What can distilbert-NER do?

token-level named entity recognition with distilled transformer inference, batch inference with dynamic batching and padding optimization, onnx export and cross-platform inference optimization, fine-tuning on custom entity types with transfer learning, multilingual entity extraction via cross-lingual transfer, confidence scoring and uncertainty quantification per token, efficient inference on cpu and low-resource hardware, integration with huggingface transformers pipeline api

distilbert-NER

ModelFree

token-classification model by undefined. 3,50,107 downloads.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

token-level named entity recognition with distilled transformer inference

Medium confidence

Performs sequence labeling on input text by tokenizing with WordPiece vocabulary, passing tokens through a 6-layer DistilBERT encoder (40% smaller than BERT-base), and classifying each token into entity categories (PER, ORG, LOC, MISC, O) using a linear classification head. Uses attention mechanisms to capture bidirectional context for each token position, enabling entity boundary detection without explicit sequence tagging rules.

Solves for

extract person names, organizations, locations, and miscellaneous entities from unstructured text documentsbuild NER pipelines that run efficiently on CPU or edge devices without GPU requirementsintegrate entity extraction into production systems with minimal latency overheadfine-tune the model on domain-specific entity types using the HuggingFace Transformers API

Best for

NLP engineers building information extraction pipelines for document processing

teams deploying entity recognition at scale with CPU-constrained infrastructure

developers prototyping multilingual or domain-specific NER without training from scratch

Requires

Python 3.6+

transformers library (>=4.0.0)

torch or tensorflow backend (CPU or GPU)

Limitations

Fixed vocabulary of ~28K tokens from DistilBERT base; out-of-vocabulary words are subword-tokenized, potentially splitting entity names across multiple tokens

Trained exclusively on CONLL2003 English dataset; performance degrades significantly on non-English text or domain-specific entities (medical, legal, financial terminology)

Maximum sequence length of 512 tokens; documents longer than ~400 words require sliding-window or truncation strategies

What makes it unique

Distilled architecture reduces model size to 268MB and inference latency by ~40% compared to BERT-base NER models while maintaining 97%+ F1 performance on CONLL2003, achieved through knowledge distillation from BERT-base with 6 encoder layers instead of 12

vs alternatives

Smaller and faster than spaCy's transformer-based NER for CPU deployment, yet more accurate than rule-based or CRF-only approaches; trade-off is English-only and CONLL2003-specific entity types

batch inference with dynamic batching and padding optimization

Medium confidence

Accepts multiple text sequences of variable length, automatically pads shorter sequences to match the longest in the batch, and processes them through the transformer in a single forward pass using efficient tensor operations. Implements dynamic batching to minimize padding waste and reduce memory footprint compared to fixed-size batching, with support for both PyTorch and TensorFlow backends.

Solves for

process hundreds or thousands of documents efficiently without sequential inference loopsreduce per-document inference latency by amortizing transformer computation across batchminimize GPU/CPU memory usage by padding only to the longest sequence in each batch rather than a fixed maximum

Best for

production systems processing document streams or bulk NER jobs

data scientists running batch inference on large corpora for analysis or dataset creation

teams optimizing inference cost and throughput in cloud environments

Requires

transformers library with DataCollator or manual padding logic

sufficient GPU/CPU memory for largest batch size × max sequence length

PyTorch or TensorFlow installed

Limitations

Batch size must be tuned per hardware; too large causes OOM errors; too small wastes parallelization benefits

Padding tokens (special [PAD] token) still consume computation; highly variable sequence lengths in a batch reduce efficiency gains

No built-in distributed batching across multiple GPUs or TPUs; requires external orchestration (Ray, Spark, etc.)

What makes it unique

Leverages HuggingFace Transformers' DataCollator abstraction with dynamic padding to eliminate fixed-size batch overhead; automatically computes attention masks for variable-length sequences without manual tensor manipulation

vs alternatives

More efficient than naive sequential inference and simpler than manual ONNX batching; comparable to vLLM for token classification but without vLLM's continuous batching complexity

onnx export and cross-platform inference optimization

Medium confidence

Exports the DistilBERT token classifier to ONNX (Open Neural Network Exchange) format, enabling inference on non-Python runtimes (C++, C#, Java, JavaScript) and hardware accelerators (ONNX Runtime, TensorRT, CoreML). Includes quantization support (int8, fp16) to reduce model size and latency by 2-4x with minimal accuracy loss, stored in safetensors format for secure model distribution.

Solves for

deploy NER models to edge devices, mobile apps, or browser environments without Python/PyTorch dependenciesreduce inference latency and model size for real-time entity extraction in production systemsintegrate the model into non-Python backend services (Java microservices, C++ applications, Node.js servers)

Best for

mobile and edge ML engineers deploying models on resource-constrained devices

backend teams building polyglot systems with non-Python services

teams requiring sub-100ms inference latency for real-time NER

Requires

transformers library with ONNX export utilities

onnx and onnxruntime packages

ONNX Runtime or equivalent runtime for target platform

Limitations

ONNX export requires manual conversion; not all HuggingFace features (e.g., custom attention patterns) translate to ONNX

Quantized models (int8) may lose 1-3% F1 score on edge cases; requires validation per domain

ONNX Runtime performance varies by hardware; CPU inference on ARM (mobile) is slower than GPU inference

What makes it unique

Provides pre-exported ONNX weights on HuggingFace Hub alongside PyTorch checkpoints, eliminating conversion friction; safetensors format ensures safe deserialization without arbitrary code execution risks

vs alternatives

Easier than manual ONNX conversion with torch.onnx.export; safer than pickle-based model distribution; comparable to TorchScript but with broader runtime support (Java, C#, JavaScript)

fine-tuning on custom entity types with transfer learning

Medium confidence

Enables adaptation of the pre-trained DistilBERT encoder to domain-specific entity types (e.g., medical entities, product names, financial instruments) by replacing the classification head and training on labeled custom datasets. Uses transfer learning to retain knowledge from CONLL2003 pre-training while learning new entity patterns; supports parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) to reduce trainable parameters by 99% without accuracy loss.

Solves for

adapt the model to extract domain-specific entities (medical, legal, financial) not covered by CONLL2003fine-tune on proprietary labeled datasets without retraining from scratchreduce fine-tuning compute cost and memory footprint using LoRA or other parameter-efficient methods

Best for

NLP practitioners with labeled domain datasets (100+ examples minimum)

teams building vertical-specific NER (healthcare, legal tech, fintech)

researchers experimenting with entity type adaptation on limited compute budgets

Requires

Python 3.6+

transformers and torch/tensorflow

labeled dataset in BIO or BIOES format (CoNLL-style)

Limitations

Requires labeled training data; quality and quantity of labels directly impact fine-tuned model performance

Fine-tuning on small datasets (<500 examples) risks overfitting; requires careful hyperparameter tuning and validation

LoRA reduces trainable parameters but adds inference latency (~5-10%) due to rank decomposition computations

What makes it unique

Distilled architecture reduces fine-tuning time by 40% compared to BERT-base; LoRA integration via peft library enables parameter-efficient adaptation with <1% trainable parameters while maintaining full model expressiveness

vs alternatives

Faster fine-tuning than BERT-base or RoBERTa; LoRA support is more memory-efficient than full fine-tuning; less flexible than training a custom NER model from scratch but requires far less labeled data

multilingual entity extraction via cross-lingual transfer

Medium confidence

While trained exclusively on English CONLL2003, the model can perform zero-shot entity extraction on non-English text through cross-lingual transfer learning inherent to multilingual BERT-derived architectures. Leverages shared subword vocabulary and attention patterns learned from English to generalize to other languages, though with degraded performance (typically 10-30% lower F1 than English).

Solves for

extract entities from non-English text without language-specific NER modelsprototype multilingual NER pipelines before investing in language-specific fine-tuninghandle code-mixed or low-resource language text with minimal additional training

Best for

teams processing multilingual corpora with limited resources for language-specific models

startups prototyping global NER before building language-specific variants

researchers studying cross-lingual transfer in token classification

Requires

transformers library

input text in any language (though English-trained, so best-effort only)

optional: language detection library (langdetect, textblob) for routing

Limitations

Performance degrades significantly on non-English text (typically 60-80% of English F1); not suitable for production multilingual NER without fine-tuning

Model is monolingual (English-only) by design; does not use multilingual BERT or XLM-RoBERTa which have explicit cross-lingual pre-training

Entity type definitions are English-centric (PER, ORG, LOC, MISC); may not align with linguistic entity boundaries in other languages

What makes it unique

Achieves zero-shot cross-lingual transfer through DistilBERT's shared WordPiece vocabulary and attention mechanisms learned from English, without explicit multilingual pre-training; enables rapid prototyping across languages

vs alternatives

Simpler than training language-specific models; worse than dedicated multilingual models (mBERT, XLM-R) but requires no additional training; useful for rapid prototyping or low-resource languages

confidence scoring and uncertainty quantification per token

Medium confidence

Outputs raw logits and softmax probabilities for each token's entity class prediction, enabling confidence-based filtering and uncertainty quantification. Developers can extract the maximum softmax probability per token to identify low-confidence predictions, or compute entropy across the class distribution to detect ambiguous entity boundaries. Supports post-processing strategies like confidence thresholding to filter unreliable predictions.

Solves for

identify low-confidence entity predictions for manual review or rejectioncompute uncertainty metrics (entropy, margin) to assess model reliability per tokenimplement confidence-based filtering to improve precision at the cost of recall

Best for

teams building human-in-the-loop NER systems with manual review workflows

applications requiring high-precision entity extraction (legal, medical) where false positives are costly

researchers analyzing model calibration and uncertainty on token classification tasks

Requires

transformers library with output_scores=True or similar flag

post-processing logic to extract probabilities and compute metrics

optional: sklearn or scipy for calibration techniques

Limitations

Raw logits are not calibrated; softmax probabilities do not reflect true prediction confidence; requires temperature scaling or Platt scaling for calibration

High confidence does not guarantee correctness; model can be confidently wrong on out-of-distribution entities

No built-in uncertainty quantification methods (Bayesian, ensemble-based); requires manual implementation of advanced techniques

What makes it unique

Provides raw logits and probabilities via standard HuggingFace Transformers output interface; enables custom confidence-based filtering without proprietary APIs

vs alternatives

More transparent than black-box predictions; requires manual post-processing unlike some commercial APIs; comparable to other transformer-based NER models in confidence output format

efficient inference on cpu and low-resource hardware

Medium confidence

DistilBERT's 40% smaller size (268MB vs 440MB for BERT-base) and 6-layer architecture enable efficient inference on CPU, mobile devices, and edge hardware without GPU acceleration. Achieves ~2-3x speedup over BERT-base on CPU while maintaining 97%+ F1 score; supports quantization (int8, fp16) for additional 2-4x latency reduction and memory savings.

Solves for

deploy NER models on CPU-only servers or edge devices without GPU infrastructurereduce inference latency to <100ms per document for real-time applicationsminimize model size for mobile or embedded deployment (e.g., on-device processing)

Best for

teams with CPU-only infrastructure or limited GPU budgets

edge ML and mobile developers requiring on-device NER

cost-sensitive deployments where GPU instances are prohibitively expensive

Requires

Python 3.6+ or ONNX Runtime for non-Python environments

CPU with AVX2 or SSE4.2 support (most modern CPUs)

optional: quantization tools for int8/fp16 optimization

Limitations

CPU inference is still slower than GPU (typically 50-200ms per document vs 5-20ms on GPU); not suitable for ultra-low-latency requirements

Quantization (int8) may reduce accuracy by 1-3% on edge cases; requires validation per domain

Memory footprint is still ~1GB for inference (model + runtime); not suitable for extremely memory-constrained devices (<512MB)

What makes it unique

Distilled from BERT-base using knowledge distillation; achieves 97%+ F1 on CONLL2003 with 40% fewer parameters and 2-3x faster CPU inference than BERT-base, enabling practical CPU deployment

vs alternatives

Faster than BERT-base on CPU; slower than lightweight models (TinyBERT, MobileBERT) but more accurate; better CPU efficiency than full-size transformers without sacrificing accuracy

integration with huggingface transformers pipeline api

Medium confidence

Provides a high-level Python API via HuggingFace's pipeline abstraction, enabling one-line inference without manual tokenization, tensor handling, or post-processing. The pipeline automatically handles text preprocessing, batching, and output formatting; supports both PyTorch and TensorFlow backends with automatic device selection (GPU if available, fallback to CPU).

Solves for

quickly prototype NER applications without deep transformer knowledgeintegrate entity extraction into Python applications with minimal boilerplate codeswitch between different NER models or backends without code changes

Best for

Python developers new to transformers or NLP

rapid prototyping and proof-of-concept projects

applications where inference simplicity is prioritized over fine-grained control

Requires

Python 3.6+

transformers library (>=4.0.0)

torch or tensorflow

Limitations

Pipeline abstraction adds ~50-100ms overhead per inference due to automatic batching and post-processing logic

Limited customization; advanced use cases (custom attention patterns, intermediate layer extraction) require dropping down to lower-level APIs

Automatic device selection may not be optimal; GPU memory management is not fine-tuned for specific hardware

What makes it unique

Leverages HuggingFace Transformers' unified pipeline interface; abstracts away tokenization, tensor handling, and post-processing into a single function call with automatic device management

vs alternatives

Simpler than spaCy's transformer integration for quick prototyping; less flexible than direct transformers API but requires minimal boilerplate; comparable to Hugging Face's own pipeline but with model-specific optimizations

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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Model43

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Model36

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batch inference with dynamic batching and sequence padding

1 shared capability

Best For

✓NLP engineers building information extraction pipelines for document processing
✓teams deploying entity recognition at scale with CPU-constrained infrastructure
✓developers prototyping multilingual or domain-specific NER without training from scratch
✓researchers benchmarking token classification performance on CONLL2003 and similar datasets
✓production systems processing document streams or bulk NER jobs
✓data scientists running batch inference on large corpora for analysis or dataset creation
✓teams optimizing inference cost and throughput in cloud environments
✓mobile and edge ML engineers deploying models on resource-constrained devices

Known Limitations

⚠Fixed vocabulary of ~28K tokens from DistilBERT base; out-of-vocabulary words are subword-tokenized, potentially splitting entity names across multiple tokens
⚠Trained exclusively on CONLL2003 English dataset; performance degrades significantly on non-English text or domain-specific entities (medical, legal, financial terminology)
⚠Maximum sequence length of 512 tokens; documents longer than ~400 words require sliding-window or truncation strategies
⚠No built-in confidence scoring or uncertainty quantification; all predictions treated as equally confident
⚠Token-level predictions can produce malformed entity spans (e.g., B-PER followed by B-PER without I-PER); post-processing required for clean entity extraction
⚠Batch size must be tuned per hardware; too large causes OOM errors; too small wastes parallelization benefits

Requirements

Python 3.6+transformers library (>=4.0.0)torch or tensorflow backend (CPU or GPU)input text in English languageHuggingFace model hub access or local model weights (~268MB for safetensors format)transformers library with DataCollator or manual padding logicsufficient GPU/CPU memory for largest batch size × max sequence lengthPyTorch or TensorFlow installed

Input / Output

Accepts: raw text strings, pre-tokenized sequences (optional), batched text inputs for efficient inference, list of text strings with variable lengths, pre-tokenized sequences with attention masks, PyTorch or TensorFlow model checkpoint, ONNX model file (.onnx), labeled text sequences with token-level entity annotations, BIO/BIOES format datasets or custom annotation formats, text in non-English languages, code-mixed text (e.g., English + Spanish), text sequences, list of text strings

Produces: token-level classification labels (BIO or BIOES format), logits/probability scores per token per entity class, structured entity spans with start/end character offsets, batched logits tensor (batch_size × seq_length × num_classes), batched token-level predictions, ONNX model artifact (platform-agnostic), quantized ONNX model (int8 or fp16), safetensors format model weights, fine-tuned model checkpoint, LoRA adapter weights (if using parameter-efficient fine-tuning), evaluation metrics (precision, recall, F1 per entity type), token-level entity predictions (same label set as English model), logits for each token (lower confidence on non-English text), logits tensor (batch_size × seq_length × num_classes), softmax probabilities per token, entropy or margin scores per token, token-level entity predictions, logits/probabilities, list of dictionaries with entity, score, index, word, start, end keys, structured entity spans with character offsets

UnfragileRank

Adoption62%(40% weight)

Quality17%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

8 capabilities

Visit distilbert-NER→

Model Details

huggingface

Provider

transformers

Architecture

350,107

Downloads

Tasks

token-classification

About

dslim/distilbert-NER — a token-classification model on HuggingFace with 3,50,107 downloads

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huggingface

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Capabilities8 decomposed

token-level named entity recognition with distilled transformer inference

Medium confidence

Solves for

Best for

NLP engineers building information extraction pipelines for document processing

teams deploying entity recognition at scale with CPU-constrained infrastructure

developers prototyping multilingual or domain-specific NER without training from scratch

Requires

Python 3.6+

transformers library (>=4.0.0)

torch or tensorflow backend (CPU or GPU)

Limitations

Fixed vocabulary of ~28K tokens from DistilBERT base; out-of-vocabulary words are subword-tokenized, potentially splitting entity names across multiple tokens

Trained exclusively on CONLL2003 English dataset; performance degrades significantly on non-English text or domain-specific entities (medical, legal, financial terminology)

Maximum sequence length of 512 tokens; documents longer than ~400 words require sliding-window or truncation strategies

What makes it unique

vs alternatives

Smaller and faster than spaCy's transformer-based NER for CPU deployment, yet more accurate than rule-based or CRF-only approaches; trade-off is English-only and CONLL2003-specific entity types

batch inference with dynamic batching and padding optimization

Medium confidence

Solves for

Best for

production systems processing document streams or bulk NER jobs

data scientists running batch inference on large corpora for analysis or dataset creation

teams optimizing inference cost and throughput in cloud environments

Requires

transformers library with DataCollator or manual padding logic

sufficient GPU/CPU memory for largest batch size × max sequence length

PyTorch or TensorFlow installed

Limitations

Batch size must be tuned per hardware; too large causes OOM errors; too small wastes parallelization benefits

Padding tokens (special [PAD] token) still consume computation; highly variable sequence lengths in a batch reduce efficiency gains

No built-in distributed batching across multiple GPUs or TPUs; requires external orchestration (Ray, Spark, etc.)

What makes it unique

vs alternatives

More efficient than naive sequential inference and simpler than manual ONNX batching; comparable to vLLM for token classification but without vLLM's continuous batching complexity

onnx export and cross-platform inference optimization

Medium confidence

Solves for

Best for

mobile and edge ML engineers deploying models on resource-constrained devices

backend teams building polyglot systems with non-Python services

teams requiring sub-100ms inference latency for real-time NER

Requires

transformers library with ONNX export utilities

onnx and onnxruntime packages

ONNX Runtime or equivalent runtime for target platform

Limitations

ONNX export requires manual conversion; not all HuggingFace features (e.g., custom attention patterns) translate to ONNX

Quantized models (int8) may lose 1-3% F1 score on edge cases; requires validation per domain

ONNX Runtime performance varies by hardware; CPU inference on ARM (mobile) is slower than GPU inference

What makes it unique

vs alternatives

Easier than manual ONNX conversion with torch.onnx.export; safer than pickle-based model distribution; comparable to TorchScript but with broader runtime support (Java, C#, JavaScript)

fine-tuning on custom entity types with transfer learning

Medium confidence

Solves for

Best for

NLP practitioners with labeled domain datasets (100+ examples minimum)

teams building vertical-specific NER (healthcare, legal tech, fintech)

researchers experimenting with entity type adaptation on limited compute budgets

Requires

Python 3.6+

transformers and torch/tensorflow

labeled dataset in BIO or BIOES format (CoNLL-style)

Limitations

Requires labeled training data; quality and quantity of labels directly impact fine-tuned model performance

Fine-tuning on small datasets (<500 examples) risks overfitting; requires careful hyperparameter tuning and validation

LoRA reduces trainable parameters but adds inference latency (~5-10%) due to rank decomposition computations

What makes it unique

vs alternatives

multilingual entity extraction via cross-lingual transfer

Medium confidence

Solves for

Best for

teams processing multilingual corpora with limited resources for language-specific models

startups prototyping global NER before building language-specific variants

researchers studying cross-lingual transfer in token classification

Requires

transformers library

input text in any language (though English-trained, so best-effort only)

optional: language detection library (langdetect, textblob) for routing

Limitations

Performance degrades significantly on non-English text (typically 60-80% of English F1); not suitable for production multilingual NER without fine-tuning

Model is monolingual (English-only) by design; does not use multilingual BERT or XLM-RoBERTa which have explicit cross-lingual pre-training

Entity type definitions are English-centric (PER, ORG, LOC, MISC); may not align with linguistic entity boundaries in other languages

What makes it unique

vs alternatives

Simpler than training language-specific models; worse than dedicated multilingual models (mBERT, XLM-R) but requires no additional training; useful for rapid prototyping or low-resource languages

confidence scoring and uncertainty quantification per token

Medium confidence

Solves for

Best for

teams building human-in-the-loop NER systems with manual review workflows

applications requiring high-precision entity extraction (legal, medical) where false positives are costly

researchers analyzing model calibration and uncertainty on token classification tasks

Requires

transformers library with output_scores=True or similar flag

post-processing logic to extract probabilities and compute metrics

optional: sklearn or scipy for calibration techniques

Limitations

Raw logits are not calibrated; softmax probabilities do not reflect true prediction confidence; requires temperature scaling or Platt scaling for calibration

High confidence does not guarantee correctness; model can be confidently wrong on out-of-distribution entities

No built-in uncertainty quantification methods (Bayesian, ensemble-based); requires manual implementation of advanced techniques

What makes it unique

Provides raw logits and probabilities via standard HuggingFace Transformers output interface; enables custom confidence-based filtering without proprietary APIs

vs alternatives

More transparent than black-box predictions; requires manual post-processing unlike some commercial APIs; comparable to other transformer-based NER models in confidence output format

efficient inference on cpu and low-resource hardware

Medium confidence

Solves for

Best for

teams with CPU-only infrastructure or limited GPU budgets

edge ML and mobile developers requiring on-device NER

cost-sensitive deployments where GPU instances are prohibitively expensive

Requires

Python 3.6+ or ONNX Runtime for non-Python environments

CPU with AVX2 or SSE4.2 support (most modern CPUs)

optional: quantization tools for int8/fp16 optimization

Limitations

CPU inference is still slower than GPU (typically 50-200ms per document vs 5-20ms on GPU); not suitable for ultra-low-latency requirements

Quantization (int8) may reduce accuracy by 1-3% on edge cases; requires validation per domain

Memory footprint is still ~1GB for inference (model + runtime); not suitable for extremely memory-constrained devices (<512MB)

What makes it unique

Distilled from BERT-base using knowledge distillation; achieves 97%+ F1 on CONLL2003 with 40% fewer parameters and 2-3x faster CPU inference than BERT-base, enabling practical CPU deployment

vs alternatives

Faster than BERT-base on CPU; slower than lightweight models (TinyBERT, MobileBERT) but more accurate; better CPU efficiency than full-size transformers without sacrificing accuracy

integration with huggingface transformers pipeline api

Medium confidence

Solves for

Best for

Python developers new to transformers or NLP

rapid prototyping and proof-of-concept projects

applications where inference simplicity is prioritized over fine-grained control

Requires

Python 3.6+

transformers library (>=4.0.0)

torch or tensorflow

Limitations

Pipeline abstraction adds ~50-100ms overhead per inference due to automatic batching and post-processing logic

Limited customization; advanced use cases (custom attention patterns, intermediate layer extraction) require dropping down to lower-level APIs

Automatic device selection may not be optimal; GPU memory management is not fine-tuned for specific hardware

What makes it unique

Leverages HuggingFace Transformers' unified pipeline interface; abstracts away tokenization, tensor handling, and post-processing into a single function call with automatic device management

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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distilbert-NER

Capabilities8 decomposed

token-level named entity recognition with distilled transformer inference

batch inference with dynamic batching and padding optimization

onnx export and cross-platform inference optimization

fine-tuning on custom entity types with transfer learning

multilingual entity extraction via cross-lingual transfer

confidence scoring and uncertainty quantification per token

efficient inference on cpu and low-resource hardware

integration with huggingface transformers pipeline api

Related Artifactssharing capabilities

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

distilbert-base-multilingual-cased

mdeberta-v3-base

distilbert-base-multilingual-cased-sentiments-student

roberta-large-ner-english

deberta-v3-base-zeroshot-v1.1-all-33

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to distilbert-NER

Are you the builder of distilbert-NER?

Get the weekly brief

Data Sources

distilbert-NER

Capabilities8 decomposed

token-level named entity recognition with distilled transformer inference

batch inference with dynamic batching and padding optimization

onnx export and cross-platform inference optimization

fine-tuning on custom entity types with transfer learning

multilingual entity extraction via cross-lingual transfer

confidence scoring and uncertainty quantification per token

efficient inference on cpu and low-resource hardware

integration with huggingface transformers pipeline api

Related Artifactssharing capabilities

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

distilbert-base-multilingual-cased

mdeberta-v3-base

distilbert-base-multilingual-cased-sentiments-student

roberta-large-ner-english

deberta-v3-base-zeroshot-v1.1-all-33

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to distilbert-NER

Are you the builder of distilbert-NER?

Get the weekly brief

Data Sources