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surazbhandari
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all-MiniLM-L6-v2-ProductMatching

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
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1 Parent(s): 0ada64f

Initial upload of fine-tuned Product Matching model

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Files changed (11) hide show
  1. 1_Pooling/config.json +8 -8
  2. README.md +827 -827
  3. config.json +24 -24
  4. config_sentence_transformers.json +12 -12
  5. modules.json +18 -18
  6. sentence_bert_config.json +2 -2
  7. special_tokens_map.json +35 -35
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +61 -61
  10. upload_to_hf.py +28 -24
  11. vocab.txt +762 -762
1_Pooling/config.json CHANGED
@@ -1,10 +1,10 @@
1
  {
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- "word_embedding_dimension": 384,
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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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- "include_prompt": true
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  }
 
1
  {
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+ "word_embedding_dimension": 384,
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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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+ "include_prompt": true
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  }
README.md CHANGED
@@ -79,9 +79,9 @@ The model was fine-tuned on a diverse corpus covering:
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80
  ```
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  SentenceTransformer(
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- (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
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- (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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- (2): Normalize()
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  )
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  ```
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@@ -168,24 +168,24 @@ You can finetune this model on your own dataset.
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  * Size: 9,950,000 training samples
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  * Columns: <code>anchor</code>, <code>positive</code>, and <code>pair_type</code>
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  * Approximate statistics based on the first 1000 samples:
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- | | anchor | positive | pair_type |
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- |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
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- | type | string | string | string |
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- | details | <ul><li>min: 6 tokens</li><li>mean: 11.91 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.71 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
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  * Samples:
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- | anchor | positive | pair_type |
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- |:------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------|
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- | <code>Swingline \| Pen Set \| Electric \| Blue</code> | <code>New Swingline Pen Set Letter 8.5x11</code> | <code>title_title</code> |
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- | <code>Sticky Notes by Herman Miller - 200-Sheet Blue</code> | <code>Herman Miller Sticky Notes - Leather - Recycled - Legal</code> | <code>title_title</code> |
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- | <code>Post-it Paper Shredder Blank Bluetooth</code> | <code>Post-it Paper Shredder w/ Bluetooth, Inkjet</code> | <code>title_title</code> |
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  * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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- ```json
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- {
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- "scale": 20.0,
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- "similarity_fct": "cos_sim",
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- "gather_across_devices": false
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- }
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- ```
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190
  ### Evaluation Dataset
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@@ -195,24 +195,24 @@ You can finetune this model on your own dataset.
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  * Size: 50,000 evaluation samples
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  * Columns: <code>anchor</code>, <code>positive</code>, and <code>pair_type</code>
197
  * Approximate statistics based on the first 1000 samples:
198
- | | anchor | positive | pair_type |
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- |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
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- | type | string | string | string |
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- | details | <ul><li>min: 6 tokens</li><li>mean: 12.21 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.91 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
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  * Samples:
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- | anchor | positive | pair_type |
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- |:-----------------------------------------------------|:--------------------------------------------------------------|:-------------------------|
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- | <code>Whiteboard by Staples - Red Heavy Duty</code> | <code>Staples Whiteboard Inkjet/Black</code> | <code>title_title</code> |
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- | <code>Avery Blank Printer, Blue</code> | <code>Avery Printer w/ Ergonomic, Red</code> | <code>title_title</code> |
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- | <code>Copy Paper by Quartet - Wireless 8.5x11</code> | <code>Quartet Copy Paper - Dot Grid - Mesh - Bluetooth</code> | <code>title_title</code> |
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  * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
209
- ```json
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- {
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- "scale": 20.0,
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- "similarity_fct": "cos_sim",
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- "gather_across_devices": false
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- }
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- ```
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217
  ### Training Hyperparameters
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  #### Non-Default Hyperparameters
@@ -352,786 +352,786 @@ You can finetune this model on your own dataset.
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  ### Training Logs
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  <details><summary>Click to expand</summary>
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- | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:-----:|:-------------:|:---------------:|
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- | 0.0000 | 1 | 0.6075 | - |
358
- | 0.0013 | 100 | 0.6294 | - |
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- | 0.0026 | 200 | 0.5563 | - |
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- | 0.0039 | 300 | 0.4578 | - |
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- | 0.0051 | 400 | 0.3912 | - |
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- | 0.0064 | 500 | 0.3496 | - |
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- | 0.0077 | 600 | 0.3141 | - |
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- | 0.0090 | 700 | 0.2718 | - |
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- | 0.0103 | 800 | 0.2624 | - |
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- | 0.0116 | 900 | 0.2408 | - |
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- | 0.0129 | 1000 | 0.2305 | - |
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- | 0.0142 | 1100 | 0.2188 | - |
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- | 0.0154 | 1200 | 0.2131 | - |
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- | 0.0167 | 1300 | 0.1898 | - |
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- | 0.0180 | 1400 | 0.188 | - |
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- | 0.0193 | 1500 | 0.1749 | - |
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- | 0.0206 | 1600 | 0.1734 | - |
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- | 0.0219 | 1700 | 0.1718 | - |
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- | 0.0232 | 1800 | 0.1535 | - |
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- | 0.0244 | 1900 | 0.1507 | - |
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- | 0.0257 | 2000 | 0.1573 | 0.2381 |
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- | 0.0270 | 2100 | 0.1336 | - |
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- | 0.0283 | 2200 | 0.1252 | - |
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- | 0.0296 | 2300 | 0.1318 | - |
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- | 0.0309 | 2400 | 0.1206 | - |
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- | 0.0322 | 2500 | 0.1239 | - |
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- | 0.0334 | 2600 | 0.1213 | - |
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- | 0.0347 | 2700 | 0.1137 | - |
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- | 0.0360 | 2800 | 0.1142 | - |
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- | 0.0373 | 2900 | 0.1052 | - |
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- | 0.0386 | 3000 | 0.1061 | - |
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- | 0.0399 | 3100 | 0.0991 | - |
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- | 0.0412 | 3200 | 0.0971 | - |
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- | 0.0425 | 3300 | 0.101 | - |
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- | 0.0437 | 3400 | 0.0885 | - |
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- | 0.0450 | 3500 | 0.0953 | - |
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- | 0.0463 | 3600 | 0.0944 | - |
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- | 0.0476 | 3700 | 0.0886 | - |
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- | 0.0489 | 3800 | 0.0822 | - |
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- | 0.0502 | 3900 | 0.0855 | - |
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- | 0.0515 | 4000 | 0.0845 | 0.2062 |
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- | 0.0527 | 4100 | 0.0829 | - |
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- | 0.0540 | 4200 | 0.0809 | - |
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- | 0.0553 | 4300 | 0.0782 | - |
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- | 0.0566 | 4400 | 0.0779 | - |
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- | 0.0579 | 4500 | 0.08 | - |
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- | 0.0592 | 4600 | 0.071 | - |
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- | 0.0605 | 4700 | 0.0784 | - |
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- | 0.0617 | 4800 | 0.0708 | - |
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- | 0.0630 | 4900 | 0.0771 | - |
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- | 0.0643 | 5000 | 0.0724 | - |
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- | 0.0656 | 5100 | 0.0714 | - |
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- | 0.0669 | 5200 | 0.0694 | - |
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- | 0.0682 | 5300 | 0.0713 | - |
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- | 0.0695 | 5400 | 0.068 | - |
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- | 0.0708 | 5500 | 0.0691 | - |
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- | 0.0720 | 5600 | 0.0612 | - |
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- | 0.0733 | 5700 | 0.0675 | - |
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- | 0.0746 | 5800 | 0.0706 | - |
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- | 0.0759 | 5900 | 0.0661 | - |
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- | 0.0772 | 6000 | 0.0669 | 0.1683 |
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- | 0.0785 | 6100 | 0.0643 | - |
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- | 0.0798 | 6200 | 0.0666 | - |
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- | 0.0810 | 6300 | 0.0678 | - |
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- | 0.0823 | 6400 | 0.0675 | - |
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- | 0.0836 | 6500 | 0.0616 | - |
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- | 0.0849 | 6600 | 0.0647 | - |
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- | 0.0862 | 6700 | 0.0681 | - |
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- | 0.0875 | 6800 | 0.0636 | - |
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- | 0.0888 | 6900 | 0.0674 | - |
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- | 0.0900 | 7000 | 0.0671 | - |
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- | 0.0913 | 7100 | 0.0612 | - |
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- | 0.0926 | 7200 | 0.0609 | - |
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- | 0.0939 | 7300 | 0.068 | - |
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- | 0.0952 | 7400 | 0.0663 | - |
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- | 0.0965 | 7500 | 0.0626 | - |
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- | 0.0978 | 7600 | 0.0624 | - |
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- | 0.0991 | 7700 | 0.0588 | - |
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- | 0.1003 | 7800 | 0.062 | - |
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- | 0.1016 | 7900 | 0.0605 | - |
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- | 0.1029 | 8000 | 0.056 | 0.1785 |
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- | 0.1042 | 8100 | 0.0565 | - |
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- | 0.1055 | 8200 | 0.0601 | - |
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- | 0.1068 | 8300 | 0.0639 | - |
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- | 0.1081 | 8400 | 0.0598 | - |
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- | 0.1093 | 8500 | 0.0601 | - |
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- | 0.1106 | 8600 | 0.0669 | - |
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- | 0.1119 | 8700 | 0.0644 | - |
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- | 0.1132 | 8800 | 0.0592 | - |
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- | 0.1145 | 8900 | 0.0609 | - |
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- | 0.1158 | 9000 | 0.0624 | - |
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- | 0.1171 | 9100 | 0.0608 | - |
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- | 0.1184 | 9200 | 0.0625 | - |
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- | 0.1196 | 9300 | 0.0592 | - |
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- | 0.1209 | 9400 | 0.0536 | - |
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- | 0.1222 | 9500 | 0.0606 | - |
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- | 0.1248 | 9700 | 0.0592 | - |
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- | 0.1274 | 9900 | 0.057 | - |
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- | 0.1286 | 10000 | 0.0544 | 0.1685 |
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- | 0.1312 | 10200 | 0.0531 | - |
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- | 0.1325 | 10300 | 0.0581 | - |
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- | 0.1351 | 10500 | 0.05 | - |
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- | 0.1364 | 10600 | 0.0583 | - |
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- | 0.1389 | 10800 | 0.0561 | - |
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- | 0.1415 | 11000 | 0.0558 | - |
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- | 0.1428 | 11100 | 0.0555 | - |
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- | 0.1467 | 11400 | 0.0584 | - |
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- | 0.1479 | 11500 | 0.054 | - |
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- | 0.1492 | 11600 | 0.0542 | - |
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- | 0.1505 | 11700 | 0.0567 | - |
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- | 0.1518 | 11800 | 0.0502 | - |
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- | 0.1531 | 11900 | 0.0536 | - |
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- | 0.1544 | 12000 | 0.0512 | 0.1556 |
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- | 0.1557 | 12100 | 0.0529 | - |
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- | 0.1569 | 12200 | 0.0528 | - |
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- | 0.1801 | 14000 | 0.0517 | 0.1407 |
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- | 0.1917 | 14900 | 0.0543 | - |
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- | 0.1930 | 15000 | 0.0503 | - |
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- | 0.1942 | 15100 | 0.0513 | - |
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- | 0.1955 | 15200 | 0.053 | - |
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- | 0.1968 | 15300 | 0.0517 | - |
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- | 0.1981 | 15400 | 0.0515 | - |
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- | 0.1994 | 15500 | 0.0548 | - |
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- | 0.2007 | 15600 | 0.0513 | - |
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- | 0.2020 | 15700 | 0.0476 | - |
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- | 0.2033 | 15800 | 0.0522 | - |
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- | 0.2045 | 15900 | 0.0496 | - |
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- | 0.2058 | 16000 | 0.0516 | 0.1394 |
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- | 0.2084 | 16200 | 0.0502 | - |
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- | 0.2097 | 16300 | 0.0494 | - |
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- | 0.2110 | 16400 | 0.0487 | - |
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- | 0.2123 | 16500 | 0.0482 | - |
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- | 0.2135 | 16600 | 0.0458 | - |
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- | 0.2161 | 16800 | 0.0497 | - |
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- | 0.2174 | 16900 | 0.0508 | - |
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- | 0.2187 | 17000 | 0.0518 | - |
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- | 0.2200 | 17100 | 0.0538 | - |
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- | 0.2213 | 17200 | 0.0497 | - |
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- | 0.2226 | 17300 | 0.053 | - |
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- | 0.2238 | 17400 | 0.0452 | - |
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- | 0.2251 | 17500 | 0.0479 | - |
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- | 0.2264 | 17600 | 0.0484 | - |
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- | 0.2277 | 17700 | 0.0467 | - |
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- | 0.2290 | 17800 | 0.0478 | - |
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- | 0.2303 | 17900 | 0.0544 | - |
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- | 0.2316 | 18000 | 0.0426 | 0.1438 |
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- | 0.2328 | 18100 | 0.0501 | - |
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- | 0.2341 | 18200 | 0.0463 | - |
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- | 0.2354 | 18300 | 0.0521 | - |
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- | 0.2367 | 18400 | 0.0454 | - |
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- | 0.2380 | 18500 | 0.0548 | - |
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- | 0.2393 | 18600 | 0.0484 | - |
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- | 0.2406 | 18700 | 0.0443 | - |
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- | 0.2418 | 18800 | 0.0489 | - |
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- | 0.2431 | 18900 | 0.0502 | - |
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- | 0.2444 | 19000 | 0.0469 | - |
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- | 0.2457 | 19100 | 0.0468 | - |
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- | 0.2470 | 19200 | 0.0532 | - |
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- | 0.2483 | 19300 | 0.0486 | - |
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- | 0.2496 | 19400 | 0.0529 | - |
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- | 0.2509 | 19500 | 0.0499 | - |
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- | 0.2521 | 19600 | 0.0524 | - |
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- | 0.2534 | 19700 | 0.0491 | - |
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- | 0.2547 | 19800 | 0.0472 | - |
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- | 0.2560 | 19900 | 0.0463 | - |
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- | 0.2573 | 20000 | 0.047 | 0.1307 |
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- | 0.2586 | 20100 | 0.0471 | - |
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- | 0.2599 | 20200 | 0.0488 | - |
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- | 0.2611 | 20300 | 0.0476 | - |
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- | 0.2624 | 20400 | 0.0477 | - |
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- | 0.2637 | 20500 | 0.0448 | - |
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- | 0.2650 | 20600 | 0.0476 | - |
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- | 0.2663 | 20700 | 0.0484 | - |
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- | 0.2676 | 20800 | 0.0444 | - |
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- | 0.2689 | 20900 | 0.0452 | - |
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- | 0.2701 | 21000 | 0.0497 | - |
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- | 0.2714 | 21100 | 0.0524 | - |
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- | 0.2727 | 21200 | 0.0443 | - |
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- | 0.2740 | 21300 | 0.0479 | - |
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- | 0.2753 | 21400 | 0.0483 | - |
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- | 0.2766 | 21500 | 0.0509 | - |
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- | 0.2779 | 21600 | 0.0522 | - |
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- | 0.2792 | 21700 | 0.046 | - |
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- | 0.2804 | 21800 | 0.0476 | - |
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- | 0.2817 | 21900 | 0.0452 | - |
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- | 0.2830 | 22000 | 0.0471 | 0.1385 |
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- | 0.2843 | 22100 | 0.0421 | - |
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- | 0.2856 | 22200 | 0.0481 | - |
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- | 0.2869 | 22300 | 0.0501 | - |
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- | 0.2882 | 22400 | 0.0481 | - |
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- | 0.2894 | 22500 | 0.0492 | - |
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- | 0.2907 | 22600 | 0.0461 | - |
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- | 0.2920 | 22700 | 0.0427 | - |
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- | 0.2933 | 22800 | 0.0453 | - |
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- | 0.2946 | 22900 | 0.0519 | - |
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- | 0.2959 | 23000 | 0.0497 | - |
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- | 0.2972 | 23100 | 0.0439 | - |
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- | 0.2984 | 23200 | 0.046 | - |
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- | 0.2997 | 23300 | 0.0456 | - |
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- | 0.3010 | 23400 | 0.0477 | - |
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- | 0.3023 | 23500 | 0.0427 | - |
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- | 0.3036 | 23600 | 0.0482 | - |
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- | 0.3049 | 23700 | 0.0435 | - |
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- | 0.3062 | 23800 | 0.0475 | - |
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- | 0.3075 | 23900 | 0.0472 | - |
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- | 0.3087 | 24000 | 0.046 | 0.1366 |
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- | 0.3100 | 24100 | 0.0425 | - |
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- | 0.3113 | 24200 | 0.0451 | - |
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- | 0.3126 | 24300 | 0.0504 | - |
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- | 0.3139 | 24400 | 0.0529 | - |
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- | 0.3152 | 24500 | 0.0484 | - |
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- | 0.3165 | 24600 | 0.0483 | - |
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- | 0.3177 | 24700 | 0.0443 | - |
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- | 0.3190 | 24800 | 0.0463 | - |
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- | 0.3203 | 24900 | 0.0455 | - |
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- | 0.3216 | 25000 | 0.0488 | - |
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- | 0.3229 | 25100 | 0.0446 | - |
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- | 0.3242 | 25200 | 0.0456 | - |
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- | 0.3255 | 25300 | 0.0452 | - |
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- | 0.3268 | 25400 | 0.0444 | - |
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- | 0.3280 | 25500 | 0.0458 | - |
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- | 0.3293 | 25600 | 0.0447 | - |
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- | 0.3306 | 25700 | 0.0463 | - |
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- | 0.3319 | 25800 | 0.0453 | - |
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- | 0.3332 | 25900 | 0.0457 | - |
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- | 0.3345 | 26000 | 0.048 | 0.1264 |
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- | 0.3358 | 26100 | 0.0509 | - |
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- | 0.3370 | 26200 | 0.0471 | - |
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- | 0.3383 | 26300 | 0.0448 | - |
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- | 0.3396 | 26400 | 0.0461 | - |
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- | 0.3409 | 26500 | 0.0433 | - |
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- | 0.3422 | 26600 | 0.0476 | - |
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- | 0.3435 | 26700 | 0.043 | - |
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- | 0.3448 | 26800 | 0.0472 | - |
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- | 0.3460 | 26900 | 0.0482 | - |
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- | 0.3473 | 27000 | 0.0466 | - |
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- | 0.3486 | 27100 | 0.0492 | - |
629
- | 0.3499 | 27200 | 0.0466 | - |
630
- | 0.3512 | 27300 | 0.0479 | - |
631
- | 0.3525 | 27400 | 0.0466 | - |
632
- | 0.3538 | 27500 | 0.0446 | - |
633
- | 0.3551 | 27600 | 0.0428 | - |
634
- | 0.3563 | 27700 | 0.0496 | - |
635
- | 0.3576 | 27800 | 0.0472 | - |
636
- | 0.3589 | 27900 | 0.0472 | - |
637
- | 0.3602 | 28000 | 0.0411 | 0.1280 |
638
- | 0.3615 | 28100 | 0.0466 | - |
639
- | 0.3628 | 28200 | 0.0463 | - |
640
- | 0.3641 | 28300 | 0.0463 | - |
641
- | 0.3653 | 28400 | 0.0471 | - |
642
- | 0.3666 | 28500 | 0.0469 | - |
643
- | 0.3679 | 28600 | 0.0447 | - |
644
- | 0.3692 | 28700 | 0.0468 | - |
645
- | 0.3705 | 28800 | 0.0405 | - |
646
- | 0.3718 | 28900 | 0.0481 | - |
647
- | 0.3731 | 29000 | 0.046 | - |
648
- | 0.3743 | 29100 | 0.0498 | - |
649
- | 0.3756 | 29200 | 0.0486 | - |
650
- | 0.3769 | 29300 | 0.0418 | - |
651
- | 0.3782 | 29400 | 0.0433 | - |
652
- | 0.3795 | 29500 | 0.0447 | - |
653
- | 0.3808 | 29600 | 0.0447 | - |
654
- | 0.3821 | 29700 | 0.0435 | - |
655
- | 0.3834 | 29800 | 0.0482 | - |
656
- | 0.3846 | 29900 | 0.0468 | - |
657
- | 0.3859 | 30000 | 0.0409 | 0.1324 |
658
- | 0.3872 | 30100 | 0.039 | - |
659
- | 0.3885 | 30200 | 0.0441 | - |
660
- | 0.3898 | 30300 | 0.0454 | - |
661
- | 0.3911 | 30400 | 0.044 | - |
662
- | 0.3924 | 30500 | 0.0431 | - |
663
- | 0.3936 | 30600 | 0.0409 | - |
664
- | 0.3949 | 30700 | 0.0459 | - |
665
- | 0.3962 | 30800 | 0.0494 | - |
666
- | 0.3975 | 30900 | 0.0416 | - |
667
- | 0.3988 | 31000 | 0.0474 | - |
668
- | 0.4001 | 31100 | 0.0394 | - |
669
- | 0.4014 | 31200 | 0.0448 | - |
670
- | 0.4027 | 31300 | 0.0429 | - |
671
- | 0.4039 | 31400 | 0.0472 | - |
672
- | 0.4052 | 31500 | 0.0425 | - |
673
- | 0.4065 | 31600 | 0.0445 | - |
674
- | 0.4078 | 31700 | 0.0427 | - |
675
- | 0.4091 | 31800 | 0.0449 | - |
676
- | 0.4104 | 31900 | 0.0485 | - |
677
- | 0.4117 | 32000 | 0.0417 | 0.1271 |
678
- | 0.4129 | 32100 | 0.0475 | - |
679
- | 0.4142 | 32200 | 0.0427 | - |
680
- | 0.4155 | 32300 | 0.0412 | - |
681
- | 0.4168 | 32400 | 0.042 | - |
682
- | 0.4181 | 32500 | 0.0509 | - |
683
- | 0.4194 | 32600 | 0.0475 | - |
684
- | 0.4207 | 32700 | 0.0412 | - |
685
- | 0.4219 | 32800 | 0.0413 | - |
686
- | 0.4232 | 32900 | 0.0447 | - |
687
- | 0.4245 | 33000 | 0.0426 | - |
688
- | 0.4258 | 33100 | 0.0432 | - |
689
- | 0.4271 | 33200 | 0.0432 | - |
690
- | 0.4284 | 33300 | 0.0433 | - |
691
- | 0.4297 | 33400 | 0.0473 | - |
692
- | 0.4310 | 33500 | 0.0438 | - |
693
- | 0.4322 | 33600 | 0.0487 | - |
694
- | 0.4335 | 33700 | 0.0438 | - |
695
- | 0.4348 | 33800 | 0.046 | - |
696
- | 0.4361 | 33900 | 0.0418 | - |
697
- | 0.4374 | 34000 | 0.044 | 0.1293 |
698
- | 0.4387 | 34100 | 0.0442 | - |
699
- | 0.4400 | 34200 | 0.0442 | - |
700
- | 0.4412 | 34300 | 0.0429 | - |
701
- | 0.4425 | 34400 | 0.0465 | - |
702
- | 0.4438 | 34500 | 0.051 | - |
703
- | 0.4451 | 34600 | 0.0408 | - |
704
- | 0.4464 | 34700 | 0.0448 | - |
705
- | 0.4477 | 34800 | 0.0488 | - |
706
- | 0.4490 | 34900 | 0.0465 | - |
707
- | 0.4502 | 35000 | 0.0439 | - |
708
- | 0.4515 | 35100 | 0.0437 | - |
709
- | 0.4528 | 35200 | 0.0428 | - |
710
- | 0.4541 | 35300 | 0.0401 | - |
711
- | 0.4554 | 35400 | 0.0423 | - |
712
- | 0.4567 | 35500 | 0.0404 | - |
713
- | 0.4580 | 35600 | 0.0396 | - |
714
- | 0.4593 | 35700 | 0.0427 | - |
715
- | 0.4605 | 35800 | 0.0438 | - |
716
- | 0.4618 | 35900 | 0.0427 | - |
717
- | 0.4631 | 36000 | 0.0412 | 0.1277 |
718
- | 0.4644 | 36100 | 0.0431 | - |
719
- | 0.4657 | 36200 | 0.0435 | - |
720
- | 0.4670 | 36300 | 0.045 | - |
721
- | 0.4683 | 36400 | 0.0439 | - |
722
- | 0.4695 | 36500 | 0.0447 | - |
723
- | 0.4708 | 36600 | 0.0464 | - |
724
- | 0.4721 | 36700 | 0.0425 | - |
725
- | 0.4734 | 36800 | 0.0523 | - |
726
- | 0.4747 | 36900 | 0.0468 | - |
727
- | 0.4760 | 37000 | 0.0441 | - |
728
- | 0.4773 | 37100 | 0.0421 | - |
729
- | 0.4785 | 37200 | 0.0439 | - |
730
- | 0.4798 | 37300 | 0.0413 | - |
731
- | 0.4811 | 37400 | 0.0482 | - |
732
- | 0.4824 | 37500 | 0.0427 | - |
733
- | 0.4837 | 37600 | 0.0384 | - |
734
- | 0.4850 | 37700 | 0.0429 | - |
735
- | 0.4863 | 37800 | 0.0449 | - |
736
- | 0.4876 | 37900 | 0.0429 | - |
737
- | 0.4888 | 38000 | 0.0433 | 0.1259 |
738
- | 0.4901 | 38100 | 0.0429 | - |
739
- | 0.4914 | 38200 | 0.0441 | - |
740
- | 0.4927 | 38300 | 0.0443 | - |
741
- | 0.4940 | 38400 | 0.0503 | - |
742
- | 0.4953 | 38500 | 0.0403 | - |
743
- | 0.4966 | 38600 | 0.0437 | - |
744
- | 0.4978 | 38700 | 0.0447 | - |
745
- | 0.4991 | 38800 | 0.0434 | - |
746
- | 0.5004 | 38900 | 0.0403 | - |
747
- | 0.5017 | 39000 | 0.0431 | - |
748
- | 0.5030 | 39100 | 0.0422 | - |
749
- | 0.5043 | 39200 | 0.0477 | - |
750
- | 0.5056 | 39300 | 0.0435 | - |
751
- | 0.5069 | 39400 | 0.0438 | - |
752
- | 0.5081 | 39500 | 0.0444 | - |
753
- | 0.5094 | 39600 | 0.0451 | - |
754
- | 0.5107 | 39700 | 0.0405 | - |
755
- | 0.5120 | 39800 | 0.0427 | - |
756
- | 0.5133 | 39900 | 0.0477 | - |
757
- | 0.5146 | 40000 | 0.046 | 0.1268 |
758
- | 0.5159 | 40100 | 0.0446 | - |
759
- | 0.5171 | 40200 | 0.0436 | - |
760
- | 0.5184 | 40300 | 0.0485 | - |
761
- | 0.5197 | 40400 | 0.0462 | - |
762
- | 0.5210 | 40500 | 0.0455 | - |
763
- | 0.5223 | 40600 | 0.0429 | - |
764
- | 0.5236 | 40700 | 0.0401 | - |
765
- | 0.5249 | 40800 | 0.0453 | - |
766
- | 0.5261 | 40900 | 0.0454 | - |
767
- | 0.5274 | 41000 | 0.0472 | - |
768
- | 0.5287 | 41100 | 0.0416 | - |
769
- | 0.5300 | 41200 | 0.0395 | - |
770
- | 0.5313 | 41300 | 0.0418 | - |
771
- | 0.5326 | 41400 | 0.0437 | - |
772
- | 0.5339 | 41500 | 0.0413 | - |
773
- | 0.5352 | 41600 | 0.0429 | - |
774
- | 0.5364 | 41700 | 0.0428 | - |
775
- | 0.5377 | 41800 | 0.046 | - |
776
- | 0.5390 | 41900 | 0.0447 | - |
777
- | 0.5403 | 42000 | 0.0424 | 0.1250 |
778
- | 0.5416 | 42100 | 0.0437 | - |
779
- | 0.5429 | 42200 | 0.0432 | - |
780
- | 0.5442 | 42300 | 0.0439 | - |
781
- | 0.5454 | 42400 | 0.0437 | - |
782
- | 0.5467 | 42500 | 0.0455 | - |
783
- | 0.5480 | 42600 | 0.0436 | - |
784
- | 0.5493 | 42700 | 0.0421 | - |
785
- | 0.5506 | 42800 | 0.0445 | - |
786
- | 0.5519 | 42900 | 0.0413 | - |
787
- | 0.5532 | 43000 | 0.0431 | - |
788
- | 0.5544 | 43100 | 0.0441 | - |
789
- | 0.5557 | 43200 | 0.0431 | - |
790
- | 0.5570 | 43300 | 0.0433 | - |
791
- | 0.5583 | 43400 | 0.0459 | - |
792
- | 0.5596 | 43500 | 0.0441 | - |
793
- | 0.5609 | 43600 | 0.0433 | - |
794
- | 0.5622 | 43700 | 0.0411 | - |
795
- | 0.5635 | 43800 | 0.0426 | - |
796
- | 0.5647 | 43900 | 0.0428 | - |
797
- | 0.5660 | 44000 | 0.0423 | 0.1245 |
798
- | 0.5673 | 44100 | 0.0436 | - |
799
- | 0.5686 | 44200 | 0.0424 | - |
800
- | 0.5699 | 44300 | 0.044 | - |
801
- | 0.5712 | 44400 | 0.0386 | - |
802
- | 0.5725 | 44500 | 0.0412 | - |
803
- | 0.5737 | 44600 | 0.0423 | - |
804
- | 0.5750 | 44700 | 0.0416 | - |
805
- | 0.5763 | 44800 | 0.0414 | - |
806
- | 0.5776 | 44900 | 0.0412 | - |
807
- | 0.5789 | 45000 | 0.044 | - |
808
- | 0.5802 | 45100 | 0.0411 | - |
809
- | 0.5815 | 45200 | 0.0422 | - |
810
- | 0.5827 | 45300 | 0.0438 | - |
811
- | 0.5840 | 45400 | 0.0449 | - |
812
- | 0.5853 | 45500 | 0.046 | - |
813
- | 0.5866 | 45600 | 0.0423 | - |
814
- | 0.5879 | 45700 | 0.0402 | - |
815
- | 0.5892 | 45800 | 0.0424 | - |
816
- | 0.5905 | 45900 | 0.0424 | - |
817
- | 0.5918 | 46000 | 0.0406 | 0.1246 |
818
- | 0.5930 | 46100 | 0.0454 | - |
819
- | 0.5943 | 46200 | 0.0466 | - |
820
- | 0.5956 | 46300 | 0.0423 | - |
821
- | 0.5969 | 46400 | 0.0444 | - |
822
- | 0.5982 | 46500 | 0.0404 | - |
823
- | 0.5995 | 46600 | 0.0435 | - |
824
- | 0.6008 | 46700 | 0.0416 | - |
825
- | 0.6020 | 46800 | 0.0471 | - |
826
- | 0.6033 | 46900 | 0.0417 | - |
827
- | 0.6046 | 47000 | 0.0402 | - |
828
- | 0.6059 | 47100 | 0.0464 | - |
829
- | 0.6072 | 47200 | 0.0387 | - |
830
- | 0.6085 | 47300 | 0.0423 | - |
831
- | 0.6098 | 47400 | 0.0417 | - |
832
- | 0.6111 | 47500 | 0.0391 | - |
833
- | 0.6123 | 47600 | 0.0448 | - |
834
- | 0.6136 | 47700 | 0.0407 | - |
835
- | 0.6149 | 47800 | 0.0396 | - |
836
- | 0.6162 | 47900 | 0.0456 | - |
837
- | 0.6175 | 48000 | 0.0431 | 0.1253 |
838
- | 0.6188 | 48100 | 0.0443 | - |
839
- | 0.6201 | 48200 | 0.0419 | - |
840
- | 0.6213 | 48300 | 0.0434 | - |
841
- | 0.6226 | 48400 | 0.0406 | - |
842
- | 0.6239 | 48500 | 0.0437 | - |
843
- | 0.6252 | 48600 | 0.0402 | - |
844
- | 0.6265 | 48700 | 0.0436 | - |
845
- | 0.6278 | 48800 | 0.0386 | - |
846
- | 0.6291 | 48900 | 0.0466 | - |
847
- | 0.6303 | 49000 | 0.0406 | - |
848
- | 0.6316 | 49100 | 0.0404 | - |
849
- | 0.6329 | 49200 | 0.0447 | - |
850
- | 0.6342 | 49300 | 0.0446 | - |
851
- | 0.6355 | 49400 | 0.0438 | - |
852
- | 0.6368 | 49500 | 0.0449 | - |
853
- | 0.6381 | 49600 | 0.0404 | - |
854
- | 0.6394 | 49700 | 0.0389 | - |
855
- | 0.6406 | 49800 | 0.0406 | - |
856
- | 0.6419 | 49900 | 0.0398 | - |
857
- | 0.6432 | 50000 | 0.0382 | 0.1255 |
858
- | 0.6445 | 50100 | 0.0383 | - |
859
- | 0.6458 | 50200 | 0.0422 | - |
860
- | 0.6471 | 50300 | 0.0442 | - |
861
- | 0.6484 | 50400 | 0.0408 | - |
862
- | 0.6496 | 50500 | 0.0408 | - |
863
- | 0.6509 | 50600 | 0.0366 | - |
864
- | 0.6522 | 50700 | 0.0409 | - |
865
- | 0.6535 | 50800 | 0.0413 | - |
866
- | 0.6548 | 50900 | 0.0448 | - |
867
- | 0.6561 | 51000 | 0.0416 | - |
868
- | 0.6574 | 51100 | 0.0394 | - |
869
- | 0.6586 | 51200 | 0.0375 | - |
870
- | 0.6599 | 51300 | 0.0411 | - |
871
- | 0.6612 | 51400 | 0.043 | - |
872
- | 0.6625 | 51500 | 0.0479 | - |
873
- | 0.6638 | 51600 | 0.0378 | - |
874
- | 0.6651 | 51700 | 0.0426 | - |
875
- | 0.6664 | 51800 | 0.0383 | - |
876
- | 0.6677 | 51900 | 0.0391 | - |
877
- | 0.6689 | 52000 | 0.0433 | 0.1218 |
878
- | 0.6702 | 52100 | 0.039 | - |
879
- | 0.6715 | 52200 | 0.0435 | - |
880
- | 0.6728 | 52300 | 0.0393 | - |
881
- | 0.6741 | 52400 | 0.04 | - |
882
- | 0.6754 | 52500 | 0.0438 | - |
883
- | 0.6767 | 52600 | 0.0466 | - |
884
- | 0.6779 | 52700 | 0.0435 | - |
885
- | 0.6792 | 52800 | 0.042 | - |
886
- | 0.6805 | 52900 | 0.0473 | - |
887
- | 0.6818 | 53000 | 0.0376 | - |
888
- | 0.6831 | 53100 | 0.0396 | - |
889
- | 0.6844 | 53200 | 0.0411 | - |
890
- | 0.6857 | 53300 | 0.0419 | - |
891
- | 0.6869 | 53400 | 0.0423 | - |
892
- | 0.6882 | 53500 | 0.0374 | - |
893
- | 0.6895 | 53600 | 0.043 | - |
894
- | 0.6908 | 53700 | 0.0444 | - |
895
- | 0.6921 | 53800 | 0.0427 | - |
896
- | 0.6934 | 53900 | 0.0456 | - |
897
- | 0.6947 | 54000 | 0.0407 | 0.1234 |
898
- | 0.6960 | 54100 | 0.0438 | - |
899
- | 0.6972 | 54200 | 0.0403 | - |
900
- | 0.6985 | 54300 | 0.041 | - |
901
- | 0.6998 | 54400 | 0.0395 | - |
902
- | 0.7011 | 54500 | 0.0447 | - |
903
- | 0.7024 | 54600 | 0.0412 | - |
904
- | 0.7037 | 54700 | 0.0406 | - |
905
- | 0.7050 | 54800 | 0.0446 | - |
906
- | 0.7062 | 54900 | 0.0426 | - |
907
- | 0.7075 | 55000 | 0.0428 | - |
908
- | 0.7088 | 55100 | 0.042 | - |
909
- | 0.7101 | 55200 | 0.0429 | - |
910
- | 0.7114 | 55300 | 0.0446 | - |
911
- | 0.7127 | 55400 | 0.0408 | - |
912
- | 0.7140 | 55500 | 0.0364 | - |
913
- | 0.7153 | 55600 | 0.0369 | - |
914
- | 0.7165 | 55700 | 0.0463 | - |
915
- | 0.7178 | 55800 | 0.0423 | - |
916
- | 0.7191 | 55900 | 0.0408 | - |
917
- | 0.7204 | 56000 | 0.0386 | 0.1252 |
918
- | 0.7217 | 56100 | 0.0434 | - |
919
- | 0.7230 | 56200 | 0.0392 | - |
920
- | 0.7243 | 56300 | 0.0399 | - |
921
- | 0.7255 | 56400 | 0.0421 | - |
922
- | 0.7268 | 56500 | 0.0434 | - |
923
- | 0.7281 | 56600 | 0.037 | - |
924
- | 0.7294 | 56700 | 0.0414 | - |
925
- | 0.7307 | 56800 | 0.0399 | - |
926
- | 0.7320 | 56900 | 0.0404 | - |
927
- | 0.7333 | 57000 | 0.0443 | - |
928
- | 0.7345 | 57100 | 0.0402 | - |
929
- | 0.7358 | 57200 | 0.0425 | - |
930
- | 0.7371 | 57300 | 0.0391 | - |
931
- | 0.7384 | 57400 | 0.0418 | - |
932
- | 0.7397 | 57500 | 0.0409 | - |
933
- | 0.7410 | 57600 | 0.0435 | - |
934
- | 0.7423 | 57700 | 0.0414 | - |
935
- | 0.7436 | 57800 | 0.0412 | - |
936
- | 0.7448 | 57900 | 0.0392 | - |
937
- | 0.7461 | 58000 | 0.04 | 0.1244 |
938
- | 0.7474 | 58100 | 0.0392 | - |
939
- | 0.7487 | 58200 | 0.0399 | - |
940
- | 0.7500 | 58300 | 0.041 | - |
941
- | 0.7513 | 58400 | 0.0395 | - |
942
- | 0.7526 | 58500 | 0.0413 | - |
943
- | 0.7538 | 58600 | 0.0394 | - |
944
- | 0.7551 | 58700 | 0.0451 | - |
945
- | 0.7564 | 58800 | 0.0454 | - |
946
- | 0.7577 | 58900 | 0.0434 | - |
947
- | 0.7590 | 59000 | 0.0417 | - |
948
- | 0.7603 | 59100 | 0.0422 | - |
949
- | 0.7616 | 59200 | 0.0415 | - |
950
- | 0.7628 | 59300 | 0.0412 | - |
951
- | 0.7641 | 59400 | 0.044 | - |
952
- | 0.7654 | 59500 | 0.0368 | - |
953
- | 0.7667 | 59600 | 0.0418 | - |
954
- | 0.7680 | 59700 | 0.0427 | - |
955
- | 0.7693 | 59800 | 0.0421 | - |
956
- | 0.7706 | 59900 | 0.0418 | - |
957
- | 0.7719 | 60000 | 0.0438 | 0.1229 |
958
- | 0.7731 | 60100 | 0.0393 | - |
959
- | 0.7744 | 60200 | 0.0411 | - |
960
- | 0.7757 | 60300 | 0.0376 | - |
961
- | 0.7770 | 60400 | 0.0404 | - |
962
- | 0.7783 | 60500 | 0.0412 | - |
963
- | 0.7796 | 60600 | 0.0404 | - |
964
- | 0.7809 | 60700 | 0.0447 | - |
965
- | 0.7821 | 60800 | 0.0436 | - |
966
- | 0.7834 | 60900 | 0.04 | - |
967
- | 0.7847 | 61000 | 0.0408 | - |
968
- | 0.7860 | 61100 | 0.0458 | - |
969
- | 0.7873 | 61200 | 0.041 | - |
970
- | 0.7886 | 61300 | 0.0384 | - |
971
- | 0.7899 | 61400 | 0.0419 | - |
972
- | 0.7911 | 61500 | 0.0433 | - |
973
- | 0.7924 | 61600 | 0.0457 | - |
974
- | 0.7937 | 61700 | 0.0388 | - |
975
- | 0.7950 | 61800 | 0.0416 | - |
976
- | 0.7963 | 61900 | 0.0437 | - |
977
- | 0.7976 | 62000 | 0.0374 | 0.1237 |
978
- | 0.7989 | 62100 | 0.0351 | - |
979
- | 0.8002 | 62200 | 0.0387 | - |
980
- | 0.8014 | 62300 | 0.0457 | - |
981
- | 0.8027 | 62400 | 0.0424 | - |
982
- | 0.8040 | 62500 | 0.039 | - |
983
- | 0.8053 | 62600 | 0.0429 | - |
984
- | 0.8066 | 62700 | 0.0387 | - |
985
- | 0.8079 | 62800 | 0.0379 | - |
986
- | 0.8092 | 62900 | 0.0377 | - |
987
- | 0.8104 | 63000 | 0.0401 | - |
988
- | 0.8117 | 63100 | 0.0393 | - |
989
- | 0.8130 | 63200 | 0.0409 | - |
990
- | 0.8143 | 63300 | 0.0394 | - |
991
- | 0.8156 | 63400 | 0.0386 | - |
992
- | 0.8169 | 63500 | 0.0394 | - |
993
- | 0.8182 | 63600 | 0.0404 | - |
994
- | 0.8195 | 63700 | 0.0409 | - |
995
- | 0.8207 | 63800 | 0.0419 | - |
996
- | 0.8220 | 63900 | 0.0384 | - |
997
- | 0.8233 | 64000 | 0.0437 | 0.1241 |
998
- | 0.8246 | 64100 | 0.0377 | - |
999
- | 0.8259 | 64200 | 0.0444 | - |
1000
- | 0.8272 | 64300 | 0.0407 | - |
1001
- | 0.8285 | 64400 | 0.0403 | - |
1002
- | 0.8297 | 64500 | 0.0354 | - |
1003
- | 0.8310 | 64600 | 0.0408 | - |
1004
- | 0.8323 | 64700 | 0.0409 | - |
1005
- | 0.8336 | 64800 | 0.0383 | - |
1006
- | 0.8349 | 64900 | 0.0382 | - |
1007
- | 0.8362 | 65000 | 0.0423 | - |
1008
- | 0.8375 | 65100 | 0.0433 | - |
1009
- | 0.8387 | 65200 | 0.0384 | - |
1010
- | 0.8400 | 65300 | 0.0411 | - |
1011
- | 0.8413 | 65400 | 0.0413 | - |
1012
- | 0.8426 | 65500 | 0.04 | - |
1013
- | 0.8439 | 65600 | 0.0443 | - |
1014
- | 0.8452 | 65700 | 0.0377 | - |
1015
- | 0.8465 | 65800 | 0.0411 | - |
1016
- | 0.8478 | 65900 | 0.0403 | - |
1017
- | 0.8490 | 66000 | 0.0405 | 0.1221 |
1018
- | 0.8503 | 66100 | 0.0447 | - |
1019
- | 0.8516 | 66200 | 0.0427 | - |
1020
- | 0.8529 | 66300 | 0.0422 | - |
1021
- | 0.8542 | 66400 | 0.0402 | - |
1022
- | 0.8555 | 66500 | 0.0428 | - |
1023
- | 0.8568 | 66600 | 0.0428 | - |
1024
- | 0.8580 | 66700 | 0.0407 | - |
1025
- | 0.8593 | 66800 | 0.0405 | - |
1026
- | 0.8606 | 66900 | 0.0442 | - |
1027
- | 0.8619 | 67000 | 0.0409 | - |
1028
- | 0.8632 | 67100 | 0.0405 | - |
1029
- | 0.8645 | 67200 | 0.0422 | - |
1030
- | 0.8658 | 67300 | 0.0411 | - |
1031
- | 0.8670 | 67400 | 0.0437 | - |
1032
- | 0.8683 | 67500 | 0.0417 | - |
1033
- | 0.8696 | 67600 | 0.0407 | - |
1034
- | 0.8709 | 67700 | 0.0395 | - |
1035
- | 0.8722 | 67800 | 0.0418 | - |
1036
- | 0.8735 | 67900 | 0.0401 | - |
1037
- | 0.8748 | 68000 | 0.0399 | 0.1231 |
1038
- | 0.8761 | 68100 | 0.0393 | - |
1039
- | 0.8773 | 68200 | 0.0418 | - |
1040
- | 0.8786 | 68300 | 0.0418 | - |
1041
- | 0.8799 | 68400 | 0.0361 | - |
1042
- | 0.8812 | 68500 | 0.0397 | - |
1043
- | 0.8825 | 68600 | 0.0342 | - |
1044
- | 0.8838 | 68700 | 0.0432 | - |
1045
- | 0.8851 | 68800 | 0.0377 | - |
1046
- | 0.8863 | 68900 | 0.0397 | - |
1047
- | 0.8876 | 69000 | 0.0409 | - |
1048
- | 0.8889 | 69100 | 0.0404 | - |
1049
- | 0.8902 | 69200 | 0.0443 | - |
1050
- | 0.8915 | 69300 | 0.0395 | - |
1051
- | 0.8928 | 69400 | 0.0409 | - |
1052
- | 0.8941 | 69500 | 0.0403 | - |
1053
- | 0.8953 | 69600 | 0.0418 | - |
1054
- | 0.8966 | 69700 | 0.0372 | - |
1055
- | 0.8979 | 69800 | 0.0369 | - |
1056
- | 0.8992 | 69900 | 0.0413 | - |
1057
- | 0.9005 | 70000 | 0.0382 | 0.1234 |
1058
- | 0.9018 | 70100 | 0.0392 | - |
1059
- | 0.9031 | 70200 | 0.0416 | - |
1060
- | 0.9044 | 70300 | 0.0414 | - |
1061
- | 0.9056 | 70400 | 0.0381 | - |
1062
- | 0.9069 | 70500 | 0.0386 | - |
1063
- | 0.9082 | 70600 | 0.0384 | - |
1064
- | 0.9095 | 70700 | 0.0386 | - |
1065
- | 0.9108 | 70800 | 0.0442 | - |
1066
- | 0.9121 | 70900 | 0.0352 | - |
1067
- | 0.9134 | 71000 | 0.036 | - |
1068
- | 0.9146 | 71100 | 0.0421 | - |
1069
- | 0.9159 | 71200 | 0.0391 | - |
1070
- | 0.9172 | 71300 | 0.0399 | - |
1071
- | 0.9185 | 71400 | 0.0448 | - |
1072
- | 0.9198 | 71500 | 0.0357 | - |
1073
- | 0.9211 | 71600 | 0.0423 | - |
1074
- | 0.9224 | 71700 | 0.0404 | - |
1075
- | 0.9237 | 71800 | 0.0412 | - |
1076
- | 0.9249 | 71900 | 0.0424 | - |
1077
- | 0.9262 | 72000 | 0.0397 | 0.1226 |
1078
- | 0.9275 | 72100 | 0.0425 | - |
1079
- | 0.9288 | 72200 | 0.0405 | - |
1080
- | 0.9301 | 72300 | 0.0411 | - |
1081
- | 0.9314 | 72400 | 0.0433 | - |
1082
- | 0.9327 | 72500 | 0.0414 | - |
1083
- | 0.9339 | 72600 | 0.0387 | - |
1084
- | 0.9352 | 72700 | 0.0418 | - |
1085
- | 0.9365 | 72800 | 0.0404 | - |
1086
- | 0.9378 | 72900 | 0.0397 | - |
1087
- | 0.9391 | 73000 | 0.0379 | - |
1088
- | 0.9404 | 73100 | 0.0437 | - |
1089
- | 0.9417 | 73200 | 0.0451 | - |
1090
- | 0.9429 | 73300 | 0.0403 | - |
1091
- | 0.9442 | 73400 | 0.0374 | - |
1092
- | 0.9455 | 73500 | 0.0398 | - |
1093
- | 0.9468 | 73600 | 0.0399 | - |
1094
- | 0.9481 | 73700 | 0.0422 | - |
1095
- | 0.9494 | 73800 | 0.0388 | - |
1096
- | 0.9507 | 73900 | 0.0397 | - |
1097
- | 0.9520 | 74000 | 0.0359 | 0.1227 |
1098
- | 0.9532 | 74100 | 0.0371 | - |
1099
- | 0.9545 | 74200 | 0.04 | - |
1100
- | 0.9558 | 74300 | 0.0433 | - |
1101
- | 0.9571 | 74400 | 0.0405 | - |
1102
- | 0.9584 | 74500 | 0.0387 | - |
1103
- | 0.9597 | 74600 | 0.042 | - |
1104
- | 0.9610 | 74700 | 0.0426 | - |
1105
- | 0.9622 | 74800 | 0.0397 | - |
1106
- | 0.9635 | 74900 | 0.0403 | - |
1107
- | 0.9648 | 75000 | 0.0415 | - |
1108
- | 0.9661 | 75100 | 0.0429 | - |
1109
- | 0.9674 | 75200 | 0.0446 | - |
1110
- | 0.9687 | 75300 | 0.0404 | - |
1111
- | 0.9700 | 75400 | 0.0397 | - |
1112
- | 0.9712 | 75500 | 0.0357 | - |
1113
- | 0.9725 | 75600 | 0.0391 | - |
1114
- | 0.9738 | 75700 | 0.0433 | - |
1115
- | 0.9751 | 75800 | 0.0405 | - |
1116
- | 0.9764 | 75900 | 0.0424 | - |
1117
- | 0.9777 | 76000 | 0.0439 | 0.1226 |
1118
- | 0.9790 | 76100 | 0.0422 | - |
1119
- | 0.9803 | 76200 | 0.0438 | - |
1120
- | 0.9815 | 76300 | 0.0391 | - |
1121
- | 0.9828 | 76400 | 0.0414 | - |
1122
- | 0.9841 | 76500 | 0.0396 | - |
1123
- | 0.9854 | 76600 | 0.0379 | - |
1124
- | 0.9867 | 76700 | 0.0397 | - |
1125
- | 0.9880 | 76800 | 0.0385 | - |
1126
- | 0.9893 | 76900 | 0.0422 | - |
1127
- | 0.9905 | 77000 | 0.0418 | - |
1128
- | 0.9918 | 77100 | 0.0417 | - |
1129
- | 0.9931 | 77200 | 0.0399 | - |
1130
- | 0.9944 | 77300 | 0.0383 | - |
1131
- | 0.9957 | 77400 | 0.041 | - |
1132
- | 0.9970 | 77500 | 0.0411 | - |
1133
- | 0.9983 | 77600 | 0.0409 | - |
1134
- | 0.9995 | 77700 | 0.0393 | - |
1135
 
1136
  </details>
1137
 
@@ -1151,25 +1151,25 @@ You can finetune this model on your own dataset.
1151
  #### Sentence Transformers
1152
  ```bibtex
1153
  @inproceedings{reimers-2019-sentence-bert,
1154
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1155
- author = "Reimers, Nils and Gurevych, Iryna",
1156
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1157
- month = "11",
1158
- year = "2019",
1159
- publisher = "Association for Computational Linguistics",
1160
- url = "https://arxiv.org/abs/1908.10084",
1161
  }
1162
  ```
1163
 
1164
  #### MultipleNegativesRankingLoss
1165
  ```bibtex
1166
  @misc{henderson2017efficient,
1167
- title={Efficient Natural Language Response Suggestion for Smart Reply},
1168
- author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
1169
- year={2017},
1170
- eprint={1705.00652},
1171
- archivePrefix={arXiv},
1172
- primaryClass={cs.CL}
1173
  }
1174
  ```
1175
 
 
79
 
80
  ```
81
  SentenceTransformer(
82
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
83
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
84
+ (2): Normalize()
85
  )
86
  ```
87
 
 
168
  * Size: 9,950,000 training samples
169
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>pair_type</code>
170
  * Approximate statistics based on the first 1000 samples:
171
+ | | anchor | positive | pair_type |
172
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
173
+ | type | string | string | string |
174
+ | details | <ul><li>min: 6 tokens</li><li>mean: 11.91 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.71 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
175
  * Samples:
176
+ | anchor | positive | pair_type |
177
+ |:------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------|
178
+ | <code>Swingline \| Pen Set \| Electric \| Blue</code> | <code>New Swingline Pen Set Letter 8.5x11</code> | <code>title_title</code> |
179
+ | <code>Sticky Notes by Herman Miller - 200-Sheet Blue</code> | <code>Herman Miller Sticky Notes - Leather - Recycled - Legal</code> | <code>title_title</code> |
180
+ | <code>Post-it Paper Shredder Blank Bluetooth</code> | <code>Post-it Paper Shredder w/ Bluetooth, Inkjet</code> | <code>title_title</code> |
181
  * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
182
+ ```json
183
+ {
184
+ "scale": 20.0,
185
+ "similarity_fct": "cos_sim",
186
+ "gather_across_devices": false
187
+ }
188
+ ```
189
 
190
  ### Evaluation Dataset
191
 
 
195
  * Size: 50,000 evaluation samples
196
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>pair_type</code>
197
  * Approximate statistics based on the first 1000 samples:
198
+ | | anchor | positive | pair_type |
199
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
200
+ | type | string | string | string |
201
+ | details | <ul><li>min: 6 tokens</li><li>mean: 12.21 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.91 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
202
  * Samples:
203
+ | anchor | positive | pair_type |
204
+ |:-----------------------------------------------------|:--------------------------------------------------------------|:-------------------------|
205
+ | <code>Whiteboard by Staples - Red Heavy Duty</code> | <code>Staples Whiteboard Inkjet/Black</code> | <code>title_title</code> |
206
+ | <code>Avery Blank Printer, Blue</code> | <code>Avery Printer w/ Ergonomic, Red</code> | <code>title_title</code> |
207
+ | <code>Copy Paper by Quartet - Wireless 8.5x11</code> | <code>Quartet Copy Paper - Dot Grid - Mesh - Bluetooth</code> | <code>title_title</code> |
208
  * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
209
+ ```json
210
+ {
211
+ "scale": 20.0,
212
+ "similarity_fct": "cos_sim",
213
+ "gather_across_devices": false
214
+ }
215
+ ```
216
 
217
  ### Training Hyperparameters
218
  #### Non-Default Hyperparameters
 
352
  ### Training Logs
353
  <details><summary>Click to expand</summary>
354
 
355
+ | Epoch | Step | Training Loss | Validation Loss |
356
  |:------:|:-----:|:-------------:|:---------------:|
357
+ | 0.0000 | 1 | 0.6075 | - |
358
+ | 0.0013 | 100 | 0.6294 | - |
359
+ | 0.0026 | 200 | 0.5563 | - |
360
+ | 0.0039 | 300 | 0.4578 | - |
361
+ | 0.0051 | 400 | 0.3912 | - |
362
+ | 0.0064 | 500 | 0.3496 | - |
363
+ | 0.0077 | 600 | 0.3141 | - |
364
+ | 0.0090 | 700 | 0.2718 | - |
365
+ | 0.0103 | 800 | 0.2624 | - |
366
+ | 0.0116 | 900 | 0.2408 | - |
367
+ | 0.0129 | 1000 | 0.2305 | - |
368
+ | 0.0142 | 1100 | 0.2188 | - |
369
+ | 0.0154 | 1200 | 0.2131 | - |
370
+ | 0.0167 | 1300 | 0.1898 | - |
371
+ | 0.0180 | 1400 | 0.188 | - |
372
+ | 0.0193 | 1500 | 0.1749 | - |
373
+ | 0.0206 | 1600 | 0.1734 | - |
374
+ | 0.0219 | 1700 | 0.1718 | - |
375
+ | 0.0232 | 1800 | 0.1535 | - |
376
+ | 0.0244 | 1900 | 0.1507 | - |
377
+ | 0.0257 | 2000 | 0.1573 | 0.2381 |
378
+ | 0.0270 | 2100 | 0.1336 | - |
379
+ | 0.0283 | 2200 | 0.1252 | - |
380
+ | 0.0296 | 2300 | 0.1318 | - |
381
+ | 0.0309 | 2400 | 0.1206 | - |
382
+ | 0.0322 | 2500 | 0.1239 | - |
383
+ | 0.0334 | 2600 | 0.1213 | - |
384
+ | 0.0347 | 2700 | 0.1137 | - |
385
+ | 0.0360 | 2800 | 0.1142 | - |
386
+ | 0.0373 | 2900 | 0.1052 | - |
387
+ | 0.0386 | 3000 | 0.1061 | - |
388
+ | 0.0399 | 3100 | 0.0991 | - |
389
+ | 0.0412 | 3200 | 0.0971 | - |
390
+ | 0.0425 | 3300 | 0.101 | - |
391
+ | 0.0437 | 3400 | 0.0885 | - |
392
+ | 0.0450 | 3500 | 0.0953 | - |
393
+ | 0.0463 | 3600 | 0.0944 | - |
394
+ | 0.0476 | 3700 | 0.0886 | - |
395
+ | 0.0489 | 3800 | 0.0822 | - |
396
+ | 0.0502 | 3900 | 0.0855 | - |
397
+ | 0.0515 | 4000 | 0.0845 | 0.2062 |
398
+ | 0.0527 | 4100 | 0.0829 | - |
399
+ | 0.0540 | 4200 | 0.0809 | - |
400
+ | 0.0553 | 4300 | 0.0782 | - |
401
+ | 0.0566 | 4400 | 0.0779 | - |
402
+ | 0.0579 | 4500 | 0.08 | - |
403
+ | 0.0592 | 4600 | 0.071 | - |
404
+ | 0.0605 | 4700 | 0.0784 | - |
405
+ | 0.0617 | 4800 | 0.0708 | - |
406
+ | 0.0630 | 4900 | 0.0771 | - |
407
+ | 0.0643 | 5000 | 0.0724 | - |
408
+ | 0.0656 | 5100 | 0.0714 | - |
409
+ | 0.0669 | 5200 | 0.0694 | - |
410
+ | 0.0682 | 5300 | 0.0713 | - |
411
+ | 0.0695 | 5400 | 0.068 | - |
412
+ | 0.0708 | 5500 | 0.0691 | - |
413
+ | 0.0720 | 5600 | 0.0612 | - |
414
+ | 0.0733 | 5700 | 0.0675 | - |
415
+ | 0.0746 | 5800 | 0.0706 | - |
416
+ | 0.0759 | 5900 | 0.0661 | - |
417
+ | 0.0772 | 6000 | 0.0669 | 0.1683 |
418
+ | 0.0785 | 6100 | 0.0643 | - |
419
+ | 0.0798 | 6200 | 0.0666 | - |
420
+ | 0.0810 | 6300 | 0.0678 | - |
421
+ | 0.0823 | 6400 | 0.0675 | - |
422
+ | 0.0836 | 6500 | 0.0616 | - |
423
+ | 0.0849 | 6600 | 0.0647 | - |
424
+ | 0.0862 | 6700 | 0.0681 | - |
425
+ | 0.0875 | 6800 | 0.0636 | - |
426
+ | 0.0888 | 6900 | 0.0674 | - |
427
+ | 0.0900 | 7000 | 0.0671 | - |
428
+ | 0.0913 | 7100 | 0.0612 | - |
429
+ | 0.0926 | 7200 | 0.0609 | - |
430
+ | 0.0939 | 7300 | 0.068 | - |
431
+ | 0.0952 | 7400 | 0.0663 | - |
432
+ | 0.0965 | 7500 | 0.0626 | - |
433
+ | 0.0978 | 7600 | 0.0624 | - |
434
+ | 0.0991 | 7700 | 0.0588 | - |
435
+ | 0.1003 | 7800 | 0.062 | - |
436
+ | 0.1016 | 7900 | 0.0605 | - |
437
+ | 0.1029 | 8000 | 0.056 | 0.1785 |
438
+ | 0.1042 | 8100 | 0.0565 | - |
439
+ | 0.1055 | 8200 | 0.0601 | - |
440
+ | 0.1068 | 8300 | 0.0639 | - |
441
+ | 0.1081 | 8400 | 0.0598 | - |
442
+ | 0.1093 | 8500 | 0.0601 | - |
443
+ | 0.1106 | 8600 | 0.0669 | - |
444
+ | 0.1119 | 8700 | 0.0644 | - |
445
+ | 0.1132 | 8800 | 0.0592 | - |
446
+ | 0.1145 | 8900 | 0.0609 | - |
447
+ | 0.1158 | 9000 | 0.0624 | - |
448
+ | 0.1171 | 9100 | 0.0608 | - |
449
+ | 0.1184 | 9200 | 0.0625 | - |
450
+ | 0.1196 | 9300 | 0.0592 | - |
451
+ | 0.1209 | 9400 | 0.0536 | - |
452
+ | 0.1222 | 9500 | 0.0606 | - |
453
+ | 0.1235 | 9600 | 0.0586 | - |
454
+ | 0.1248 | 9700 | 0.0592 | - |
455
+ | 0.1261 | 9800 | 0.0585 | - |
456
+ | 0.1274 | 9900 | 0.057 | - |
457
+ | 0.1286 | 10000 | 0.0544 | 0.1685 |
458
+ | 0.1299 | 10100 | 0.0532 | - |
459
+ | 0.1312 | 10200 | 0.0531 | - |
460
+ | 0.1325 | 10300 | 0.0581 | - |
461
+ | 0.1338 | 10400 | 0.0518 | - |
462
+ | 0.1351 | 10500 | 0.05 | - |
463
+ | 0.1364 | 10600 | 0.0583 | - |
464
+ | 0.1376 | 10700 | 0.0553 | - |
465
+ | 0.1389 | 10800 | 0.0561 | - |
466
+ | 0.1402 | 10900 | 0.0549 | - |
467
+ | 0.1415 | 11000 | 0.0558 | - |
468
+ | 0.1428 | 11100 | 0.0555 | - |
469
+ | 0.1441 | 11200 | 0.0531 | - |
470
+ | 0.1454 | 11300 | 0.0598 | - |
471
+ | 0.1467 | 11400 | 0.0584 | - |
472
+ | 0.1479 | 11500 | 0.054 | - |
473
+ | 0.1492 | 11600 | 0.0542 | - |
474
+ | 0.1505 | 11700 | 0.0567 | - |
475
+ | 0.1518 | 11800 | 0.0502 | - |
476
+ | 0.1531 | 11900 | 0.0536 | - |
477
+ | 0.1544 | 12000 | 0.0512 | 0.1556 |
478
+ | 0.1557 | 12100 | 0.0529 | - |
479
+ | 0.1569 | 12200 | 0.0528 | - |
480
+ | 0.1582 | 12300 | 0.0538 | - |
481
+ | 0.1595 | 12400 | 0.0515 | - |
482
+ | 0.1608 | 12500 | 0.0514 | - |
483
+ | 0.1621 | 12600 | 0.0562 | - |
484
+ | 0.1634 | 12700 | 0.0524 | - |
485
+ | 0.1647 | 12800 | 0.0531 | - |
486
+ | 0.1659 | 12900 | 0.0508 | - |
487
+ | 0.1672 | 13000 | 0.0531 | - |
488
+ | 0.1685 | 13100 | 0.0477 | - |
489
+ | 0.1698 | 13200 | 0.0526 | - |
490
+ | 0.1711 | 13300 | 0.0499 | - |
491
+ | 0.1724 | 13400 | 0.0533 | - |
492
+ | 0.1737 | 13500 | 0.0511 | - |
493
+ | 0.1750 | 13600 | 0.0553 | - |
494
+ | 0.1762 | 13700 | 0.0487 | - |
495
+ | 0.1775 | 13800 | 0.0486 | - |
496
+ | 0.1788 | 13900 | 0.052 | - |
497
+ | 0.1801 | 14000 | 0.0517 | 0.1407 |
498
+ | 0.1814 | 14100 | 0.051 | - |
499
+ | 0.1827 | 14200 | 0.0512 | - |
500
+ | 0.1840 | 14300 | 0.0526 | - |
501
+ | 0.1852 | 14400 | 0.0548 | - |
502
+ | 0.1865 | 14500 | 0.0545 | - |
503
+ | 0.1878 | 14600 | 0.0465 | - |
504
+ | 0.1891 | 14700 | 0.0508 | - |
505
+ | 0.1904 | 14800 | 0.0502 | - |
506
+ | 0.1917 | 14900 | 0.0543 | - |
507
+ | 0.1930 | 15000 | 0.0503 | - |
508
+ | 0.1942 | 15100 | 0.0513 | - |
509
+ | 0.1955 | 15200 | 0.053 | - |
510
+ | 0.1968 | 15300 | 0.0517 | - |
511
+ | 0.1981 | 15400 | 0.0515 | - |
512
+ | 0.1994 | 15500 | 0.0548 | - |
513
+ | 0.2007 | 15600 | 0.0513 | - |
514
+ | 0.2020 | 15700 | 0.0476 | - |
515
+ | 0.2033 | 15800 | 0.0522 | - |
516
+ | 0.2045 | 15900 | 0.0496 | - |
517
+ | 0.2058 | 16000 | 0.0516 | 0.1394 |
518
+ | 0.2071 | 16100 | 0.0484 | - |
519
+ | 0.2084 | 16200 | 0.0502 | - |
520
+ | 0.2097 | 16300 | 0.0494 | - |
521
+ | 0.2110 | 16400 | 0.0487 | - |
522
+ | 0.2123 | 16500 | 0.0482 | - |
523
+ | 0.2135 | 16600 | 0.0458 | - |
524
+ | 0.2148 | 16700 | 0.0524 | - |
525
+ | 0.2161 | 16800 | 0.0497 | - |
526
+ | 0.2174 | 16900 | 0.0508 | - |
527
+ | 0.2187 | 17000 | 0.0518 | - |
528
+ | 0.2200 | 17100 | 0.0538 | - |
529
+ | 0.2213 | 17200 | 0.0497 | - |
530
+ | 0.2226 | 17300 | 0.053 | - |
531
+ | 0.2238 | 17400 | 0.0452 | - |
532
+ | 0.2251 | 17500 | 0.0479 | - |
533
+ | 0.2264 | 17600 | 0.0484 | - |
534
+ | 0.2277 | 17700 | 0.0467 | - |
535
+ | 0.2290 | 17800 | 0.0478 | - |
536
+ | 0.2303 | 17900 | 0.0544 | - |
537
+ | 0.2316 | 18000 | 0.0426 | 0.1438 |
538
+ | 0.2328 | 18100 | 0.0501 | - |
539
+ | 0.2341 | 18200 | 0.0463 | - |
540
+ | 0.2354 | 18300 | 0.0521 | - |
541
+ | 0.2367 | 18400 | 0.0454 | - |
542
+ | 0.2380 | 18500 | 0.0548 | - |
543
+ | 0.2393 | 18600 | 0.0484 | - |
544
+ | 0.2406 | 18700 | 0.0443 | - |
545
+ | 0.2418 | 18800 | 0.0489 | - |
546
+ | 0.2431 | 18900 | 0.0502 | - |
547
+ | 0.2444 | 19000 | 0.0469 | - |
548
+ | 0.2457 | 19100 | 0.0468 | - |
549
+ | 0.2470 | 19200 | 0.0532 | - |
550
+ | 0.2483 | 19300 | 0.0486 | - |
551
+ | 0.2496 | 19400 | 0.0529 | - |
552
+ | 0.2509 | 19500 | 0.0499 | - |
553
+ | 0.2521 | 19600 | 0.0524 | - |
554
+ | 0.2534 | 19700 | 0.0491 | - |
555
+ | 0.2547 | 19800 | 0.0472 | - |
556
+ | 0.2560 | 19900 | 0.0463 | - |
557
+ | 0.2573 | 20000 | 0.047 | 0.1307 |
558
+ | 0.2586 | 20100 | 0.0471 | - |
559
+ | 0.2599 | 20200 | 0.0488 | - |
560
+ | 0.2611 | 20300 | 0.0476 | - |
561
+ | 0.2624 | 20400 | 0.0477 | - |
562
+ | 0.2637 | 20500 | 0.0448 | - |
563
+ | 0.2650 | 20600 | 0.0476 | - |
564
+ | 0.2663 | 20700 | 0.0484 | - |
565
+ | 0.2676 | 20800 | 0.0444 | - |
566
+ | 0.2689 | 20900 | 0.0452 | - |
567
+ | 0.2701 | 21000 | 0.0497 | - |
568
+ | 0.2714 | 21100 | 0.0524 | - |
569
+ | 0.2727 | 21200 | 0.0443 | - |
570
+ | 0.2740 | 21300 | 0.0479 | - |
571
+ | 0.2753 | 21400 | 0.0483 | - |
572
+ | 0.2766 | 21500 | 0.0509 | - |
573
+ | 0.2779 | 21600 | 0.0522 | - |
574
+ | 0.2792 | 21700 | 0.046 | - |
575
+ | 0.2804 | 21800 | 0.0476 | - |
576
+ | 0.2817 | 21900 | 0.0452 | - |
577
+ | 0.2830 | 22000 | 0.0471 | 0.1385 |
578
+ | 0.2843 | 22100 | 0.0421 | - |
579
+ | 0.2856 | 22200 | 0.0481 | - |
580
+ | 0.2869 | 22300 | 0.0501 | - |
581
+ | 0.2882 | 22400 | 0.0481 | - |
582
+ | 0.2894 | 22500 | 0.0492 | - |
583
+ | 0.2907 | 22600 | 0.0461 | - |
584
+ | 0.2920 | 22700 | 0.0427 | - |
585
+ | 0.2933 | 22800 | 0.0453 | - |
586
+ | 0.2946 | 22900 | 0.0519 | - |
587
+ | 0.2959 | 23000 | 0.0497 | - |
588
+ | 0.2972 | 23100 | 0.0439 | - |
589
+ | 0.2984 | 23200 | 0.046 | - |
590
+ | 0.2997 | 23300 | 0.0456 | - |
591
+ | 0.3010 | 23400 | 0.0477 | - |
592
+ | 0.3023 | 23500 | 0.0427 | - |
593
+ | 0.3036 | 23600 | 0.0482 | - |
594
+ | 0.3049 | 23700 | 0.0435 | - |
595
+ | 0.3062 | 23800 | 0.0475 | - |
596
+ | 0.3075 | 23900 | 0.0472 | - |
597
+ | 0.3087 | 24000 | 0.046 | 0.1366 |
598
+ | 0.3100 | 24100 | 0.0425 | - |
599
+ | 0.3113 | 24200 | 0.0451 | - |
600
+ | 0.3126 | 24300 | 0.0504 | - |
601
+ | 0.3139 | 24400 | 0.0529 | - |
602
+ | 0.3152 | 24500 | 0.0484 | - |
603
+ | 0.3165 | 24600 | 0.0483 | - |
604
+ | 0.3177 | 24700 | 0.0443 | - |
605
+ | 0.3190 | 24800 | 0.0463 | - |
606
+ | 0.3203 | 24900 | 0.0455 | - |
607
+ | 0.3216 | 25000 | 0.0488 | - |
608
+ | 0.3229 | 25100 | 0.0446 | - |
609
+ | 0.3242 | 25200 | 0.0456 | - |
610
+ | 0.3255 | 25300 | 0.0452 | - |
611
+ | 0.3268 | 25400 | 0.0444 | - |
612
+ | 0.3280 | 25500 | 0.0458 | - |
613
+ | 0.3293 | 25600 | 0.0447 | - |
614
+ | 0.3306 | 25700 | 0.0463 | - |
615
+ | 0.3319 | 25800 | 0.0453 | - |
616
+ | 0.3332 | 25900 | 0.0457 | - |
617
+ | 0.3345 | 26000 | 0.048 | 0.1264 |
618
+ | 0.3358 | 26100 | 0.0509 | - |
619
+ | 0.3370 | 26200 | 0.0471 | - |
620
+ | 0.3383 | 26300 | 0.0448 | - |
621
+ | 0.3396 | 26400 | 0.0461 | - |
622
+ | 0.3409 | 26500 | 0.0433 | - |
623
+ | 0.3422 | 26600 | 0.0476 | - |
624
+ | 0.3435 | 26700 | 0.043 | - |
625
+ | 0.3448 | 26800 | 0.0472 | - |
626
+ | 0.3460 | 26900 | 0.0482 | - |
627
+ | 0.3473 | 27000 | 0.0466 | - |
628
+ | 0.3486 | 27100 | 0.0492 | - |
629
+ | 0.3499 | 27200 | 0.0466 | - |
630
+ | 0.3512 | 27300 | 0.0479 | - |
631
+ | 0.3525 | 27400 | 0.0466 | - |
632
+ | 0.3538 | 27500 | 0.0446 | - |
633
+ | 0.3551 | 27600 | 0.0428 | - |
634
+ | 0.3563 | 27700 | 0.0496 | - |
635
+ | 0.3576 | 27800 | 0.0472 | - |
636
+ | 0.3589 | 27900 | 0.0472 | - |
637
+ | 0.3602 | 28000 | 0.0411 | 0.1280 |
638
+ | 0.3615 | 28100 | 0.0466 | - |
639
+ | 0.3628 | 28200 | 0.0463 | - |
640
+ | 0.3641 | 28300 | 0.0463 | - |
641
+ | 0.3653 | 28400 | 0.0471 | - |
642
+ | 0.3666 | 28500 | 0.0469 | - |
643
+ | 0.3679 | 28600 | 0.0447 | - |
644
+ | 0.3692 | 28700 | 0.0468 | - |
645
+ | 0.3705 | 28800 | 0.0405 | - |
646
+ | 0.3718 | 28900 | 0.0481 | - |
647
+ | 0.3731 | 29000 | 0.046 | - |
648
+ | 0.3743 | 29100 | 0.0498 | - |
649
+ | 0.3756 | 29200 | 0.0486 | - |
650
+ | 0.3769 | 29300 | 0.0418 | - |
651
+ | 0.3782 | 29400 | 0.0433 | - |
652
+ | 0.3795 | 29500 | 0.0447 | - |
653
+ | 0.3808 | 29600 | 0.0447 | - |
654
+ | 0.3821 | 29700 | 0.0435 | - |
655
+ | 0.3834 | 29800 | 0.0482 | - |
656
+ | 0.3846 | 29900 | 0.0468 | - |
657
+ | 0.3859 | 30000 | 0.0409 | 0.1324 |
658
+ | 0.3872 | 30100 | 0.039 | - |
659
+ | 0.3885 | 30200 | 0.0441 | - |
660
+ | 0.3898 | 30300 | 0.0454 | - |
661
+ | 0.3911 | 30400 | 0.044 | - |
662
+ | 0.3924 | 30500 | 0.0431 | - |
663
+ | 0.3936 | 30600 | 0.0409 | - |
664
+ | 0.3949 | 30700 | 0.0459 | - |
665
+ | 0.3962 | 30800 | 0.0494 | - |
666
+ | 0.3975 | 30900 | 0.0416 | - |
667
+ | 0.3988 | 31000 | 0.0474 | - |
668
+ | 0.4001 | 31100 | 0.0394 | - |
669
+ | 0.4014 | 31200 | 0.0448 | - |
670
+ | 0.4027 | 31300 | 0.0429 | - |
671
+ | 0.4039 | 31400 | 0.0472 | - |
672
+ | 0.4052 | 31500 | 0.0425 | - |
673
+ | 0.4065 | 31600 | 0.0445 | - |
674
+ | 0.4078 | 31700 | 0.0427 | - |
675
+ | 0.4091 | 31800 | 0.0449 | - |
676
+ | 0.4104 | 31900 | 0.0485 | - |
677
+ | 0.4117 | 32000 | 0.0417 | 0.1271 |
678
+ | 0.4129 | 32100 | 0.0475 | - |
679
+ | 0.4142 | 32200 | 0.0427 | - |
680
+ | 0.4155 | 32300 | 0.0412 | - |
681
+ | 0.4168 | 32400 | 0.042 | - |
682
+ | 0.4181 | 32500 | 0.0509 | - |
683
+ | 0.4194 | 32600 | 0.0475 | - |
684
+ | 0.4207 | 32700 | 0.0412 | - |
685
+ | 0.4219 | 32800 | 0.0413 | - |
686
+ | 0.4232 | 32900 | 0.0447 | - |
687
+ | 0.4245 | 33000 | 0.0426 | - |
688
+ | 0.4258 | 33100 | 0.0432 | - |
689
+ | 0.4271 | 33200 | 0.0432 | - |
690
+ | 0.4284 | 33300 | 0.0433 | - |
691
+ | 0.4297 | 33400 | 0.0473 | - |
692
+ | 0.4310 | 33500 | 0.0438 | - |
693
+ | 0.4322 | 33600 | 0.0487 | - |
694
+ | 0.4335 | 33700 | 0.0438 | - |
695
+ | 0.4348 | 33800 | 0.046 | - |
696
+ | 0.4361 | 33900 | 0.0418 | - |
697
+ | 0.4374 | 34000 | 0.044 | 0.1293 |
698
+ | 0.4387 | 34100 | 0.0442 | - |
699
+ | 0.4400 | 34200 | 0.0442 | - |
700
+ | 0.4412 | 34300 | 0.0429 | - |
701
+ | 0.4425 | 34400 | 0.0465 | - |
702
+ | 0.4438 | 34500 | 0.051 | - |
703
+ | 0.4451 | 34600 | 0.0408 | - |
704
+ | 0.4464 | 34700 | 0.0448 | - |
705
+ | 0.4477 | 34800 | 0.0488 | - |
706
+ | 0.4490 | 34900 | 0.0465 | - |
707
+ | 0.4502 | 35000 | 0.0439 | - |
708
+ | 0.4515 | 35100 | 0.0437 | - |
709
+ | 0.4528 | 35200 | 0.0428 | - |
710
+ | 0.4541 | 35300 | 0.0401 | - |
711
+ | 0.4554 | 35400 | 0.0423 | - |
712
+ | 0.4567 | 35500 | 0.0404 | - |
713
+ | 0.4580 | 35600 | 0.0396 | - |
714
+ | 0.4593 | 35700 | 0.0427 | - |
715
+ | 0.4605 | 35800 | 0.0438 | - |
716
+ | 0.4618 | 35900 | 0.0427 | - |
717
+ | 0.4631 | 36000 | 0.0412 | 0.1277 |
718
+ | 0.4644 | 36100 | 0.0431 | - |
719
+ | 0.4657 | 36200 | 0.0435 | - |
720
+ | 0.4670 | 36300 | 0.045 | - |
721
+ | 0.4683 | 36400 | 0.0439 | - |
722
+ | 0.4695 | 36500 | 0.0447 | - |
723
+ | 0.4708 | 36600 | 0.0464 | - |
724
+ | 0.4721 | 36700 | 0.0425 | - |
725
+ | 0.4734 | 36800 | 0.0523 | - |
726
+ | 0.4747 | 36900 | 0.0468 | - |
727
+ | 0.4760 | 37000 | 0.0441 | - |
728
+ | 0.4773 | 37100 | 0.0421 | - |
729
+ | 0.4785 | 37200 | 0.0439 | - |
730
+ | 0.4798 | 37300 | 0.0413 | - |
731
+ | 0.4811 | 37400 | 0.0482 | - |
732
+ | 0.4824 | 37500 | 0.0427 | - |
733
+ | 0.4837 | 37600 | 0.0384 | - |
734
+ | 0.4850 | 37700 | 0.0429 | - |
735
+ | 0.4863 | 37800 | 0.0449 | - |
736
+ | 0.4876 | 37900 | 0.0429 | - |
737
+ | 0.4888 | 38000 | 0.0433 | 0.1259 |
738
+ | 0.4901 | 38100 | 0.0429 | - |
739
+ | 0.4914 | 38200 | 0.0441 | - |
740
+ | 0.4927 | 38300 | 0.0443 | - |
741
+ | 0.4940 | 38400 | 0.0503 | - |
742
+ | 0.4953 | 38500 | 0.0403 | - |
743
+ | 0.4966 | 38600 | 0.0437 | - |
744
+ | 0.4978 | 38700 | 0.0447 | - |
745
+ | 0.4991 | 38800 | 0.0434 | - |
746
+ | 0.5004 | 38900 | 0.0403 | - |
747
+ | 0.5017 | 39000 | 0.0431 | - |
748
+ | 0.5030 | 39100 | 0.0422 | - |
749
+ | 0.5043 | 39200 | 0.0477 | - |
750
+ | 0.5056 | 39300 | 0.0435 | - |
751
+ | 0.5069 | 39400 | 0.0438 | - |
752
+ | 0.5081 | 39500 | 0.0444 | - |
753
+ | 0.5094 | 39600 | 0.0451 | - |
754
+ | 0.5107 | 39700 | 0.0405 | - |
755
+ | 0.5120 | 39800 | 0.0427 | - |
756
+ | 0.5133 | 39900 | 0.0477 | - |
757
+ | 0.5146 | 40000 | 0.046 | 0.1268 |
758
+ | 0.5159 | 40100 | 0.0446 | - |
759
+ | 0.5171 | 40200 | 0.0436 | - |
760
+ | 0.5184 | 40300 | 0.0485 | - |
761
+ | 0.5197 | 40400 | 0.0462 | - |
762
+ | 0.5210 | 40500 | 0.0455 | - |
763
+ | 0.5223 | 40600 | 0.0429 | - |
764
+ | 0.5236 | 40700 | 0.0401 | - |
765
+ | 0.5249 | 40800 | 0.0453 | - |
766
+ | 0.5261 | 40900 | 0.0454 | - |
767
+ | 0.5274 | 41000 | 0.0472 | - |
768
+ | 0.5287 | 41100 | 0.0416 | - |
769
+ | 0.5300 | 41200 | 0.0395 | - |
770
+ | 0.5313 | 41300 | 0.0418 | - |
771
+ | 0.5326 | 41400 | 0.0437 | - |
772
+ | 0.5339 | 41500 | 0.0413 | - |
773
+ | 0.5352 | 41600 | 0.0429 | - |
774
+ | 0.5364 | 41700 | 0.0428 | - |
775
+ | 0.5377 | 41800 | 0.046 | - |
776
+ | 0.5390 | 41900 | 0.0447 | - |
777
+ | 0.5403 | 42000 | 0.0424 | 0.1250 |
778
+ | 0.5416 | 42100 | 0.0437 | - |
779
+ | 0.5429 | 42200 | 0.0432 | - |
780
+ | 0.5442 | 42300 | 0.0439 | - |
781
+ | 0.5454 | 42400 | 0.0437 | - |
782
+ | 0.5467 | 42500 | 0.0455 | - |
783
+ | 0.5480 | 42600 | 0.0436 | - |
784
+ | 0.5493 | 42700 | 0.0421 | - |
785
+ | 0.5506 | 42800 | 0.0445 | - |
786
+ | 0.5519 | 42900 | 0.0413 | - |
787
+ | 0.5532 | 43000 | 0.0431 | - |
788
+ | 0.5544 | 43100 | 0.0441 | - |
789
+ | 0.5557 | 43200 | 0.0431 | - |
790
+ | 0.5570 | 43300 | 0.0433 | - |
791
+ | 0.5583 | 43400 | 0.0459 | - |
792
+ | 0.5596 | 43500 | 0.0441 | - |
793
+ | 0.5609 | 43600 | 0.0433 | - |
794
+ | 0.5622 | 43700 | 0.0411 | - |
795
+ | 0.5635 | 43800 | 0.0426 | - |
796
+ | 0.5647 | 43900 | 0.0428 | - |
797
+ | 0.5660 | 44000 | 0.0423 | 0.1245 |
798
+ | 0.5673 | 44100 | 0.0436 | - |
799
+ | 0.5686 | 44200 | 0.0424 | - |
800
+ | 0.5699 | 44300 | 0.044 | - |
801
+ | 0.5712 | 44400 | 0.0386 | - |
802
+ | 0.5725 | 44500 | 0.0412 | - |
803
+ | 0.5737 | 44600 | 0.0423 | - |
804
+ | 0.5750 | 44700 | 0.0416 | - |
805
+ | 0.5763 | 44800 | 0.0414 | - |
806
+ | 0.5776 | 44900 | 0.0412 | - |
807
+ | 0.5789 | 45000 | 0.044 | - |
808
+ | 0.5802 | 45100 | 0.0411 | - |
809
+ | 0.5815 | 45200 | 0.0422 | - |
810
+ | 0.5827 | 45300 | 0.0438 | - |
811
+ | 0.5840 | 45400 | 0.0449 | - |
812
+ | 0.5853 | 45500 | 0.046 | - |
813
+ | 0.5866 | 45600 | 0.0423 | - |
814
+ | 0.5879 | 45700 | 0.0402 | - |
815
+ | 0.5892 | 45800 | 0.0424 | - |
816
+ | 0.5905 | 45900 | 0.0424 | - |
817
+ | 0.5918 | 46000 | 0.0406 | 0.1246 |
818
+ | 0.5930 | 46100 | 0.0454 | - |
819
+ | 0.5943 | 46200 | 0.0466 | - |
820
+ | 0.5956 | 46300 | 0.0423 | - |
821
+ | 0.5969 | 46400 | 0.0444 | - |
822
+ | 0.5982 | 46500 | 0.0404 | - |
823
+ | 0.5995 | 46600 | 0.0435 | - |
824
+ | 0.6008 | 46700 | 0.0416 | - |
825
+ | 0.6020 | 46800 | 0.0471 | - |
826
+ | 0.6033 | 46900 | 0.0417 | - |
827
+ | 0.6046 | 47000 | 0.0402 | - |
828
+ | 0.6059 | 47100 | 0.0464 | - |
829
+ | 0.6072 | 47200 | 0.0387 | - |
830
+ | 0.6085 | 47300 | 0.0423 | - |
831
+ | 0.6098 | 47400 | 0.0417 | - |
832
+ | 0.6111 | 47500 | 0.0391 | - |
833
+ | 0.6123 | 47600 | 0.0448 | - |
834
+ | 0.6136 | 47700 | 0.0407 | - |
835
+ | 0.6149 | 47800 | 0.0396 | - |
836
+ | 0.6162 | 47900 | 0.0456 | - |
837
+ | 0.6175 | 48000 | 0.0431 | 0.1253 |
838
+ | 0.6188 | 48100 | 0.0443 | - |
839
+ | 0.6201 | 48200 | 0.0419 | - |
840
+ | 0.6213 | 48300 | 0.0434 | - |
841
+ | 0.6226 | 48400 | 0.0406 | - |
842
+ | 0.6239 | 48500 | 0.0437 | - |
843
+ | 0.6252 | 48600 | 0.0402 | - |
844
+ | 0.6265 | 48700 | 0.0436 | - |
845
+ | 0.6278 | 48800 | 0.0386 | - |
846
+ | 0.6291 | 48900 | 0.0466 | - |
847
+ | 0.6303 | 49000 | 0.0406 | - |
848
+ | 0.6316 | 49100 | 0.0404 | - |
849
+ | 0.6329 | 49200 | 0.0447 | - |
850
+ | 0.6342 | 49300 | 0.0446 | - |
851
+ | 0.6355 | 49400 | 0.0438 | - |
852
+ | 0.6368 | 49500 | 0.0449 | - |
853
+ | 0.6381 | 49600 | 0.0404 | - |
854
+ | 0.6394 | 49700 | 0.0389 | - |
855
+ | 0.6406 | 49800 | 0.0406 | - |
856
+ | 0.6419 | 49900 | 0.0398 | - |
857
+ | 0.6432 | 50000 | 0.0382 | 0.1255 |
858
+ | 0.6445 | 50100 | 0.0383 | - |
859
+ | 0.6458 | 50200 | 0.0422 | - |
860
+ | 0.6471 | 50300 | 0.0442 | - |
861
+ | 0.6484 | 50400 | 0.0408 | - |
862
+ | 0.6496 | 50500 | 0.0408 | - |
863
+ | 0.6509 | 50600 | 0.0366 | - |
864
+ | 0.6522 | 50700 | 0.0409 | - |
865
+ | 0.6535 | 50800 | 0.0413 | - |
866
+ | 0.6548 | 50900 | 0.0448 | - |
867
+ | 0.6561 | 51000 | 0.0416 | - |
868
+ | 0.6574 | 51100 | 0.0394 | - |
869
+ | 0.6586 | 51200 | 0.0375 | - |
870
+ | 0.6599 | 51300 | 0.0411 | - |
871
+ | 0.6612 | 51400 | 0.043 | - |
872
+ | 0.6625 | 51500 | 0.0479 | - |
873
+ | 0.6638 | 51600 | 0.0378 | - |
874
+ | 0.6651 | 51700 | 0.0426 | - |
875
+ | 0.6664 | 51800 | 0.0383 | - |
876
+ | 0.6677 | 51900 | 0.0391 | - |
877
+ | 0.6689 | 52000 | 0.0433 | 0.1218 |
878
+ | 0.6702 | 52100 | 0.039 | - |
879
+ | 0.6715 | 52200 | 0.0435 | - |
880
+ | 0.6728 | 52300 | 0.0393 | - |
881
+ | 0.6741 | 52400 | 0.04 | - |
882
+ | 0.6754 | 52500 | 0.0438 | - |
883
+ | 0.6767 | 52600 | 0.0466 | - |
884
+ | 0.6779 | 52700 | 0.0435 | - |
885
+ | 0.6792 | 52800 | 0.042 | - |
886
+ | 0.6805 | 52900 | 0.0473 | - |
887
+ | 0.6818 | 53000 | 0.0376 | - |
888
+ | 0.6831 | 53100 | 0.0396 | - |
889
+ | 0.6844 | 53200 | 0.0411 | - |
890
+ | 0.6857 | 53300 | 0.0419 | - |
891
+ | 0.6869 | 53400 | 0.0423 | - |
892
+ | 0.6882 | 53500 | 0.0374 | - |
893
+ | 0.6895 | 53600 | 0.043 | - |
894
+ | 0.6908 | 53700 | 0.0444 | - |
895
+ | 0.6921 | 53800 | 0.0427 | - |
896
+ | 0.6934 | 53900 | 0.0456 | - |
897
+ | 0.6947 | 54000 | 0.0407 | 0.1234 |
898
+ | 0.6960 | 54100 | 0.0438 | - |
899
+ | 0.6972 | 54200 | 0.0403 | - |
900
+ | 0.6985 | 54300 | 0.041 | - |
901
+ | 0.6998 | 54400 | 0.0395 | - |
902
+ | 0.7011 | 54500 | 0.0447 | - |
903
+ | 0.7024 | 54600 | 0.0412 | - |
904
+ | 0.7037 | 54700 | 0.0406 | - |
905
+ | 0.7050 | 54800 | 0.0446 | - |
906
+ | 0.7062 | 54900 | 0.0426 | - |
907
+ | 0.7075 | 55000 | 0.0428 | - |
908
+ | 0.7088 | 55100 | 0.042 | - |
909
+ | 0.7101 | 55200 | 0.0429 | - |
910
+ | 0.7114 | 55300 | 0.0446 | - |
911
+ | 0.7127 | 55400 | 0.0408 | - |
912
+ | 0.7140 | 55500 | 0.0364 | - |
913
+ | 0.7153 | 55600 | 0.0369 | - |
914
+ | 0.7165 | 55700 | 0.0463 | - |
915
+ | 0.7178 | 55800 | 0.0423 | - |
916
+ | 0.7191 | 55900 | 0.0408 | - |
917
+ | 0.7204 | 56000 | 0.0386 | 0.1252 |
918
+ | 0.7217 | 56100 | 0.0434 | - |
919
+ | 0.7230 | 56200 | 0.0392 | - |
920
+ | 0.7243 | 56300 | 0.0399 | - |
921
+ | 0.7255 | 56400 | 0.0421 | - |
922
+ | 0.7268 | 56500 | 0.0434 | - |
923
+ | 0.7281 | 56600 | 0.037 | - |
924
+ | 0.7294 | 56700 | 0.0414 | - |
925
+ | 0.7307 | 56800 | 0.0399 | - |
926
+ | 0.7320 | 56900 | 0.0404 | - |
927
+ | 0.7333 | 57000 | 0.0443 | - |
928
+ | 0.7345 | 57100 | 0.0402 | - |
929
+ | 0.7358 | 57200 | 0.0425 | - |
930
+ | 0.7371 | 57300 | 0.0391 | - |
931
+ | 0.7384 | 57400 | 0.0418 | - |
932
+ | 0.7397 | 57500 | 0.0409 | - |
933
+ | 0.7410 | 57600 | 0.0435 | - |
934
+ | 0.7423 | 57700 | 0.0414 | - |
935
+ | 0.7436 | 57800 | 0.0412 | - |
936
+ | 0.7448 | 57900 | 0.0392 | - |
937
+ | 0.7461 | 58000 | 0.04 | 0.1244 |
938
+ | 0.7474 | 58100 | 0.0392 | - |
939
+ | 0.7487 | 58200 | 0.0399 | - |
940
+ | 0.7500 | 58300 | 0.041 | - |
941
+ | 0.7513 | 58400 | 0.0395 | - |
942
+ | 0.7526 | 58500 | 0.0413 | - |
943
+ | 0.7538 | 58600 | 0.0394 | - |
944
+ | 0.7551 | 58700 | 0.0451 | - |
945
+ | 0.7564 | 58800 | 0.0454 | - |
946
+ | 0.7577 | 58900 | 0.0434 | - |
947
+ | 0.7590 | 59000 | 0.0417 | - |
948
+ | 0.7603 | 59100 | 0.0422 | - |
949
+ | 0.7616 | 59200 | 0.0415 | - |
950
+ | 0.7628 | 59300 | 0.0412 | - |
951
+ | 0.7641 | 59400 | 0.044 | - |
952
+ | 0.7654 | 59500 | 0.0368 | - |
953
+ | 0.7667 | 59600 | 0.0418 | - |
954
+ | 0.7680 | 59700 | 0.0427 | - |
955
+ | 0.7693 | 59800 | 0.0421 | - |
956
+ | 0.7706 | 59900 | 0.0418 | - |
957
+ | 0.7719 | 60000 | 0.0438 | 0.1229 |
958
+ | 0.7731 | 60100 | 0.0393 | - |
959
+ | 0.7744 | 60200 | 0.0411 | - |
960
+ | 0.7757 | 60300 | 0.0376 | - |
961
+ | 0.7770 | 60400 | 0.0404 | - |
962
+ | 0.7783 | 60500 | 0.0412 | - |
963
+ | 0.7796 | 60600 | 0.0404 | - |
964
+ | 0.7809 | 60700 | 0.0447 | - |
965
+ | 0.7821 | 60800 | 0.0436 | - |
966
+ | 0.7834 | 60900 | 0.04 | - |
967
+ | 0.7847 | 61000 | 0.0408 | - |
968
+ | 0.7860 | 61100 | 0.0458 | - |
969
+ | 0.7873 | 61200 | 0.041 | - |
970
+ | 0.7886 | 61300 | 0.0384 | - |
971
+ | 0.7899 | 61400 | 0.0419 | - |
972
+ | 0.7911 | 61500 | 0.0433 | - |
973
+ | 0.7924 | 61600 | 0.0457 | - |
974
+ | 0.7937 | 61700 | 0.0388 | - |
975
+ | 0.7950 | 61800 | 0.0416 | - |
976
+ | 0.7963 | 61900 | 0.0437 | - |
977
+ | 0.7976 | 62000 | 0.0374 | 0.1237 |
978
+ | 0.7989 | 62100 | 0.0351 | - |
979
+ | 0.8002 | 62200 | 0.0387 | - |
980
+ | 0.8014 | 62300 | 0.0457 | - |
981
+ | 0.8027 | 62400 | 0.0424 | - |
982
+ | 0.8040 | 62500 | 0.039 | - |
983
+ | 0.8053 | 62600 | 0.0429 | - |
984
+ | 0.8066 | 62700 | 0.0387 | - |
985
+ | 0.8079 | 62800 | 0.0379 | - |
986
+ | 0.8092 | 62900 | 0.0377 | - |
987
+ | 0.8104 | 63000 | 0.0401 | - |
988
+ | 0.8117 | 63100 | 0.0393 | - |
989
+ | 0.8130 | 63200 | 0.0409 | - |
990
+ | 0.8143 | 63300 | 0.0394 | - |
991
+ | 0.8156 | 63400 | 0.0386 | - |
992
+ | 0.8169 | 63500 | 0.0394 | - |
993
+ | 0.8182 | 63600 | 0.0404 | - |
994
+ | 0.8195 | 63700 | 0.0409 | - |
995
+ | 0.8207 | 63800 | 0.0419 | - |
996
+ | 0.8220 | 63900 | 0.0384 | - |
997
+ | 0.8233 | 64000 | 0.0437 | 0.1241 |
998
+ | 0.8246 | 64100 | 0.0377 | - |
999
+ | 0.8259 | 64200 | 0.0444 | - |
1000
+ | 0.8272 | 64300 | 0.0407 | - |
1001
+ | 0.8285 | 64400 | 0.0403 | - |
1002
+ | 0.8297 | 64500 | 0.0354 | - |
1003
+ | 0.8310 | 64600 | 0.0408 | - |
1004
+ | 0.8323 | 64700 | 0.0409 | - |
1005
+ | 0.8336 | 64800 | 0.0383 | - |
1006
+ | 0.8349 | 64900 | 0.0382 | - |
1007
+ | 0.8362 | 65000 | 0.0423 | - |
1008
+ | 0.8375 | 65100 | 0.0433 | - |
1009
+ | 0.8387 | 65200 | 0.0384 | - |
1010
+ | 0.8400 | 65300 | 0.0411 | - |
1011
+ | 0.8413 | 65400 | 0.0413 | - |
1012
+ | 0.8426 | 65500 | 0.04 | - |
1013
+ | 0.8439 | 65600 | 0.0443 | - |
1014
+ | 0.8452 | 65700 | 0.0377 | - |
1015
+ | 0.8465 | 65800 | 0.0411 | - |
1016
+ | 0.8478 | 65900 | 0.0403 | - |
1017
+ | 0.8490 | 66000 | 0.0405 | 0.1221 |
1018
+ | 0.8503 | 66100 | 0.0447 | - |
1019
+ | 0.8516 | 66200 | 0.0427 | - |
1020
+ | 0.8529 | 66300 | 0.0422 | - |
1021
+ | 0.8542 | 66400 | 0.0402 | - |
1022
+ | 0.8555 | 66500 | 0.0428 | - |
1023
+ | 0.8568 | 66600 | 0.0428 | - |
1024
+ | 0.8580 | 66700 | 0.0407 | - |
1025
+ | 0.8593 | 66800 | 0.0405 | - |
1026
+ | 0.8606 | 66900 | 0.0442 | - |
1027
+ | 0.8619 | 67000 | 0.0409 | - |
1028
+ | 0.8632 | 67100 | 0.0405 | - |
1029
+ | 0.8645 | 67200 | 0.0422 | - |
1030
+ | 0.8658 | 67300 | 0.0411 | - |
1031
+ | 0.8670 | 67400 | 0.0437 | - |
1032
+ | 0.8683 | 67500 | 0.0417 | - |
1033
+ | 0.8696 | 67600 | 0.0407 | - |
1034
+ | 0.8709 | 67700 | 0.0395 | - |
1035
+ | 0.8722 | 67800 | 0.0418 | - |
1036
+ | 0.8735 | 67900 | 0.0401 | - |
1037
+ | 0.8748 | 68000 | 0.0399 | 0.1231 |
1038
+ | 0.8761 | 68100 | 0.0393 | - |
1039
+ | 0.8773 | 68200 | 0.0418 | - |
1040
+ | 0.8786 | 68300 | 0.0418 | - |
1041
+ | 0.8799 | 68400 | 0.0361 | - |
1042
+ | 0.8812 | 68500 | 0.0397 | - |
1043
+ | 0.8825 | 68600 | 0.0342 | - |
1044
+ | 0.8838 | 68700 | 0.0432 | - |
1045
+ | 0.8851 | 68800 | 0.0377 | - |
1046
+ | 0.8863 | 68900 | 0.0397 | - |
1047
+ | 0.8876 | 69000 | 0.0409 | - |
1048
+ | 0.8889 | 69100 | 0.0404 | - |
1049
+ | 0.8902 | 69200 | 0.0443 | - |
1050
+ | 0.8915 | 69300 | 0.0395 | - |
1051
+ | 0.8928 | 69400 | 0.0409 | - |
1052
+ | 0.8941 | 69500 | 0.0403 | - |
1053
+ | 0.8953 | 69600 | 0.0418 | - |
1054
+ | 0.8966 | 69700 | 0.0372 | - |
1055
+ | 0.8979 | 69800 | 0.0369 | - |
1056
+ | 0.8992 | 69900 | 0.0413 | - |
1057
+ | 0.9005 | 70000 | 0.0382 | 0.1234 |
1058
+ | 0.9018 | 70100 | 0.0392 | - |
1059
+ | 0.9031 | 70200 | 0.0416 | - |
1060
+ | 0.9044 | 70300 | 0.0414 | - |
1061
+ | 0.9056 | 70400 | 0.0381 | - |
1062
+ | 0.9069 | 70500 | 0.0386 | - |
1063
+ | 0.9082 | 70600 | 0.0384 | - |
1064
+ | 0.9095 | 70700 | 0.0386 | - |
1065
+ | 0.9108 | 70800 | 0.0442 | - |
1066
+ | 0.9121 | 70900 | 0.0352 | - |
1067
+ | 0.9134 | 71000 | 0.036 | - |
1068
+ | 0.9146 | 71100 | 0.0421 | - |
1069
+ | 0.9159 | 71200 | 0.0391 | - |
1070
+ | 0.9172 | 71300 | 0.0399 | - |
1071
+ | 0.9185 | 71400 | 0.0448 | - |
1072
+ | 0.9198 | 71500 | 0.0357 | - |
1073
+ | 0.9211 | 71600 | 0.0423 | - |
1074
+ | 0.9224 | 71700 | 0.0404 | - |
1075
+ | 0.9237 | 71800 | 0.0412 | - |
1076
+ | 0.9249 | 71900 | 0.0424 | - |
1077
+ | 0.9262 | 72000 | 0.0397 | 0.1226 |
1078
+ | 0.9275 | 72100 | 0.0425 | - |
1079
+ | 0.9288 | 72200 | 0.0405 | - |
1080
+ | 0.9301 | 72300 | 0.0411 | - |
1081
+ | 0.9314 | 72400 | 0.0433 | - |
1082
+ | 0.9327 | 72500 | 0.0414 | - |
1083
+ | 0.9339 | 72600 | 0.0387 | - |
1084
+ | 0.9352 | 72700 | 0.0418 | - |
1085
+ | 0.9365 | 72800 | 0.0404 | - |
1086
+ | 0.9378 | 72900 | 0.0397 | - |
1087
+ | 0.9391 | 73000 | 0.0379 | - |
1088
+ | 0.9404 | 73100 | 0.0437 | - |
1089
+ | 0.9417 | 73200 | 0.0451 | - |
1090
+ | 0.9429 | 73300 | 0.0403 | - |
1091
+ | 0.9442 | 73400 | 0.0374 | - |
1092
+ | 0.9455 | 73500 | 0.0398 | - |
1093
+ | 0.9468 | 73600 | 0.0399 | - |
1094
+ | 0.9481 | 73700 | 0.0422 | - |
1095
+ | 0.9494 | 73800 | 0.0388 | - |
1096
+ | 0.9507 | 73900 | 0.0397 | - |
1097
+ | 0.9520 | 74000 | 0.0359 | 0.1227 |
1098
+ | 0.9532 | 74100 | 0.0371 | - |
1099
+ | 0.9545 | 74200 | 0.04 | - |
1100
+ | 0.9558 | 74300 | 0.0433 | - |
1101
+ | 0.9571 | 74400 | 0.0405 | - |
1102
+ | 0.9584 | 74500 | 0.0387 | - |
1103
+ | 0.9597 | 74600 | 0.042 | - |
1104
+ | 0.9610 | 74700 | 0.0426 | - |
1105
+ | 0.9622 | 74800 | 0.0397 | - |
1106
+ | 0.9635 | 74900 | 0.0403 | - |
1107
+ | 0.9648 | 75000 | 0.0415 | - |
1108
+ | 0.9661 | 75100 | 0.0429 | - |
1109
+ | 0.9674 | 75200 | 0.0446 | - |
1110
+ | 0.9687 | 75300 | 0.0404 | - |
1111
+ | 0.9700 | 75400 | 0.0397 | - |
1112
+ | 0.9712 | 75500 | 0.0357 | - |
1113
+ | 0.9725 | 75600 | 0.0391 | - |
1114
+ | 0.9738 | 75700 | 0.0433 | - |
1115
+ | 0.9751 | 75800 | 0.0405 | - |
1116
+ | 0.9764 | 75900 | 0.0424 | - |
1117
+ | 0.9777 | 76000 | 0.0439 | 0.1226 |
1118
+ | 0.9790 | 76100 | 0.0422 | - |
1119
+ | 0.9803 | 76200 | 0.0438 | - |
1120
+ | 0.9815 | 76300 | 0.0391 | - |
1121
+ | 0.9828 | 76400 | 0.0414 | - |
1122
+ | 0.9841 | 76500 | 0.0396 | - |
1123
+ | 0.9854 | 76600 | 0.0379 | - |
1124
+ | 0.9867 | 76700 | 0.0397 | - |
1125
+ | 0.9880 | 76800 | 0.0385 | - |
1126
+ | 0.9893 | 76900 | 0.0422 | - |
1127
+ | 0.9905 | 77000 | 0.0418 | - |
1128
+ | 0.9918 | 77100 | 0.0417 | - |
1129
+ | 0.9931 | 77200 | 0.0399 | - |
1130
+ | 0.9944 | 77300 | 0.0383 | - |
1131
+ | 0.9957 | 77400 | 0.041 | - |
1132
+ | 0.9970 | 77500 | 0.0411 | - |
1133
+ | 0.9983 | 77600 | 0.0409 | - |
1134
+ | 0.9995 | 77700 | 0.0393 | - |
1135
 
1136
  </details>
1137
 
 
1151
  #### Sentence Transformers
1152
  ```bibtex
1153
  @inproceedings{reimers-2019-sentence-bert,
1154
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1155
+ author = "Reimers, Nils and Gurevych, Iryna",
1156
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1157
+ month = "11",
1158
+ year = "2019",
1159
+ publisher = "Association for Computational Linguistics",
1160
+ url = "https://arxiv.org/abs/1908.10084",
1161
  }
1162
  ```
1163
 
1164
  #### MultipleNegativesRankingLoss
1165
  ```bibtex
1166
  @misc{henderson2017efficient,
1167
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
1168
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
1169
+ year={2017},
1170
+ eprint={1705.00652},
1171
+ archivePrefix={arXiv},
1172
+ primaryClass={cs.CL}
1173
  }
1174
  ```
1175
 
config.json CHANGED
@@ -1,26 +1,26 @@
1
  {
2
- "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
3
- "architectures": [
4
- "BertModel"
5
- ],
6
- "attention_probs_dropout_prob": 0.1,
7
- "classifier_dropout": null,
8
- "gradient_checkpointing": false,
9
- "hidden_act": "gelu",
10
- "hidden_dropout_prob": 0.1,
11
- "hidden_size": 384,
12
- "initializer_range": 0.02,
13
- "intermediate_size": 1536,
14
- "layer_norm_eps": 1e-12,
15
- "max_position_embeddings": 512,
16
- "model_type": "bert",
17
- "num_attention_heads": 12,
18
- "num_hidden_layers": 6,
19
- "pad_token_id": 0,
20
- "position_embedding_type": "absolute",
21
- "torch_dtype": "float32",
22
- "transformers_version": "4.46.3",
23
- "type_vocab_size": 2,
24
- "use_cache": true,
25
- "vocab_size": 30522
26
  }
 
1
  {
2
+ "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 384,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 1536,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 6,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.46.3",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 30522
26
  }
config_sentence_transformers.json CHANGED
@@ -1,14 +1,14 @@
1
  {
2
- "__version__": {
3
- "sentence_transformers": "5.2.3",
4
- "transformers": "4.46.3",
5
- "pytorch": "2.10.0"
6
- },
7
- "model_type": "SentenceTransformer",
8
- "prompts": {
9
- "query": "",
10
- "document": ""
11
- },
12
- "default_prompt_name": null,
13
- "similarity_fn_name": "cosine"
14
  }
 
1
  {
2
+ "__version__": {
3
+ "sentence_transformers": "5.2.3",
4
+ "transformers": "4.46.3",
5
+ "pytorch": "2.10.0"
6
+ },
7
+ "model_type": "SentenceTransformer",
8
+ "prompts": {
9
+ "query": "",
10
+ "document": ""
11
+ },
12
+ "default_prompt_name": null,
13
+ "similarity_fn_name": "cosine"
14
  }
modules.json CHANGED
@@ -1,20 +1,20 @@
1
  [
2
- {
3
- "idx": 0,
4
- "name": "0",
5
- "path": "",
6
- "type": "sentence_transformers.models.Transformer"
7
- },
8
- {
9
- "idx": 1,
10
- "name": "1",
11
- "path": "1_Pooling",
12
- "type": "sentence_transformers.models.Pooling"
13
- },
14
- {
15
- "idx": 2,
16
- "name": "2",
17
- "path": "2_Normalize",
18
- "type": "sentence_transformers.models.Normalize"
19
- }
20
  ]
 
1
  [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
  ]
sentence_bert_config.json CHANGED
@@ -1,4 +1,4 @@
1
  {
2
- "max_seq_length": 256,
3
- "do_lower_case": false
4
  }
 
1
  {
2
+ "max_seq_length": 256,
3
+ "do_lower_case": false
4
  }
special_tokens_map.json CHANGED
@@ -1,37 +1,37 @@
1
  {
2
- "cls_token": {
3
- "content": "[CLS]",
4
- "lstrip": false,
5
- "normalized": false,
6
- "rstrip": false,
7
- "single_word": false
8
- },
9
- "mask_token": {
10
- "content": "[MASK]",
11
- "lstrip": false,
12
- "normalized": false,
13
- "rstrip": false,
14
- "single_word": false
15
- },
16
- "pad_token": {
17
- "content": "[PAD]",
18
- "lstrip": false,
19
- "normalized": false,
20
- "rstrip": false,
21
- "single_word": false
22
- },
23
- "sep_token": {
24
- "content": "[SEP]",
25
- "lstrip": false,
26
- "normalized": false,
27
- "rstrip": false,
28
- "single_word": false
29
- },
30
- "unk_token": {
31
- "content": "[UNK]",
32
- "lstrip": false,
33
- "normalized": false,
34
- "rstrip": false,
35
- "single_word": false
36
- }
37
  }
 
1
  {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
  }
tokenizer.json CHANGED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json CHANGED
@@ -1,64 +1,64 @@
1
  {
2
- "added_tokens_decoder": {
3
- "0": {
4
- "content": "[PAD]",
5
- "lstrip": false,
6
- "normalized": false,
7
- "rstrip": false,
8
- "single_word": false,
9
- "special": true
10
- },
11
- "100": {
12
- "content": "[UNK]",
13
- "lstrip": false,
14
- "normalized": false,
15
- "rstrip": false,
16
- "single_word": false,
17
- "special": true
18
- },
19
- "101": {
20
- "content": "[CLS]",
21
- "lstrip": false,
22
- "normalized": false,
23
- "rstrip": false,
24
- "single_word": false,
25
- "special": true
26
- },
27
- "102": {
28
- "content": "[SEP]",
29
- "lstrip": false,
30
- "normalized": false,
31
- "rstrip": false,
32
- "single_word": false,
33
- "special": true
34
- },
35
- "103": {
36
- "content": "[MASK]",
37
- "lstrip": false,
38
- "normalized": false,
39
- "rstrip": false,
40
- "single_word": false,
41
- "special": true
42
- }
43
  },
44
- "clean_up_tokenization_spaces": false,
45
- "cls_token": "[CLS]",
46
- "do_basic_tokenize": true,
47
- "do_lower_case": true,
48
- "mask_token": "[MASK]",
49
- "max_length": 128,
50
- "model_max_length": 256,
51
- "never_split": null,
52
- "pad_to_multiple_of": null,
53
- "pad_token": "[PAD]",
54
- "pad_token_type_id": 0,
55
- "padding_side": "right",
56
- "sep_token": "[SEP]",
57
- "stride": 0,
58
- "strip_accents": null,
59
- "tokenize_chinese_chars": true,
60
- "tokenizer_class": "BertTokenizer",
61
- "truncation_side": "right",
62
- "truncation_strategy": "longest_first",
63
- "unk_token": "[UNK]"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64
  }
 
1
  {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
10
  },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
49
+ "max_length": 128,
50
+ "model_max_length": 256,
51
+ "never_split": null,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "[PAD]",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
  }
upload_to_hf.py CHANGED
@@ -2,30 +2,34 @@ import os
2
  from huggingface_hub import HfApi
3
 
4
  def upload_model():
5
- # Configuration
6
- repo_id = "surazbhandari/all-MiniLM-L6-v2-ProductMatching"
7
- local_dir = "/Users/surajbhandari/Documents/antigravity/all-MiniLM-L6-v2-ProductMatching"
8
-
9
- # Initialize API
10
- api = HfApi()
11
-
12
- print(f"🚀 Starting upload to: https://huggingface.co/{{repo_id}}")
13
- print(f"📁 Local directory: {{local_dir}}")
14
-
15
- try:
16
- # Upload the entire folder
17
- api.upload_folder(
18
- folder_path=local_dir,
19
- repo_id=repo_id,
20
- repo_type="model",
21
- commit_message="Initial upload of fine-tuned Product Matching model",
22
- )
23
- print("\n✅ Upload successful!")
24
- print(f"Check your model here: https://huggingface.co/{{repo_id}}")
25
 
26
- except Exception as e:
27
- print(f"\n❌ Error during upload: {{str(e)}}")
28
- print("\nNote: Make sure you are logged in using `huggingface-cli login` or have HUGGING_FACE_HUB_TOKEN set.")
 
 
 
 
 
 
 
 
 
 
29
 
30
  if __name__ == "__main__":
31
- upload_model()
 
2
  from huggingface_hub import HfApi
3
 
4
  def upload_model():
5
+ # Configuration
6
+ repo_id = "surazbhandari/all-MiniLM-L6-v2-ProductMatching"
7
+ local_dir = "/Users/surajbhandari/Documents/antigravity/all-MiniLM-L6-v2-ProductMatching"
8
+
9
+ # Initialize API
10
+ api = HfApi()
11
+
12
+ print(f" Starting upload to: https://huggingface.co/{repo_id}")
13
+ print(f" Local directory: {local_dir}")
14
+
15
+ try:
16
+ # Check if token is available
17
+ if not os.environ.get("HUGGING_FACE_HUB_TOKEN"):
18
+ print("\nNote: HUGGING_FACE_HUB_TOKEN not found in environment. Using locally cached credentials.")
 
 
 
 
 
 
19
 
20
+ # Upload the entire folder
21
+ api.upload_folder(
22
+ folder_path=local_dir,
23
+ repo_id=repo_id,
24
+ repo_type="model",
25
+ commit_message="Initial upload of fine-tuned Product Matching model",
26
+ )
27
+ print("\n Upload successful!")
28
+ print(f"Check your model here: https://huggingface.co/{repo_id}")
29
+
30
+ except Exception as e:
31
+ print(f"\n Error during upload: {str(e)}")
32
+ print("\nNote: Make sure you are logged in using `huggingface-cli login` or have HUGGING_FACE_HUB_TOKEN set.")
33
 
34
  if __name__ == "__main__":
35
+ upload_model()
vocab.txt CHANGED
@@ -1613,387 +1613,387 @@ z
1613
  ⊕
1614
  ⊗
1615
  ⋅
1616
- ─
1617
- │
1618
- ■
1619
- ▪
1620
- ●
1621
- ★
1622
- ☆
1623
- ☉
1624
- ♠
1625
- ♣
1626
- ♥
1627
- ♦
1628
- ♭
1629
- ♯
1630
- ⟨
1631
- ⟩
1632
- ⱼ
1633
- ⺩
1634
- ⺼
1635
- ⽥
1636
- 、
1637
- 。
1638
- 〈
1639
- 〉
1640
- 《
1641
- 》
1642
- 「
1643
- 」
1644
- 『
1645
- 』
1646
- 〜
1647
- あ
1648
- い
1649
- う
1650
- え
1651
- お
1652
- か
1653
- き
1654
- く
1655
- け
1656
- こ
1657
- さ
1658
- し
1659
- す
1660
- せ
1661
- そ
1662
- た
1663
- ち
1664
- っ
1665
- つ
1666
- て
1667
- と
1668
- な
1669
- に
1670
- ぬ
1671
- ね
1672
- の
1673
- は
1674
- ひ
1675
- ふ
1676
- へ
1677
- ほ
1678
- ま
1679
- み
1680
- む
1681
- め
1682
- も
1683
- や
1684
- ゆ
1685
- よ
1686
- ら
1687
- り
1688
- る
1689
- れ
1690
- ろ
1691
- を
1692
- ん
1693
- ァ
1694
- ア
1695
- ィ
1696
- イ
1697
- ウ
1698
- ェ
1699
- エ
1700
- オ
1701
- カ
1702
- キ
1703
- ク
1704
- ケ
1705
- コ
1706
- サ
1707
- シ
1708
- ス
1709
- セ
1710
- タ
1711
- チ
1712
- ッ
1713
- ツ
1714
- テ
1715
- ト
1716
- ナ
1717
- ニ
1718
- ノ
1719
- ハ
1720
- ヒ
1721
- フ
1722
- ヘ
1723
- ホ
1724
- マ
1725
- ミ
1726
- ム
1727
- メ
1728
- モ
1729
- ャ
1730
- ュ
1731
- ョ
1732
- ラ
1733
- リ
1734
- ル
1735
- レ
1736
- ロ
1737
- ワ
1738
- ン
1739
- ・
1740
- ー
1741
- 一
1742
- 三
1743
- 上
1744
- 下
1745
- 不
1746
- 世
1747
- 中
1748
- 主
1749
- 久
1750
- 之
1751
- 也
1752
- 事
1753
- 二
1754
- 五
1755
- 井
1756
- 京
1757
- 人
1758
- 亻
1759
- 仁
1760
- 介
1761
- 代
1762
- 仮
1763
- 伊
1764
- 会
1765
- 佐
1766
- 侍
1767
- 保
1768
- 信
1769
- 健
1770
- 元
1771
- 光
1772
- 八
1773
- 公
1774
- 内
1775
- 出
1776
- 分
1777
- 前
1778
- 劉
1779
- 力
1780
- 加
1781
- 勝
1782
- 北
1783
- 区
1784
- 十
1785
- 千
1786
- 南
1787
- 博
1788
- 原
1789
- 口
1790
- 古
1791
- 史
1792
- 司
1793
- 合
1794
- 吉
1795
- 同
1796
- 名
1797
- 和
1798
- 囗
1799
- 四
1800
- 国
1801
- 國
1802
- 土
1803
- 地
1804
- 坂
1805
- 城
1806
- 堂
1807
- 場
1808
- 士
1809
- 夏
1810
- 外
1811
- 大
1812
- 天
1813
- 太
1814
- 夫
1815
- 奈
1816
- 女
1817
- 子
1818
- 学
1819
- 宀
1820
- 宇
1821
- 安
1822
- 宗
1823
- 定
1824
- 宣
1825
- 宮
1826
- 家
1827
- 宿
1828
- 寺
1829
- 將
1830
- 小
1831
- 尚
1832
- 山
1833
- 岡
1834
- 島
1835
- 崎
1836
- 川
1837
- 州
1838
- 巿
1839
- 帝
1840
- 平
1841
- 年
1842
- 幸
1843
- 广
1844
- 弘
1845
- 張
1846
- 彳
1847
- 後
1848
- 御
1849
- 德
1850
- 心
1851
- 忄
1852
- 志
1853
- 忠
1854
- 愛
1855
- 成
1856
- 我
1857
- 戦
1858
- 戸
1859
- 手
1860
- 扌
1861
- 政
1862
- 文
1863
- 新
1864
- 方
1865
- 日
1866
- 明
1867
- 星
1868
- 春
1869
- 昭
1870
- 智
1871
- 曲
1872
- 書
1873
- 月
1874
- 有
1875
- 朝
1876
- 木
1877
- 本
1878
- 李
1879
- 村
1880
- 東
1881
- 松
1882
- 林
1883
- 森
1884
- 楊
1885
- 樹
1886
- 橋
1887
- 歌
1888
- 止
1889
- 正
1890
- 武
1891
- 比
1892
- 氏
1893
- 民
1894
- 水
1895
- 氵
1896
- 氷
1897
- 永
1898
- 江
1899
- 沢
1900
- 河
1901
- 治
1902
- 法
1903
- 海
1904
- 清
1905
- 漢
1906
- 瀬
1907
- 火
1908
- 版
1909
- 犬
1910
- 王
1911
- 生
1912
- 田
1913
- 男
1914
- 疒
1915
- 発
1916
- 白
1917
- 的
1918
- 皇
1919
- 目
1920
- 相
1921
- 省
1922
- 真
1923
- 石
1924
- 示
1925
- 社
1926
- 神
1927
- 福
1928
- 禾
1929
- 秀
1930
- 秋
1931
- 空
1932
- 立
1933
- 章
1934
- 竹
1935
- 糹
1936
- 美
1937
- 義
1938
- 耳
1939
- 良
1940
- 艹
1941
- 花
1942
- 英
1943
- 華
1944
- 葉
1945
- 藤
1946
- 行
1947
- 街
1948
- 西
1949
- 見
1950
- 訁
1951
- 語
1952
- 谷
1953
- 貝
1954
- 貴
1955
- 車
1956
- 軍
1957
- 辶
1958
- 道
1959
- 郎
1960
- 郡
1961
- 部
1962
- 都
1963
- 里
1964
- 野
1965
- 金
1966
- 鈴
1967
- 镇
1968
- 長
1969
- 門
1970
- 間
1971
- 阝
1972
- 阿
1973
- 陳
1974
- 陽
1975
- 雄
1976
- 青
1977
- 面
1978
- 風
1979
- 食
1980
- 香
1981
- 馬
1982
- 高
1983
- 龍
1984
- 龸
1985
- fi
1986
- fl
1987
- ！
1988
- （
1989
- ）
1990
- ，
1991
- －
1992
- ．
1993
- ／
1994
- ：
1995
- ？
1996
- ～
1997
  the
1998
  of
1999
  and
@@ -24100,7 +24100,7 @@ mn
24100
  montenegrin
24101
  packard
24102
  ##unciation
24103
- ##♭
24104
  ##kki
24105
  reclaim
24106
  scholastic
@@ -30140,383 +30140,383 @@ necessitated
30140
  ##⊕
30141
  ##⊗
30142
  ##⋅
30143
- ##─
30144
- ##│
30145
- ##■
30146
- ##▪
30147
- ##●
30148
- ##★
30149
- ##☆
30150
- ##☉
30151
- ##♠
30152
- ##♣
30153
- ##♥
30154
- ##♦
30155
- ##♯
30156
- ##⟨
30157
- ##⟩
30158
- ##ⱼ
30159
- ##⺩
30160
- ##⺼
30161
- ##⽥
30162
- ##、
30163
- ##。
30164
- ##〈
30165
- ##〉
30166
- ##《
30167
- ##》
30168
- ##「
30169
- ##」
30170
- ##『
30171
- ##』
30172
- ##〜
30173
- ##あ
30174
- ##い
30175
- ##う
30176
- ##え
30177
- ##お
30178
- ##か
30179
- ##き
30180
- ##く
30181
- ##け
30182
- ##こ
30183
- ##さ
30184
- ##し
30185
- ##す
30186
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30187
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30188
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30189
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30190
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30192
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30193
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30196
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30197
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30199
- ##は
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30202
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30203
- ##ほ
30204
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30205
- ##み
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- ##め
30208
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30209
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30210
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- ##よ
30212
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30213
- ##り
30214
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30215
- ##れ
30216
- ##ろ
30217
- ##を
30218
- ##ん
30219
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30232
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30243
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30244
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30245
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30247
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30248
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30249
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30250
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30251
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30252
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30253
- ##メ
30254
- ##モ
30255
- ##ャ
30256
- ##ュ
30257
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30258
- ##ラ
30259
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30260
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30261
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30262
- ##ロ
30263
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30264
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30265
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30266
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30267
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30268
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30269
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30270
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30271
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30272
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30273
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30274
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30275
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30276
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30277
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30278
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30279
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30280
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30281
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30282
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30283
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30284
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30285
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30286
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30287
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30288
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30289
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30290
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30291
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30292
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30293
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30294
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30295
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30296
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30297
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30298
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30299
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30300
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30301
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30302
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30303
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30304
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30305
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30306
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30307
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30308
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30309
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30310
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30311
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30312
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30313
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30314
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30315
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30316
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30318
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30319
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30320
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30321
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30322
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30323
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30324
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30325
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30326
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30327
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30328
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30329
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30330
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30331
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30332
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30333
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30334
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30335
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30336
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30337
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30338
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30339
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30340
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30341
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30342
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30343
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30344
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30345
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30346
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30347
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30348
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30349
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30350
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30351
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30352
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30353
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30354
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30355
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30356
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30357
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30358
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30359
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30360
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30361
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30362
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30363
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30364
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30365
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30366
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30367
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30368
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30369
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30370
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30371
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30372
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30373
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30374
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30375
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30376
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30377
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30378
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30379
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30380
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30381
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30382
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30383
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30384
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30385
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30386
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30387
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30388
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30389
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30390
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30391
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30392
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30393
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30394
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30395
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30396
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30397
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30398
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30399
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30400
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30401
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30402
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30403
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30404
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30405
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30406
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30407
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30408
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30409
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30410
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30411
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30412
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30413
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30414
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30415
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30416
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30417
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30418
- ##氏
30419
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30420
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30421
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30422
- ##氷
30423
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30424
- ##江
30425
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30426
- ##河
30427
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30428
- ##法
30429
- ##海
30430
- ##清
30431
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30432
- ##瀬
30433
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30434
- ##版
30435
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30436
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30437
- ##生
30438
- ##田
30439
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30440
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30441
- ##発
30442
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30443
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30444
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30445
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30446
- ##相
30447
- ##省
30448
- ##真
30449
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30450
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30451
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30452
- ##神
30453
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30454
- ##禾
30455
- ##秀
30456
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30457
- ##空
30458
- ##立
30459
- ##章
30460
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30461
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30462
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30463
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30464
- ##耳
30465
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30466
- ##艹
30467
- ##花
30468
- ##英
30469
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30470
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30471
- ##藤
30472
- ##行
30473
- ##街
30474
- ##西
30475
- ##見
30476
- ##訁
30477
- ##語
30478
- ##谷
30479
- ##貝
30480
- ##貴
30481
- ##車
30482
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30483
- ##辶
30484
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30485
- ##郎
30486
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30487
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30488
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30489
- ##里
30490
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30491
- ##金
30492
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30493
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30494
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30495
- ##門
30496
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30497
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30498
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30499
- ##陳
30500
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30501
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30502
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30503
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30504
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30505
- ##食
30506
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30507
- ##馬
30508
- ##高
30509
- ##龍
30510
- ##龸
30511
- ##fi
30512
- ##fl
30513
- ##！
30514
- ##（
30515
- ##）
30516
- ##，
30517
- ##－
30518
- ##．
30519
- ##／
30520
- ##：
30521
- ##？
30522
- ##～
 
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  the
1998
  of
1999
  and
 
24100
  montenegrin
24101
  packard
24102
  ##unciation
24103
+ ##
24104
  ##kki
24105
  reclaim
24106
  scholastic
 
30140
  ##⊕
30141
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30142
  ##⋅
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