444409 GPT2

run.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+device = torch.device('cuda')
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model: GPT2LMHeadModel = GPT2LMHeadModel.from_pretrained('gpt2', pad_token_id=tokenizer.eos_token_id)
+model.to(device)
+
+# In[2]:
+
+
+import lzma
+
+
+def read_xz_file(fname):
+    with lzma.open(fname, mode='rt', encoding='utf-8') as f:
+        return [line.strip() for line in f.readlines()]
+
+
+# In[3]:
+
+
+dev_input_raw = read_xz_file('dev-0/in.tsv.xz')
+test_input_raw = read_xz_file('test-A/in.tsv.xz')
+
+
+# In[4]:
+
+
+def get_contexts(input_text):
+    all_fields = input_text.replace(r'\n', ' ').split('\t')
+    return {'left': all_fields[6], 'right': all_fields[7]}
+
+
+dev_input_contexts = [get_contexts(input_text) for input_text in dev_input_raw]
+
+# In[5]:
+
+
+test_input_contexts = [get_contexts(input_text) for input_text in test_input_raw]
+
+# In[6]:
+
+
+from tqdm import tqdm
+
+tokenizer.truncation_side = 'left'
+
+
+def predict_words(dataset):
+    preds = []
+    for entry in tqdm(dataset):
+        text = f"{entry['left']}"
+        src = tokenizer.encode(text, return_tensors="pt", truncation=True).to(device)
+        output = model.generate(src, max_length=len(src[0]) + 1, do_sample=True, top_k=0, temperature=0.8,
+                                num_return_sequences=1, no_repeat_ngram_size=2)
+        generated_word = tokenizer.decode(output[0], skip_special_tokens=True).split(' ')[-1]
+        preds.append(f'{generated_word.strip()}:0.99 :0.01')
+    return preds
+
+
+# In[7]:
+
+
+dev_preds = predict_words(dev_input_contexts)
+with open('dev-0/out.tsv', 'w') as f:
+    f.writelines(line + '\n' for line in dev_preds)
+
+# In[8]:
+
+
+test_preds = predict_words(test_input_contexts)
+with open('test-A/out.tsv', 'w') as f:
+    f.writelines(line + '\n' for line in test_preds)