Instructions to use WesScivetti/GPT-BERT_Random_Seed2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/GPT-BERT_Random_Seed2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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library_name: transformers
pipeline_tag: fill-mask
tags:
- custom-code
- masked-language-modeling
---
# WesScivetti/GPT-BERT_Random_Seed2
Custom GPT-BERT checkpoint from the NPN filtered-corpus training experiments.
This repository includes the architecture code required by Transformers. Example:
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
repo_id = "WesScivetti/GPT-BERT_Random_Seed2"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()
```
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