Sponsored by Vmake Labs Video Enhancer
Toolify Toolify logo
AI Models
NER-BERT-AI-Model-using-annotated-corpus-ner

AventIQ-AI / NER-BERT-AI-Model-using-annotated-corpus-ner

Run on huggingface.co
huggingface.co
Total runs: 7
24-hour runs: 0
7-day runs: -2
30-day runs: 1
Model's Last Updated: May 22 2025

Introduction of NER-BERT-AI-Model-using-annotated-corpus-ner

Model Details of NER-BERT-AI-Model-using-annotated-corpus-ner

🧠 NER-BERT-AI-Model-using-annotated-corpus-ner

A BERT-based Named Entity Recognition (NER) model fine-tuned on the Entity Annotated Corpus. It classifies tokens in text into predefined entity types such as Person (PER), Organization (ORG), and Location (LOC). This model is well-suited for information extraction, resume parsing, and chatbot applications.

✨ Model Highlights
  • 📌 Based on bert-base-cased (by Google)
  • 🔍 Fine-tuned on the Entity Annotated Corpus ( ner_dataset.csv )
  • ⚡ Supports prediction of 3 entity types: PER, ORG, LOC
  • 💾 Compatible with Hugging Face pipeline() for easy inference
🧠 Intended Uses
  • Resume and document parsing
  • Chatbots and virtual assistants
  • Named entity tagging in structured documents
  • Search and information retrieval systems
  • News or content analysis
🚫 Limitations
  • Trained only on English formal texts
  • May not generalize well to informal text or domain-specific jargon
  • Subword tokenization may split entities (e.g., "Cupertino" → "Cup", "##ert", "##ino")
  • Limited to the entities available in the original dataset (PER, ORG, LOC only)
🏋️‍♂️ Training Details
Field Value
Base Model bert-base-cased
Dataset Entity Annotated Corpus
Framework PyTorch with Transformers
Epochs 3
Batch Size 16
Max Length 128 tokens
Optimizer AdamW
Loss CrossEntropyLoss (token-level)
Device Trained on CUDA-enabled GPU
📊 Evaluation Metrics
Metric Score
Precision 83.15
Recall 83.85
F1-Score 83.50
🔎 Label Mapping
Label ID Entity Type
0 O
1 B-PER
2 I-PER
3 B-ORG
4 I-ORG
5 B-LOC
6 I-LOC
🚀 Usage 
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

model_name = "/AventIQ-AI/NER-BERT-AI-Model-using-annotated-corpus-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)
🧩 Quantization

Post-training quantization can be applied using PyTorch to reduce model size and improve inference performance, especially on edge devices.

🗂 Repository Structure 
.
├── model/               # Trained model files
├── tokenizer_config/    # Tokenizer and vocab files
├── model.safensors/     # Model in safetensors format
├── README.md            # Model card
🤝 Contributing

We welcome feedback, bug reports, and improvements! Feel free to open an issue or submit a pull request.

Runs of AventIQ-AI NER-BERT-AI-Model-using-annotated-corpus-ner on huggingface.co

7
Total runs
0
24-hour runs
-1
3-day runs
-2
7-day runs
1
30-day runs

More Information About NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co Model

NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co

NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co is an AI model on huggingface.co that provides NER-BERT-AI-Model-using-annotated-corpus-ner's model effect (), which can be used instantly with this AventIQ-AI NER-BERT-AI-Model-using-annotated-corpus-ner model. huggingface.co supports a free trial of the NER-BERT-AI-Model-using-annotated-corpus-ner model, and also provides paid use of the NER-BERT-AI-Model-using-annotated-corpus-ner. Support call NER-BERT-AI-Model-using-annotated-corpus-ner model through api, including Node.js, Python, http.

NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co Url

https://huggingface.co/AventIQ-AI/NER-BERT-AI-Model-using-annotated-corpus-ner

AventIQ-AI NER-BERT-AI-Model-using-annotated-corpus-ner online free

NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co is an online trial and call api platform, which integrates NER-BERT-AI-Model-using-annotated-corpus-ner's modeling effects, including api services, and provides a free online trial of NER-BERT-AI-Model-using-annotated-corpus-ner, you can try NER-BERT-AI-Model-using-annotated-corpus-ner online for free by clicking the link below.

AventIQ-AI NER-BERT-AI-Model-using-annotated-corpus-ner online free url in huggingface.co:

https://huggingface.co/AventIQ-AI/NER-BERT-AI-Model-using-annotated-corpus-ner

NER-BERT-AI-Model-using-annotated-corpus-ner install

NER-BERT-AI-Model-using-annotated-corpus-ner is an open source model from GitHub that offers a free installation service, and any user can find NER-BERT-AI-Model-using-annotated-corpus-ner on GitHub to install. At the same time, huggingface.co provides the effect of NER-BERT-AI-Model-using-annotated-corpus-ner install, users can directly use NER-BERT-AI-Model-using-annotated-corpus-ner installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

NER-BERT-AI-Model-using-annotated-corpus-ner install url in huggingface.co:

https://huggingface.co/AventIQ-AI/NER-BERT-AI-Model-using-annotated-corpus-ner

Url of NER-BERT-AI-Model-using-annotated-corpus-ner

NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co Url

Provider of NER-BERT-AI-Model-using-annotated-corpus-ner huggingface.co

AventIQ-AI
ORGANIZATIONS
huggingface.co

Other API from AventIQ-AI