Upload 4 files

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 import json
-from llmlingua import PromptCompressor
+#from llmlingua import PromptCompressor
+from utils_llmlingua2_test import PromptCompressor
 import tiktoken
 
 compressors = {
     "xlm-roberta": PromptCompressor(
-        model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
-        #model_name='qminh369/token-classification-llmlingua2-xlm-roberta-42k_merge_1_epoch',
+        #model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
+        model_name='qminh369/token-classification-llmlingua2-xlm-roberta-42k_merge_1_epoch',
         use_llmlingua2=True,
         device_map="cpu"
     )
@@ -26,7 +27,8 @@ def compress(original_prompt, compression_rate, base_model="xlm-roberta", force_
             force_tokens=force_tokens,
             chunk_end_tokens=chunk_end_tokens,
             return_word_label=True,
-            drop_consecutive=True
+            drop_consecutive=True,
+            force_reserve_digit=True,
             )
 
     compressed_prompt = results["compressed_prompt"]

core_utils_llmlingua2.py

@@ -0,0 +1,149 @@
+import os
+import random
+import string
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+class TokenClfDataset(Dataset):     # Hàm tạo custom dataset
+    def __init__(
+        self,
+        texts,
+        max_len=512,    # 256 (phobert)  512 (xlm-roberta)
+        tokenizer=None,
+        model_name="m_bert",
+    ):
+        self.len = len(texts)
+        self.texts = texts
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+        self.model_name = model_name
+        if "m_bert" in model_name:
+            self.cls_token = "[CLS]"
+            self.sep_token = "[SEP]"
+            self.unk_token = "[UNK]"
+            self.pad_token = "[PAD]"
+            self.mask_token = "[MASK]"
+        elif "xlm-roberta-large" in model_name:
+            self.bos_token = "<s>"
+            self.eos_token = "</s>"
+            self.sep_token = "</s>"
+            self.cls_token = "<s>"
+            self.unk_token = "<unk>"
+            self.pad_token = "<pad>"
+            self.mask_token = "<mask>"
+        elif "xlm-roberta" in model_name:
+            self.bos_token = "<s>"
+            self.eos_token = "</s>"
+            self.sep_token = "</s>"
+            self.cls_token = "<s>"
+            self.unk_token = "<unk>"
+            self.pad_token = "<pad>"
+            self.mask_token = "<mask>"
+        elif "phobert" in model_name:
+            self.bos_token = "<s>"
+            self.eos_token = "</s>"
+            self.sep_token = "</s>"
+            self.cls_token = "<s>"
+            self.unk_token = "<unk>"
+            self.pad_token = "<pad>"
+            self.mask_token = "<mask>"
+        #else: raise NotImplementedError()
+
+    def __getitem__(self, index):
+        text = self.texts[index]
+        tokenized_text = self.tokenizer.tokenize(text)
+
+        tokenized_text = (
+            [self.cls_token] + tokenized_text + [self.sep_token]
+        )  # add special tokens
+
+        if len(tokenized_text) > self.max_len:
+            tokenized_text = tokenized_text[: self.max_len]
+        else:
+            tokenized_text = tokenized_text + [
+                self.pad_token for _ in range(self.max_len - len(tokenized_text))
+            ]
+
+        attn_mask = [1 if tok != self.pad_token else 0 for tok in tokenized_text]
+
+        ids = self.tokenizer.convert_tokens_to_ids(tokenized_text)
+
+        return {
+            "ids": torch.tensor(ids, dtype=torch.long),
+            "mask": torch.tensor(attn_mask, dtype=torch.long),
+        }
+
+    def __len__(self):
+        return self.len
+
+
+def seed_everything(seed: int):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def is_begin_of_new_word(token, model_name, force_tokens, token_map):   # Thêm kí tự bắt đầu vào từ mới
+    if "m_bert" in model_name:
+        if token.lstrip("##") in force_tokens or token.lstrip("##") in set(
+            token_map.values()
+        ):
+            return True
+        return not token.startswith("##")
+    elif "xlm-roberta-large" in model_name:
+        #print("xlm-roberta-large")
+        if (
+            token in string.punctuation
+            or token in force_tokens
+            or token in set(token_map.values())
+        ):
+            return True
+        return token.startswith("▁")    # check xem token có bắt đầu bằng kí tự "_" hay ko  -> Trả về False
+    elif "xlm-roberta" in model_name:
+        #print("xlm-roberta-large")
+        if (
+            token in string.punctuation
+            or token in force_tokens
+            or token in set(token_map.values())
+        ):
+            return True
+        return token.startswith("▁")  
+    elif "phobert" in model_name:
+        #print("minh phobert")
+        #print("xlm-roberta-large")
+        if (
+            token in string.punctuation     # điều kiện hoặc
+            or token in force_tokens
+            or token in set(token_map.values())
+        ):
+            return True
+        #return token.startswith("▁") # 
+        #return not token.startswith("▁") 
+        #return not token.startswith("@@")
+        return not token.endswith("@@")
+        #return token.startswith("@@")
+    #else: raise NotImplementedError()
+
+def replace_added_token(token, token_map):
+    for ori_token, new_token in token_map.items():
+        token = token.replace(new_token, ori_token)
+    return token
+
+def get_pure_token(token, model_name):  # hàm get pure token trả về token gốc (sau khi loại bỏ kí tự đặc biệt subword)
+    if "m_bert" in model_name:
+        return token.lstrip("##")
+    elif "xlm-roberta-large" in model_name:
+        return token.lstrip("▁")        # bỏ kí tự "_" ở phía bên trái của từ
+    elif "xlm-roberta" in model_name:
+        return token.lstrip("▁")        # bỏ kí tự "_" ở ph��a bên trái của từ
+    elif "phobert" in model_name:
+        #return token.lstrip("▁")
+        #return token.lstrip("@@")
+        return token.rstrip("@@")
+    # else: raise NotImplementedError()

requirements.txt

@@ -1,4 +1,3 @@
 gradio
 accelerate
-llmlingua==0.2.1
 tiktoken