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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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datasets: |
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- conll2003 |
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model-index: |
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- name: bert-finetuned-ner |
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results: [] |
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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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# bert-finetuned-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0814 |
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## Model description |
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bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). |
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Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. |
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If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available. |
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# How to Use |
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You can use this model with Transformers pipeline for NER. |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner") |
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model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "My name is Wolfgang and I live in Berlin" |
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ner_results = nlp(example) |
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print(ner_results) |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.0181 | 1.0 | 1756 | 0.1301 | |
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| 0.0166 | 2.0 | 3512 | 0.0762 | |
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| 0.0064 | 3.0 | 5268 | 0.0814 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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