DistilBERT Base Cased for Named Entity Recognition
| Property | Value |
|---|---|
| Parameter Count | 65.2M |
| License | Apache 2.0 |
| F1 Score | 98.7% |
| Dataset | CoNLL-2003 |
| Tensor Type | F32 |
What is distilbert-base-cased-finetuned-conll03-english?
This model is a fine-tuned version of DistilBERT base cased specifically optimized for Named Entity Recognition (NER) tasks using the CoNLL-2003 English dataset. It maintains case sensitivity, meaning it treats "english" and "English" as distinct tokens, making it particularly suitable for tasks where capitalization carries semantic meaning.
Implementation Details
The model was trained using Transformers version 4.3.1 and Datasets version 1.3.0. It achieves impressive metrics on the validation set, including 98.34% accuracy, 98.58% precision, and 98.82% recall. The model utilizes the standard DistilBERT architecture while being optimized for token classification tasks.
- Token-level classification optimized for NER tasks
- Case-sensitive implementation for improved accuracy
- Trained with label_all_tokens parameter enabled
- Includes entity-level metrics evaluation
Core Capabilities
- Named Entity Recognition with state-of-the-art performance
- Efficient processing with 65.2M parameters
- High accuracy (98.34%) and F1 score (98.7%)
- Optimal for production environments with F32 tensor support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its case-sensitive approach to NER tasks and its exceptional performance metrics, making it particularly suitable for applications where precise entity recognition is crucial. Its relatively small parameter count (65.2M) makes it more efficient than larger models while maintaining high accuracy.
Q: What are the recommended use cases?
The model is ideal for applications requiring Named Entity Recognition in English text where case sensitivity is important, such as proper noun detection, organization name extraction, and location identification. It's particularly suited for production environments where both accuracy and processing efficiency are priorities.
