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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/captionbert-8192-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbstractPhil/captionbert-8192-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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" | |
| } |