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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
5413416
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verified ·
1 Parent(s): 85ac989

CE fine-tuned epoch 3/3 best_val=0.5588

Browse files
Files changed (2) hide show
  1. README.md +11 -11
  2. model.safetensors +1 -1
README.md CHANGED
@@ -28,16 +28,16 @@ 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: 0.5454545454545454
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  name: Accuracy
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  - type: accuracy_threshold
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- value: 1.165013074874878
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  name: Accuracy Threshold
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  - type: f1
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  value: 0.6677471636952999
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  name: F1
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  - type: f1_threshold
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- value: -9.491117477416992
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  name: F1 Threshold
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  - type: precision
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  value: 0.5018270401948843
@@ -46,7 +46,7 @@ model-index:
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  value: 0.9975786924939467
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  name: Recall
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  - type: average_precision
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- value: 0.5451929668211959
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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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- # [-0.2177 0.5965 -3.8169 -1.2369 0.4599]
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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 | 0.5455 |
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- | accuracy_threshold | 1.165 |
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  | f1 | 0.6677 |
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- | f1_threshold | -9.4911 |
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  | precision | 0.5018 |
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  | recall | 0.9976 |
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- | **average_precision** | **0.5452** |
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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 | 0.5452 |
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  ### Training Time
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- - **Training**: 34.1 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: 0.5587878787878788
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  name: Accuracy
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  - type: accuracy_threshold
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+ value: 0.892870306968689
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  name: Accuracy Threshold
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  - type: f1
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  value: 0.6677471636952999
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  name: F1
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  - type: f1_threshold
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+ value: -8.320409774780273
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  name: F1 Threshold
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  - type: precision
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  value: 0.5018270401948843
 
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  value: 0.9975786924939467
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  name: Recall
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  - type: average_precision
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+ value: 0.5638184038907261
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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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+ # [-0.0067 0.3615 -3.1055 -0.8462 0.1024]
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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 | 0.5588 |
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+ | accuracy_threshold | 0.8929 |
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  | f1 | 0.6677 |
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+ | f1_threshold | -8.3204 |
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  | precision | 0.5018 |
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  | recall | 0.9976 |
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+ | **average_precision** | **0.5638** |
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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 | 0.5638 |
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  ### Training Time
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+ - **Training**: 33.0 seconds
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  ### Framework Versions
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  - Python: 3.12.13
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  size 90866404
 
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