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
| { | |
| "profile": "_applied", | |
| "run_name": "captionbert-8192-b", | |
| "hf_repo": "AbstractPhil/captionbert-8192-v2-B", | |
| "sources": [ | |
| { | |
| "repo": "AbstractPhil/conceptual-captions-12m-webdataset-berts", | |
| "n_chunks": 66, | |
| "chunk_rows": 500000, | |
| "missing": {} | |
| } | |
| ], | |
| "experts": [ | |
| "bert", | |
| "modern", | |
| "roberta", | |
| "albert", | |
| "distil" | |
| ], | |
| "ref_expert": "bert", | |
| "ref_hf_name": "google-bert/bert-base-uncased", | |
| "require_all_experts": true, | |
| "caption_field": "caption_llava", | |
| "caption_field_candidates": [ | |
| "caption_llava", | |
| "caption", | |
| "caption_llava_short" | |
| ], | |
| "work_dir": "/content/cbv2", | |
| "keep_expert_shards": false, | |
| "disk_floor_gb": 25.0, | |
| "ram_resident": true, | |
| "preflight": true, | |
| "artifact_repo": "AbstractPhil/captionbert-8192-v2", | |
| "targets_repo": "AbstractPhil/captionbert-8192-v2-consensus", | |
| "push_targets": true, | |
| "hf_push": true, | |
| "push_every_min": 30.0, | |
| "keep_local_ckpts": 3, | |
| "parity_chunk": 0, | |
| "parity_n": 64, | |
| "parity_min_cos": 0.999, | |
| "fit_chunks": [ | |
| 0, | |
| 11, | |
| 22, | |
| 33, | |
| 44, | |
| 55 | |
| ], | |
| "fit_rows_per_chunk": 4000, | |
| "holdout_chunks": [ | |
| 60, | |
| 61 | |
| ], | |
| "fit_seed": 0, | |
| "d_model": 512, | |
| "n_heads": 8, | |
| "n_layers": 12, | |
| "d_ff": 2048, | |
| "max_len": 8192, | |
| "output_dim": 768, | |
| "dropout": 0.1, | |
| "pooling": "mean", | |
| "max_tokens": 256, | |
| "length_bucketing": true, | |
| "bucket_window": 64, | |
| "grad_checkpointing": true, | |
| "vram_probe": true, | |
| "epochs": 4, | |
| "batch_size": 2048, | |
| "lr": 0.0006, | |
| "min_lr": 1e-06, | |
| "warmup_steps": 2000, | |
| "grad_clip": 1.0, | |
| "seed": 42, | |
| "amp": true, | |
| "num_workers": 0, | |
| "log_every": 50, | |
| "eval_every": 1000, | |
| "ckpt_every_min": 20.0, | |
| "fresh_start": true, | |
| "allow_mismatched_resume": false, | |
| "nce_weight": 1.0, | |
| "mse_weight": 1.0, | |
| "nce_temperature": 0.07, | |
| "cv_weight": 0.0, | |
| "cv_target": 0.084, | |
| "run_stage0": false, | |
| "run_stage1": false, | |
| "run_stage2": false, | |
| "run_stage3": true, | |
| "resume": true | |
| } |