| {"repo_id":"ConfidenceTransformer","entity_id":"py:eval","uri":"program://ConfidenceTransformer/module/eval#L1-L79","kind":"module","name":"eval","path":"eval.py","language":"python","start_line":1,"end_line":79,"context_start_line":1,"context_end_line":79,"code":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Model\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer # Import the class from main.py\n\n# Load the trained model and tokenizer\nmodel_name = 'confidence_model'\nmodel_path = model_name\ntokenizer_path = model_name\ntokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)\n\nmodel = ConfidenceEnhancedTransformer.from_pretrained(model_path, attn_implementation=\"eager\")\n\n# Move model to GPU if available\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Set the model to evaluation mode\nmodel.eval()\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\n\n# Ensure the example text is not empty and preprocess it\nexample_text = dataset['text'][3].strip() # Get the first example from the dataset and strip whitespace\nif not example_text:\n raise ValueError(\"The example text from the dataset is empty. Please check the dataset.\")\n\n# Example usage\nif __name__ == \"__main__\":\n prompt = \"tefewafwef aoasdfsfasdfsadfdfasdsdijfoiwej\"\n inputs = tokenizer(prompt, return_tensors='pt').to(device)\n \n # Check if input_ids is empty\n if inputs.input_ids.size(1) == 0:\n raise ValueError(\"The input text resulted in an empty input_ids tensor. Please check the input text.\")\n \n with torch.no_grad():\n outputs = model(\n input_ids=inputs.input_ids,\n attention_mask=inputs.attention_mask,\n num_dropout_samples=10 # Increased from 5 to 10\n )\n # Get the confidence score\n confidence_score = outputs['confidence_score'].item()\n ood_score = outputs['ood_score'].item()\n print(f\"Refined Confidence Score: {confidence_score}\")\n print(f\"OOD Score: {ood_score}\")\n\n # Decode and print the generated text (not part of the confidence mechanism)\n generated_text = tokenizer.decode(outputs['lm_logits'].argmax(-1).squeeze().tolist())\n print(f\"OOD example: {prompt}\")\n print(f\"Generated Text: {generated_text}\")\n\n # Evaluate on an in-distribution example from WikiText-2\n inputs = tokenizer(example_text, return_tensors='pt').to(device)\n \n # Check if input_ids is empty\n if inputs.input_ids.size(1) == 0:\n raise ValueError(\"The example text resulted in an empty input_ids tensor. Please check the example text.\")\n \n with torch.no_grad():\n outputs = model(\n input_ids=inputs.input_ids,\n attention_mask=inputs.attention_mask,\n num_dropout_samples=10\n )\n # Get the confidence score for the in-distribution example\n confidence_score = outputs['confidence_score'].item()\n ood_score = outputs['ood_score'].item()\n print(f\"In-Distribution Example - Refined Confidence Score: {confidence_score}\")\n print(f\"In-Distribution Example - OOD Score: {ood_score}\")\n\n # Decode and print the generated text for the in-distribution example\n generated_text = tokenizer.decode(outputs['lm_logits'].argmax(-1).squeeze().tolist())\n print(f\"example text: {example_text}\")\n print(f\"In-Distribution Example - Generated Text: {generated_text}\")","source_hash":"3578bf0e13cd974bc6904184ebfe9ffbf435d2522c4e913c4c68d1f6dc98ef44","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:main","uri":"program://ConfidenceTransformer/module/main#L1-L135","kind":"module","name":"main","path":"main.py","language":"python","start_line":1,"end_line":135,"context_start_line":1,"context_end_line":135,"code":"import torch\nimport torch.nn as nn\nfrom transformers import GPT2LMHeadModel, GPT2Model, GPT2Tokenizer\nimport torch.nn.functional as F\n\nclass ConfidenceEnhancedTransformer(GPT2LMHeadModel):\n def __init__(self, config):\n super(ConfidenceEnhancedTransformer, self).__init__(config)\n self.transformer = GPT2Model(config)\n #self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Language modeling head\n\n # Confidence scoring head for epistemic uncertainty and OOD detection\n self.confidence_head = nn.Sequential(\n nn.Linear(config.n_embd, 128),\n nn.ReLU(),\n nn.Linear(128, 1), # Single output for confidence score\n nn.Sigmoid() # Confidence score between 0 and 1\n )\n \n # OOD detector head\n self.ood_detector = nn.Linear(config.n_embd, 1) # Auxiliary head for OOD detection\n self.init_weights()\n\n def forward(self, input_ids, attention_mask=None, labels=None, num_dropout_samples=10):\n # Standard forward pass through transformer\n outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n labels=labels,\n output_attentions=True,\n output_hidden_states=True,\n return_dict=True,\n )\n lm_logits = outputs.logits # [batch_size, sequence_length, vocab_size]\n hidden_states = outputs.hidden_states[-1] # Get the last hidden state\n attentions = outputs.attentions # Attention weights\n\n # Base confidence score from hidden states\n base_confidence_score = self.confidence_head(hidden_states.mean(dim=1))\n\n # Attention-based confidence signal\n attention_entropy = []\n for attn_layer in attentions:\n attn_probs = attn_layer.mean(dim=1) # Mean over heads\n attn_entropy = -torch.sum(attn_probs * torch.log(attn_probs + 1e-12), dim=-1)\n attention_entropy.append(attn_entropy.mean(dim=-1)) # Mean over tokens\n avg_attention_entropy = torch.stack(attention_entropy).mean(dim=0) # Mean over layers\n\n # Monte Carlo Dropout for variance estimation\n variance_confidence = 0.0\n dropout_scores = []\n if num_dropout_samples > 1:\n original_mode = self.training # Save original mode\n self.train() # Enable dropout layers\n for _ in range(num_dropout_samples):\n # Removed torch.no_grad() to ensure dropout behaves correctly\n dropout_outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True,\n return_dict=True,\n )\n dropout_hidden_states = dropout_outputs.hidden_states[-1]\n dropout_confidence = self.confidence_head(dropout_hidden_states.mean(dim=1))\n dropout_scores.append(dropout_confidence)\n \n self.train(original_mode) # Restore original mode\n # Calculate variance of dropout predictions as a confidence measure\n dropout_scores = torch.stack(dropout_scores) # [num_samples, batch_size, 1]\n variance_confidence = torch.var(dropout_scores, dim=0).mean()\n else:\n variance_confidence = torch.tensor(0.0).to(hidden_states.device)\n\n # OOD detection score\n ood_score = torch.sigmoid(self.ood_detector(hidden_states.mean(dim=1))).squeeze()\n\n # Adjust this line to handle ood_score shape correctly\n if len(ood_score.shape) == 0:\n ood_score = ood_score.unsqueeze(0) # Add batch dimension if it's a scalar\n\n # Combine all signals into a refined confidence score\n refined_confidence_score = (\n base_confidence_score \n - variance_confidence # Lower confidence if high variance\n - avg_attention_entropy.unsqueeze(1) # Lower confidence if high attention entropy\n - ood_score.unsqueeze(1) # Lower confidence if high OOD score\n ).clamp(0, 1) # Ensure the final score is between 0 and 1\n\n # Calculate total loss if labels are provided\n total_loss = None\n if labels is not None:\n # Language modeling loss\n shift_logits = lm_logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n lm_loss = nn.CrossEntropyLoss()(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n\n # Entropy-based confidence loss\n token_probs = F.softmax(shift_logits, dim=-1)\n entropy = -torch.sum(token_probs * torch.log(token_probs + 1e-12), dim=-1).mean(dim=-1)\n confidence_loss = nn.MSELoss()(base_confidence_score.squeeze(), 1 - entropy)\n\n # OOD loss\n ood_loss = nn.BCELoss()(ood_score, torch.zeros_like(ood_score)) # Penalty for high OOD score\n\n # Combine all losses with adjusted weights\n total_loss = lm_loss + 0.5 * confidence_loss + 0.3 * ood_loss # Increased weights\n\n return {\n 'loss': total_loss,\n 'lm_logits': lm_logits,\n 'confidence_score': refined_confidence_score, # Refined confidence score\n 'ood_score': ood_score, # Out-of-distribution score\n 'base_confidence_score': base_confidence_score.squeeze(), # Added for logging\n 'variance_confidence': variance_confidence, # Added for logging\n 'avg_attention_entropy': avg_attention_entropy # Added for logging\n }\n\n# Example usage of the model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2', attn_implementation='eager')\n\n# Example input\ninput_text = \"What is the capital of USA?\"\ninput_tokens = tokenizer(input_text, return_tensors=\"pt\")\noutputs = model(input_tokens['input_ids'], num_dropout_samples=10)\n\n# Get the confidence score\nconfidence_score = outputs['confidence_score'].item()\nood_score = outputs['ood_score'].item()\nprint(f\"Refined Confidence Score: {confidence_score}\")\nprint(f\"OOD Score: {ood_score}\")\n\n# Decode and print the generated text (not part of the confidence mechanism)\ngenerated_text = tokenizer.decode(outputs['lm_logits'].argmax(-1).squeeze().tolist())\nprint(f\"Generated Text: {generated_text}\")","source_hash":"0bf4a6d8cb4435d83ba0509db804e4c03aefa41e5e87fca50ffea0cd28079b65","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:main.ConfidenceEnhancedTransformer","uri":"program://ConfidenceTransformer/class/main.ConfidenceEnhancedTransformer#L6-L116","kind":"class","name":"ConfidenceEnhancedTransformer","path":"main.py","language":"python","start_line":6,"end_line":116,"context_start_line":1,"context_end_line":135,"code":"import torch\nimport torch.nn as nn\nfrom transformers import GPT2LMHeadModel, GPT2Model, GPT2Tokenizer\nimport torch.nn.functional as F\n\nclass ConfidenceEnhancedTransformer(GPT2LMHeadModel):\n def __init__(self, config):\n super(ConfidenceEnhancedTransformer, self).__init__(config)\n self.transformer = GPT2Model(config)\n #self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Language modeling head\n\n # Confidence scoring head for epistemic uncertainty and OOD detection\n self.confidence_head = nn.Sequential(\n nn.Linear(config.n_embd, 128),\n nn.ReLU(),\n nn.Linear(128, 1), # Single output for confidence score\n nn.Sigmoid() # Confidence score between 0 and 1\n )\n \n # OOD detector head\n self.ood_detector = nn.Linear(config.n_embd, 1) # Auxiliary head for OOD detection\n self.init_weights()\n\n def forward(self, input_ids, attention_mask=None, labels=None, num_dropout_samples=10):\n # Standard forward pass through transformer\n outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n labels=labels,\n output_attentions=True,\n output_hidden_states=True,\n return_dict=True,\n )\n lm_logits = outputs.logits # [batch_size, sequence_length, vocab_size]\n hidden_states = outputs.hidden_states[-1] # Get the last hidden state\n attentions = outputs.attentions # Attention weights\n\n # Base confidence score from hidden states\n base_confidence_score = self.confidence_head(hidden_states.mean(dim=1))\n\n # Attention-based confidence signal\n attention_entropy = []\n for attn_layer in attentions:\n attn_probs = attn_layer.mean(dim=1) # Mean over heads\n attn_entropy = -torch.sum(attn_probs * torch.log(attn_probs + 1e-12), dim=-1)\n attention_entropy.append(attn_entropy.mean(dim=-1)) # Mean over tokens\n avg_attention_entropy = torch.stack(attention_entropy).mean(dim=0) # Mean over layers\n\n # Monte Carlo Dropout for variance estimation\n variance_confidence = 0.0\n dropout_scores = []\n if num_dropout_samples > 1:\n original_mode = self.training # Save original mode\n self.train() # Enable dropout layers\n for _ in range(num_dropout_samples):\n # Removed torch.no_grad() to ensure dropout behaves correctly\n dropout_outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True,\n return_dict=True,\n )\n dropout_hidden_states = dropout_outputs.hidden_states[-1]\n dropout_confidence = self.confidence_head(dropout_hidden_states.mean(dim=1))\n dropout_scores.append(dropout_confidence)\n \n self.train(original_mode) # Restore original mode\n # Calculate variance of dropout predictions as a confidence measure\n dropout_scores = torch.stack(dropout_scores) # [num_samples, batch_size, 1]\n variance_confidence = torch.var(dropout_scores, dim=0).mean()\n else:\n variance_confidence = torch.tensor(0.0).to(hidden_states.device)\n\n # OOD detection score\n ood_score = torch.sigmoid(self.ood_detector(hidden_states.mean(dim=1))).squeeze()\n\n # Adjust this line to handle ood_score shape correctly\n if len(ood_score.shape) == 0:\n ood_score = ood_score.unsqueeze(0) # Add batch dimension if it's a scalar\n\n # Combine all signals into a refined confidence score\n refined_confidence_score = (\n base_confidence_score \n - variance_confidence # Lower confidence if high variance\n - avg_attention_entropy.unsqueeze(1) # Lower confidence if high attention entropy\n - ood_score.unsqueeze(1) # Lower confidence if high OOD score\n ).clamp(0, 1) # Ensure the final score is between 0 and 1\n\n # Calculate total loss if labels are provided\n total_loss = None\n if labels is not None:\n # Language modeling loss\n shift_logits = lm_logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n lm_loss = nn.CrossEntropyLoss()(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n\n # Entropy-based confidence loss\n token_probs = F.softmax(shift_logits, dim=-1)\n entropy = -torch.sum(token_probs * torch.log(token_probs + 1e-12), dim=-1).mean(dim=-1)\n confidence_loss = nn.MSELoss()(base_confidence_score.squeeze(), 1 - entropy)\n\n # OOD loss\n ood_loss = nn.BCELoss()(ood_score, torch.zeros_like(ood_score)) # Penalty for high OOD score\n\n # Combine all losses with adjusted weights\n total_loss = lm_loss + 0.5 * confidence_loss + 0.3 * ood_loss # Increased weights\n\n return {\n 'loss': total_loss,\n 'lm_logits': lm_logits,\n 'confidence_score': refined_confidence_score, # Refined confidence score\n 'ood_score': ood_score, # Out-of-distribution score\n 'base_confidence_score': base_confidence_score.squeeze(), # Added for logging\n 'variance_confidence': variance_confidence, # Added for logging\n 'avg_attention_entropy': avg_attention_entropy # Added for logging\n }\n\n# Example usage of the model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2', attn_implementation='eager')\n\n# Example input\ninput_text = \"What is the capital of USA?\"\ninput_tokens = tokenizer(input_text, return_tensors=\"pt\")\noutputs = model(input_tokens['input_ids'], num_dropout_samples=10)\n\n# Get the confidence score\nconfidence_score = outputs['confidence_score'].item()\nood_score = outputs['ood_score'].item()\nprint(f\"Refined Confidence Score: {confidence_score}\")\nprint(f\"OOD Score: {ood_score}\")\n\n# Decode and print the generated text (not part of the confidence mechanism)\ngenerated_text = tokenizer.decode(outputs['lm_logits'].argmax(-1).squeeze().tolist())\nprint(f\"Generated Text: {generated_text}\")","source_hash":"0bf4a6d8cb4435d83ba0509db804e4c03aefa41e5e87fca50ffea0cd28079b65","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:main.__init__","uri":"program://ConfidenceTransformer/function/main.__init__#L7-L22","kind":"function","name":"__init__","path":"main.py","language":"python","start_line":7,"end_line":22,"context_start_line":1,"context_end_line":42,"code":"import torch\nimport torch.nn as nn\nfrom transformers import GPT2LMHeadModel, GPT2Model, GPT2Tokenizer\nimport torch.nn.functional as F\n\nclass ConfidenceEnhancedTransformer(GPT2LMHeadModel):\n def __init__(self, config):\n super(ConfidenceEnhancedTransformer, self).__init__(config)\n self.transformer = GPT2Model(config)\n #self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Language modeling head\n\n # Confidence scoring head for epistemic uncertainty and OOD detection\n self.confidence_head = nn.Sequential(\n nn.Linear(config.n_embd, 128),\n nn.ReLU(),\n nn.Linear(128, 1), # Single output for confidence score\n nn.Sigmoid() # Confidence score between 0 and 1\n )\n \n # OOD detector head\n self.ood_detector = nn.Linear(config.n_embd, 1) # Auxiliary head for OOD detection\n self.init_weights()\n\n def forward(self, input_ids, attention_mask=None, labels=None, num_dropout_samples=10):\n # Standard forward pass through transformer\n outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n labels=labels,\n output_attentions=True,\n output_hidden_states=True,\n return_dict=True,\n )\n lm_logits = outputs.logits # [batch_size, sequence_length, vocab_size]\n hidden_states = outputs.hidden_states[-1] # Get the last hidden state\n attentions = outputs.attentions # Attention weights\n\n # Base confidence score from hidden states\n base_confidence_score = self.confidence_head(hidden_states.mean(dim=1))\n\n # Attention-based confidence signal\n attention_entropy = []","source_hash":"0bf4a6d8cb4435d83ba0509db804e4c03aefa41e5e87fca50ffea0cd28079b65","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:main.forward","uri":"program://ConfidenceTransformer/function/main.forward#L24-L116","kind":"function","name":"forward","path":"main.py","language":"python","start_line":24,"end_line":116,"context_start_line":4,"context_end_line":135,"code":"import torch.nn.functional as F\n\nclass ConfidenceEnhancedTransformer(GPT2LMHeadModel):\n def __init__(self, config):\n super(ConfidenceEnhancedTransformer, self).__init__(config)\n self.transformer = GPT2Model(config)\n #self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Language modeling head\n\n # Confidence scoring head for epistemic uncertainty and OOD detection\n self.confidence_head = nn.Sequential(\n nn.Linear(config.n_embd, 128),\n nn.ReLU(),\n nn.Linear(128, 1), # Single output for confidence score\n nn.Sigmoid() # Confidence score between 0 and 1\n )\n \n # OOD detector head\n self.ood_detector = nn.Linear(config.n_embd, 1) # Auxiliary head for OOD detection\n self.init_weights()\n\n def forward(self, input_ids, attention_mask=None, labels=None, num_dropout_samples=10):\n # Standard forward pass through transformer\n outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n labels=labels,\n output_attentions=True,\n output_hidden_states=True,\n return_dict=True,\n )\n lm_logits = outputs.logits # [batch_size, sequence_length, vocab_size]\n hidden_states = outputs.hidden_states[-1] # Get the last hidden state\n attentions = outputs.attentions # Attention weights\n\n # Base confidence score from hidden states\n base_confidence_score = self.confidence_head(hidden_states.mean(dim=1))\n\n # Attention-based confidence signal\n attention_entropy = []\n for attn_layer in attentions:\n attn_probs = attn_layer.mean(dim=1) # Mean over heads\n attn_entropy = -torch.sum(attn_probs * torch.log(attn_probs + 1e-12), dim=-1)\n attention_entropy.append(attn_entropy.mean(dim=-1)) # Mean over tokens\n avg_attention_entropy = torch.stack(attention_entropy).mean(dim=0) # Mean over layers\n\n # Monte Carlo Dropout for variance estimation\n variance_confidence = 0.0\n dropout_scores = []\n if num_dropout_samples > 1:\n original_mode = self.training # Save original mode\n self.train() # Enable dropout layers\n for _ in range(num_dropout_samples):\n # Removed torch.no_grad() to ensure dropout behaves correctly\n dropout_outputs = super().forward(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True,\n return_dict=True,\n )\n dropout_hidden_states = dropout_outputs.hidden_states[-1]\n dropout_confidence = self.confidence_head(dropout_hidden_states.mean(dim=1))\n dropout_scores.append(dropout_confidence)\n \n self.train(original_mode) # Restore original mode\n # Calculate variance of dropout predictions as a confidence measure\n dropout_scores = torch.stack(dropout_scores) # [num_samples, batch_size, 1]\n variance_confidence = torch.var(dropout_scores, dim=0).mean()\n else:\n variance_confidence = torch.tensor(0.0).to(hidden_states.device)\n\n # OOD detection score\n ood_score = torch.sigmoid(self.ood_detector(hidden_states.mean(dim=1))).squeeze()\n\n # Adjust this line to handle ood_score shape correctly\n if len(ood_score.shape) == 0:\n ood_score = ood_score.unsqueeze(0) # Add batch dimension if it's a scalar\n\n # Combine all signals into a refined confidence score\n refined_confidence_score = (\n base_confidence_score \n - variance_confidence # Lower confidence if high variance\n - avg_attention_entropy.unsqueeze(1) # Lower confidence if high attention entropy\n - ood_score.unsqueeze(1) # Lower confidence if high OOD score\n ).clamp(0, 1) # Ensure the final score is between 0 and 1\n\n # Calculate total loss if labels are provided\n total_loss = None\n if labels is not None:\n # Language modeling loss\n shift_logits = lm_logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n lm_loss = nn.CrossEntropyLoss()(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n\n # Entropy-based confidence loss\n token_probs = F.softmax(shift_logits, dim=-1)\n entropy = -torch.sum(token_probs * torch.log(token_probs + 1e-12), dim=-1).mean(dim=-1)\n confidence_loss = nn.MSELoss()(base_confidence_score.squeeze(), 1 - entropy)\n\n # OOD loss\n ood_loss = nn.BCELoss()(ood_score, torch.zeros_like(ood_score)) # Penalty for high OOD score\n\n # Combine all losses with adjusted weights\n total_loss = lm_loss + 0.5 * confidence_loss + 0.3 * ood_loss # Increased weights\n\n return {\n 'loss': total_loss,\n 'lm_logits': lm_logits,\n 'confidence_score': refined_confidence_score, # Refined confidence score\n 'ood_score': ood_score, # Out-of-distribution score\n 'base_confidence_score': base_confidence_score.squeeze(), # Added for logging\n 'variance_confidence': variance_confidence, # Added for logging\n 'avg_attention_entropy': avg_attention_entropy # Added for logging\n }\n\n# Example usage of the model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2', attn_implementation='eager')\n\n# Example input\ninput_text = \"What is the capital of USA?\"\ninput_tokens = tokenizer(input_text, return_tensors=\"pt\")\noutputs = model(input_tokens['input_ids'], num_dropout_samples=10)\n\n# Get the confidence score\nconfidence_score = outputs['confidence_score'].item()\nood_score = outputs['ood_score'].item()\nprint(f\"Refined Confidence Score: {confidence_score}\")\nprint(f\"OOD Score: {ood_score}\")\n\n# Decode and print the generated text (not part of the confidence mechanism)\ngenerated_text = tokenizer.decode(outputs['lm_logits'].argmax(-1).squeeze().tolist())\nprint(f\"Generated Text: {generated_text}\")","source_hash":"0bf4a6d8cb4435d83ba0509db804e4c03aefa41e5e87fca50ffea0cd28079b65","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train","uri":"program://ConfidenceTransformer/module/train#L1-L214","kind":"module","name":"train","path":"train.py","language":"python","start_line":1,"end_line":214,"context_start_line":1,"context_end_line":214,"code":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import GPT2Tokenizer, get_linear_schedule_with_warmup\nfrom tqdm import tqdm\nimport torch.optim as optim\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:\n tokenized_text = tokenizer.encode(text)\n for i in range(0, len(tokenized_text) - block_size + 1, block_size):\n self.examples.append(\n torch.tensor(tokenized_text[i:i + block_size], dtype=torch.long)\n )\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return self.examples[i]\n\n# Load tokenizer and model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\ntexts = dataset['text']\n\n# Split the dataset into training and validation sets\ntrain_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)\n\n# Prepare datasets\ntrain_dataset = TextDataset(\n tokenizer=tokenizer,\n texts=train_texts,\n block_size=128\n)\nval_dataset = TextDataset(\n tokenizer=tokenizer,\n texts=val_texts,\n block_size=128\n)\n\n# Create DataLoaders\ntrain_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)\nval_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False)\n\n# Prepare optimizer and scheduler\noptimizer = optim.AdamW(model.parameters(), lr=5e-5)\ntotal_steps = len(train_dataloader) * num_epochs\nscheduler = get_linear_schedule_with_warmup(\n optimizer,\n num_warmup_steps=0,\n num_training_steps=total_steps\n)\n\n# Move model to GPU if available\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Define ECE computation function\ndef compute_ece(preds, confidences, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n ece = 0.0\n for i in range(n_bins):\n bin_lower = bin_boundaries[i]\n bin_upper = bin_boundaries[i + 1]\n in_bin = (confidences > bin_lower) & (confidences <= bin_upper)\n prop_in_bin = np.mean(in_bin)\n if prop_in_bin > 0:\n accuracy_in_bin = np.mean(preds[in_bin] == preds[in_bin]) # Adjust based on your prediction mechanism\n avg_confidence_in_bin = np.mean(confidences[in_bin])\n ece += np.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin\n return ece\n\n# Define Reliability Diagram function\ndef reliability_diagram(confidences, predictions, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n bin_centers = (bin_boundaries[:-1] + bin_boundaries[1:]) / 2\n accuracy = np.zeros(n_bins)\n confidence = np.zeros(n_bins)\n prop = np.zeros(n_bins)\n\n for i in range(n_bins):\n in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n prop[i] = np.mean(in_bin)\n if prop[i] > 0:\n accuracy[i] = np.mean(predictions[in_bin] == predictions[in_bin]) # Replace with actual labels\n confidence[i] = np.mean(confidences[in_bin])\n\n plt.figure(figsize=(8, 6))\n plt.plot(bin_centers, accuracy, marker='o', label='Accuracy')\n plt.plot(bin_centers, confidence, marker='s', label='Confidence')\n plt.fill_between(bin_boundaries[:-1], 0, 1, color='gray', alpha=0.1)\n plt.xlabel('Confidence')\n plt.ylabel('Accuracy')\n plt.legend()\n plt.title('Reliability Diagram')\n plt.show()\n\n# Training loop\nmodel.train()\nglobal_step = 0\nfor epoch in range(num_epochs):\n print(f\"Epoch {epoch + 1}/{num_epochs}\")\n epoch_loss = 0\n for batch in tqdm(train_dataloader):\n inputs = batch.to(device)\n labels = inputs.clone()\n\n optimizer.zero_grad()\n\n outputs = model(\n input_ids=inputs,\n labels=labels,\n num_dropout_samples=15 # You can adjust this number\n )\n loss = outputs['loss']\n loss.backward()\n\n # Check gradients for confidence_head and ood_detector\n for name, param in model.named_parameters():\n if 'confidence_head' in name or 'ood_detector' in name:\n if param.grad is not None:\n print(f\"Gradient for {name}: {param.grad.mean().item()}\")\n\n optimizer.step()\n scheduler.step()\n\n epoch_loss += loss.item()\n global_step += 1\n\n # Log intermediate values for debugging\n base_confidence_score = outputs['base_confidence_score'].mean().item()\n variance_confidence = outputs['variance_confidence'].item()\n avg_attention_entropy = outputs['avg_attention_entropy'].mean().item()\n ood_score = outputs['ood_score'].mean().item()\n\n print(f\"Step {global_step}: Loss = {loss.item():.4f}, \"\n f\"Base Confidence Score = {base_confidence_score:.4f}, \"\n f\"Variance Confidence = {variance_confidence:.4f}, \"\n f\"Avg Attention Entropy = {avg_attention_entropy:.4f}, \"\n f\"OOD Score = {ood_score:.4f}\")\n\n # Save the model every 500 steps\n if global_step % save_steps == 0:\n model.save_pretrained(f'model_step_{global_step}.pth')\n tokenizer.save_pretrained(f'tokenizer_step_{global_step}.pth')\n\n avg_train_loss = epoch_loss / len(train_dataloader)\n print(f\"Average Training Loss: {avg_train_loss:.4f}\")\n\n # Validation phase\n model.eval()\n val_loss = 0\n all_val_preds = []\n all_val_confidences = []\n with torch.no_grad():\n for val_batch in tqdm(val_dataloader, desc=\"Validation\"):\n val_inputs = val_batch.to(device)\n val_labels = val_inputs.clone()\n\n val_outputs = model(\n input_ids=val_inputs,\n labels=val_labels,\n num_dropout_samples=15\n )\n val_loss += val_outputs['loss'].item()\n\n # Collect predictions and confidence scores for calibration\n val_confidences = val_outputs['base_confidence_score'].cpu().numpy()\n # Assuming you're using the model's logits to derive predictions\n val_logits = val_outputs['logits']\n val_preds = torch.argmax(val_logits, dim=-1).cpu().numpy()\n all_val_preds.extend(val_preds.flatten())\n all_val_confidences.extend(val_confidences.flatten())\n\n avg_val_loss = val_loss / len(val_dataloader)\n print(f\"Average Validation Loss after Epoch {epoch + 1}: {avg_val_loss:.4f}\")\n\n # Calculate ECE\n ece = compute_ece(\n preds=np.array(all_val_preds),\n confidences=np.array(all_val_confidences),\n n_bins=10\n )\n print(f\"Expected Calibration Error (ECE) after Epoch {epoch + 1}: {ece:.4f}\")\n\n # Plot Reliability Diagram\n reliability_diagram(\n confidences=np.array(all_val_confidences),\n predictions=np.array(all_val_preds),\n n_bins=10\n )\n\n # Reset model to training mode\n model.train()\n\n# Save the final trained model\nmodel.save_pretrained('confidence_model')\ntokenizer.save_pretrained('confidence_model')","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.TextDataset","uri":"program://ConfidenceTransformer/class/train.TextDataset#L17-L32","kind":"class","name":"TextDataset","path":"train.py","language":"python","start_line":17,"end_line":32,"context_start_line":1,"context_end_line":52,"code":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import GPT2Tokenizer, get_linear_schedule_with_warmup\nfrom tqdm import tqdm\nimport torch.optim as optim\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:\n tokenized_text = tokenizer.encode(text)\n for i in range(0, len(tokenized_text) - block_size + 1, block_size):\n self.examples.append(\n torch.tensor(tokenized_text[i:i + block_size], dtype=torch.long)\n )\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return self.examples[i]\n\n# Load tokenizer and model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\ntexts = dataset['text']\n\n# Split the dataset into training and validation sets\ntrain_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)\n\n# Prepare datasets\ntrain_dataset = TextDataset(\n tokenizer=tokenizer,\n texts=train_texts,\n block_size=128\n)\nval_dataset = TextDataset(\n tokenizer=tokenizer,","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.compute_ece","uri":"program://ConfidenceTransformer/function/train.compute_ece#L75-L87","kind":"function","name":"compute_ece","path":"train.py","language":"python","start_line":75,"end_line":87,"context_start_line":55,"context_end_line":107,"code":")\n\n# Create DataLoaders\ntrain_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)\nval_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False)\n\n# Prepare optimizer and scheduler\noptimizer = optim.AdamW(model.parameters(), lr=5e-5)\ntotal_steps = len(train_dataloader) * num_epochs\nscheduler = get_linear_schedule_with_warmup(\n optimizer,\n num_warmup_steps=0,\n num_training_steps=total_steps\n)\n\n# Move model to GPU if available\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Define ECE computation function\ndef compute_ece(preds, confidences, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n ece = 0.0\n for i in range(n_bins):\n bin_lower = bin_boundaries[i]\n bin_upper = bin_boundaries[i + 1]\n in_bin = (confidences > bin_lower) & (confidences <= bin_upper)\n prop_in_bin = np.mean(in_bin)\n if prop_in_bin > 0:\n accuracy_in_bin = np.mean(preds[in_bin] == preds[in_bin]) # Adjust based on your prediction mechanism\n avg_confidence_in_bin = np.mean(confidences[in_bin])\n ece += np.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin\n return ece\n\n# Define Reliability Diagram function\ndef reliability_diagram(confidences, predictions, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n bin_centers = (bin_boundaries[:-1] + bin_boundaries[1:]) / 2\n accuracy = np.zeros(n_bins)\n confidence = np.zeros(n_bins)\n prop = np.zeros(n_bins)\n\n for i in range(n_bins):\n in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n prop[i] = np.mean(in_bin)\n if prop[i] > 0:\n accuracy[i] = np.mean(predictions[in_bin] == predictions[in_bin]) # Replace with actual labels\n confidence[i] = np.mean(confidences[in_bin])\n\n plt.figure(figsize=(8, 6))\n plt.plot(bin_centers, accuracy, marker='o', label='Accuracy')\n plt.plot(bin_centers, confidence, marker='s', label='Confidence')\n plt.fill_between(bin_boundaries[:-1], 0, 1, color='gray', alpha=0.1)","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.reliability_diagram","uri":"program://ConfidenceTransformer/function/train.reliability_diagram#L90-L112","kind":"function","name":"reliability_diagram","path":"train.py","language":"python","start_line":90,"end_line":112,"context_start_line":70,"context_end_line":132,"code":"# Move model to GPU if available\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Define ECE computation function\ndef compute_ece(preds, confidences, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n ece = 0.0\n for i in range(n_bins):\n bin_lower = bin_boundaries[i]\n bin_upper = bin_boundaries[i + 1]\n in_bin = (confidences > bin_lower) & (confidences <= bin_upper)\n prop_in_bin = np.mean(in_bin)\n if prop_in_bin > 0:\n accuracy_in_bin = np.mean(preds[in_bin] == preds[in_bin]) # Adjust based on your prediction mechanism\n avg_confidence_in_bin = np.mean(confidences[in_bin])\n ece += np.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin\n return ece\n\n# Define Reliability Diagram function\ndef reliability_diagram(confidences, predictions, n_bins=10):\n bin_boundaries = np.linspace(0, 1, n_bins + 1)\n bin_centers = (bin_boundaries[:-1] + bin_boundaries[1:]) / 2\n accuracy = np.zeros(n_bins)\n confidence = np.zeros(n_bins)\n prop = np.zeros(n_bins)\n\n for i in range(n_bins):\n in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n prop[i] = np.mean(in_bin)\n if prop[i] > 0:\n accuracy[i] = np.mean(predictions[in_bin] == predictions[in_bin]) # Replace with actual labels\n confidence[i] = np.mean(confidences[in_bin])\n\n plt.figure(figsize=(8, 6))\n plt.plot(bin_centers, accuracy, marker='o', label='Accuracy')\n plt.plot(bin_centers, confidence, marker='s', label='Confidence')\n plt.fill_between(bin_boundaries[:-1], 0, 1, color='gray', alpha=0.1)\n plt.xlabel('Confidence')\n plt.ylabel('Accuracy')\n plt.legend()\n plt.title('Reliability Diagram')\n plt.show()\n\n# Training loop\nmodel.train()\nglobal_step = 0\nfor epoch in range(num_epochs):\n print(f\"Epoch {epoch + 1}/{num_epochs}\")\n epoch_loss = 0\n for batch in tqdm(train_dataloader):\n inputs = batch.to(device)\n labels = inputs.clone()\n\n optimizer.zero_grad()\n\n outputs = model(\n input_ids=inputs,\n labels=labels,\n num_dropout_samples=15 # You can adjust this number\n )\n loss = outputs['loss']\n loss.backward()","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.__init__","uri":"program://ConfidenceTransformer/function/train.__init__#L18-L26","kind":"function","name":"__init__","path":"train.py","language":"python","start_line":18,"end_line":26,"context_start_line":1,"context_end_line":46,"code":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import GPT2Tokenizer, get_linear_schedule_with_warmup\nfrom tqdm import tqdm\nimport torch.optim as optim\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:\n tokenized_text = tokenizer.encode(text)\n for i in range(0, len(tokenized_text) - block_size + 1, block_size):\n self.examples.append(\n torch.tensor(tokenized_text[i:i + block_size], dtype=torch.long)\n )\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return self.examples[i]\n\n# Load tokenizer and model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\ntexts = dataset['text']\n\n# Split the dataset into training and validation sets\ntrain_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)\n\n# Prepare datasets\ntrain_dataset = TextDataset(","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.__len__","uri":"program://ConfidenceTransformer/function/train.__len__#L28-L29","kind":"function","name":"__len__","path":"train.py","language":"python","start_line":28,"end_line":29,"context_start_line":8,"context_end_line":49,"code":"from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:\n tokenized_text = tokenizer.encode(text)\n for i in range(0, len(tokenized_text) - block_size + 1, block_size):\n self.examples.append(\n torch.tensor(tokenized_text[i:i + block_size], dtype=torch.long)\n )\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return self.examples[i]\n\n# Load tokenizer and model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\ntexts = dataset['text']\n\n# Split the dataset into training and validation sets\ntrain_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)\n\n# Prepare datasets\ntrain_dataset = TextDataset(\n tokenizer=tokenizer,\n texts=train_texts,\n block_size=128","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:train.__getitem__","uri":"program://ConfidenceTransformer/function/train.__getitem__#L31-L32","kind":"function","name":"__getitem__","path":"train.py","language":"python","start_line":31,"end_line":32,"context_start_line":11,"context_end_line":52,"code":"import matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:\n tokenized_text = tokenizer.encode(text)\n for i in range(0, len(tokenized_text) - block_size + 1, block_size):\n self.examples.append(\n torch.tensor(tokenized_text[i:i + block_size], dtype=torch.long)\n )\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return self.examples[i]\n\n# Load tokenizer and model\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\nmodel = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n\n# Load the WikiText-2 dataset\ndataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\ntexts = dataset['text']\n\n# Split the dataset into training and validation sets\ntrain_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)\n\n# Prepare datasets\ntrain_dataset = TextDataset(\n tokenizer=tokenizer,\n texts=train_texts,\n block_size=128\n)\nval_dataset = TextDataset(\n tokenizer=tokenizer,","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test","uri":"program://ConfidenceTransformer/module/test#L1-L74","kind":"module","name":"test","path":"test.py","language":"python","start_line":1,"end_line":74,"context_start_line":1,"context_end_line":74,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])\n\n def test_training_step(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-5)\n \n self.model.train()\n optimizer.zero_grad()\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n loss = outputs['loss']\n loss.backward()\n optimizer.step()\n \n self.assertIsNotNone(loss)\n self.assertGreater(loss.item(), 0.0)\n\nif __name__ == '__main__':\n unittest.main()","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.TestConfidenceEnhancedTransformer","uri":"program://ConfidenceTransformer/class/test.TestConfidenceEnhancedTransformer#L6-L71","kind":"class","name":"TestConfidenceEnhancedTransformer","path":"test.py","language":"python","start_line":6,"end_line":71,"context_start_line":1,"context_end_line":74,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])\n\n def test_training_step(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-5)\n \n self.model.train()\n optimizer.zero_grad()\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n loss = outputs['loss']\n loss.backward()\n optimizer.step()\n \n self.assertIsNotNone(loss)\n self.assertGreater(loss.item(), 0.0)\n\nif __name__ == '__main__':\n unittest.main()","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.setUpClass","uri":"program://ConfidenceTransformer/function/test.setUpClass#L9-L12","kind":"function","name":"setUpClass","path":"test.py","language":"python","start_line":9,"end_line":12,"context_start_line":1,"context_end_line":32,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n ","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_initialization","uri":"program://ConfidenceTransformer/function/test.test_initialization#L14-L15","kind":"function","name":"test_initialization","path":"test.py","language":"python","start_line":14,"end_line":15,"context_start_line":1,"context_end_line":35,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_forward_pass","uri":"program://ConfidenceTransformer/function/test.test_forward_pass#L17-L25","kind":"function","name":"test_forward_pass","path":"test.py","language":"python","start_line":17,"end_line":25,"context_start_line":1,"context_end_line":45,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_confidence_score_range","uri":"program://ConfidenceTransformer/function/test.test_confidence_score_range#L27-L35","kind":"function","name":"test_confidence_score_range","path":"test.py","language":"python","start_line":27,"end_line":35,"context_start_line":7,"context_end_line":55,"code":"\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_ood_score_range","uri":"program://ConfidenceTransformer/function/test.test_ood_score_range#L37-L45","kind":"function","name":"test_ood_score_range","path":"test.py","language":"python","start_line":37,"end_line":45,"context_start_line":17,"context_end_line":65,"code":" def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n self.assertIn('lm_logits', outputs)\n self.assertIn('confidence_score', outputs)\n self.assertIn('ood_score', outputs)\n\n def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])\n\n def test_training_step(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-5)\n \n self.model.train()\n optimizer.zero_grad()\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_loss_calculation","uri":"program://ConfidenceTransformer/function/test.test_loss_calculation#L47-L55","kind":"function","name":"test_loss_calculation","path":"test.py","language":"python","start_line":47,"end_line":55,"context_start_line":27,"context_end_line":74,"code":" def test_confidence_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n confidence_score = outputs['confidence_score'].item()\n self.assertGreaterEqual(confidence_score, 0.0)\n self.assertLessEqual(confidence_score, 1.0)\n\n def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])\n\n def test_training_step(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-5)\n \n self.model.train()\n optimizer.zero_grad()\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n loss = outputs['loss']\n loss.backward()\n optimizer.step()\n \n self.assertIsNotNone(loss)\n self.assertGreater(loss.item(), 0.0)\n\nif __name__ == '__main__':\n unittest.main()","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"py:test.test_training_step","uri":"program://ConfidenceTransformer/function/test.test_training_step#L57-L71","kind":"function","name":"test_training_step","path":"test.py","language":"python","start_line":57,"end_line":71,"context_start_line":37,"context_end_line":74,"code":" def test_ood_score_range(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)\n \n ood_score = outputs['ood_score'].item()\n self.assertGreaterEqual(ood_score, 0.0)\n self.assertLessEqual(ood_score, 1.0)\n\n def test_loss_calculation(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n \n self.assertIn('loss', outputs)\n self.assertIsNotNone(outputs['loss'])\n\n def test_training_step(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n labels = input_tokens['input_ids'].clone()\n optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-5)\n \n self.model.train()\n optimizer.zero_grad()\n outputs = self.model(input_tokens['input_ids'], labels=labels, num_dropout_samples=2)\n loss = outputs['loss']\n loss.backward()\n optimizer.step()\n \n self.assertIsNotNone(loss)\n self.assertGreater(loss.item(), 0.0)\n\nif __name__ == '__main__':\n unittest.main()","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"file:eval.py","uri":"program://ConfidenceTransformer/file/eval.py","kind":"file","name":"eval.py","path":"eval.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Model\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer # Import the class from main.py\n\n# Load the trained model and tokenizer\nmodel_name = 'confidence_model'\nmodel_path = model_name\ntokenizer_path = model_name\ntokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)\n\nmodel = ConfidenceEnhancedTransformer.from_pretrained(model_path, attn_implementation=\"eager\")\n\n# Move model to GPU if available\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Set the model to evaluation mode\nmodel.eval()","source_hash":"3578bf0e13cd974bc6904184ebfe9ffbf435d2522c4e913c4c68d1f6dc98ef44","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"file:main.py","uri":"program://ConfidenceTransformer/file/main.py","kind":"file","name":"main.py","path":"main.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nimport torch.nn as nn\nfrom transformers import GPT2LMHeadModel, GPT2Model, GPT2Tokenizer\nimport torch.nn.functional as F\n\nclass ConfidenceEnhancedTransformer(GPT2LMHeadModel):\n def __init__(self, config):\n super(ConfidenceEnhancedTransformer, self).__init__(config)\n self.transformer = GPT2Model(config)\n #self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Language modeling head\n\n # Confidence scoring head for epistemic uncertainty and OOD detection\n self.confidence_head = nn.Sequential(\n nn.Linear(config.n_embd, 128),\n nn.ReLU(),\n nn.Linear(128, 1), # Single output for confidence score\n nn.Sigmoid() # Confidence score between 0 and 1\n )\n \n # OOD detector head\n self.ood_detector = nn.Linear(config.n_embd, 1) # Auxiliary head for OOD detection","source_hash":"0bf4a6d8cb4435d83ba0509db804e4c03aefa41e5e87fca50ffea0cd28079b65","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"file:train.py","uri":"program://ConfidenceTransformer/file/train.py","kind":"file","name":"train.py","path":"train.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import GPT2Tokenizer, get_linear_schedule_with_warmup\nfrom tqdm import tqdm\nimport torch.optim as optim\nfrom datasets import load_dataset\nfrom main import ConfidenceEnhancedTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_epochs = 3\nsave_steps = 500 # Save the model every 500 steps\n\n# Define a custom dataset\nclass TextDataset(Dataset):\n def __init__(self, tokenizer, texts, block_size=128):\n self.examples = []\n\n for text in texts:","source_hash":"fca41099ce250271c231f639b57270deadda7aaebce2cdea10a73247db8e086b","truncated":false} |
| {"repo_id":"ConfidenceTransformer","entity_id":"file:test.py","uri":"program://ConfidenceTransformer/file/test.py","kind":"file","name":"test.py","path":"test.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import unittest\nimport torch\nfrom transformers import GPT2Tokenizer\nfrom main import ConfidenceEnhancedTransformer\n\nclass TestConfidenceEnhancedTransformer(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n cls.model = ConfidenceEnhancedTransformer.from_pretrained('gpt2')\n cls.model.eval() # Set model to evaluation mode\n\n def test_initialization(self):\n self.assertIsInstance(self.model, ConfidenceEnhancedTransformer)\n\n def test_forward_pass(self):\n input_text = \"What is the capital of USA?\"\n input_tokens = self.tokenizer(input_text, return_tensors=\"pt\")\n with torch.no_grad():\n outputs = self.model(input_tokens['input_ids'], num_dropout_samples=2)","source_hash":"ba380ce5eaed855a01a22a5d730ef8e9e6c7f260a060b9c2a08938a0f06823b9","truncated":false} |
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