Usuń 'bigram.py'

bigram.py

@@ -1,36 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-import sys
-
-tokenizer = AutoTokenizer.from_pretrained("roberta-base")
-model = AutoModelForMaskedLM.from_pretrained("roberta-base")
-
-for line in sys.stdin:
-    line_splited = line.split("\t")
-    left_context = line_splited[6].split(" ")[-1]
-    right_context = line_splited[7].split(" ")[0]
-
-    word = "[MASK]"
-
-    text = f"{left_context} {word} {right_context}"
-
-    input_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt", max_length=512, truncation=True)
-
-    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1][0]
-
-    with torch.no_grad():
-        outputs = model(input_ids)
-        predictions = outputs[0][0, mask_token_index].softmax(dim=0)
-
-    top_k = 1000
-    top_k_tokens = torch.topk(predictions, top_k).indices.tolist()
-    result = ''
-    prob_sum = 0
-    for token in top_k_tokens:
-        word = tokenizer.convert_ids_to_tokens([token])[0]
-        prob = predictions[token].item()
-        prob_sum += prob
-        result += f"{word}:{prob} "
-    diff = 1.0 - prob_sum
-    result += f":{diff}"
-    print(result)