Feature Extraction
Transformers
TensorBoard
Safetensors
English
captionbert_v2
sentence-similarity
consensus-distillation
geometric-deep-learning
amoe
custom_code
Instructions to use AbstractPhil/captionbert-8192-v2-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/captionbert-8192-v2-b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbstractPhil/captionbert-8192-v2-b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2-b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 556 Bytes
fde50dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"architectures": [
"CaptionBertV2Model"
],
"model_type": "captionbert_v2",
"auto_map": {
"AutoConfig": "modeling_captionbert.CaptionBertV2Config",
"AutoModel": "modeling_captionbert.CaptionBertV2Model"
},
"vocab_size": 30522,
"hidden_size": 512,
"num_hidden_layers": 12,
"num_attention_heads": 8,
"intermediate_size": 2048,
"output_dim": 768,
"max_position_embeddings": 8192,
"hidden_dropout_prob": 0.1,
"pad_token_id": 0,
"pooling": "mean",
"torch_dtype": "float32",
"tokenizer_class": "BertTokenizerFast"
} |