.41324031352996826
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  name: F1 Threshold
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  - type: precision
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- value: 0.5405046480743692
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  name: Precision
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  - type: recall
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- value: 0.9854721549636803
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  name: Recall
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  - type: average_precision
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- value: 0.6568676757267966
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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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- # [0.6498 0.5873 0.6027 0.6833 0.4922]
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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 | 0.6352 |
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- | accuracy_threshold | 0.5756 |
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- | f1 | 0.6981 |
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- | f1_threshold | 0.4132 |
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- | precision | 0.5405 |
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- | recall | 0.9855 |
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- | **average_precision** | **0.6569** |
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  <!--
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  ## Bias, Risks and Limitations
@@ -326,6 +326,10 @@ You can finetune this model on your own dataset.
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  | 0.5391 | 1000 | 0.6662 | - |
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  | 0.8086 | 1500 | 0.6504 | - |
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  | -1 | -1 | - | 0.6569 |
 
 
 
 
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  ### Training Time
 
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  type: ce-val
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  metrics:
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  - type: accuracy
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+ value: 0.6896969696969697
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  name: Accuracy
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  - type: accuracy_threshold
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+ value: 0.5847750902175903
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  name: Accuracy Threshold
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  - type: f1
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+ value: 0.7074707470747076
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  name: F1
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  - type: f1_threshold
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+ value: 0.3505881428718567
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  name: F1 Threshold
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  - type: precision
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+ value: 0.5630372492836676
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  name: Precision
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  - type: recall
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+ value: 0.9515738498789347
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  name: Recall
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  - type: average_precision
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+ value: 0.7391973708035351
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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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+ # [0.7715 0.6352 0.7843 0.844 0.4491]
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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 | 0.6897 |
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+ | accuracy_threshold | 0.5848 |
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+ | f1 | 0.7075 |
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+ | f1_threshold | 0.3506 |
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+ | precision | 0.563 |
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+ | recall | 0.9516 |
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+ | **average_precision** | **0.7392** |
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  <!--
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  ## Bias, Risks and Limitations
 
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  | 0.5391 | 1000 | 0.6662 | - |
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  | 0.8086 | 1500 | 0.6504 | - |
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  | -1 | -1 | - | 0.6569 |
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+ | 0.2695 | 500 | 0.6129 | - |
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+ | 0.5391 | 1000 | 0.6091 | - |
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+ | 0.8086 | 1500 | 0.5937 | - |
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+ | -1 | -1 | - | 0.7392 |
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  ### Training Time
model.safetensors CHANGED
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