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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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language: en |
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widget: |
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- text: "My name is Scott and I live in Columbus." |
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- text: "My name is Scott and I am calling from Buffalo, NY. I would like to file a complain with United Airlines." |
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- text: "Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne." |
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datasets: |
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- conll2003 |
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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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model-index: |
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- name: bert-large-uncased-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9504719600222099 |
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- name: Recall |
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type: recall |
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value: 0.9574896520863632 |
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- name: F1 |
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type: f1 |
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value: 0.9539679001337494 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9885618059637473 |
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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-large-uncased-finetuned-ner |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co./bert-large-uncased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0778 |
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- Precision: 0.9505 |
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- Recall: 0.9575 |
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- F1: 0.9540 |
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- Accuracy: 0.9886 |
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## Model description |
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More information needed |
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#### Limitations and bias |
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This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. |
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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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```python |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner") |
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model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "My name is Scott and I live in Ohio" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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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: 16 |
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- eval_batch_size: 64 |
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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: 10 |
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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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| 0.1997 | 1.0 | 878 | 0.0576 | 0.9316 | 0.9257 | 0.9286 | 0.9837 | |
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| 0.04 | 2.0 | 1756 | 0.0490 | 0.9400 | 0.9513 | 0.9456 | 0.9870 | |
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| 0.0199 | 3.0 | 2634 | 0.0557 | 0.9436 | 0.9540 | 0.9488 | 0.9879 | |
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| 0.0112 | 4.0 | 3512 | 0.0602 | 0.9443 | 0.9569 | 0.9506 | 0.9881 | |
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| 0.0068 | 5.0 | 4390 | 0.0631 | 0.9451 | 0.9589 | 0.9520 | 0.9882 | |
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| 0.0044 | 6.0 | 5268 | 0.0638 | 0.9510 | 0.9567 | 0.9538 | 0.9885 | |
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| 0.003 | 7.0 | 6146 | 0.0722 | 0.9495 | 0.9560 | 0.9527 | 0.9885 | |
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| 0.0016 | 8.0 | 7024 | 0.0762 | 0.9491 | 0.9595 | 0.9543 | 0.9887 | |
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| 0.0018 | 9.0 | 7902 | 0.0769 | 0.9496 | 0.9542 | 0.9519 | 0.9883 | |
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| 0.0009 | 10.0 | 8780 | 0.0778 | 0.9505 | 0.9575 | 0.9540 | 0.9886 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.8.1+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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