Instructions to use SAVSNET/PetBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAVSNET/PetBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SAVSNET/PetBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SAVSNET/PetBERT") model = AutoModelForMaskedLM.from_pretrained("SAVSNET/PetBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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- **Model type:** Masked Language Model
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- **Language(s) (NLP):** English
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- **License:** openrail
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- **Finetuned from model [optional]:** [
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### Model Sources
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## Training Details
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### Training Data
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- **Model type:** Masked Language Model
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- **Language(s) (NLP):** English
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- **License:** openrail
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- **Finetuned from model [optional]:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
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### Model Sources
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```
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## Training Details
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### Training Data
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