If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! distilbert-NER is the fine-tuned version of DistilBERT, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of Named Entity Recognition (NER). This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model…
Open-weight model · Token classification
biomedical-ner-all
by D 4 Data Community d4data/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.).
Parameters66M
Context512
Weights531.5 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads230.5k
Runs On
What it takes to serve biomedical-ner-all (66M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By D 4 Data Community, published under apache-2.0, revision 015a4050c9ac.
About the Model
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
- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
- Carbon emission: 0.0279399890043426 Kg
- Training time: 30.16527 minutes
- GPU used : 1 x GeForce RTX 3060 Laptop GPU
Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18
Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")
Author
Configuration
- Architecture
- DistilBertForTokenClassification
- Context length (tokens)
- 512
- Vocabulary size
- 30,522
- Stored precision
- float32
- Model type
- distilbert
Identity and Version
- Repository
- d4data/biomedical-ner-all
- Publisher
- D 4 Data Community
- Task
- Token classification
- Modality
- Text
- Library
- transformers
- Parameters
- 66M parameters
- Languages
- en
- Revision
- 015a4050c9ac99722e61c547aa9b4282bcbedc7f
- First published
- 2022-06-19
- Last updated
- 2023-07-02
Files and Weights
9 files, 532.4 MB in total. The weights are 2 files totalling 531.5 MB in bin, safetensors.
Weights2 files · 531.5 MB
Configuration2 files · 5.1 KB
Tokenizer3 files · 943.4 KB
Documentation1 file · 3.1 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 265.7 MB | d744b846a71c |
| pytorch_model.bin | Weights | 265.7 MB | b027673a3307 |
| config.json | Configuration | 5.0 KB | — |
| special_tokens_map.json | Configuration | 125 B | — |
| README.md | Documentation | 3.1 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 711.5 KB | — |
| tokenizer_config.json | Tokenizer | 373 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 531.5 MB
Download from D 4 Data Community
Released by D 4 Data Community through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 531.5 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About biomedical-ner-all
How much GPU memory does biomedical-ner-all need?
About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (66M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run biomedical-ner-all on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use biomedical-ner-all commercially?
Yes. biomedical-ner-all is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is biomedical-ner-all's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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