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firqaaa
/
indo-setfit-bert-base-p2

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
firqaaa commited on
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Add SetFit model

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Files changed (5) hide show
  1. README.md +802 -145
  2. config.json +1 -1
  3. config_setfit.json +2 -2
  4. model.safetensors +1 -1
  5. model_head.pkl +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ model-index:
32
  split: test
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  metrics:
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  - type: accuracy
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- value: 0.4316742081447964
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  name: Accuracy
37
  ---
38
 
@@ -64,20 +64,20 @@ 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)
65
 
66
  ### Model Labels
67
- | Label | Examples |
68
- |:---------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
69
- | sangat positif | <ul><li>"tema universal untuk menjadi orang yang lebih baik melalui cinta belum pernah difilmkan dengan cara yang lebih menarik daripada di `baran. '"</li><li>'hangat dalam penggambaran manusia sehari-hari yang penuh kasih namun tidak konsisten, santai dalam langkah tenang yang sempurna, dan bangga dalam pesannya.'</li><li>'kecerdasan yang mendalam serta kasih sayang yang hangat dan menyelimuti terhembus dari setiap bingkainya.'</li></ul> |
70
- | sangat negatif | <ul><li>'sebuah film kejam yang dibuat oleh seseorang yang pasti membaca catcher in the rye tetapi jelas-jelas menderita disleksia'</li><li>"all the queen's men adalah film perang kemunduran yang gagal dalam berbagai tingkatan, sehingga harus membayar ganti rugi kepada pemirsa."</li><li>'... ini bahkan bukan film yang bisa kita nikmati sebagai pelarian ringan; ini adalah ketakutan dan frustrasi yang dipicu hingga tingkat yang tidak dapat ditoleransi.'</li></ul> |
71
- | positif | <ul><li>'direktur kredit ramsay karena mengambil cerita yang terkadang mustahil dan membuatnya terasa realistis.'</li><li>'patut diberi hormat hanya karena mencoba menjadi lebih kompleks daripada film rata-rata Anda.'</li><li>'tapi secara keseluruhan, Anda akan menyukai film ini.'</li></ul> |
72
- | negatif | <ul><li>'semua orang harus disalahkan di sini.'</li><li>'tontonan hingar bingar -lrb- di acara TV -rrb- biasanya telah diragi oleh pesona yang secara mencolok hilang dari ledakan layar lebar gadis-gadis itu.'</li><li>'alur cerita, karakter, drama, emosi, ide – semuanya tidak relevan dengan pengalaman melihat raja kalajengking.'</li></ul> |
73
- | netral | <ul><li>'affleck hanya menciptakan garis besar untuk sebuah peran yang masih perlu ia kembangkan, sebuah peran yang dengan mudah dipenuhi dengan otoritas.'</li><li>'sutradara oliver parker bekerja keras untuk mengubah kehidupan menjadi pentingnya bersikap sungguh-sungguh sehingga dia mungkin menarik satu atau dua otot.'</li><li>'clayburgh dan tambor adalah pemain yang menawan; tak satu pun dari mereka pantas menerima Eric Schaeffer.'</li></ul> |
74
 
75
  ## Evaluation
76
 
77
  ### Metrics
78
  | Label | Accuracy |
79
  |:--------|:---------|
80
- | **all** | 0.4317 |
81
 
82
  ## Uses
83
 
@@ -127,17 +127,17 @@ preds = model("itu curang.")
127
  ## Training Details
128
 
129
  ### Training Set Metrics
130
- | Training set | Min | Median | Max |
131
- |:-------------|:----|:-------|:----|
132
- | Word count | 2 | 15.476 | 46 |
133
 
134
  | Label | Training Sample Count |
135
  |:---------------|:----------------------|
136
- | sangat negatif | 200 |
137
- | negatif | 200 |
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- | netral | 200 |
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- | positif | 200 |
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- | sangat positif | 200 |
141
 
142
  ### Training Hyperparameters
143
  - batch_size: (128, 128)
@@ -157,134 +157,791 @@ preds = model("itu curang.")
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  - load_best_model_at_end: True
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159
  ### Training Results
160
- | Epoch | Step | Training Loss | Validation Loss |
161
- |:-------:|:--------:|:-------------:|:---------------:|
162
- | 0.0002 | 1 | 0.3317 | - |
163
- | 0.008 | 50 | 0.2883 | - |
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- | 0.016 | 100 | 0.2625 | - |
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- | 0.024 | 150 | 0.2516 | - |
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- | 0.032 | 200 | 0.2075 | - |
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- | 0.04 | 250 | 0.184 | - |
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- | 0.048 | 300 | 0.1632 | - |
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- | 0.056 | 350 | 0.1105 | - |
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- | 0.064 | 400 | 0.1109 | - |
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- | 0.072 | 450 | 0.0934 | - |
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- | 0.08 | 500 | 0.0518 | - |
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- | 0.088 | 550 | 0.0246 | - |
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- | 0.096 | 600 | 0.0133 | - |
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- | 0.104 | 650 | 0.0056 | - |
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- | 0.112 | 700 | 0.006 | - |
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- | 0.12 | 750 | 0.0072 | - |
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- | 0.128 | 800 | 0.0179 | - |
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- | 0.136 | 850 | 0.0025 | - |
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- | 0.144 | 900 | 0.0019 | - |
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- | 0.152 | 950 | 0.0008 | - |
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- | 0.16 | 1000 | 0.0009 | - |
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- | 0.168 | 1050 | 0.0016 | - |
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- | 0.176 | 1100 | 0.0008 | - |
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- | 0.184 | 1150 | 0.0009 | - |
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- | 0.192 | 1200 | 0.0006 | - |
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- | 0.2 | 1250 | 0.0112 | - |
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- | 0.208 | 1300 | 0.0007 | - |
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- | 0.216 | 1350 | 0.0005 | - |
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- | 0.224 | 1400 | 0.0006 | - |
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- | 0.232 | 1450 | 0.0004 | - |
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- | 0.24 | 1500 | 0.0003 | - |
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- | 0.248 | 1550 | 0.0111 | - |
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- | 0.256 | 1600 | 0.0007 | - |
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- | 0.264 | 1650 | 0.0004 | - |
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- | 0.272 | 1700 | 0.0068 | - |
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- | 0.28 | 1750 | 0.0006 | - |
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- | 0.288 | 1800 | 0.008 | - |
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- | 0.296 | 1850 | 0.0004 | - |
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- | 0.304 | 1900 | 0.0009 | - |
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- | 0.312 | 1950 | 0.0004 | - |
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- | 0.32 | 2000 | 0.0003 | - |
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- | 0.328 | 2050 | 0.0034 | - |
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- | 0.336 | 2100 | 0.0003 | - |
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- | 0.344 | 2150 | 0.0002 | - |
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- | 0.352 | 2200 | 0.0002 | - |
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- | 0.36 | 2250 | 0.0002 | - |
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- | 0.368 | 2300 | 0.0002 | - |
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- | 0.376 | 2350 | 0.0002 | - |
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- | 0.384 | 2400 | 0.0002 | - |
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- | 0.392 | 2450 | 0.0001 | - |
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- | 0.4 | 2500 | 0.0002 | - |
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- | 0.408 | 2550 | 0.0001 | - |
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- | 0.416 | 2600 | 0.0001 | - |
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- | 0.424 | 2650 | 0.0002 | - |
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- | 0.432 | 2700 | 0.0001 | - |
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- | 0.44 | 2750 | 0.0001 | - |
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- | 0.448 | 2800 | 0.0001 | - |
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- | 0.456 | 2850 | 0.0003 | - |
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- | 0.464 | 2900 | 0.0001 | - |
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- | 0.472 | 2950 | 0.0001 | - |
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- | 0.48 | 3000 | 0.0004 | - |
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- | 0.488 | 3050 | 0.0002 | - |
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- | 0.496 | 3100 | 0.0001 | - |
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- | 0.504 | 3150 | 0.0003 | - |
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- | 0.512 | 3200 | 0.0001 | - |
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- | 0.52 | 3250 | 0.0001 | - |
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- | 0.528 | 3300 | 0.0002 | - |
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- | 0.536 | 3350 | 0.0001 | - |
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- | 0.544 | 3400 | 0.0001 | - |
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- | 0.552 | 3450 | 0.0001 | - |
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- | 0.56 | 3500 | 0.0001 | - |
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- | 0.568 | 3550 | 0.0001 | - |
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- | 0.576 | 3600 | 0.0001 | - |
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- | 0.584 | 3650 | 0.0001 | - |
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- | 0.592 | 3700 | 0.0001 | - |
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- | 0.6 | 3750 | 0.0 | - |
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- | 0.608 | 3800 | 0.0001 | - |
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- | 0.616 | 3850 | 0.0001 | - |
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- | 0.624 | 3900 | 0.0001 | - |
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- | 0.632 | 3950 | 0.0001 | - |
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- | 0.64 | 4000 | 0.0003 | - |
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- | 0.648 | 4050 | 0.0001 | - |
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- | 0.656 | 4100 | 0.0001 | - |
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- | 0.664 | 4150 | 0.0001 | - |
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- | 0.672 | 4200 | 0.0001 | - |
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- | 0.68 | 4250 | 0.0001 | - |
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- | 0.688 | 4300 | 0.0001 | - |
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- | 0.696 | 4350 | 0.0001 | - |
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- | 0.704 | 4400 | 0.0001 | - |
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- | 0.712 | 4450 | 0.0001 | - |
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- | 0.72 | 4500 | 0.0001 | - |
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- | 0.728 | 4550 | 0.0001 | - |
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- | 0.736 | 4600 | 0.0001 | - |
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- | 0.744 | 4650 | 0.0001 | - |
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- | 0.752 | 4700 | 0.0001 | - |
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- | 0.76 | 4750 | 0.0001 | - |
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- | 0.768 | 4800 | 0.0001 | - |
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- | 0.776 | 4850 | 0.0001 | - |
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- | 0.784 | 4900 | 0.0001 | - |
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- | 0.792 | 4950 | 0.0001 | - |
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- | 0.8 | 5000 | 0.0 | - |
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- | 0.808 | 5050 | 0.0001 | - |
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- | 0.816 | 5100 | 0.0001 | - |
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- | 0.824 | 5150 | 0.0001 | - |
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- | 0.832 | 5200 | 0.0 | - |
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- | 0.84 | 5250 | 0.0001 | - |
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- | 0.848 | 5300 | 0.0001 | - |
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- | 0.856 | 5350 | 0.0 | - |
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- | 0.864 | 5400 | 0.0001 | - |
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- | 0.872 | 5450 | 0.0001 | - |
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- | 0.88 | 5500 | 0.0001 | - |
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- | 0.888 | 5550 | 0.0001 | - |
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- | 0.896 | 5600 | 0.0 | - |
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- | 0.904 | 5650 | 0.0001 | - |
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- | 0.912 | 5700 | 0.0001 | - |
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- | 0.92 | 5750 | 0.0001 | - |
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- | 0.928 | 5800 | 0.0 | - |
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- | 0.936 | 5850 | 0.0 | - |
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- | 0.944 | 5900 | 0.0 | - |
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- | 0.952 | 5950 | 0.0 | - |
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- | 0.96 | 6000 | 0.0 | - |
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- | 0.968 | 6050 | 0.0 | - |
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- | 0.976 | 6100 | 0.0001 | - |
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- | 0.984 | 6150 | 0.0 | - |
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- | 0.992 | 6200 | 0.0 | - |
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- | **1.0** | **6250** | **0.0** | **0.3546** |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
288
 
289
  * The bold row denotes the saved checkpoint.
290
  ### Framework Versions
 
32
  split: test
33
  metrics:
34
  - type: accuracy
35
+ value: 0.4248868778280543
36
  name: Accuracy
37
  ---
38
 
 
64
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
65
 
66
  ### Model Labels
67
+ | Label | Examples |
68
+ |:---------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
69
+ | negatif | <ul><li>'orang aneh berkaki delapan tidak akan bergabung dengan jajaran film monster\\/fiksi ilmiah hebat yang kita sukai ...'</li><li>"Terlepas dari latar Hawaii, hiasan fiksi ilmiah, dan beberapa momen slapstick yang gaduh, plot dasar `` lilo '' bisa saja diambil dari naskah kuno Shirley Temple yang berlumuran air mata."</li><li>'ini adalah film yang sangat tidak aman dalam kemampuannya untuk menggairahkan sehingga menghasilkan bukan hanya satu tapi dua badai petir palsu untuk menggarisbawahi aksinya.'</li></ul> |
70
+ | positif | <ul><li>'plot dari comeback curlers sebenarnya tidak terlalu menarik, tapi yang aku suka dari pria dengan sapu dan yang spesial adalah bagaimana filmnya mengetahui apa yang unik dan nyentrik dari orang Kanada.'</li><li>'sebuah studi psikologis yang dingin, merenung, namun bergema secara diam-diam mengenai ketegangan dan ketidakbahagiaan dalam rumah tangga.'</li><li>'seperti yang biasa mereka katakan di film-film fiksi ilmiah tahun 1950-an, tanda-tanda adalah penghormatan terhadap hadiah Shyamalan, yang sedemikian rupa sehingga kita akan terus mengawasi langit untuk proyek berikutnya.'</li></ul> |
71
+ | sangat negatif | <ul><li>"benar-benar transparan adalah serangan tanpa henti dari naskah tersebut berupa lelucon-lelucon seks memalukan yang berbau penulisan ulang naskah yang dirancang untuk membuat film tersebut mendapat peringkat `` lebih keren '' pg-13."</li><li>'bagaikan latihan improvisasi yang buruk, karakter-karakter yang ditulis secara dangkal mengoceh dengan membosankan tentang kehidupan, cinta, dan seni yang sedang mereka perjuangkan untuk ciptakan.'</li><li>'dari semua Halloween, ini yang paling tidak menarik secara visual.'</li></ul> |
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+ | netral | <ul><li>'film ini tidak menghormati hukum, kebenaran politik, atau kesopanan umum, namun menampilkan sesuatu yang lebih penting: rasa hormat terhadap orang-orang yang cacat dan gila.'</li><li>'tertahan oleh kekhidmatannya sendiri.'</li><li>'lebih banyak pertunjukan vaudeville daripada narasi yang dibangun dengan baik, namun dalam hal ini tidak menyinggung dan sebenarnya agak manis.'</li></ul> |
73
+ | sangat positif | <ul><li>'hawn dan sarandon membentuk ikatan akting yang menjadikan banger bersaudara studi karakter yang menarik sambil tertawa.'</li><li>'isabelle huppert unggul sebagai mika yang penuh teka-teki dan anna mouglais adalah bakat muda baru yang menakjubkan dalam salah satu misteri psikologis chabrol yang paling intens.'</li><li>'williams menciptakan gambaran menakjubkan seperti sopir taksi tentang seorang pria yang tertatih-tatih di ambang kewarasan.'</li></ul> |
74
 
75
  ## Evaluation
76
 
77
  ### Metrics
78
  | Label | Accuracy |
79
  |:--------|:---------|
80
+ | **all** | 0.4249 |
81
 
82
  ## Uses
83
 
 
127
  ## Training Details
128
 
129
  ### Training Set Metrics
130
+ | Training set | Min | Median | Max |
131
+ |:-------------|:----|:--------|:----|
132
+ | Word count | 1 | 15.7676 | 46 |
133
 
134
  | Label | Training Sample Count |
135
  |:---------------|:----------------------|
136
+ | sangat negatif | 500 |
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+ | negatif | 500 |
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+ | netral | 500 |
139
+ | positif | 500 |
140
+ | sangat positif | 500 |
141
 
142
  ### Training Hyperparameters
143
  - batch_size: (128, 128)
 
157
  - load_best_model_at_end: True
158
 
159
  ### Training Results
160
+ | Epoch | Step | Training Loss | Validation Loss |
161
+ |:-------:|:---------:|:-------------:|:---------------:|
162
+ | 0.0000 | 1 | 0.3367 | - |
163
+ | 0.0013 | 50 | 0.3139 | - |
164
+ | 0.0026 | 100 | 0.3005 | - |
165
+ | 0.0038 | 150 | 0.2627 | - |
166
+ | 0.0051 | 200 | 0.2701 | - |
167
+ | 0.0064 | 250 | 0.2647 | - |
168
+ | 0.0077 | 300 | 0.2646 | - |
169
+ | 0.0090 | 350 | 0.2494 | - |
170
+ | 0.0102 | 400 | 0.2356 | - |
171
+ | 0.0115 | 450 | 0.2093 | - |
172
+ | 0.0128 | 500 | 0.2187 | - |
173
+ | 0.0141 | 550 | 0.2131 | - |
174
+ | 0.0154 | 600 | 0.2288 | - |
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+ | 0.0166 | 650 | 0.1996 | - |
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+ | 0.0179 | 700 | 0.1825 | - |
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+ | 0.0192 | 750 | 0.1887 | - |
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+ | 0.0205 | 800 | 0.1809 | - |
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+ | 0.0218 | 850 | 0.1756 | - |
180
+ | 0.0230 | 900 | 0.155 | - |
181
+ | 0.0243 | 950 | 0.1462 | - |
182
+ | 0.0256 | 1000 | 0.1455 | - |
183
+ | 0.0269 | 1050 | 0.1547 | - |
184
+ | 0.0282 | 1100 | 0.0863 | - |
185
+ | 0.0294 | 1150 | 0.1362 | - |
186
+ | 0.0307 | 1200 | 0.1096 | - |
187
+ | 0.0320 | 1250 | 0.0898 | - |
188
+ | 0.0333 | 1300 | 0.1202 | - |
189
+ | 0.0346 | 1350 | 0.0916 | - |
190
+ | 0.0358 | 1400 | 0.0918 | - |
191
+ | 0.0371 | 1450 | 0.1022 | - |
192
+ | 0.0384 | 1500 | 0.0518 | - |
193
+ | 0.0397 | 1550 | 0.0587 | - |
194
+ | 0.0410 | 1600 | 0.0526 | - |
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+ | 0.0422 | 1650 | 0.0461 | - |
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+ | 0.0435 | 1700 | 0.0617 | - |
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+ | 0.0448 | 1750 | 0.0426 | - |
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+ | 0.0461 | 1800 | 0.0347 | - |
199
+ | 0.0474 | 1850 | 0.0255 | - |
200
+ | 0.0486 | 1900 | 0.0349 | - |
201
+ | 0.0499 | 1950 | 0.0121 | - |
202
+ | 0.0512 | 2000 | 0.0164 | - |
203
+ | 0.0525 | 2050 | 0.0077 | - |
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+ | 0.0538 | 2100 | 0.0084 | - |
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+ | 0.0550 | 2150 | 0.006 | - |
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+ | 0.0563 | 2200 | 0.0143 | - |
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+ | 0.0576 | 2250 | 0.0123 | - |
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+ | 0.0589 | 2300 | 0.0154 | - |
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+ | 0.0602 | 2350 | 0.0108 | - |
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+ | 0.0614 | 2400 | 0.0041 | - |
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+ | 0.0627 | 2450 | 0.0048 | - |
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+ | 0.0640 | 2500 | 0.0103 | - |
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+ | 0.0653 | 2550 | 0.0099 | - |
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+ | 0.0666 | 2600 | 0.026 | - |
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+ | 0.0678 | 2650 | 0.0095 | - |
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+ | 0.0691 | 2700 | 0.0091 | - |
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+ | 0.0704 | 2750 | 0.0041 | - |
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+ | 0.0717 | 2800 | 0.005 | - |
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+ | 0.0730 | 2850 | 0.0024 | - |
220
+ | 0.0742 | 2900 | 0.0013 | - |
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+ | 0.0755 | 2950 | 0.0067 | - |
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+ | 0.0768 | 3000 | 0.0009 | - |
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+ | 0.0781 | 3050 | 0.0042 | - |
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+ | 0.0794 | 3100 | 0.0039 | - |
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+ | 0.0806 | 3150 | 0.0023 | - |
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+ | 0.0819 | 3200 | 0.0032 | - |
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+ | 0.0832 | 3250 | 0.0071 | - |
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+ | 0.0845 | 3300 | 0.013 | - |
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+ | 0.0858 | 3350 | 0.015 | - |
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+ | 0.0870 | 3400 | 0.0013 | - |
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+ | 0.0883 | 3450 | 0.0012 | - |
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+ | 0.0896 | 3500 | 0.0017 | - |
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+ | 0.0909 | 3550 | 0.002 | - |
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+ | 0.0922 | 3600 | 0.0247 | - |
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+ | 0.0934 | 3650 | 0.0044 | - |
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+ | 0.0947 | 3700 | 0.0004 | - |
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+ | 0.0960 | 3750 | 0.0031 | - |
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+ | 0.0973 | 3800 | 0.0235 | - |
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+ | 0.0986 | 3850 | 0.0017 | - |
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+ | 0.0998 | 3900 | 0.001 | - |
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+ | 0.1011 | 3950 | 0.0065 | - |
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+ | 0.1024 | 4000 | 0.0043 | - |
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+ | 0.1037 | 4050 | 0.0051 | - |
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+ | 0.1050 | 4100 | 0.0009 | - |
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+ | 0.1062 | 4150 | 0.0006 | - |
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+ | 0.1075 | 4200 | 0.0081 | - |
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+ | 0.1088 | 4250 | 0.0005 | - |
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+ | 0.1101 | 4300 | 0.0155 | - |
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+ | 0.1114 | 4350 | 0.0091 | - |
250
+ | 0.1126 | 4400 | 0.0187 | - |
251
+ | 0.1139 | 4450 | 0.0011 | - |
252
+ | 0.1152 | 4500 | 0.0037 | - |
253
+ | 0.1165 | 4550 | 0.0033 | - |
254
+ | 0.1178 | 4600 | 0.0006 | - |
255
+ | 0.1190 | 4650 | 0.0024 | - |
256
+ | 0.1203 | 4700 | 0.0008 | - |
257
+ | 0.1216 | 4750 | 0.0007 | - |
258
+ | 0.1229 | 4800 | 0.0012 | - |
259
+ | 0.1242 | 4850 | 0.0113 | - |
260
+ | 0.1254 | 4900 | 0.0004 | - |
261
+ | 0.1267 | 4950 | 0.0059 | - |
262
+ | 0.1280 | 5000 | 0.0004 | - |
263
+ | 0.1293 | 5050 | 0.001 | - |
264
+ | 0.1306 | 5100 | 0.0001 | - |
265
+ | 0.1318 | 5150 | 0.002 | - |
266
+ | 0.1331 | 5200 | 0.0006 | - |
267
+ | 0.1344 | 5250 | 0.0007 | - |
268
+ | 0.1357 | 5300 | 0.0026 | - |
269
+ | 0.1370 | 5350 | 0.0079 | - |
270
+ | 0.1382 | 5400 | 0.001 | - |
271
+ | 0.1395 | 5450 | 0.0065 | - |
272
+ | 0.1408 | 5500 | 0.0009 | - |
273
+ | 0.1421 | 5550 | 0.0008 | - |
274
+ | 0.1434 | 5600 | 0.0003 | - |
275
+ | 0.1446 | 5650 | 0.0002 | - |
276
+ | 0.1459 | 5700 | 0.0001 | - |
277
+ | 0.1472 | 5750 | 0.0027 | - |
278
+ | 0.1485 | 5800 | 0.0002 | - |
279
+ | 0.1498 | 5850 | 0.0002 | - |
280
+ | 0.1510 | 5900 | 0.0003 | - |
281
+ | 0.1523 | 5950 | 0.0001 | - |
282
+ | 0.1536 | 6000 | 0.0061 | - |
283
+ | 0.1549 | 6050 | 0.0066 | - |
284
+ | 0.1562 | 6100 | 0.0015 | - |
285
+ | 0.1574 | 6150 | 0.016 | - |
286
+ | 0.1587 | 6200 | 0.0009 | - |
287
+ | 0.1600 | 6250 | 0.0062 | - |
288
+ | 0.1613 | 6300 | 0.0002 | - |
289
+ | 0.1626 | 6350 | 0.0002 | - |
290
+ | 0.1638 | 6400 | 0.0002 | - |
291
+ | 0.1651 | 6450 | 0.0153 | - |
292
+ | 0.1664 | 6500 | 0.0031 | - |
293
+ | 0.1677 | 6550 | 0.0003 | - |
294
+ | 0.1690 | 6600 | 0.0009 | - |
295
+ | 0.1702 | 6650 | 0.0043 | - |
296
+ | 0.1715 | 6700 | 0.0007 | - |
297
+ | 0.1728 | 6750 | 0.0002 | - |
298
+ | 0.1741 | 6800 | 0.0001 | - |
299
+ | 0.1754 | 6850 | 0.0003 | - |
300
+ | 0.1766 | 6900 | 0.0013 | - |
301
+ | 0.1779 | 6950 | 0.0003 | - |
302
+ | 0.1792 | 7000 | 0.0002 | - |
303
+ | 0.1805 | 7050 | 0.0001 | - |
304
+ | 0.1818 | 7100 | 0.0001 | - |
305
+ | 0.1830 | 7150 | 0.0001 | - |
306
+ | 0.1843 | 7200 | 0.0001 | - |
307
+ | 0.1856 | 7250 | 0.0003 | - |
308
+ | 0.1869 | 7300 | 0.0001 | - |
309
+ | 0.1882 | 7350 | 0.0002 | - |
310
+ | 0.1894 | 7400 | 0.0012 | - |
311
+ | 0.1907 | 7450 | 0.0001 | - |
312
+ | 0.1920 | 7500 | 0.0002 | - |
313
+ | 0.1933 | 7550 | 0.0002 | - |
314
+ | 0.1946 | 7600 | 0.0003 | - |
315
+ | 0.1958 | 7650 | 0.0014 | - |
316
+ | 0.1971 | 7700 | 0.0093 | - |
317
+ | 0.1984 | 7750 | 0.0001 | - |
318
+ | 0.1997 | 7800 | 0.0005 | - |
319
+ | 0.2010 | 7850 | 0.0001 | - |
320
+ | 0.2022 | 7900 | 0.0001 | - |
321
+ | 0.2035 | 7950 | 0.0058 | - |
322
+ | 0.2048 | 8000 | 0.0002 | - |
323
+ | 0.2061 | 8050 | 0.0001 | - |
324
+ | 0.2074 | 8100 | 0.0002 | - |
325
+ | 0.2086 | 8150 | 0.0003 | - |
326
+ | 0.2099 | 8200 | 0.0003 | - |
327
+ | 0.2112 | 8250 | 0.0068 | - |
328
+ | 0.2125 | 8300 | 0.0004 | - |
329
+ | 0.2138 | 8350 | 0.0002 | - |
330
+ | 0.2150 | 8400 | 0.0001 | - |
331
+ | 0.2163 | 8450 | 0.0002 | - |
332
+ | 0.2176 | 8500 | 0.0001 | - |
333
+ | 0.2189 | 8550 | 0.0002 | - |
334
+ | 0.2202 | 8600 | 0.0001 | - |
335
+ | 0.2214 | 8650 | 0.0001 | - |
336
+ | 0.2227 | 8700 | 0.0001 | - |
337
+ | 0.2240 | 8750 | 0.0001 | - |
338
+ | 0.2253 | 8800 | 0.0001 | - |
339
+ | 0.2266 | 8850 | 0.0006 | - |
340
+ | 0.2278 | 8900 | 0.0 | - |
341
+ | 0.2291 | 8950 | 0.0 | - |
342
+ | 0.2304 | 9000 | 0.0001 | - |
343
+ | 0.2317 | 9050 | 0.0 | - |
344
+ | 0.2330 | 9100 | 0.0001 | - |
345
+ | 0.2342 | 9150 | 0.0 | - |
346
+ | 0.2355 | 9200 | 0.0001 | - |
347
+ | 0.2368 | 9250 | 0.0 | - |
348
+ | 0.2381 | 9300 | 0.0001 | - |
349
+ | 0.2394 | 9350 | 0.0001 | - |
350
+ | 0.2406 | 9400 | 0.0 | - |
351
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352
+ | 0.2432 | 9500 | 0.0001 | - |
353
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354
+ | 0.2458 | 9600 | 0.0001 | - |
355
+ | 0.2470 | 9650 | 0.0001 | - |
356
+ | 0.2483 | 9700 | 0.003 | - |
357
+ | 0.2496 | 9750 | 0.0077 | - |
358
+ | 0.2509 | 9800 | 0.0099 | - |
359
+ | 0.2522 | 9850 | 0.0223 | - |
360
+ | 0.2534 | 9900 | 0.0002 | - |
361
+ | 0.2547 | 9950 | 0.0001 | - |
362
+ | 0.2560 | 10000 | 0.003 | - |
363
+ | 0.2573 | 10050 | 0.0118 | - |
364
+ | 0.2586 | 10100 | 0.0002 | - |
365
+ | 0.2598 | 10150 | 0.0022 | - |
366
+ | 0.2611 | 10200 | 0.0001 | - |
367
+ | 0.2624 | 10250 | 0.0077 | - |
368
+ | 0.2637 | 10300 | 0.0003 | - |
369
+ | 0.2650 | 10350 | 0.0 | - |
370
+ | 0.2662 | 10400 | 0.0074 | - |
371
+ | 0.2675 | 10450 | 0.0072 | - |
372
+ | 0.2688 | 10500 | 0.0001 | - |
373
+ | 0.2701 | 10550 | 0.008 | - |
374
+ | 0.2714 | 10600 | 0.0001 | - |
375
+ | 0.2726 | 10650 | 0.0001 | - |
376
+ | 0.2739 | 10700 | 0.0 | - |
377
+ | 0.2752 | 10750 | 0.0001 | - |
378
+ | 0.2765 | 10800 | 0.0074 | - |
379
+ | 0.2778 | 10850 | 0.0001 | - |
380
+ | 0.2790 | 10900 | 0.0001 | - |
381
+ | 0.2803 | 10950 | 0.0003 | - |
382
+ | 0.2816 | 11000 | 0.0004 | - |
383
+ | 0.2829 | 11050 | 0.0078 | - |
384
+ | 0.2842 | 11100 | 0.0 | - |
385
+ | 0.2854 | 11150 | 0.0001 | - |
386
+ | 0.2867 | 11200 | 0.0001 | - |
387
+ | 0.2880 | 11250 | 0.0001 | - |
388
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389
+ | 0.2906 | 11350 | 0.0001 | - |
390
+ | 0.2918 | 11400 | 0.0001 | - |
391
+ | 0.2931 | 11450 | 0.0004 | - |
392
+ | 0.2944 | 11500 | 0.0002 | - |
393
+ | 0.2957 | 11550 | 0.0 | - |
394
+ | 0.2970 | 11600 | 0.0 | - |
395
+ | 0.2982 | 11650 | 0.0078 | - |
396
+ | 0.2995 | 11700 | 0.0 | - |
397
+ | 0.3008 | 11750 | 0.0005 | - |
398
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399
+ | 0.3034 | 11850 | 0.0 | - |
400
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401
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402
+ | 0.3072 | 12000 | 0.0006 | - |
403
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404
+ | 0.3098 | 12100 | 0.0001 | - |
405
+ | 0.3110 | 12150 | 0.0 | - |
406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
+ | 0.3635 | 14200 | 0.0074 | - |
447
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448
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449
+ | 0.3674 | 14350 | 0.0001 | - |
450
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451
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452
+ | 0.3712 | 14500 | 0.0 | - |
453
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454
+ | 0.3738 | 14600 | 0.0 | - |
455
+ | 0.3750 | 14650 | 0.0002 | - |
456
+ | 0.3763 | 14700 | 0.0001 | - |
457
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458
+ | 0.3789 | 14800 | 0.0001 | - |
459
+ | 0.3802 | 14850 | 0.0 | - |
460
+ | 0.3814 | 14900 | 0.0001 | - |
461
+ | 0.3827 | 14950 | 0.0 | - |
462
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463
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464
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465
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466
+ | 0.3891 | 15200 | 0.0 | - |
467
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468
+ | 0.3917 | 15300 | 0.0001 | - |
469
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470
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471
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472
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473
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474
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475
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476
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477
+ | 0.4032 | 15750 | 0.0001 | - |
478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
+ | 0.4250 | 16600 | 0.0 | - |
495
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496
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497
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498
+ | 0.4301 | 16800 | 0.0 | - |
499
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500
+ | 0.4326 | 16900 | 0.0 | - |
501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
+ | 0.4506 | 17600 | 0.0 | - |
515
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516
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517
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518
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519
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520
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521
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522
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523
+ | 0.4621 | 18050 | 0.0001 | - |
524
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525
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526
+ | 0.4659 | 18200 | 0.0001 | - |
527
+ | 0.4672 | 18250 | 0.0 | - |
528
+ | 0.4685 | 18300 | 0.0 | - |
529
+ | 0.4698 | 18350 | 0.0001 | - |
530
+ | 0.4710 | 18400 | 0.0 | - |
531
+ | 0.4723 | 18450 | 0.0 | - |
532
+ | 0.4736 | 18500 | 0.0 | - |
533
+ | 0.4749 | 18550 | 0.0 | - |
534
+ | 0.4762 | 18600 | 0.0 | - |
535
+ | 0.4774 | 18650 | 0.0 | - |
536
+ | 0.4787 | 18700 | 0.0 | - |
537
+ | 0.4800 | 18750 | 0.0 | - |
538
+ | 0.4813 | 18800 | 0.0 | - |
539
+ | 0.4826 | 18850 | 0.0 | - |
540
+ | 0.4838 | 18900 | 0.0 | - |
541
+ | 0.4851 | 18950 | 0.0 | - |
542
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543
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544
+ | 0.4890 | 19100 | 0.0 | - |
545
+ | 0.4902 | 19150 | 0.0 | - |
546
+ | 0.4915 | 19200 | 0.0 | - |
547
+ | 0.4928 | 19250 | 0.0 | - |
548
+ | 0.4941 | 19300 | 0.0 | - |
549
+ | 0.4954 | 19350 | 0.0 | - |
550
+ | 0.4966 | 19400 | 0.0 | - |
551
+ | 0.4979 | 19450 | 0.0 | - |
552
+ | 0.4992 | 19500 | 0.0 | - |
553
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554
+ | 0.5018 | 19600 | 0.0 | - |
555
+ | 0.5030 | 19650 | 0.0 | - |
556
+ | 0.5043 | 19700 | 0.0 | - |
557
+ | 0.5056 | 19750 | 0.0 | - |
558
+ | 0.5069 | 19800 | 0.0 | - |
559
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560
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561
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562
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563
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564
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565
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566
+ | 0.5171 | 20200 | 0.0 | - |
567
+ | 0.5184 | 20250 | 0.0 | - |
568
+ | 0.5197 | 20300 | 0.0 | - |
569
+ | 0.5210 | 20350 | 0.0 | - |
570
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571
+ | 0.5235 | 20450 | 0.0 | - |
572
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573
+ | 0.5261 | 20550 | 0.0 | - |
574
+ | 0.5274 | 20600 | 0.0 | - |
575
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576
+ | 0.5299 | 20700 | 0.0 | - |
577
+ | 0.5312 | 20750 | 0.0 | - |
578
+ | 0.5325 | 20800 | 0.0 | - |
579
+ | 0.5338 | 20850 | 0.0 | - |
580
+ | 0.5350 | 20900 | 0.0 | - |
581
+ | 0.5363 | 20950 | 0.0 | - |
582
+ | 0.5376 | 21000 | 0.0 | - |
583
+ | 0.5389 | 21050 | 0.0 | - |
584
+ | 0.5402 | 21100 | 0.0 | - |
585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
+ | 0.5594 | 21850 | 0.0 | - |
600
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601
+ | 0.5619 | 21950 | 0.0 | - |
602
+ | 0.5632 | 22000 | 0.0 | - |
603
+ | 0.5645 | 22050 | 0.0 | - |
604
+ | 0.5658 | 22100 | 0.0 | - |
605
+ | 0.5670 | 22150 | 0.0 | - |
606
+ | 0.5683 | 22200 | 0.0 | - |
607
+ | 0.5696 | 22250 | 0.0 | - |
608
+ | 0.5709 | 22300 | 0.0 | - |
609
+ | 0.5722 | 22350 | 0.0 | - |
610
+ | 0.5734 | 22400 | 0.0 | - |
611
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612
+ | 0.5760 | 22500 | 0.0 | - |
613
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614
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615
+ | 0.5798 | 22650 | 0.0 | - |
616
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617
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618
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619
+ | 0.5850 | 22850 | 0.0 | - |
620
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621
+ | 0.5875 | 22950 | 0.0 | - |
622
+ | 0.5888 | 23000 | 0.0 | - |
623
+ | 0.5901 | 23050 | 0.0 | - |
624
+ | 0.5914 | 23100 | 0.0 | - |
625
+ | 0.5926 | 23150 | 0.0 | - |
626
+ | 0.5939 | 23200 | 0.0 | - |
627
+ | 0.5952 | 23250 | 0.0 | - |
628
+ | 0.5965 | 23300 | 0.0 | - |
629
+ | 0.5978 | 23350 | 0.0 | - |
630
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631
+ | 0.6003 | 23450 | 0.0 | - |
632
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633
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634
+ | 0.6042 | 23600 | 0.0 | - |
635
+ | 0.6054 | 23650 | 0.0 | - |
636
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946
  * The bold row denotes the saved checkpoint.
947
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