Update README.md

README.md

@@ -1,111 +1,118 @@
----
-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 senior data synthesis model of SPEED. 
-
-## Usage
-
-Below is an example to synthesize classification data using this senior generator. 
-
-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
-import re
-
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-from prompts_synthesis import get_create_classify_data_prompt
-from utils import fix_common_json_errors_and_loads
-
-
-LLAMA3_PROMPT = """
-{prompt} [/INST]
-""".strip("\n")
-
-# Each query must come with a one-sentence instruction that describes the task
-tasks = [
-    'Identify the intended age group for educational technology products.',
-    'Classify businesses based on their operational hours.'
-]
-language = 'English'
-
-prompts = [LLAMA3_PROMPT.format(prompt=get_create_classify_data_prompt(task=task, language=language)[1]['content']) for task in tasks]
-
-tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
-model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
-model.to("cuda:0")
-model.eval()
-tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
-tokenizer.padding_side = "left"
-tokenizer.truncation_side = "left"
-
-with torch.inference_mode():
-    # 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)
-
-    # Set the generation parameters
-    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(' '))
-
-# Format outputs
-bad_cnt=0
-outputs = []
-for i, result in enumerate(batch_results):
-    try:
-        output = fix_common_json_errors_and_loads(result)
-        user_query = output.get("input_text", "")
-        positive_document = output.get("label", "")
-        hard_negative_document = output.get("misleading_label", "")
-    except:
-        bad_cnt+=1
-        continue
-    out_data = {
-        "query": user_query,
-        "positives": [positive_document],
-        "negatives": [hard_negative_document],
-        "language": "English",
-        "task_definition": tasks[i],
-    }
-    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 senior data synthesis model of SPEED. 
+
+## Usage
+
+Below is an example to synthesize classification data using this senior generator. 
+
+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
+import re
+
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from prompts_synthesis import get_create_classify_data_prompt
+from utils import fix_common_json_errors_and_loads
+
+
+LLAMA3_PROMPT = """
+{prompt} [/INST]
+""".strip("\n")
+
+# Each query must come with a one-sentence instruction that describes the task
+tasks = [
+    'Identify the intended age group for educational technology products.',
+    'Classify businesses based on their operational hours.'
+]
+language = 'English'
+
+prompts = [LLAMA3_PROMPT.format(prompt=get_create_classify_data_prompt(task=task, language=language)[1]['content']) for task in tasks]
+
+tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
+model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
+model.to("cuda:0")
+model.eval()
+tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
+tokenizer.padding_side = "left"
+tokenizer.truncation_side = "left"
+
+with torch.inference_mode():
+    # 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)
+
+    # Set the generation parameters
+    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(' '))
+
+# Format outputs
+bad_cnt=0
+outputs = []
+for i, result in enumerate(batch_results):
+    try:
+        output = fix_common_json_errors_and_loads(result)
+        user_query = output.get("input_text", "")
+        positive_document = output.get("label", "")
+        hard_negative_document = output.get("misleading_label", "")
+    except:
+        bad_cnt+=1
+        continue
+    out_data = {
+        "query": user_query,
+        "positives": [positive_document],
+        "negatives": [hard_negative_document],
+        "language": "English",
+        "task_definition": tasks[i],
+    }
+    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