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albert-base-v2-finetuned-ner

This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0626
  • Precision: 0.9252
  • Recall: 0.9330
  • F1: 0.9291
  • Accuracy: 0.9848

Model description

More information needed

limitations

Limitations and bias

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.

How to use

You can use this model with Transformers pipeline for NER.

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")

model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Scott and I live in Ohio"
ner_results = nlp(example)
print(ner_results)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 220 0.0863 0.8827 0.8969 0.8898 0.9773
No log 2.0 440 0.0652 0.8951 0.9199 0.9073 0.9809
0.1243 3.0 660 0.0626 0.9191 0.9208 0.9200 0.9827
0.1243 4.0 880 0.0585 0.9227 0.9281 0.9254 0.9843
0.0299 5.0 1100 0.0626 0.9252 0.9330 0.9291 0.9848

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.8.1+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0
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Finetuned from

Dataset used to train Jorgeutd/albert-base-v2-finetuned-ner

Evaluation results