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--- |
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license: apache-2.0 |
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base_model: distilroberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: distilroberta-ConLL2003 |
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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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# Model Description |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on [ConLL2003 dataset](https://huggingface.co/datasets/conll2003). |
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It achieves the following results on the evaluation set in Named Entity Recognition (NER)/Token Classification task: |
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- Loss: 0.0585 |
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- F1: 0.9536 |
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# Model Performance |
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- 1st Place: This fine-tuned model is topped on the best scores ( F1: 94.6%) from [Named Entity Recognition (NER) on CoNLL 2003 (English)]((https://paperswithcode.com/sota/named-entity-recognition-ner-on-conll-2003)). |
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- 6th Place: This fine-tuned model is ranked in the 6th place from the [Token Classification on conll2003 leaderboard](https://paperswithcode.com/sota/token-classification-on-conll2003) |
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## Model Usage |
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```python |
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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("jinhybr/distilroberta-ConLL2003") |
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model = AutoModelForTokenClassification.from_pretrained("jinhybr/distilroberta-ConLL2003") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) |
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example = "My name is Tao Jin and live in Canada" |
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ner_results = nlp(example) |
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print(ner_results) |
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[{'entity_group': 'PER', 'score': 0.99686015, 'word': ' Tao Jin', 'start': 11, 'end': 18}, {'entity_group': 'LOC', 'score': 0.9996836, 'word': ' Canada', 'start': 31, 'end': 37}] |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 24 |
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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: 6.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.1666 | 1.0 | 439 | 0.0621 | 0.9345 | |
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| 0.0499 | 2.0 | 878 | 0.0564 | 0.9391 | |
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| 0.0273 | 3.0 | 1317 | 0.0553 | 0.9469 | |
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| 0.0167 | 4.0 | 1756 | 0.0553 | 0.9492 | |
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| 0.0103 | 5.0 | 2195 | 0.0572 | 0.9516 | |
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| 0.0068 | 6.0 | 2634 | 0.0585 | 0.9536 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
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