Instructions to use WesScivetti/GPT-BERT_Random_Seed3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/GPT-BERT_Random_Seed3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/GPT-BERT_Random_Seed3", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("WesScivetti/GPT-BERT_Random_Seed3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| pipeline_tag: fill-mask | |
| tags: | |
| - custom-code | |
| - masked-language-modeling | |
| # WesScivetti/GPT-BERT_Random_Seed3 | |
| 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_Seed3" | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) | |
| model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True) | |
| model.eval() | |
| ``` | |