biomedical-ner-all
An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
Params
66 M
Context
512
Downloads 30d
231 K
Likes
195
Download history
tracking started — chart appears after 7 days of snapshots (2 recorded)231 K downloads in the last 30 days
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.3 GB | 0.8 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- DistilBertForTokenClassification
- Parameters
- 66 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 30,522
- Licence
- apache-2.0
- First seen on the Hub
- 2022-06-19
- Training datasets
- undisclosed
- Added to our catalog
- 2026-09-19
Compare with any token-classification model