Instructions to use nirantk/splade-v3-lexical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirantk/splade-v3-lexical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nirantk/splade-v3-lexical")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nirantk/splade-v3-lexical") model = AutoModelForMaskedLM.from_pretrained("nirantk/splade-v3-lexical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download data_args.json from nirantk/splade-v3-lexical: direct link, hf CLI and curl.
- Browser
- Download file 409 Bytes
-
https://huggingface.co/nirantk/splade-v3-lexical/resolve/refs%2Fpr%2F1/data_args.json
- Command line
-
hf download hf://nirantk/splade-v3-lexical@refs/pr/1/data_args.json
-
curl -L -o data_args.json https://huggingface.co/nirantk/splade-v3-lexical/resolve/refs%2Fpr%2F1/data_args.json
409 Bytes
| { | |
| "training_data_type": "pkl_dict", | |
| "training_data_path": "/gfs-ssd/project/neuralsearch/new_scores/ensemble_scores_5050.pkl.gz", | |
| "document_dir": "/nfs/data/neuralsearch/msmarco/documents/raw.tsv", | |
| "query_dir": "/nfs/data/neuralsearch/msmarco/training_queries/raw.tsv", | |
| "qrels_path": "/nfs/data/neuralsearch/msmarco/training_queries/qrels.json", | |
| "n_negatives": 8, | |
| "n_queries": -1 | |
| } |