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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- token-classification |
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- ner |
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- nlp |
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datasets: |
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- conll2003 |
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language: |
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- en |
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pipeline_tag: token-classification |
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library_name: transformers |
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base_model: bert-base-uncased |
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model-index: |
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- name: harpertokenNER |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition (NER) |
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dataset: |
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name: CoNLL-2003 |
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type: conll2003 |
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metrics: |
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- name: Validation Loss |
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type: loss |
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value: 0.0474 |
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widget: |
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- text: "Apple is buying a U.K. startup for $1 billion" |
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--- |
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# harpertokenNER |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [CoNLL-2003](https://huggingface.co/datasets/eriktks/conll2003) dataset. It achieves a validation loss of **0.0474** on the evaluation set. |
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## Model Description |
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This is a token classification model fine-tuned for **Named Entity Recognition (NER)** on the CoNLL-2003 dataset, built on the `bert-base-uncased` architecture. It identifies entities like people, organizations, and locations in text. Optimized for CPU use. Uploaded by [harpertoken](https://huggingface.co/harpertoken). |
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## Intended Uses & Limitations |
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### Intended Uses |
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- Extracting named entities from unstructured text (e.g., news articles, reports) |
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- Powering NLP pipelines on CPU-based systems |
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- Research or lightweight production use |
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### Limitations |
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- Trained on English text from CoNLL-2003, so it may not generalize well to other languages or domains |
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- Uses `bert-base-uncased` tokenization (lowercase-only), potentially missing case-sensitive nuances |
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- Optimized for NER; additional tuning needed for other token-classification tasks |
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## Training and Evaluation Data |
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The model was trained and evaluated on the *CoNLL-2003 dataset*, a standard NER benchmark. It features annotated English news articles with entities like persons, organizations, and locations, split into training, validation, and test sets. Metrics here reflect the evaluation subset. |
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## Training Procedure |
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### Training Hyperparameters |
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The following hyperparameters were used during training: |
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- **learning_rate**: 2e-05 |
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- **train_batch_size**: 8 |
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- **eval_batch_size**: 8 |
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- **seed**: 42 |
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- **optimizer**: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- **lr_scheduler_type**: linear |
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- **lr_scheduler_warmup_steps**: 500 |
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- **num_epochs**: 3 |
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### Training Results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.048 | 1.0 | 1756 | 0.0531 | |
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| 0.0251 | 2.0 | 3512 | 0.0473 | |
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| 0.016 | 3.0 | 5268 | 0.0474 | |
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### Framework Versions |
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- **Transformers**: 4.28.1 |
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- **PyTorch**: 2.0.1 |
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- **Datasets**: 1.18.3 |
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- **Tokenizers**: 0.13.3 |
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