Instructions to use SRDdev/HingMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/HingMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/HingMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM") model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM") - Notebooks
- Google Colab
- Kaggle
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README.md
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### HingMaskedLM
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This model trained for Masked Language Modeling for Hinglish Data.
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### Dataset
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Hinglish-Top [Dataset](https://huggingface.co/datasets/WillHeld/hinglish_top) columns
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### Citation
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Author: @[SRDdev](https://huggingface.co/SRDdev)
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Name
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framework
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Year: Jan 2023
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Pipeline
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Github
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LinkedIn
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```
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### HingMaskedLM
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MaskedLM is a pre-training technique used in Natural Language Processing (NLP) for deep-learning models like Transformers. It is a variant of language modeling where a portion of the input text is masked, and the model is trained to predict the masked tokens based on the context provided by the unmasked tokens. This model is trained for Masked Language Modeling for `Hinglish Data`.
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### Dataset
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Hinglish-Top [Dataset](https://huggingface.co/datasets/WillHeld/hinglish_top) columns
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### Citation
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Author: @[SRDdev](https://huggingface.co/SRDdev)
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Name: Shreyas Dixit
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framework: Pytorch
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Year: Jan 2023
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Pipeline: fill-mask
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Github: https://github.com/SRDdev
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LinkedIn: https://www.linkedin.com/in/srddev/
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```
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