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Model: NECOUDBFM/Jellyfish-7B Source: Original Platform
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---
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license: cc-by-nc-4.0
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language:
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- en
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---
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# Jellyfish-7B
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<!-- Provide a quick summary of what the model is/does. -->
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<!--
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<img src="https://i.imgur.com/d8Bl04i.png" alt="PicToModel" width="330"/>
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-->
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<img src="https://i.imgur.com/E1vqCIw.png" alt="PicToModel" width="330"/>
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Jellyfish model with other sizes are available here:
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[Jellyfish-8B](https://huggingface.co/NECOUDBFM/Jellyfish-8B)
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[Jellyfish-13B](https://huggingface.co/NECOUDBFM/Jellyfish-13B)
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## Model Details
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Jellyfish-7B is a large language model equipped with 7 billion parameters.
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We fine-tuned the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model using a subset of the [Jellyfish-Instruct](https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct) dataset.
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For interpretability, the winning rate of Jellyfish-7B against GPT-3.5-turbo (evaluated by GPT-4) is 56.36%.
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More details about the model can be found in the [Jellyfish paper](https://arxiv.org/abs/2312.01678).
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- **Developed by:** Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
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- **Contact: dongyuyang@nec.com**
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- **Funded by:** NEC Corporation, Osaka University
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- **Language(s) (NLP):** English
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- **License:** Non-Commercial Creative Commons license (CC BY-NC-4.0)
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- **Finetuned from model:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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## Citation
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If you find our work useful, please give us credit by citing:
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```
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@article{zhang2023jellyfish,
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title={Jellyfish: A Large Language Model for Data Preprocessing},
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author={Zhang, Haochen and Dong, Yuyang and Xiao, Chuan and Oyamada, Masafumi},
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journal={arXiv preprint arXiv:2312.01678},
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year={2023}
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}
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```
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## Performance on seen tasks
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| Task | Type | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | GPT-4o | Table-GPT | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
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|-----------------|--------|-------------------|-----------------|--------|--------|--------|-----------|--------------|--------------|---------------|
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| Error Detection | Seen | Adult | *99.10* | 99.10 | 92.01 | 83.58 | -- | 77.40 | 73.74 | **99.33** |
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| Error Detection | Seen | Hospital | 94.40 | **97.80** | 90.74 | 44.76 | -- | 94.51 | 93.40 | *95.59* |
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| Error Detection | Unseen | Flights | 81.00 | -- | **83.48** | 66.01 | -- | 69.15 | 66.21 | *82.52* |
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| Error Detection | Unseen | Rayyan | 79.00 | -- | *81.95* | 68.53 | -- | 75.07 | 81.06 | **90.65** |
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| Data Imputation | Seen | Buy | 96.50 | 98.50 | **100** | **100** | -- | 98.46 | 98.46 | **100** |
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| Data Imputation | Seen | Restaurant | 77.20 | 88.40 | **97.67** | 90.70 | -- | 89.53 | 87.21 | 89.53 |
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| Data Imputation | Unseen | Flipkart | 68.00 | -- | **89.94** | 83.20 | -- | 87.14 | *87.48* | 81.68 |
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| Data Imputation | Unseen | Phone | 86.70 | -- | **90.79** | 86.78 | -- | 86.52 | 85.68 | *87.21* |
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| Schema Matching | Seen | MIMIC-III | 20.00 | -- | 40.00 | 29.41 | -- | **53.33** | *45.45* | 40.00 |
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| Schema Matching | Seen | Synthea | 38.50 | 45.20 | **66.67** | 6.56 | -- | 55.56 | 47.06 | 56.00 |
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| Schema Matching | Unseen | CMS | *50.00* | -- | 19.35 | 22.22 | -- | 42.86 | 38.10 | **59.29** |
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| Entity Matching | Seen | Amazon-Google | 75.58 | 63.50 | 74.21 | 70.91 | 70.10 | **81.69** | *81.42* | 81.34 |
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| Entity Matching | Seen | Beer | 94.37 | **100** | **100** | 90.32 | 96.30 | **100.00** | **100.00** | 96.77 |
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| Entity Matching | Seen | DBLP-ACM | **98.99** | 96.60 | 97.44 | 95.87 | 93.80 | 98.65 | 98.77 | *98.98* |
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| Entity Matching | Seen | DBLP-GoogleScholar| *95.70* | 83.80 | 91.87 | 90.45 | 92.40 | 94.88 | 95.03 | **98.51** |
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| Entity Matching | Seen | Fodors-Zagats | **100** | **100** | **100** | 93.62 | **100** | **100** | **100** | **100** |
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| Entity Matching | Seen | iTunes-Amazon | 97.06 | *98.20*| **100** | 98.18 | 94.30 | 96.30 | 96.30 | 98.11 |
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| Entity Matching | Unseen | Abt-Buy | 89.33 | -- | **92.77** | 78.73 | -- | 86.06 | 88.84 | *89.58* |
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| Entity Matching | Unseen | Walmart-Amazon | 86.89 | 87.00 | **90.27** | 79.19 | 82.40 | 84.91 | 85.24 | *89.42* |
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| Avg | | | 80.44 | - | *84.17* | 72.58 | - | 82.74 | 81.55 | **86.02** |
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_For GPT-3.5 and GPT-4, we used the few-shot approach on all datasets. For Jellyfish models, the few-shot approach is disabled on seen datasets and enabled on unseen datasets._
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_Accuracy as the metric for data imputation and the F1 score for other tasks._
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1.
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[HoloDetect](https://arxiv.org/abs/1904.02285) for Error Detection seen datasets
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[RAHA](https://dl.acm.org/doi/10.1145/3299869.3324956) for Error Detection unseen datasets
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[IPM](https://ieeexplore.ieee.org/document/9458712) for Data Imputation
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[SMAT](https://www.researchgate.net/publication/353920530_SMAT_An_Attention-Based_Deep_Learning_Solution_to_the_Automation_of_Schema_Matching) for Schema Matching
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[Ditto](https://arxiv.org/abs/2004.00584) for Entity Matching
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2.
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[Large Language Models as Data Preprocessors](https://arxiv.org/abs/2308.16361)
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## Performance on unseen tasks
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### Column Type Annotation
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| Dataset | RoBERTa (159 shots)<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 | GPT-4o | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
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|--------|-----------------|--------|--------|--------|--------------|--------------|---------------|
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| SOTAB | 79.20 | 89.47 | 91.55 | 65.05 | 83 | 76.33 | 82 |
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_Few-shot is disabled for Jellyfish models._
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1. Results from [Column Type Annotation using ChatGPT](https://arxiv.org/abs/2306.00745)
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### Attribute Value Extraction
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| Dataset |Stable Beluga 2 70B<sup>1</sup> | SOLAR 70B<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 <sup>1</sup>| GPT-4o | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
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| ---- | ---- | ---- | ---- | ---- | ---- | ----| ----| ----|
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| AE-110k | 52.10 | 49.20 | 61.30 | 55.50 | 55.77 | 56.09 |59.55 | 58.12 |
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| OA-Mine | 50.80 | 55.20 | 62.70 | 68.90 | 60.20 | 51.98 | 59.22 | 55.96 |
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_Few-shot is disabled for Jellyfish models._
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1. Results from [Product Attribute Value Extraction using Large Language Models](https://arxiv.org/abs/2310.12537)
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## Prompt Template
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```
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{system message}
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[INST]:
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{prompt} (without the {})
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[\INST]]
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```
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## Training Details
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### Training Method
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We used LoRA to speed up the training process, targeting the q_proj, k_proj, v_proj, and o_proj modules.
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## Uses
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To accelerate the inference, we strongly recommend running Jellyfish using [vLLM](https://github.com/vllm-project/vllm).
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Python Script
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We provide two simple Python code examples for inference using the Jellyfish model.
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#### Using Transformers and Torch Modules
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<div style="height: auto; max-height: 400px; overflow-y: scroll;">
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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import torch
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if torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# Model will be automatically downloaded from HuggingFace model hub if not cached.
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# Model files will be cached in "~/.cache/huggingface/hub/models--NECOUDBFM--Jellyfish/" by default.
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# You can also download the model manually and replace the model name with the path to the model files.
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model = AutoModelForCausalLM.from_pretrained(
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"NECOUDBFM/Jellyfish",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("NECOUDBFM/Jellyfish")
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system_message = "You are an AI assistant that follows instruction extremely well. Help as much as you can."
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# You need to define the user_message variable based on the task and the data you want to test on.
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user_message = "Hello, world."
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prompt = f"{system_message}\n\n[INST]:\n\n{user_message}\n\n[\INST]]"
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inputs = tokenizer(prompt, return_tensors="pt")
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input_ids = inputs["input_ids"].to(device)
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# You can modify the sampling parameters according to your needs.
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generation_config = GenerationConfig(
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do_samples=True,
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temperature=0.35,
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top_p=0.9,
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)
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with torch.no_grad():
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generation_output = model.generate(
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input_ids=input_ids,
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generation_config=generation_config,
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return_dict_in_generate=True,
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output_scores=True,
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max_new_tokens=1024,
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pad_token_id=tokenizer.eos_token_id,
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repetition_penalty=1.15,
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)
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output = generation_output[0]
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response = tokenizer.decode(
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output[:, input_ids.shape[-1] :][0], skip_special_tokens=True
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).strip()
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print(response)
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```
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</div>
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#### Using vLLM
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<div style="height: auto; max-height: 400px; overflow-y: scroll;">
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```python
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from vllm import LLM, SamplingParams
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# To use vllm for inference, you need to download the model files either using HuggingFace model hub or manually.
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# You should modify the path to the model according to your local environment.
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path_to_model = (
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"/workspace/models/Jellyfish"
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)
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model = LLM(model=path_to_model)
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# You can modify the sampling parameters according to your needs.
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# Caution: The stop parameter should not be changed.
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sampling_params = SamplingParams(
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temperature=0.35,
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top_p=0.9,
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max_tokens=1024,
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stop=["[INST]"],
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)
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system_message = "You are an AI assistant that follows instruction extremely well. Help as much as you can."
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# You need to define the user_message variable based on the task and the data you want to test on.
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user_message = "Hello, world."
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prompt = f"{system_message}\n\n[INST]:\n\n{user_message}\n\n[\INST]]"
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outputs = model.generate(prompt, sampling_params)
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response = outputs[0].outputs[0].text.strip()
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print(response)
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```
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</div>
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## Prompts
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We provide the prompts used for both fine-tuning and inference.
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You can structure your data according to these prompts.
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### System Message
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```
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You are an AI assistant that follows instruction extremely well.
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User will give you a question. Your task is to answer as faithfully as you can.
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```
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### For Error Detection
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_There are two forms of the error detection task.
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In the first form, a complete record row is provided, and the task is to determine if a specific value is erroneous.
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In the second form, only the value of a specific attribute is given, and the decision about its correctness is based solely on the attribute's name and value.
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The subsequent prompt examples pertain to these two forms, respectively._
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```
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Your task is to determine if there is an error in the value of a specific attribute within the whole record provided.
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The attributes may include {attribute 1}, {attribute 2}, ...
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Errors may include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense given the context of the whole record.
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Record [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
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Attribute for Verification: [{attribute X}: {attribute X value}]
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Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
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```
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```
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Your task is to determine if there is an error in the value of a specific attribute.
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The attributes may belong to a {keyword} record and could be one of the following: {attribute 1}, {attribute 2}, ...
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Errors can include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense for that attribute.
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Note: Missing values (N/A or \"nan\") are not considered errors.
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Attribute for Verification: [{attribute X}: {attribute X value}]
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Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
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```
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### For Data Imputation
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```
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You are presented with a {keyword} record that is missing a specific attribute: {attribute X}.
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Your task is to deduce or infer the value of {attribute X} using the available information in the record.
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You may be provided with fields like {attribute 1}, {attribute 2}, ... to help you in the inference.
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Record: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
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Based on the provided record, what would you infer is the value for the missing attribute {attribute X}?
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Answer only the value of {attribute X}.
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```
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### For Schema Matching
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```
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Your task is to determine if the two attributes (columns) are semantically equivalent in the context of merging two tables.
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Each attribute will be provided by its name and a brief description.
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Your goal is to assess if they refer to the same information based on these names and descriptions provided.
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Attribute A is [name: {value of name}, description: {value of description}].
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Attribute B is [name: {value of name}, description: {value of description}].
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Are Attribute A and Attribute B semantically equivalent? Choose your answer from: [Yes, No].
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```
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### For Entity Matching
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```
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You are tasked with determining whether two records listed below are the same based on the information provided.
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Carefully compare the {attribute 1}, {attribute 2}... for each record before making your decision.
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Note that missing values (N/A or \"nan\") should not be used as a basis for your decision.
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Record A: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
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Record B: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
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Are record A and record B the same entity? Choose your answer from: [Yes, No].
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```
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### For Column Type Annotation
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We follow the prompt in [Column Type Annotation using ChatGPT](https://arxiv.org/abs/2306.00745) (text+inst+2-step).
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### For Attribute Value Extraction
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We follow the prompt in [Product Attribute Value Extraction using Large Language Models](https://arxiv.org/abs/2310.12537) (textual, w/o examples).
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26
config.json
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config.json
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{
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"_name_or_path": "/shared/hddfs1/groups/kbl-cgmgrp-kbl/users/dong/jellyfish/model/Mistral-7B-Instruct-v0.2",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.34.1",
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"use_cache": true,
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"vocab_size": 32000
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}
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6
generation_config.json
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6
generation_config.json
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
91122
tokenizer.json
Normal file
91122
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user