Diwank Singh commited on
Commit
5f36a54
1 Parent(s): fcca1db

Signed-off-by: Diwank Singh <diwank.singh@gmail.com>

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  {
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  "types": {
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- "139759749873328_Dense": "sentence_transformers.models.Dense",
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  },
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- "139759749873328_Dense",
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- "139771065711008_Dense",
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  ],
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- "139771065971808_Dense",
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- "139759749869296_Dense",
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- "139759749864928_Normalize"
 
 
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  ]
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  },
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  "parameters": {
 
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  {
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  "types": {
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  },
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+ "140228101622896_Dense",
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+ "140225464235392_Dense",
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+ "140225464235968_Dense",
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+ "140225464231168_Normalize"
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  ],
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+ "140225464223632_Dense",
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+ "140225464236064_Dense",
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+ "140225464233232_Dropout",
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+ "140225464231312_Dense",
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+ "140225464232416_Dense",
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+ "140225464235536_Normalize"
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  ]
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  },
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  "parameters": {
README.md CHANGED
@@ -53,12 +53,12 @@ The model was trained with the parameters:
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  **Loss**:
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- `sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
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  Parameters of the fit()-Method:
59
  ```
60
  {
61
- "epochs": 6,
62
  "evaluation_steps": 2000,
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  "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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  "max_grad_norm": 1,
@@ -83,12 +83,20 @@ SentenceTransformer(
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  (2): Asym(
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  (dialog-0): Dense({'in_features': 1024, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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  (dialog-1): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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- (dialog-2): Dense({'in_features': 2048, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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- (dialog-3): Normalize()
 
 
 
 
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  (fact-0): Dense({'in_features': 1024, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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  (fact-1): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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- (fact-2): Dense({'in_features': 2048, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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- (fact-3): Normalize()
 
 
 
 
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  )
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  )
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  ```
 
53
 
54
  **Loss**:
55
 
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+ `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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  Parameters of the fit()-Method:
59
  ```
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  {
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+ "epochs": 4,
62
  "evaluation_steps": 2000,
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  "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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  "max_grad_norm": 1,
 
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  (2): Asym(
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  (dialog-0): Dense({'in_features': 1024, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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  (dialog-1): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (dialog-2): Dropout(
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+ (dropout_layer): Dropout(p=0.1, inplace=False)
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+ )
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+ (dialog-3): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (dialog-4): Dense({'in_features': 2048, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (dialog-5): Normalize()
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  (fact-0): Dense({'in_features': 1024, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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  (fact-1): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (fact-2): Dropout(
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+ (dropout_layer): Dropout(p=0.1, inplace=False)
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+ )
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+ (fact-3): Dense({'in_features': 2048, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (fact-4): Dense({'in_features': 2048, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ (fact-5): Normalize()
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  )
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  )
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  ```