transformers pytorch xlm-roberta token-classification generated_from_trainer dataset:xtreme base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
datasets:
- xtreme
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: multilingual-xlm-roberta-for-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: xtreme
      type: xtreme
      config: PAN-X.de
      split: validation
      args: PAN-X.de
    metrics:
    - type: f1
      value: 0.8607623700505596
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# multilingual-xlm-roberta-for-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.

#### How to use

You can use this model with Transformers *pipeline* for NER.

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Tirendaz/roberta-base-NER")
model = AutoModelForTokenClassification.from_pretrained("Tirendaz/roberta-base-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: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 263  | 0.1627          | 0.8229 |
| 0.214         | 2.0   | 526  | 0.1410          | 0.8472 |
| 0.214         | 3.0   | 789  | 0.1343          | 0.8608 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
