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yNilay
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colbert-personal-context

Sentence Similarity
Safetensors
sentence-transformers
PyLate
lfm2
ColBERT
feature-extraction
Generated from Trainer
dataset_size:5191
loss:Contrastive
Eval Results (legacy)
Model card Files
xet
 Community
yNilay commited on
Commit
57da0cc
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verified ·
1 Parent(s): 12bf4bc

Add new ColBERT model

Browse files
Files changed (3) hide show
  1. 1_Dense/model.safetensors +1 -1
  2. README.md +34 -34
  3. model.safetensors +1 -1
1_Dense/model.safetensors CHANGED
@@ -1,3 +1,3 @@
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README.md CHANGED
@@ -24,7 +24,7 @@ model-index:
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  type: unknown
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  metrics:
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  - type: accuracy
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- value: 0.9740034937858582
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  name: Accuracy
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  ---
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@@ -216,9 +216,9 @@ You can finetune this model on your own dataset.
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  * Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
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- | Metric | Value |
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- |:-------------|:----------|
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- | **accuracy** | **0.974** |
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  <!--
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  ## Bias, Risks and Limitations
@@ -413,36 +413,36 @@ You can finetune this model on your own dataset.
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  ### Training Logs
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  | Epoch | Step | Training Loss | Validation Loss | accuracy |
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  |:-------:|:-------:|:-------------:|:---------------:|:--------:|
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- | 0.1233 | 10 | 3.3759 | - | - |
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- | 0.2465 | 20 | 1.1565 | - | - |
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- | 0.3698 | 30 | 0.5595 | - | - |
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- | 0.4931 | 40 | 0.4625 | - | - |
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- | 0.6163 | 50 | 0.3728 | - | - |
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- | 0.7396 | 60 | 0.3595 | - | - |
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- | 0.8629 | 70 | 0.3632 | - | - |
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- | 0.9861 | 80 | 0.3566 | - | - |
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- | 0 | 0 | - | - | 0.9636 |
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- | 1.0 | 82 | - | 1.0872 | - |
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- | 1.0986 | 90 | 0.2931 | - | - |
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- | 1.2219 | 100 | 0.2994 | - | - |
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- | 1.3451 | 110 | 0.2319 | - | - |
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- | 1.4684 | 120 | 0.2172 | - | - |
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- | 1.5917 | 130 | 0.273 | - | - |
431
- | 1.7149 | 140 | 0.2254 | - | - |
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- | 1.8382 | 150 | 0.2416 | - | - |
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- | 1.9615 | 160 | 0.2538 | - | - |
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- | 0 | 0 | - | - | 0.9688 |
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- | 2.0 | 164 | - | 0.9975 | - |
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- | 2.0740 | 170 | 0.1938 | - | - |
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- | 2.1972 | 180 | 0.1639 | - | - |
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- | 2.3205 | 190 | 0.2477 | - | - |
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- | 2.4438 | 200 | 0.1845 | - | - |
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- | 2.5670 | 210 | 0.1397 | - | - |
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- | 2.6903 | 220 | 0.2116 | - | - |
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- | 2.8136 | 230 | 0.1989 | - | - |
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- | 2.9368 | 240 | 0.1563 | - | - |
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- | 0 | 0 | - | - | 0.9740 |
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- | **3.0** | **246** | **-** | **0.8835** | **-** |
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  * The bold row denotes the saved checkpoint.
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24
  type: unknown
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  metrics:
26
  - type: accuracy
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+ value: 0.9757365584373474
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  name: Accuracy
29
  ---
30
 
 
216
 
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  * Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
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+ | Metric | Value |
220
+ |:-------------|:-----------|
221
+ | **accuracy** | **0.9757** |
222
 
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  <!--
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  ## Bias, Risks and Limitations
 
413
  ### Training Logs
414
  | Epoch | Step | Training Loss | Validation Loss | accuracy |
415
  |:-------:|:-------:|:-------------:|:---------------:|:--------:|
416
+ | 0.1233 | 10 | 3.3802 | - | - |
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+ | 0.2465 | 20 | 1.1614 | - | - |
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+ | 0.3698 | 30 | 0.5549 | - | - |
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+ | 0.4931 | 40 | 0.4642 | - | - |
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+ | 0.6163 | 50 | 0.3749 | - | - |
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+ | 0.7396 | 60 | 0.3587 | - | - |
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+ | 0.8629 | 70 | 0.349 | - | - |
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+ | 0.9861 | 80 | 0.367 | - | - |
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+ | 0 | 0 | - | - | 0.9723 |
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+ | 1.0 | 82 | - | 1.0230 | - |
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+ | 1.0986 | 90 | 0.2812 | - | - |
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+ | 1.2219 | 100 | 0.3016 | - | - |
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+ | 1.3451 | 110 | 0.2187 | - | - |
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+ | 1.4684 | 120 | 0.2104 | - | - |
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+ | 1.5917 | 130 | 0.2656 | - | - |
431
+ | 1.7149 | 140 | 0.2199 | - | - |
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+ | 1.8382 | 150 | 0.2356 | - | - |
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+ | 1.9615 | 160 | 0.2607 | - | - |
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+ | 0 | 0 | - | - | 0.9757 |
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+ | 2.0 | 164 | - | 0.9950 | - |
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+ | 2.0740 | 170 | 0.1836 | - | - |
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+ | 2.1972 | 180 | 0.1789 | - | - |
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+ | 2.3205 | 190 | 0.2342 | - | - |
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+ | 2.4438 | 200 | 0.1904 | - | - |
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+ | 2.5670 | 210 | 0.1312 | - | - |
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+ | 2.6903 | 220 | 0.1813 | - | - |
442
+ | 2.8136 | 230 | 0.1826 | - | - |
443
+ | 2.9368 | 240 | 0.1456 | - | - |
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+ | 0 | 0 | - | - | 0.9757 |
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+ | **3.0** | **246** | **-** | **0.873** | **-** |
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447
  * The bold row denotes the saved checkpoint.
448
 
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