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my-setfit-model-dataset-PG-OCR-2

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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spaly99 commited on
Commit
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1 Parent(s): 7aee559

Add SetFit model

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Files changed (3) hide show
  1. README.md +46 -59
  2. model.safetensors +1 -1
  3. model_head.pkl +1 -1
README.md CHANGED
@@ -11,11 +11,14 @@ metrics:
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  - recall
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  - f1
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  widget:
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- - text: 'VALID THRU 02/10/2015 '
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- - text: Les chaines Universal+
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- - text: "\tminute = second * 60,"
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- - text: ': Session'
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- - text: Phil Klay
 
 
 
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  pipeline_tag: text-classification
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  inference: true
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  base_model: sentence-transformers/paraphrase-mpnet-base-v2
@@ -31,16 +34,16 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.87
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  name: Accuracy
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  - type: precision
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- value: 0.8452380952380952
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  name: Precision
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  - type: recall
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- value: 0.8452380952380952
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  name: Recall
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  - type: f1
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- value: 0.8452380952380952
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  name: F1
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  ---
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@@ -72,17 +75,17 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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- | Label | Examples |
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- |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
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- | True | <ul><li>'ace} UNIV FEATURING CHAMILLIONAIRE ©) '</li><li>'ee — Pra yon t “pink you are S© Mer '</li><li>'ae PTAA Be hs B corms of Rermee '</li></ul> |
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- | False | <ul><li>'mmmmmmmmmmlli'</li><li>'Manage Cookie Preferences'</li><li>'Supply Partners'</li></ul> |
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  ## Evaluation
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  ### Metrics
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  | Label | Accuracy | Precision | Recall | F1 |
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  |:--------|:---------|:----------|:-------|:-------|
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- | **all** | 0.87 | 0.8452 | 0.8452 | 0.8452 |
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  ## Uses
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@@ -102,7 +105,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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- preds = model(": Session")
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  ```
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  <!--
@@ -134,12 +137,12 @@ preds = model(": Session")
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  ### Training Set Metrics
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  | Training set | Min | Median | Max |
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  |:-------------|:----|:-------|:----|
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- | Word count | 1 | 8.4575 | 265 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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- | False | 384 |
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- | True | 416 |
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
@@ -163,47 +166,31 @@ preds = model(": Session")
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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- | 0.0005 | 1 | 0.4077 | - |
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- | 0.025 | 50 | 0.2689 | - |
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- | 0.05 | 100 | 0.2505 | - |
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- | 0.075 | 150 | 0.1787 | - |
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- | 0.1 | 200 | 0.1602 | - |
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- | 0.125 | 250 | 0.1381 | - |
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- | 0.15 | 300 | 0.1753 | - |
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- | 0.175 | 350 | 0.018 | - |
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- | 0.2 | 400 | 0.024 | - |
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- | 0.225 | 450 | 0.0863 | - |
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- | 0.25 | 500 | 0.0504 | - |
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- | 0.275 | 550 | 0.0204 | - |
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- | 0.3 | 600 | 0.0124 | - |
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- | 0.325 | 650 | 0.066 | - |
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- | 0.35 | 700 | 0.1305 | - |
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- | 0.375 | 750 | 0.0599 | - |
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- | 0.4 | 800 | 0.0323 | - |
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- | 0.425 | 850 | 0.0039 | - |
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- | 0.45 | 900 | 0.0131 | - |
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- | 0.475 | 950 | 0.004 | - |
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- | 0.5 | 1000 | 0.0016 | - |
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- | 0.525 | 1050 | 0.0139 | - |
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- | 0.55 | 1100 | 0.0189 | - |
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- | 0.575 | 1150 | 0.0533 | - |
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- | 0.6 | 1200 | 0.0645 | - |
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- | 0.625 | 1250 | 0.0005 | - |
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- | 0.65 | 1300 | 0.0111 | - |
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- | 0.675 | 1350 | 0.002 | - |
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- | 0.7 | 1400 | 0.0082 | - |
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- | 0.725 | 1450 | 0.0009 | - |
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- | 0.75 | 1500 | 0.0018 | - |
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- | 0.775 | 1550 | 0.0003 | - |
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- | 0.8 | 1600 | 0.0108 | - |
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- | 0.825 | 1650 | 0.0009 | - |
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- | 0.85 | 1700 | 0.0003 | - |
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- | 0.875 | 1750 | 0.0009 | - |
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- | 0.9 | 1800 | 0.0038 | - |
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- | 0.925 | 1850 | 0.0406 | - |
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- | 0.95 | 1900 | 0.0012 | - |
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- | 0.975 | 1950 | 0.0024 | - |
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- | 1.0 | 2000 | 0.0004 | - |
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  ### Framework Versions
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  - Python: 3.11.0
 
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  - recall
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  - f1
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  widget:
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+ - text: Maintenance to the cambridge.org website is scheduled for 14 March at 12am
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+ – 8am GMT.
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+ - text: Quarterly Earnings
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+ - text: 'So set sail for Long John Silver''s and discover why wa''re America''s most
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+ popular sealood vestments antannro fi '
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+ - text: "\n OPEC oil price\
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+ \ annually 1960-2024\n "
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+ - text: 'RUSSELL WILSON OF THE SEATTLE SEAHAWKS — DURING SUPER BOWL XLVIII '
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  pipeline_tag: text-classification
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  inference: true
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  base_model: sentence-transformers/paraphrase-mpnet-base-v2
 
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  split: test
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  metrics:
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  - type: accuracy
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+ value: 0.8083333333333333
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  name: Accuracy
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  - type: precision
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+ value: 0.7894736842105263
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  name: Precision
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  - type: recall
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+ value: 0.8035714285714286
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  name: Recall
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  - type: f1
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+ value: 0.7964601769911505
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  name: F1
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  ---
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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+ | Label | Examples |
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+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | False | <ul><li>'Learn more about this provider'</li><li>'Verletzte und Festnahmen'</li><li>'Bulgaria'</li></ul> |
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+ | True | <ul><li>'Free Quotes on Doors '</li><li>'Pakistan Cricket Board, Gaddafi Stadium, Ferozepur Road, Lahore, Pakistan. E-Mail: careers@pcb.com.pk '</li><li>"‘here's a new predator in the urban jungle "</li></ul> |
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  ## Evaluation
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  ### Metrics
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  | Label | Accuracy | Precision | Recall | F1 |
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  |:--------|:---------|:----------|:-------|:-------|
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+ | **all** | 0.8083 | 0.7895 | 0.8036 | 0.7965 |
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  ## Uses
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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+ preds = model("Quarterly Earnings")
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  ```
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  <!--
 
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  ### Training Set Metrics
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  | Training set | Min | Median | Max |
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  |:-------------|:----|:-------|:----|
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+ | Word count | 1 | 8.2229 | 242 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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+ | False | 236 |
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+ | True | 244 |
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
 
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0008 | 1 | 0.3892 | - |
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+ | 0.0417 | 50 | 0.2262 | - |
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+ | 0.0833 | 100 | 0.2138 | - |
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+ | 0.125 | 150 | 0.1058 | - |
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+ | 0.1667 | 200 | 0.1327 | - |
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+ | 0.2083 | 250 | 0.098 | - |
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+ | 0.25 | 300 | 0.0719 | - |
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+ | 0.2917 | 350 | 0.0634 | - |
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+ | 0.3333 | 400 | 0.0021 | - |
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+ | 0.375 | 450 | 0.0084 | - |
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+ | 0.4167 | 500 | 0.0799 | - |
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+ | 0.4583 | 550 | 0.0822 | - |
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+ | 0.5 | 600 | 0.0775 | - |
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+ | 0.5417 | 650 | 0.0114 | - |
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+ | 0.5833 | 700 | 0.0013 | - |
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+ | 0.625 | 750 | 0.0121 | - |
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+ | 0.6667 | 800 | 0.1034 | - |
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+ | 0.7083 | 850 | 0.0539 | - |
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+ | 0.75 | 900 | 0.0076 | - |
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+ | 0.7917 | 950 | 0.0114 | - |
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+ | 0.8333 | 1000 | 0.0223 | - |
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+ | 0.875 | 1050 | 0.0208 | - |
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+ | 0.9167 | 1100 | 0.0246 | - |
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+ | 0.9583 | 1150 | 0.0098 | - |
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+ | 1.0 | 1200 | 0.003 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework Versions
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  - Python: 3.11.0
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