# bert-large-NER by D: Open-Weight Model Source: https://savrn.com/models/bert-large-ner Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bert-large-NER (334M 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 | 0.7 GB | 0.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.3 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [bert-large-NER on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bert-large-ner/gpus) ## SAVRN's Notes on bert-large-NER This is the larger sibling of the most downloaded English NER model in our catalog. Same author, same CoNLL-2003 training data, same four entity types, but built on BERT large with 334M parameters. The extra capacity buys some accuracy on ambiguous names at roughly three times the compute of the base model. Choose it when misses cost real money, for example in compliance or due-diligence extraction, and the base model's errors show up in review. For bulk tagging where speed matters more, the base version is the sensible default. Both carry the MIT license, so commercial use is allowed with the notice kept. SAVRN Research, 2026-09-18 ## Model Card By D, published under mit, revision 6fe43d9ec0bb. 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! ### Model description bert-large-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-large-cased model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. If you'd like to use a smaller BERT model fine-tuned on the same dataset, a [bert-base-NER](https://savrn.com/models/bert-base-ner) version is also available. ### Intended uses & limitations #### How to use You can use this model with Transformers pipeline for NER. [Read the full model card (621 words)](https://savrn.com/models/bert-large-ner/card) ## Configuration Architecture BertForTokenClassification Context length (tokens) 512 Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 28,996 Model type bert ## Identity and Version Repository dslim/bert-large-NER Publisher D Task Token classification Modality Text Library transformers Parameters 334M parameters Languages en Revision 6fe43d9ec0bba0f67e367ecd74399216fc409c7f First published 2022-03-02 Last updated 2024-10-08 ## Files and Weights 17 files, 6.7 GB in total. The weights are 5 files totalling 6.7 GB in bin, h5, msgpack, onnx, safetensors. Weights5 files · 6.7 GB Configuration5 files · 3.0 KB Tokenizer5 files · 1.1 MB Documentation1 file · 5.9 KB Repository1 file · 445 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 1.3 GB | d63d39520929 | | model.safetensors | Weights | 1.3 GB | 81d6a7a3967f | | onnx/model.onnx | Weights | 1.3 GB | 46ba1e057362 | | pytorch_model.bin | Weights | 1.3 GB | db4d615a10a1 | | tf_model.h5 | Weights | 1.3 GB | febce5a7915f | | config.json | Configuration | 1.4 KB | — | | onnx/added_tokens.json | Configuration | 82 B | — | | onnx/config.json | Configuration | 1.2 KB | — | | onnx/special_tokens_map.json | Configuration | 125 B | — | | special_tokens_map.json | Configuration | 112 B | — | | README.md | Documentation | 5.9 KB | — | | .gitattributes | Repository | 445 B | — | | onnx/tokenizer.json | Tokenizer | 668.9 KB | — | | onnx/tokenizer_config.json | Tokenizer | 1.3 KB | — | | onnx/vocab.txt | Tokenizer | 213.4 KB | — | | tokenizer_config.json | Tokenizer | 40 B | — | | vocab.txt | Tokenizer | 213.4 KB | — | ## License and Download License mit Access Open weights, no gate Download size 6.7 GB [Download from D](https://huggingface.co/dslim/bert-large-NER) Released by D through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:1810.04805](https://savrn.com/papers/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding) - Trained on (disclosed) conll2003 ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | conll2003 | Configuration conll2003Task Token ClassificationMetric AccuracyComparison conditions not established | 0.903169 | [dslim](https://huggingface.co/dslim/bert-large-NER) Publisher reported | Evaluated revision not stated | — | | conll2003 | Configuration conll2003Task Token ClassificationMetric F1Comparison conditions not established | 0.919697 | [dslim](https://huggingface.co/dslim/bert-large-NER) Publisher reported | Evaluated revision not stated | — | | conll2003 | Configuration conll2003Task Token ClassificationMetric PrecisionComparison conditions not established | 0.920025 | [dslim](https://huggingface.co/dslim/bert-large-NER) Publisher reported | Evaluated revision not stated | — | | conll2003 | Configuration conll2003Task Token ClassificationMetric RecallComparison conditions not established | 0.919369 | [dslim](https://huggingface.co/dslim/bert-large-NER) Publisher reported | Evaluated revision not stated | — | | conll2003 | Configuration conll2003Task Token ClassificationMetric lossComparison conditions not established | 0.508505 | [dslim](https://huggingface.co/dslim/bert-large-NER) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 6.7 GB | | 16-bit | 0.7 GB | | 8-bit | 0.3 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About bert-large-NER ### How much GPU memory does bert-large-NER need? About 0.8 GB at 16-bit and 0.2 GB at 4-bit: the weights (334M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bert-large-NER 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 bert-large-NER commercially? Yes. bert-large-NER is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is bert-large-NER's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [bert-large-cased-finetuned-conll03-english](https://savrn.com/models/bert-large-cased-finetuned-conll03-english) [Bayerische Staatsbibliothek](https://savrn.com/model-publishers/dbmdz) Open weights 334M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-large-cased-finetuned-conll03-english) Model · Token classification ### [OpenMed-NER-DiseaseDetect-BioMed-335M](https://savrn.com/models/openmed-ner-diseasedetect-biomed-335m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.… Open weights apache-2.0 334M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-diseasedetect-biomed-335m) Model · Token classification ### [OpenMed-NER-GenomicDetect-PubMed-335M](https://savrn.com/models/openmed-ner-genomicdetect-pubmed-335m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Gene Entity Recognition - Gene-related entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the… Open weights apache-2.0 334M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-genomicdetect-pubmed-335m) Model · Token classification ### [deid_roberta_i2b2](https://savrn.com/models/deid-roberta-i2b2) [One Brave Idea](https://savrn.com/model-publishers/obi) A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. A token can either be classified as non-PHI or as one of the 11 PHI types. Token predictions are aggregated to spans by making use of BILOU tagging. The PHI labels that were used for training and other details can be found here: Annotation Guidelines More details on how to use this model, the format of data and other useful information is present in the GitHub repo: Robust DeID. A demo on how the model works (using model predictions to de-identify a medical note) is on this space: Medical-Note-Deidentification. Steps on how this model can be used to run a forward… Open weights mit 354M parameters 514 tokens transformers [View model](https://savrn.com/models/deid-roberta-i2b2) Model · Token classification ### [gliner2.5-multi-v1](https://savrn.com/models/gliner2-5-multi-v1) [Fastino](https://savrn.com/model-publishers/fastino) GLiNER2.5 Multi is the multilingual boundary checkpoint. It is built on mDeBERTa-v3-base and is the default choice when you need entities, classification, records, and relations in one model across languages. Load it with AutoExtractor: the checkpoint's architecture field selects BoundaryExtractor automatically. Fine-tune via Fastino. Join discussions on Reddit. This card is for fastino/gliner2.5-multi-v1. All three checkpoints share the same public API. Python 3.10 or newer is required. The [local] extra pulls in PyTorch so you can load Hub checkpoints. Always use AutoExtractor for GLiNER2.5. GLiNER2.frompretrained(...) is the legacy span loader and will not dispatch this checkpoint.… Open weights apache-2.0 287M parameters gliner2 [View model](https://savrn.com/models/gliner2-5-multi-v1) Model · Token classification ### [intenter](https://savrn.com/models/intenter) [Elda](https://savrn.com/model-publishers/elda-ai) Korean-first, multilingual (ko · ja · en). 281M parameters · p50 27.8 ms. Elda-Intenter reads an utterance and returns, in one forward pass, four aligned channels: what the speaker is doing (speech act), what they are talking about (entities), and the attribute/predicate structure that binds them. It is not a general-purpose NER model. It is the perception layer of a live conversational system, built for the constraint that shapes everything else in that setting: latency budgets measured in tens of milliseconds. All figures below are on sets excluded from training by construction. KLUE-NER (dev, Korean · 600 sentences / 1,687 gold entities), entity-level F1: Korean particles attach to the… Access requested at publisher other 281M parameters transformers [View model](https://savrn.com/models/intenter) ## D [All models and datasets](https://savrn.com/model-publishers/dslim) ## Versions - [6fe43d9ec0bb](https://savrn.com/models/bert-large-ner/versions/6fe43d9ec0bb) · current 2026-09-18 ## Explore More - [All token classification models](https://savrn.com/models/tasks/token-classification) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Hugging Face record](https://huggingface.co/dslim/bert-large-NER) - [How the hub is built](https://savrn.com/model-hub/methodology)