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davanstrien
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fineweb-c-quality-reranker-v2

Text Ranking
sentence-transformers
Safetensors
multilingual
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:9220
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
davanstrien HF Staff commited on
Commit
3840896
·
verified ·
1 Parent(s): 0fa9434

Training in progress, step 174

Browse files
Files changed (3) hide show
  1. README.md +28 -18
  2. eval/CrossEncoderClassificationEvaluator_fineweb_c_eval_results.csv +1 -0
  3. model.safetensors +1 -1
README.md CHANGED
@@ -31,25 +31,25 @@ model-index:
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  type: fineweb_c_eval
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  metrics:
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  - type: accuracy
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- value: 0.8388671875
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  name: Accuracy
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  - type: accuracy_threshold
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- value: 0.7548891305923462
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  name: Accuracy Threshold
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  - type: f1
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- value: 0.37500000000000006
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  name: F1
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  - type: f1_threshold
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- value: 0.49519360065460205
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  name: F1 Threshold
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  - type: precision
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- value: 0.3132530120481928
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  name: Precision
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  - type: recall
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- value: 0.46706586826347307
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  name: Recall
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  - type: average_precision
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- value: 0.296686339444048
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  name: Average Precision
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  ---
55
 
@@ -110,7 +110,7 @@ pairs = [
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  ]
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  scores = model.predict(pairs)
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  print(scores)
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- # [0.094 0.6645 0.1461 0.2214 0.5059]
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  # Or rank different texts based on similarity to a single text
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  ranks = model.rank(
@@ -161,13 +161,13 @@ You can finetune this model on your own dataset.
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  | Metric | Value |
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  |:----------------------|:-----------|
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- | accuracy | 0.8389 |
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- | accuracy_threshold | 0.7549 |
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- | f1 | 0.375 |
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- | f1_threshold | 0.4952 |
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- | precision | 0.3133 |
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- | recall | 0.4671 |
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- | **average_precision** | **0.2967** |
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  <!--
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  ## Bias, Risks and Limitations
@@ -381,12 +381,22 @@ You can finetune this model on your own dataset.
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  | 0.3737 | 108 | 1.1537 | - | - |
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  | 0.3945 | 114 | 1.0772 | - | - |
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  | 0.4014 | 116 | - | 1.1264 | 0.2967 |
 
 
 
 
 
 
 
 
 
 
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  ### Training Time
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- - **Training**: 1.8 minutes
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- - **Evaluation**: 41.5 seconds
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- - **Total**: 2.5 minutes
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  ### Framework Versions
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  - Python: 3.12.12
 
31
  type: fineweb_c_eval
32
  metrics:
33
  - type: accuracy
34
+ value: 0.849609375
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  name: Accuracy
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  - type: accuracy_threshold
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+ value: 0.8169846534729004
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  name: Accuracy Threshold
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  - type: f1
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+ value: 0.5108433734939759
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  name: F1
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  - type: f1_threshold
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+ value: 0.6361271142959595
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  name: F1 Threshold
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  - type: precision
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+ value: 0.4274193548387097
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  name: Precision
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  - type: recall
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+ value: 0.6347305389221557
50
  name: Recall
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  - type: average_precision
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+ value: 0.4518233024149694
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  name: Average Precision
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  ---
55
 
 
110
  ]
111
  scores = model.predict(pairs)
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  print(scores)
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+ # [0.1112 0.8549 0.0385 0.0726 0.0967]
114
 
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  # Or rank different texts based on similarity to a single text
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  ranks = model.rank(
 
161
 
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  | Metric | Value |
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  |:----------------------|:-----------|
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+ | accuracy | 0.8496 |
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+ | accuracy_threshold | 0.817 |
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+ | f1 | 0.5108 |
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+ | f1_threshold | 0.6361 |
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+ | precision | 0.4274 |
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+ | recall | 0.6347 |
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+ | **average_precision** | **0.4518** |
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  <!--
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  ## Bias, Risks and Limitations
 
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  | 0.3737 | 108 | 1.1537 | - | - |
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  | 0.3945 | 114 | 1.0772 | - | - |
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  | 0.4014 | 116 | - | 1.1264 | 0.2967 |
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+ | 0.4152 | 120 | 1.0160 | - | - |
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+ | 0.4360 | 126 | 1.1722 | - | - |
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+ | 0.4567 | 132 | 1.0133 | - | - |
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+ | 0.4775 | 138 | 1.0978 | - | - |
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+ | 0.4983 | 144 | 0.9355 | - | - |
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+ | 0.5190 | 150 | 1.1273 | - | - |
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+ | 0.5398 | 156 | 0.8814 | - | - |
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+ | 0.5606 | 162 | 1.0361 | - | - |
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+ | 0.5813 | 168 | 0.9421 | - | - |
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+ | 0.6021 | 174 | 0.9503 | 0.8597 | 0.4518 |
394
 
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  ### Training Time
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+ - **Training**: 2.9 minutes
398
+ - **Evaluation**: 1.0 minutes
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+ - **Total**: 3.9 minutes
400
 
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  ### Framework Versions
402
  - Python: 3.12.12
eval/CrossEncoderClassificationEvaluator_fineweb_c_eval_results.csv CHANGED
@@ -1,3 +1,4 @@
1
  epoch,steps,Accuracy,Accuracy_Threshold,F1,F1_Threshold,Precision,Recall,Average_Precision
2
  0.20069204152249134,58,0.8369140625,0.8085842,0.29916897506925205,0.39676917,0.1945945945945946,0.6467065868263473,0.22527425150874691
3
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1
  epoch,steps,Accuracy,Accuracy_Threshold,F1,F1_Threshold,Precision,Recall,Average_Precision
2
  0.20069204152249134,58,0.8369140625,0.8085842,0.29916897506925205,0.39676917,0.1945945945945946,0.6467065868263473,0.22527425150874691
3
  0.4013840830449827,116,0.8388671875,0.75488913,0.37500000000000006,0.4951936,0.3132530120481928,0.46706586826347307,0.296686339444048
4
+ 0.6020761245674741,174,0.849609375,0.81698465,0.5108433734939759,0.6361271,0.4274193548387097,0.6347305389221557,0.4518233024149694
model.safetensors CHANGED
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