Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
scibert
scientific-text
mirror
r-compatible
Instructions to use NetworkIsLife/SciBert_Cased_DAFS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NetworkIsLife/SciBert_Cased_DAFS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NetworkIsLife/SciBert_Cased_DAFS")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NetworkIsLife/SciBert_Cased_DAFS", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 385 Bytes
f2df82e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 31116
}
|