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firqaaa
/
indo-setfit-bert-base-p1

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
firqaaa commited on
Commit
65528c0
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1 Parent(s): 5b6b921

Add SetFit model

Browse files
Files changed (6) hide show
  1. 1_Pooling/config.json +1 -3
  2. README.md +476 -166
  3. config.json +1 -1
  4. config_setfit.json +2 -2
  5. model.safetensors +1 -1
  6. model_head.pkl +1 -1
1_Pooling/config.json CHANGED
@@ -3,7 +3,5 @@
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  "pooling_mode_cls_token": false,
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  "pooling_mode_mean_tokens": true,
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  "pooling_mode_max_tokens": false,
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- "pooling_mode_mean_sqrt_len_tokens": false,
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- "pooling_mode_weightedmean_tokens": false,
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- "pooling_mode_lasttoken": false
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  }
 
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  "pooling_mode_cls_token": false,
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  "pooling_mode_mean_tokens": true,
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  "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
 
 
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  }
README.md CHANGED
@@ -32,7 +32,7 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.8
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  name: Accuracy
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  ---
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@@ -64,17 +64,17 @@ 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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66
  ### Model Labels
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- | Label | Examples |
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- |:--------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | positif | <ul><li>'benar-benar lucu'</li><li>'gulungan dari sebuah tong tong yang tersesat'</li><li>', mereka menemukan rute-rute baru melalui lingkungan yang sudah dikenal'</li></ul> |
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- | negatif | <ul><li>'tidak menarik atau berbau tidak sedap'</li><li>"telah melakukan kesalahan nyaris fatal dengan menjadi apa yang orang Inggris sebut 'terlalu pintar setengah mati'."</li><li>'untuk roboh'</li></ul> |
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72
  ## Evaluation
73
 
74
  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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- | **all** | 0.8 |
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  ## Uses
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@@ -126,12 +126,12 @@ preds = model("film yang cepat, lucu, dan sangat menghibur.")
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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 | 9.4825 | 51 |
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  | Label | Training Sample Count |
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  |:--------|:----------------------|
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- | negatif | 200 |
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- | positif | 200 |
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136
  ### Training Hyperparameters
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  - batch_size: (32, 32)
@@ -151,168 +151,478 @@ preds = model("film yang cepat, lucu, dan sangat menghibur.")
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  - load_best_model_at_end: True
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:-------:|:--------:|:-------------:|:---------------:|
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- | 0.0004 | 1 | 0.3079 | - |
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- | 0.0199 | 50 | 0.3644 | - |
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- | 0.0398 | 100 | 0.2816 | - |
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- | 0.0597 | 150 | 0.2254 | - |
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- | 0.0796 | 200 | 0.1798 | - |
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- | 0.0995 | 250 | 0.0478 | - |
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- | 0.1194 | 300 | 0.0049 | - |
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- | 0.1393 | 350 | 0.0016 | - |
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- | 0.1592 | 400 | 0.0011 | - |
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- | 0.1791 | 450 | 0.0005 | - |
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- | 0.1990 | 500 | 0.0003 | - |
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- | 0.2189 | 550 | 0.0004 | - |
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- | 0.2388 | 600 | 0.0003 | - |
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- | 0.2587 | 650 | 0.0003 | - |
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- | 0.2786 | 700 | 0.0001 | - |
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- | 0.2984 | 750 | 0.0002 | - |
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- | 0.3183 | 800 | 0.0001 | - |
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- | 0.3382 | 850 | 0.0001 | - |
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- | 0.3581 | 900 | 0.0001 | - |
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- | 0.3780 | 950 | 0.0001 | - |
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- | 0.3979 | 1000 | 0.0001 | - |
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- | 0.4178 | 1050 | 0.0001 | - |
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- | 0.4377 | 1100 | 0.0001 | - |
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- | 0.4576 | 1150 | 0.0001 | - |
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- | 0.4775 | 1200 | 0.0001 | - |
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- | 0.4974 | 1250 | 0.0001 | - |
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- | 0.5173 | 1300 | 0.0001 | - |
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- | 0.5372 | 1350 | 0.0001 | - |
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- | 0.5571 | 1400 | 0.0001 | - |
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- | 0.5969 | 1500 | 0.0001 | - |
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- | 0.6168 | 1550 | 0.0001 | - |
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- | 0.6367 | 1600 | 0.0001 | - |
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- | 0.7362 | 1850 | 0.0001 | - |
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- | 0.7561 | 1900 | 0.0001 | - |
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- | 0.7760 | 1950 | 0.0001 | - |
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- | 0.7959 | 2000 | 0.0001 | - |
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- | 0.9948 | 2500 | 0.0 | - |
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- | **1.0** | **2513** | **-** | **0.2622** |
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- | 1.0147 | 2550 | 0.0 | - |
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- | 1.0346 | 2600 | 0.0 | - |
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- | 1.1341 | 2850 | 0.0 | - |
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- | 1.1540 | 2900 | 0.0 | - |
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- | 1.1739 | 2950 | 0.0 | - |
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- | 1.1938 | 3000 | 0.0 | - |
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- | 1.2137 | 3050 | 0.0 | - |
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- | 2.0 | 5026 | - | 0.269 |
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- | 2.9845 | 7500 | 0.0 | - |
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- | 3.0 | 7539 | - | 0.2744 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
310
 
311
  * The bold row denotes the saved checkpoint.
312
  ### Framework Versions
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  - Python: 3.10.13
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  - SetFit: 1.0.3
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- - Sentence Transformers: 2.3.1
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  - Transformers: 4.36.2
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  - PyTorch: 2.1.2+cu121
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  - Datasets: 2.16.1
 
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  split: test
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  metrics:
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  - type: accuracy
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+ value: 0.82
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  name: Accuracy
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  ---
38
 
 
64
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
65
 
66
  ### Model Labels
67
+ | Label | Examples |
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+ |:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | positif | <ul><li>'Layak untuk kunjungan lain.'</li><li>'gulungan dari sebuah tong tong yang tersesat'</li><li>'adalah film yang hebat .'</li></ul> |
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+ | negatif | <ul><li>'Anda berada di rumah menonton film itu daripada di bioskop menonton yang ini.'</li><li>'dengan banyak warna biru gelap dan merah muda yang serius'</li><li>'hal buruk'</li></ul> |
71
 
72
  ## Evaluation
73
 
74
  ### Metrics
75
  | Label | Accuracy |
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  |:--------|:---------|
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+ | **all** | 0.82 |
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  ## Uses
80
 
 
126
  ### Training Set Metrics
127
  | Training set | Min | Median | Max |
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  |:-------------|:----|:-------|:----|
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+ | Word count | 1 | 9.4029 | 51 |
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  | Label | Training Sample Count |
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  |:--------|:----------------------|
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+ | negatif | 350 |
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+ | positif | 350 |
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  ### Training Hyperparameters
137
  - batch_size: (32, 32)
 
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  - load_best_model_at_end: True
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  ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.3328 | - |
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+ | 0.0065 | 50 | 0.4117 | - |
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+ | 0.0130 | 100 | 0.2903 | - |
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+ | 0.0195 | 150 | 0.3104 | - |
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+ | 0.0260 | 200 | 0.2411 | - |
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+ | 0.0326 | 250 | 0.2341 | - |
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+ | 0.0391 | 300 | 0.2144 | - |
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+ | 0.0456 | 350 | 0.1785 | - |
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+ | 0.0521 | 400 | 0.1649 | - |
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+ | 0.0586 | 450 | 0.037 | - |
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+ | 0.0651 | 500 | 0.0447 | - |
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+ | 0.0716 | 550 | 0.0472 | - |
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+ | 0.0781 | 600 | 0.0361 | - |
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+ | 0.0846 | 650 | 0.0016 | - |
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+ | 0.0912 | 700 | 0.0013 | - |
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+ | 0.0977 | 750 | 0.0011 | - |
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+ | 0.1042 | 800 | 0.0006 | - |
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+ | 0.8530 | 6550 | 0.0 | - |
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+ | **3.0** | **23037** | **-** | **0.2771** |
620
 
621
  * The bold row denotes the saved checkpoint.
622
  ### Framework Versions
623
  - Python: 3.10.13
624
  - SetFit: 1.0.3
625
+ - Sentence Transformers: 2.2.2
626
  - Transformers: 4.36.2
627
  - PyTorch: 2.1.2+cu121
628
  - Datasets: 2.16.1
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