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--- |
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license: mit |
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datasets: |
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- tner/ontonotes5 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: token-classification |
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--- |
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# NER detection powered by RoBERTa |
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Presented here is an english NER model, finetuned from [roberta-base](https://huggingface.co/FacebookAI/roberta-base). Weights available on [huggingface](https://huggingface.co/EdoardoLuciani/roberta-on-english-ner). Code available on [github](https://github.com/EdoardoLuciani/roberta-on-english-ner). |
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### Sample |
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The setup closely follows the [RobertaForTokenClassification](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaForTokenClassification) sample code: |
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```python |
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tokenizer = AutoTokenizer.from_pretrained("roberta-base") |
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model = RobertaForTokenClassification.from_pretrained("EdoardoLuciani/roberta-on-english-ner") |
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inputs = tokenizer( |
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"HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt" |
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) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_token_class_ids = logits.argmax(-1) |
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``` |
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More specific integration, along with pretty printing and output parsing, is available on [example.ipynb](example.ipynb). Here is an extract of the output: |
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``` |
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Barack Obama was born in Hawaii and served as the 44th President of the United States. |
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------------ ------ -- ----------------- |
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PERSON GPE ORDINAL GPE |
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Apple Inc. is headquartered in Cupertino, California, and was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne. |
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---------- --------- ---------- ---------- ------------- ------------ |
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ORG GPE GPE PERSON PERSON PERSON |
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On July 20, 1969, Neil Armstrong and Buzz Aldrin became the first humans to walk on the moon as part of the Apollo 11 mission. |
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------------- -------------- ----------- ----- --------- |
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DATE PERSON PERSON ORDINAL EVENT |
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``` |
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### Labels |
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The labels used follow the ones provided by [ontonotes5](https://paperswithcode.com/dataset/ontonotes-5-0) which are available [here](label.json). They are formatted in a part of the [TNER](https://github.com/asahi417/tner) project. They include: |
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``` |
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CARDINAL, DATE, PERSON, NORP, GPE, LAW, PERCENT, ORDINAL, MONEY, WORK_OF_ART, FAC, TIME, QUANTITY, PRODUCT, LANGUAGE, ORG, LOC, EVENT |
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``` |
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### Evaluation |
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Model has been evaluated both manually and with a portion of the ontonotes5 dataset never seen in training. Accuracy scores for the latter amount to 99.5%. |
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Full code used for the evaluation is available on [test.ipynb](test.ipynb) |
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### Dataset |
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Training data has been provided by the [ontonotes5](https://paperswithcode.com/dataset/ontonotes-5-0) dataset, specifically using the postprocessed dataset by [tner available on huggingface](https://huggingface.co/datasets/tner/ontonotes5) |
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Dataset has been further processed to split the labels between the model's tokens, assuring consistency in the model output. Full code for training and dataset processing is available on [train.ipynb](train.ipynb). |
