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--- |
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language: en |
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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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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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widget: |
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- text: My name is Scott and I live in Columbus. |
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- text: Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne. |
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base_model: albert-base-v2 |
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model-index: |
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- name: albert-base-v2-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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args: conll2003 |
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metrics: |
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- type: precision |
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value: 0.9252213840603477 |
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name: Precision |
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- type: recall |
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value: 0.9329732113328189 |
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name: Recall |
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- type: f1 |
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value: 0.9290811285541773 |
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name: F1 |
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- type: accuracy |
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value: 0.9848205157332728 |
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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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# albert-base-v2-finetuned-ner |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0626 |
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- Precision: 0.9252 |
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- Recall: 0.9330 |
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- F1: 0.9291 |
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- Accuracy: 0.9848 |
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## Model description |
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More information needed |
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## limitations |
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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/albert-base-v2-finetuned-ner") |
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model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/albert-base-v2-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 and evaluation data |
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More information needed |
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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: 64 |
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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: 5 |
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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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| No log | 1.0 | 220 | 0.0863 | 0.8827 | 0.8969 | 0.8898 | 0.9773 | |
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| No log | 2.0 | 440 | 0.0652 | 0.8951 | 0.9199 | 0.9073 | 0.9809 | |
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| 0.1243 | 3.0 | 660 | 0.0626 | 0.9191 | 0.9208 | 0.9200 | 0.9827 | |
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| 0.1243 | 4.0 | 880 | 0.0585 | 0.9227 | 0.9281 | 0.9254 | 0.9843 | |
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| 0.0299 | 5.0 | 1100 | 0.0626 | 0.9252 | 0.9330 | 0.9291 | 0.9848 | |
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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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