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---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: base-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8845085098992705
- name: Recall
type: recall
value: 0.9017351274787535
- name: F1
type: f1
value: 0.8930387515342801
- name: Accuracy
type: accuracy
value: 0.9782491655001615
---
# base-NER: A Named Entity Recognition (NER) Model
`base-NER` is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the CoNLL2003 dataset, designed for the task of **Named Entity Recognition (NER)**. This model can identify entities like people, organizations, locations, and more from text.
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
model = AutoModelForTokenClassification.from_pretrained("eddiegulay/base-NER")
tokenizer = AutoTokenizer.from_pretrained("eddiegulay/base-NER")
classifier = pipeline("ner", model=model, tokenizer=tokenizer)
result = classifier("My name is Edgar and I stay in Dar es Salaam")
print(result)
```
## Model Performance
The model achieved the following results on the CoNLL2003 test set:
- **Precision**: 0.8845
- **Recall**: 0.9017
- **F1-Score**: 0.8930
- **Accuracy**: 0.9782
The loss during training was 0.1129 on the validation set.
## Model Description
This model leverages the DistilBERT architecture, which is a smaller and faster version of BERT, designed for efficiency while maintaining strong performance. The model is specifically fine-tuned for NER tasks, making it ideal for entity extraction in various domains like finance, healthcare, or general text analytics.
## Intended Uses & Limitations
**Intended Uses**:
- Text extraction tasks for recognizing names of people, organizations, locations, dates, and other named entities in a sentence.
- Suitable for use in production applications where lightweight models are preferred due to memory or speed constraints.
**Limitations**:
- The model is limited to English texts, as it was trained on the CoNLL2003 dataset.
- Performance may degrade when used on domain-specific entities not present in the CoNLL2003 dataset (e.g., technical or biomedical domains).
- May struggle with ambiguous or context-dependent entity classifications.
## Training and Evaluation Data
The model was trained on the **CoNLL2003** dataset, which contains annotations for named entities in English text. It is a widely-used dataset for NER tasks, consisting of four entity types: **person**, **organization**, **location**, and **miscellaneous**.
### Dataset Configuration
- **Dataset**: CoNLL2003
- **Split**: Test set used for evaluation
- **Entity Types**: Person, Organization, Location, Miscellaneous
## Training Procedure
The model was fine-tuned for 2 epochs using a linear learning rate scheduler and an Adam optimizer.
### Training Hyperparameters
The following hyperparameters were used during training:
- **Learning Rate**: 2e-5
- **Batch Size**: 16 (train and eval)
- **Seed**: 42
- **Optimizer**: Adam (betas=(0.9,0.999), epsilon=1e-8)
- **Scheduler**: Linear
- **Epochs**: 2
### Training Results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0595 | 1.0 | 878 | 0.1046 | 0.8676 | 0.8909 | 0.8791 | 0.9762 |
| 0.0319 | 2.0 | 1756 | 0.1129 | 0.8845 | 0.9017 | 0.8930 | 0.9782 |
## Usage Example
You can use this model with Hugging Face's `transformers` library for token classification tasks:
## Framework Versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
## Future Improvements
- Fine-tuning the model on more domain-specific datasets for improved generalization.
- Implementing entity recognition for additional entity types, including products, dates, and technical terms.
Feel free to modify or add more details, especially for sections like model description, intended uses, and limitations.
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