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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- text-classification |
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- bert |
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- english |
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model-index: |
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- name: BERT Classification |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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metrics: |
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- accuracy |
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--- |
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# BERT Classification |
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## Model Overview |
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- **Model Name**: BERT Classification |
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- **Model Type**: Text Classification |
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- **Developer**: Mansoor Hamidzadeh |
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- **Framework**: Transformers |
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- **Language**: English |
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- **License**: Apache-2.0 |
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## Model Description |
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This model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) designed for text classification tasks. It categorizes text into four labels: |
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- **Label 1**: Household |
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- **Label 2**: Books |
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- **Label 3**: Clothing & Accessories |
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- **Label 4**: Electronics |
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## Technical Details |
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- **Model Size**: 109M parameters |
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- **Tensor Type**: F32 |
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- **File Format**: Safetensors |
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## How To Use |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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text='' |
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pipe = pipeline("text-classification", model="mansoorhamidzadeh/bert_classification") |
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pipe(text) |
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``` |
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## Usage |
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The model is useful for categorizing product descriptions or similar text data into predefined labels. |
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## Citation |
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If you use this model in your research or applications, please cite it as follows: |
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```bibtex |
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@misc{mansoorhamidzadeh/bert_classification, |
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author = {mansoorhamidzadeh}, |
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title = {English to Persian Translation using MT5-Small}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/mansoorhamidzadeh/bert_classification}}, |
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} |
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