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