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---
language:
- en
- multilingual
license: apache-2.0
library_name: transformers
tags:
- bert
- multilingual
- instruction-following
- text-generation
- nlp
pipeline_tag: text-generation
---
## Bert (Really just made it for fun)
## Model Description
Kirim1/Bert is a multilingual language model with comprehensive language understanding capabilities spanning over 100 languages. While primarily optimized for English, this model demonstrates strong performance across a diverse range of linguistic contexts and maintains robust instruction-following capabilities. For security purpose dont use this model
## Key Features
- **Multilingual Support**: Trained on data covering 100+ languages, enabling cross-lingual understanding and generation
- **Instruction Tuning**: Optimized for following complex instructions and performing task-oriented operations
- **English-First Design**: While multilingual, the model exhibits particular strength in English language tasks
- **Versatile Applications**: Suitable for text classification, question answering, summarization, translation, and general natural language understanding
## Intended Use
This model is designed for:
- Natural language understanding and generation tasks
- Multilingual text processing and analysis
- Instruction-following applications
- Cross-lingual information retrieval
- Text classification and sentiment analysis
- Question answering systems
## Training Data
The model was trained on a diverse multilingual corpus with emphasis on English language data, incorporating instruction-tuning datasets to enhance task-following capabilities.
## Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Kirim1/Bert")
model = AutoModel.from_pretrained("Kirim1/Bert")
# Example usage
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```
## Limitations
- While supporting 100+ languages, performance may vary across different language families
- Best results are achieved with English language inputs
- May require fine-tuning for domain-specific applications
- Performance on low-resource languages may be limited compared to high-resource languages
## Ethical Considerations
Users should be aware that language models can reflect biases present in training data. Care should be taken when deploying this model in production environments, particularly for sensitive applications or decision-making systems.
## License
This model is released under the Apache 2.0 license.
## Citation
If you use this model in your research, please cite:
```
@misc{kirim1bert,
author = {Kirim1},
title = {Bert: A Multilingual Instruction-Following Language Model},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/Kirim1/Bert}
}
``` |