metadata

{}

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

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

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

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

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]

Speeds, Sizes, Times [optional]

[More Information Needed]

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)

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]