Haon-Chen
/

speed-synthesis-8b-revisor

@@ -1,112 +1,119 @@
----
-license: mit
----
-## SPEED-synthesis-7b-senior
-[Little Giants: Synthesizing High-Quality Embedding Data at Scale](https://arxiv.org/pdf/2410.18634.pdf). Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2024
-This is the data revisor model of SPEED.
-## Usage
-Below is an example to revise s2s data using this revisor.
-The prompts and misc scripts can be found in our [github page](https://github.com/haon-chen/SPEED)
-### Transformers
-```python
-import torch
-import os
-import random
-import numpy as np
-import json
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from typing import List, Dict, Optional
-from prompts_aligning import get_create_all_revise_data_prompt
-from utils import fix_common_json_errors_and_loads_for_revisor
-LLAMA3_PROMPT = """
-{prompt} [/INST]
-""".strip("\n")
-# Each query must come with a one-sentence instruction that describes the task
-old_prompts = [
-    "You have been assigned a text matching task: Match a Stockard Channing movie title with a brief plot description.\n\nYour mission is to write one example for this task in JSON format. The JSON object must contain the following keys:\n- \"input\": a string, a random input specified by the task.\n- \"positive_document\": a string, a relevant document for the \"input\" according to the task.\n\nPlease adhere to the following guidelines:\n- The values of all fields should be in English.\n- Both the \"input\" and \"positive_document\" should be very short (a sentence or a phrase), avoid substantial word overlaps, otherwise the task would be too easy.\n- The \"input\" and \"positive_document\" should be independent of each other.\n\nYour output must always be a JSON object only, do not explain yourself or output anything else. Be creative!"
-]
-old_data = [
-    {"input": "Stockard Channing in 'The Business of Strangers', directed by Patrick Stettner.", "positive_document": "In 'The Business of Strangers', Channing stars as a businesswoman who embarks on a ruthless journey, after which she undergoes a drastic change. She faces many challenges while pursuing her goals and eventually comes out stronger."},
-]
-language = 'English'
-prompts = [LLAMA3_PROMPT.format(prompt=get_create_all_revise_data_prompt(prompt=old_prompt, data=json.dumps(data))[1]['content']) for old_prompt in old_prompts for data in old_data]
-tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
-model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
-model.to("cuda:0")
-tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
-tokenizer.padding_side = "left"
-tokenizer.truncation_side = "left"
-# Tokenize the input texts
-encodes = tokenizer(prompts, padding="longest", add_special_tokens=True, return_tensors="pt")
-input_ids = encodes.input_ids.to(model.device)
-attention_mask = encodes.attention_mask.to(model.device)
-GEN_CONFIG = {"do_sample":True, "temperature": 1.0, "top_p": 1.0, "max_new_tokens": 800}
-output = model.generate(
-    input_ids=input_ids,
-    attention_mask=attention_mask,
-    pad_token_id = tokenizer.eos_token_id,
-    **GEN_CONFIG
-)
-output_texts = tokenizer.batch_decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-batch_results = []
-for i in range(len(output_texts)):
-    batch_results.append(output_texts[i][len(prompts[i]):].strip(' '))
-bad_cnt=0
-outputs = []
-for i, result in enumerate(batch_results):
-    try:
-        content = fix_common_json_errors_and_loads_for_revisor(result)
-        revision = content["revision"]
-        reason = content["reason"]
-        user_query = revision.get("input", "")
-        positive_document = revision.get("positive_document", "")
-    except:
-        bad_cnt+=1
-        continue
-    out_data = {
-        "query": user_query,
-        "positives": [positive_document],
-        "negatives": [],
-        "language": "English",
-        "reason": reason,
-    }
-    outputs.append(out_data)
-print(bad_cnt)
-print(outputs)
-```
-## Citation
-If you find our paper or models helpful, please consider cite as follows:
-```bibtex
-@article{chen2024little,
-  title={Little Giants: Synthesizing High-Quality Embedding Data at Scale},
-  author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
-  journal={arXiv preprint arXiv:2410.18634},
-  year={2024}
-}
-```
-## Limitations

+---
+license: mit
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B
+pipeline_tag: text-generation
+tags:
+- transformers
+---
+## SPEED-synthesis-7b-senior
+[Little Giants: Synthesizing High-Quality Embedding Data at Scale](https://arxiv.org/pdf/2410.18634.pdf). Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2024
+This is the data revisor model of SPEED.
+## Usage
+Below is an example to revise s2s data using this revisor.
+The prompts and misc scripts can be found in our [github page](https://github.com/haon-chen/SPEED)
+### Transformers
+```python
+import torch
+import os
+import random
+import numpy as np
+import json
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from typing import List, Dict, Optional
+from prompts_aligning import get_create_all_revise_data_prompt
+from utils import fix_common_json_errors_and_loads_for_revisor
+LLAMA3_PROMPT = """
+{prompt} [/INST]
+""".strip("\n")
+# Each query must come with a one-sentence instruction that describes the task
+old_prompts = [
+    "You have been assigned a text matching task: Match a Stockard Channing movie title with a brief plot description.\n\nYour mission is to write one example for this task in JSON format. The JSON object must contain the following keys:\n- \"input\": a string, a random input specified by the task.\n- \"positive_document\": a string, a relevant document for the \"input\" according to the task.\n\nPlease adhere to the following guidelines:\n- The values of all fields should be in English.\n- Both the \"input\" and \"positive_document\" should be very short (a sentence or a phrase), avoid substantial word overlaps, otherwise the task would be too easy.\n- The \"input\" and \"positive_document\" should be independent of each other.\n\nYour output must always be a JSON object only, do not explain yourself or output anything else. Be creative!"
+]
+old_data = [
+    {"input": "Stockard Channing in 'The Business of Strangers', directed by Patrick Stettner.", "positive_document": "In 'The Business of Strangers', Channing stars as a businesswoman who embarks on a ruthless journey, after which she undergoes a drastic change. She faces many challenges while pursuing her goals and eventually comes out stronger."},
+]
+language = 'English'
+prompts = [LLAMA3_PROMPT.format(prompt=get_create_all_revise_data_prompt(prompt=old_prompt, data=json.dumps(data))[1]['content']) for old_prompt in old_prompts for data in old_data]
+tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
+model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
+model.to("cuda:0")
+tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
+tokenizer.padding_side = "left"
+tokenizer.truncation_side = "left"
+# Tokenize the input texts
+encodes = tokenizer(prompts, padding="longest", add_special_tokens=True, return_tensors="pt")
+input_ids = encodes.input_ids.to(model.device)
+attention_mask = encodes.attention_mask.to(model.device)
+GEN_CONFIG = {"do_sample":True, "temperature": 1.0, "top_p": 1.0, "max_new_tokens": 800}
+output = model.generate(
+    input_ids=input_ids,
+    attention_mask=attention_mask,
+    pad_token_id = tokenizer.eos_token_id,
+    **GEN_CONFIG
+)
+output_texts = tokenizer.batch_decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+batch_results = []
+for i in range(len(output_texts)):
+    batch_results.append(output_texts[i][len(prompts[i]):].strip(' '))
+bad_cnt=0
+outputs = []
+for i, result in enumerate(batch_results):
+    try:
+        content = fix_common_json_errors_and_loads_for_revisor(result)
+        revision = content["revision"]
+        reason = content["reason"]
+        user_query = revision.get("input", "")
+        positive_document = revision.get("positive_document", "")
+    except:
+        bad_cnt+=1
+        continue
+    out_data = {
+        "query": user_query,
+        "positives": [positive_document],
+        "negatives": [],
+        "language": "English",
+        "reason": reason,
+    }
+    outputs.append(out_data)
+print(bad_cnt)
+print(outputs)
+```
+## Citation
+If you find our paper or models helpful, please consider cite as follows:
+```bibtex
+@article{chen2024little,
+  title={Little Giants: Synthesizing High-Quality Embedding Data at Scale},
+  author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
+  journal={arXiv preprint arXiv:2410.18634},
+  year={2024}
+}
+```
+## Limitations