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- ---
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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 Needed]
 
 
 
 
 
 
 
 
 
 
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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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+ library_name: transformers
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+ license: apache-2.0
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+ language:
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+ - ja
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+ base_model:
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+ - FacebookAI/xlm-roberta-base
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Japanese Named Entity Recognition (NER)
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+ This model is built using XLM-RoBERTa for Japanese text to recognize named entities such as persons, organizations, locations, and other categories. The model is designed specifically for Japanese text and can be used for a variety of tasks that require entity extraction from Japanese documents or conversations.
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+ ## Table of Contents
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+ - [Overview](#overview)
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+ - [NER Tags](#ner-tags)
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+ - [Model Details](#model-details)
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+ - [Sample Input and Output](#sample-input-and-output)
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+ ## Overview
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+ Named Entity Recognition (NER) is a critical task in natural language processing (NLP) for identifying and classifying entities in text. This model recognizes named entities in Japanese, making it ideal for use in applications like document analysis, chatbots, or information retrieval in the Japanese language.
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+ ## NER Tags
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+ The model identifies the following tags:
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+ | Class ID | Tag | Description |
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+ |----------|-------|----------------------|
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+ | 0 | O | Outside any entity |
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+ | 1 | PER | Person names |
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+ | 2 | ORG | Organizations |
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+ | 3 | ORG-P | Political orgs |
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+ | 4 | ORG-O | Other orgs |
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+ | 5 | LOC | Locations |
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+ | 6 | INS | Institutions |
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+ | 7 | PRD | Products |
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+ | 8 | EVT | Events |
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+ ## Model Details
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+ - **Base Model**: `xlm-roberta-base`
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+ - **Task**: Token Classification (NER)
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+ - **Languages**: Japanese
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+ - **Input**: Japanese text
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+ - **Output**: Tokenized text with NER tags
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+ ## Sample Input and Output
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+ Here’s an example input sentence and the expected NER output.
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+ ### **Input**
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+ ```text
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+ 中国では、中国共産党による一党統治が続く。
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+ ```
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+ ### **Output**
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+ | Token | Predicted Tag |
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+ |---------|---------------|
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+ | 中国 | LOC |
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+ | では | O |
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+ | 、 | O |
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+ | 中国 | ORG-P |
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+ | 共産党 | ORG-P |
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+ | による | O |
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+ | 一党 | O |
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+ | 統治 | O |
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+ | が | O |
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+ | 続く | O |
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+ | 。 | O |
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+ ### Visualization with Gradio and spaCy
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+ The NER output is also visualized in color-coded format for ease of interpretation:
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+ **Entities Output:**
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+ - `LOC` (Location): China (中国)
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+ - `ORG-P` (Political Organization): Chinese Communist Party (中国共産党)
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+ Here’s the updated README section with the class names replacing the class IDs:
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+ ---
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+ ## Model Performance Metrics
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+ The following performance metrics were achieved by the model during evaluation:
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+ ### Overall Metrics:
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+ - **Total Accuracy**: 98.42%
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+ - **Total F1-score**: 99.33%
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+ ### Class-wise Metrics:
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+ | Class | Recall | Precision |
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+ |----------|-----------|-----------|
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+ | **O** | 99.94% | 99.00% |
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+ | **PER** | 97.53% | 98.80% |
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+ | **ORG** | 99.22% | 96.23% |
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+ | **ORG-P**| 95.30% | 99.71% |
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+ | **ORG-O**| 97.80% | 98.26% |
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+ | **LOC** | 99.03% | 96.71% |
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+ | **INS** | 98.88% | 99.07% |
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+ | **PRD** | 99.31% | 99.67% |
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+ | **EVT** | 98.96% | 98.31% |
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+ The model demonstrates strong overall performance, with particularly high F1-scores and balanced class-wise precision and recall values.
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