| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 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. |
|
|
| ```python |
| 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 |
