metadata
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilroberta-ConLL2003
results: []
distilroberta-ConLL2003
This model is a fine-tuned version of distilroberta-base on ConLL2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0585
- F1: 0.9536
The best scores for Named Entity Recognition (NER) on CoNLL 2003 (English) is 94.6% from [the ConLL2003 NER benchmark] (https://paperswithcode.com/sota/named-entity-recognition-ner-on-conll-2003), This fine-tuned Model has achieved 95.36%.
Model Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("jinhybr/distilroberta-ConLL2003")
model = AutoModelForTokenClassification.from_pretrained("jinhybr/distilroberta-ConLL2003")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Tao Jin and live in Canada"
ner_results = nlp(example)
print(ner_results)
[{'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}]
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.1666 | 1.0 | 439 | 0.0621 | 0.9345 |
| 0.0499 | 2.0 | 878 | 0.0564 | 0.9391 |
| 0.0273 | 3.0 | 1317 | 0.0553 | 0.9469 |
| 0.0167 | 4.0 | 1756 | 0.0553 | 0.9492 |
| 0.0103 | 5.0 | 2195 | 0.0572 | 0.9516 |
| 0.0068 | 6.0 | 2634 | 0.0585 | 0.9536 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1