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  library_name: transformers
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- tags: []
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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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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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
 
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- ## Citation [optional]
 
 
 
 
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
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+ pipeline_tag: text2text-generation
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  library_name: transformers
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+ tags:
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+ - t5
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+ - vietnamese
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+ - information-extraction
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+ - text2text-generation
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  ---
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+ # ViT5 Motor Extractor
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+ ## Model Card for `letran1110/vit5_motor_extractor`
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+ This is a fine-tuned [ViT5](https://huggingface.co/VietAI/vit5-base) model for extracting motor specifications from raw text descriptions. The model is trained to take in noisy or unstructured motor-related information and output structured key-value pairs such as power, voltage, poles, protection class, and more.
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+ ---
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+ ## 🧠 Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ - **Model Type:** `T5ForConditionalGeneration`
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+ - **Language(s):** Vietnamese (primary), English (partially)
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+ - **Finetuned From:** `VietAI/vit5-base`
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+ - **License:** MIT
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+ - **Framework:** 🤗 Transformers
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+ ---
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+ ## 🔧 How to Use
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+ tokenizer = AutoTokenizer.from_pretrained("letran1110/vit5_motor_extractor")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("letran1110/vit5_motor_extractor")
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+ text = "Động cơ 3 pha 5.5kW, 4 cực, điện áp 380V, vỏ nhôm, bảo vệ IP55"
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+ inputs = tokenizer(text, return_tensors="pt")
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+ outputs = model.generate(**inputs)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```
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+ ## Intended Use
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+ This model is designed to help extract structured information from motor specification descriptions (both Vietnamese and partial English), useful in:
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+ - Inventory parsing
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+ - Industrial cataloging
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+ - Smart search & indexing for motor components
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+ ## Out-of-Scope Use
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+ - Long-form document QA
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+ - General conversation
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+ - Image-based input (OCR must be done separately)
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+ ## 📚 Training
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+ Dataset: Custom dataset crawled and annotated from motor product pages
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+ Epochs: 10
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+ Batch Size: 16
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+ Max Length: 512
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+ Optimizer: AdamW
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+ ## 🧪 Evaluation
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+ Evaluation is manual by checking structured JSON outputs. Target fields include:
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+ - `motor_name`
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+ - `power`
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+ - `voltage`
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+ - `poles`
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+ - `protection`
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+ - `frame_size`
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+ - `shaft_diameter`
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+ - `material`
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+ ## 🤝 Citation
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+ If you use this model, please cite the repo:
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+ ```bibtex
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+ @misc{vit5motor2024,
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+ title={ViT5 Motor Extractor},
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+ author={letran1110},
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+ year={2024},
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+ howpublished={\url{https://huggingface.co/letran1110/vit5_motor_extractor}},
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+ }
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+ ```