metadata

license: mit
base_model: xlm-roberta-base
datasets:
  - xtreme
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: roberta-base-NER
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xtreme
          type: xtreme
          config: PAN-X.en
          split: validation
          args: PAN-X.en
        metrics:
          - name: Precision
            type: precision
            value: 0.8003614625330182
          - name: Recall
            type: recall
            value: 0.8110735418427726
          - name: F1
            type: f1
            value: 0.8056818976978517
          - name: Accuracy
            type: accuracy
            value: 0.9194332683336213
language:
  - en

roberta-base-NER

Model description

xlm-roberta-base-multilingual-cased-ner is a Named Entity Recognition model based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a XLMRoreberta-base-multilingual-cased model that was fine-tuned on an aggregation of 10 high-resourced languages.

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for NER.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner")
model = AutoModelForTokenClassification.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

Abbreviation	Description
O	Outside of a named entity
B-PER	Beginning of a person’s name right after another person’s name
I-PER	Person’s name
B-ORG	Beginning of an organisation right after another organisation
I-ORG	Organisation
B-LOC	Beginning of a location right after another location
I-LOC	Location

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 24
eval_batch_size: 24
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	417	0.3359	0.7286	0.7675	0.7476	0.8991
0.4227	2.0	834	0.2951	0.7711	0.7980	0.7843	0.9131
0.2818	3.0	1251	0.2824	0.7852	0.8076	0.7962	0.9174
0.2186	4.0	1668	0.2853	0.7934	0.8150	0.8041	0.9193
0.1801	5.0	2085	0.2935	0.8004	0.8111	0.8057	0.9194

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3