Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-eus-250steps-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-eus-250steps-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-eus-250steps-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-eus-250steps-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "intermediate_size": 2560, | |
| "max_position_embeddings": 512, | |
| "position_bucket_size": 32, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "vocab_size": 16384, | |
| "layer_norm_eps": 1e-05, | |
| "force_causal_mask": true, | |
| "classifier_dropout": 0.1, | |
| "classifier_layer_norm_eps": 1e-05, | |
| "num_labels": 2 | |
| } |