Fill-Mask
Transformers
Safetensors
Urdu
avey-b
urdu
masked-language-modeling
encoder
custom_code
Eval Results (legacy)
Instructions to use SndOpenResearch/avey-b-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SndOpenResearch/avey-b-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SndOpenResearch/avey-b-ur", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("SndOpenResearch/avey-b-ur", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download MANIFEST.json from SndOpenResearch/avey-b-ur: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/SndOpenResearch/avey-b-ur/resolve/main/MANIFEST.json
- Command line
-
hf download hf://SndOpenResearch/avey-b-ur/MANIFEST.json
-
curl -L -o MANIFEST.json https://huggingface.co/SndOpenResearch/avey-b-ur/resolve/main/MANIFEST.json
1.35 kB
| { | |
| "generated_at": "2026-09-10T09:59:10.314711+00:00", | |
| "lang": "urd_Arab", | |
| "map_url": "https://data.hplt-project.org/three/sorted/urd_Arab.map", | |
| "bins_requested": [ | |
| 10, | |
| 9 | |
| ], | |
| "prob_threshold": 0.8, | |
| "min_chars": 20, | |
| "val_fraction": 0.005, | |
| "max_docs_per_shard": 50000, | |
| "max_chars_per_shard": 500000000, | |
| "max_shards": null, | |
| "stream_download": true, | |
| "shards": [ | |
| { | |
| "url": "https://data.hplt-project.org/three/sorted/urd_Arab/10_1.jsonl.zst", | |
| "bin": 10, | |
| "raw_docs": 2162, | |
| "dropped_low_prob": 0, | |
| "dropped_empty": 0, | |
| "dropped_exact_dup": 0, | |
| "pii_spans_redacted": 24, | |
| "kept_train": 2151, | |
| "kept_val": 11, | |
| "chars_kept": 41055559 | |
| }, | |
| { | |
| "url": "https://data.hplt-project.org/three/sorted/urd_Arab/9_1.jsonl.zst", | |
| "bin": 9, | |
| "raw_docs": 29290, | |
| "dropped_low_prob": 3, | |
| "dropped_empty": 0, | |
| "dropped_exact_dup": 1, | |
| "pii_spans_redacted": 729, | |
| "kept_train": 29146, | |
| "kept_val": 140, | |
| "chars_kept": 500109810 | |
| } | |
| ], | |
| "totals": { | |
| "raw_docs": 31452, | |
| "kept_train": 31297, | |
| "kept_val": 151, | |
| "dropped_low_prob": 3, | |
| "dropped_empty": 0, | |
| "dropped_exact_dup": 1, | |
| "pii_spans_redacted": 753, | |
| "chars_kept": 541165369 | |
| }, | |
| "consolidated_counts": { | |
| "train": 31297, | |
| "val": 151 | |
| } | |
| } |