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---
base_model: distilbert-base-cased
datasets:
- conll2003
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- type: precision
value: 1.0
name: Precision
- type: recall
value: 1.0
name: Recall
- type: f1
value: 1.0
name: F1
- type: accuracy
value: 1.0
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-finetuned-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0711
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## Model description
The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER) tasks. It is based on the DistilBERT architecture, which is a smaller, faster, and lighter version of BERT. DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it efficient for deployment in production systems.
## Intended Uses & Limitations
### Intended Uses
- Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text.
- Information Extraction: Automatically identifying and classifying key information in documents.
- Text Preprocessing: Enhancing text preprocessing for downstream tasks like sentiment analysis and text summarization.
### limitations
- Domain Specificity: The model is trained on the CoNLL-2003 dataset, which primarily consists of newswire data. Performance may degrade on text from different domains.
- Language Limitation: This model is trained on English text. It may not perform well on text in other languages.
- Precision in Complex Sentences: While the model performs well on standard sentences, complex sentence structures or ambiguous contexts might pose challenges.
## Training and evaluation data
The model is fine-tuned on the CoNLL-2003 dataset, a widely-used dataset for training and evaluating NER systems. The dataset includes four types of named entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.0908 | 1.0 | 1756 | 0.0887 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0467 | 2.0 | 3512 | 0.0713 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0276 | 3.0 | 5268 | 0.0711 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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