Zainab-waheed66/code-switching-codesaviours-si26-Zainab

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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Zainab Waheed
• Funded by [optional]: Zainab Waheed
• Shared by [optional]: Zainab Waheed
• Model type: Token Classification (Named Entity Recognition / Language Identification)
• Language(s) (NLP): English, Urdu (Code-switched)
• License: MIT
• Finetuned from model [optional]: xlm-roberta-base

Model Sources [optional]

• Repository: Hugging Face Repository
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be used directly for token classification tasks on code-switched text corpora, identifying token-level attributes or categories across mixed-language sentences.

Downstream Use [optional]

The model can be integrated into larger NLP pipelines focusing on multilingual text processing, sentiment analysis preparation, or linguistic analysis of code-switching phenomena

Out-of-Scope Use

The model is not optimized for standard monolingual long-form text generation or tasks outside token-level classification. Misuse includes deploying it for high-stakes automated decision-making without human oversight.

Bias, Risks, and Limitations

Code-switched data often exhibits domain-specific linguistic patterns, slang, and spelling variations. Consequently, the model's performance may degrade when applied to conversational or formal domains significantly different from its training distribution.

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.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Zainab-waheed66/code-switching-codesaviours-si26-Zainab")
model = AutoModelForTokenClassification.from_pretrained("Zainab-waheed66/code-switching-codesaviours-si26-Zainab")

nlp = pipeline("token-classification", model=model, tokenizer=tokenizer)
results = nlp("Your code-switched sentence here")
print(results)

## Training Details

### Training Data

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Trained on code-switched datasets annotated for token classification tasks.

### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** Training regime: fp16 / bf16 mixed precision <!--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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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics
Metrics: Precision, Recall, F1-Score for token classification.
#### Testing Data

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[More Information Needed]

#### 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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### 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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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