Feature(LLMLingua-2): update the meetingbank datasets

DOCUMENT.md

@@ -31,7 +31,7 @@ llm_lingua = PromptCompressor("microsoft/phi-2")
 llm_lingua = PromptCompressor("TheBloke/Llama-2-7b-Chat-GPTQ", model_config={"revision": "main"})
 ```
 
-To try **LongLLMLingua** in your scenorias, you can use
+To try **LongLLMLingua** in your scenarios, you can use
 
 ```python
 from llmlingua import PromptCompressor
@@ -51,7 +51,7 @@ compressed_prompt = llm_lingua.compress_prompt(
 )
 ```
 
-To try **LLMLingua-2** in your scenorias, you can use
+To try **LLMLingua-2** in your scenarios, you can use
 
 ```python
 from llmlingua import PromptCompressor

README.md

@@ -148,7 +148,7 @@ llm_lingua = PromptCompressor("microsoft/phi-2")
 llm_lingua = PromptCompressor("TheBloke/Llama-2-7b-Chat-GPTQ", model_config={"revision": "main"})
 ```
 
-To try **LongLLMLingua** in your scenorias, you can use
+To try **LongLLMLingua** in your scenarios, you can use
 
 ```python
 from llmlingua import PromptCompressor
@@ -168,7 +168,7 @@ compressed_prompt = llm_lingua.compress_prompt(
 )
 ```
 
-To try **LLMLingua-2** in your scenorias, you can use
+To try **LLMLingua-2** in your scenarios, you can use
 
 ```python
 from llmlingua import PromptCompressor

experiments/llmlingua2/README.md

@@ -24,7 +24,7 @@ pip install tensorboard
 
 ## Data collection
 
-We will release our collected GPT-4 compression result at [HF](https://huggingface.co/datasets/microsoft/LLMLingua-2-data-MeetingBankComp) after review. We also provide the whole data collection pipeline at [**collect_data.sh**](data_collection/collect_data.sh) to help you construct your custom compression dataset.
+We release our collected GPT-4 compression result at [HF](https://huggingface.co/datasets/microsoft/MeetingBank-LLMCompressed) after review. We also provide the whole data collection pipeline at [**collect_data.sh**](data_collection/collect_data.sh) to help you construct your custom compression dataset.
 
 ## Model Training
 

experiments/llmlingua2/data_collection/README.md

@@ -1,10 +1,10 @@
 ### Use our collected data
 
-We will release our collected GPT-4 compression result at [HF](https://huggingface.co/datasets/microsoft/LLMLingua-2-data-MeetingBankComp) after review. To load data, simply use
+We release our collected GPT-4 compression result at [HF](https://huggingface.co/datasets/microsoft/MeetingBank-LLMCompressed) after review. To load data, simply use
 
 ```python
 from datasets import load_dataset
-data = load_dataset("microsoft/LLMLingua-2-data-MeetingBankComp", split="train")
+data = load_dataset("microsoft/MeetingBank-LLMCompressed", split="train")
 print(len(data))
 for idx, sample in enumerate(data):
     # concatenation of all chunks
@@ -16,7 +16,7 @@ for idx, sample in enumerate(data):
 To load compressed chunks along with original chunks, simply use
 ```python
 from datasets import load_dataset
-data = load_dataset("microsoft/LLMLingua-2-data-MeetingBankComp", split="train")
+data = load_dataset("microsoft/MeetingBank-LLMCompressed", split="train")
 print(len(data))
 for idx, sample in enumerate(data):
     # chunk list

experiments/llmlingua2/data_collection/collect_data.sh

@@ -1,16 +1,16 @@
 # Copyright (c) 2023 Microsoft
 # Licensed under The MIT License [see LICENSE for details]
 
-python format_data.py
+# python format_data.py
 
-python compress.py --load_origin_from ../../../results/meetingbank/origin/meetingbank_train_formated.json \
-    --compressor gpt4 \
-    --chunk_size 512 \
-    --save_path ../../../results/meetingbank/gpt-4-32k_comp/compression_cs512_meetingbank_train_formated.json
+# python compress.py --load_origin_from ../results/meetingbank/origin/meetingbank_train_formated.json \
+#     --compressor gpt4 \
+#     --chunk_size 512 \
+#     --save_path ../results/meetingbank/gpt-4-32k_comp/compression_cs512_meetingbank_train_formated.json
 
-python label_word.py --load_prompt_from ../../../results/meetingbank/gpt-4-32k_comp/compression_cs512_meetingbank_train_formated.json \
+python label_word.py --load_prompt_from microsoft/MeetingBank-LLMCompressed \
     --window_size 400 \
-    --save_path ../../../results/meetingbank/gpt-4-32k_comp/annotation_cs512_meetingbank_train_formated.json
+    --save_path ../results/meetingbank/gpt-4-32k_comp/annotation_cs512_meetingbank_train_formated.json
 
-python filter.py --load_path ../../../results/meetingbank/gpt-4-32k_comp/annotation_cs512_meetingbank_train_formated.pt \
-    --save_path ../../../results/meetingbank/gpt-4-32k_comp/annotation_kept_cs512_meetingbank_train_formated.pt
+python filter.py --load_path ../results/meetingbank/gpt-4-32k_comp/annotation_cs512_meetingbank_train_formated.pt \
+    --save_path ../results/meetingbank/gpt-4-32k_comp/annotation_kept_cs512_meetingbank_train_formated.pt

experiments/llmlingua2/data_collection/label_word.py

@@ -6,7 +6,7 @@
 import logging
 import os
 from collections import defaultdict
-
+from datasets import load_dataset
 import spacy
 import torch
 from tqdm import tqdm
@@ -58,16 +58,15 @@ def split_string(input_string, ignore_tokens=set([","])):
 def is_equal(token1, token2):
     return token1.lower() == token2.lower()
 
-
 origins, comps = [], []
-raw_data = json.load(open(args.load_prompt_from, "r"))
-for sid, sample in raw_data.items():
+meeting_bank_comp = load_dataset(args.load_prompt_from, split="train")
+for i, sample in enumerate(meeting_bank_comp):
     if len(sample["prompt_list"]) != len(sample["compressed_prompt_list"]):
-        print(f"{sid}-th length not equal")
+        print(f"{i}-th length not equal")
         continue
     origins.extend(sample["prompt_list"])
     comps.extend(sample["compressed_prompt_list"])
-
+    
 res = {}
 res_pt = defaultdict(list)
 

experiments/llmlingua2/model_training/train.sh

@@ -1,5 +1,5 @@
 # Copyright (c) 2023 Microsoft
 # Licensed under The MIT License [see LICENSE for details]
 
-python train_roberta.py --data_path ../../../results/meetingbank/gpt-4-32k_comp/annotation_cs512_meetingbank_train_formated.pt \
+python train_roberta.py --data_path ../../../results/meetingbank/gpt-4-32k_comp/annotation_kept_cs512_meetingbank_train_formated.pt \
     --save_path ../../../results/models/xlm_roberta_large_meetingbank_only.pth