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
File size: 891 Bytes
2ebd035 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"run_name": "captionbert-v2-sts-anchor",
"trunk_repo": "AbstractPhil/captionbert-8192-v2",
"trunk_ckpt": "checkpoints/best_model.pt",
"tokenizer": "google-bert/bert-base-uncased",
"d_model": 512,
"n_heads": 8,
"n_layers": 12,
"d_ff": 2048,
"max_len": 8192,
"output_dim": 768,
"pooling": "mean",
"sites": "all",
"n_slots": 16,
"K": 64,
"D": 4,
"tau": 0.1,
"hidden": 178,
"gate_init": -3.0,
"data_repo": "sentence-transformers/all-nli",
"data_config": "triplet",
"n_train": 200000,
"max_tokens": 64,
"batch_size": 256,
"temperature": 0.05,
"use_hard_negatives": true,
"steps": 4000,
"lr": 0.001,
"warmup": 200,
"grad_clip": 1.0,
"seed": 0,
"log_every": 100,
"eval_every": 500,
"out_dir": "/content/amoe_sts",
"hf_repo": "AbstractPhil/captionbert-8192-v2",
"hf_path": "amoe/sts",
"hf_private": false,
"hf_push": true
} |