Delete files extract.py with huggingface_hub

extract.py

@@ -1,86 +0,0 @@
-from pydoc import text
-
-import torch
-from src.task.model import TinyTransformerLM, TinyClassifier, TinyAnsweringModel
-
-class TextExtractor:
-
-    def __init__(
-        self,
-        model: TinyAnsweringModel,
-        tokenizer,
-        context_length,
-        device=None,
-    ):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.context_length = context_length
-
-        if device is None:
-            device = (
-                "cuda"
-                if torch.cuda.is_available()
-                else "cpu"
-            )
-
-        self.device = torch.device(device)
-        self.model.to(self.device)
-        self.model.eval()
-
-    @torch.no_grad()
-    def extract(
-        self,
-        context_text: str,  #context containing the answer
-        question_text: str  #the actual question 
-        ):
-
-        input_text = (
-                self.tokenizer.CLS_TOKEN
-                + question_text
-                + self.tokenizer.SEP_TOKEN
-                + context_text
-                + self.tokenizer.SEP_TOKEN
-                )
-        input_ids = self.tokenizer.encode(input_text)
-
-        padded_ids, mask_ids = self.tokenizer.pad(
-            [input_ids],
-            max_length=self.context_length
-            )
-
-        token_tensor = torch.tensor(
-            padded_ids[0],
-            dtype=torch.long,
-            device=self.device,
-            )
-
-        padding_mask = torch.tensor(
-            mask_ids,
-            dtype=torch.bool,
-            device=self.device,
-            )
-
-        #Typically -> embedding -> transformer -> start,end logits
-        #Forward pass on the model is refusing :-(
-        start_logits, end_logits = self.model( 
-            tokens=token_tensor.unsqueeze(0),
-            padding_mask=padding_mask
-            )
-
-
-        # Remove batch dimension: [max_length]
-        start_logits = start_logits[0]
-        end_logits = end_logits[0]
-        
-        start_predictions = torch.argmax(
-            start_logits,
-            dim=-1,
-            )
-    
-        end_predictions = torch.argmax(
-            end_logits,
-            dim=-1,
-            )
-        
-        return token_tensor, start_predictions.item(), end_predictions.item()
-