metadata

license: apache-2.0
tags:
  - generated_from_trainer
language: en
widget:
  - text: My name is Scott and I live in Columbus. I work at the Hospital.
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: albert-base-v2-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9252213840603477
          - name: Recall
            type: recall
            value: 0.9329732113328189
          - name: F1
            type: f1
            value: 0.9290811285541773
          - name: Accuracy
            type: accuracy
            value: 0.9848205157332728

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