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--- |
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base_model: distilbert-base-cased |
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datasets: |
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- conll2003 |
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license: apache-2.0 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilbert-finetuned-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- type: precision |
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value: 1.0 |
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name: Precision |
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- type: recall |
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value: 1.0 |
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name: Recall |
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- type: f1 |
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value: 1.0 |
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name: F1 |
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- type: accuracy |
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value: 1.0 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-finetuned-ner |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0711 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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- Accuracy: 1.0 |
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## Model description |
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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. |
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## Intended Uses & Limitations |
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### Intended Uses |
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- Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text. |
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- Information Extraction: Automatically identifying and classifying key information in documents. |
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- Text Preprocessing: Enhancing text preprocessing for downstream tasks like sentiment analysis and text summarization. |
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### limitations |
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- 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. |
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- Language Limitation: This model is trained on English text. It may not perform well on text in other languages. |
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- Precision in Complex Sentences: While the model performs well on standard sentences, complex sentence structures or ambiguous contexts might pose challenges. |
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## Training and evaluation data |
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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). |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| |
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| 0.0908 | 1.0 | 1756 | 0.0887 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0467 | 2.0 | 3512 | 0.0713 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0276 | 3.0 | 5268 | 0.0711 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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