README.md

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# XLM-RoBERTa Token Classification for Named Entity Recognition (NER)
This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named Entity Recognition (NER) tasks. It has been trained on the PAN-X subset of the XTREME dataset for  German Language . The model identifies the following entity types:

PER: Person names
ORG: Organization names
LOC: Location names


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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Finetuned from model [optional]:** [More Information Needed]

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## Uses
This model is suitable for multilingual NER tasks, especially in scenarios where extracting and classifying person, organization, and location names in text across different languages is required.

Applications:
Information extraction
Multilingual NER tasks
Automated text analysis for businesses
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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details
Base Model: xlm-roberta-base
Training Dataset: The model is trained on the PAN-X subset of the XTREME dataset, which includes labeled NER data for multiple languages.
Training Framework: Hugging Face transformers library with PyTorch backend.
Data Preprocessing: Tokenization was performed using XLM-RoBERTa tokenizer, with attention paid to aligning token labels to subword tokens.

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters
The model's performance is evaluated using the F1 score for NER. The predictions are aligned with gold-standard labels, ignoring sub-token predictions where appropriate.

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation
  #'''python
    import torch
    
    from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
    
    import pandas as pd
    
    
    model_checkpoint = "MassMin/xlm-roberta-base-finetuned-panx-de"  # Replace with your Hugging Face model name
    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    
    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
    
    model = AutoModelForTokenClassification.from_pretrained(model_checkpoint).to(device)
    
    
    ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, framework="pt", device=0 if torch.cuda.is_available() else -1)
    
    
    def tag_text_with_pipeline(text, ner_pipeline):
    
        # Use the NER pipeline to get predictions
        results = ner_pipeline(text)
        
        # Convert results to a DataFrame for easy viewing
        df = pd.DataFrame(results)
        df = df[['word', 'entity', 'score']]
        df.columns = ['Tokens', 'Tags', 'Score']  # Rename columns for clarity
        return df
    
    
    text = "Jeff Dean works at Google in California."
    
    result = tag_text_with_pipeline(text, ner_pipeline)
    
    print(result)


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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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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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