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--- |
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license: mit |
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base_model: xlm-roberta-base |
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datasets: |
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- xtreme |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-base-NER |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: xtreme |
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type: xtreme |
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config: PAN-X.en |
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split: validation |
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args: PAN-X.en |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8003614625330182 |
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- name: Recall |
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type: recall |
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value: 0.8110735418427726 |
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- name: F1 |
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type: f1 |
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value: 0.8056818976978517 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9194332683336213 |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-base-NER |
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## Model description |
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**xlm-roberta-base-multilingual-cased-ner** is a **Named Entity Recognition** model based on a fine-tuned XLM-RoBERTa base model. |
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It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). |
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Specifically, this model is a *XLMRoreberta-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages. |
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## Intended uses & limitations |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner") |
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model = AutoModelForTokenClassification.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "My name is Wolfgang and I live in Berlin" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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Abbreviation|Description |
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-|- |
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O|Outside of a named entity |
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B-PER |Beginning of a person’s name right after another person’s name |
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I-PER |Person’s name |
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B-ORG |Beginning of an organisation right after another organisation |
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I-ORG |Organisation |
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B-LOC |Beginning of a location right after another location |
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I-LOC |Location |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 417 | 0.3359 | 0.7286 | 0.7675 | 0.7476 | 0.8991 | |
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| 0.4227 | 2.0 | 834 | 0.2951 | 0.7711 | 0.7980 | 0.7843 | 0.9131 | |
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| 0.2818 | 3.0 | 1251 | 0.2824 | 0.7852 | 0.8076 | 0.7962 | 0.9174 | |
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| 0.2186 | 4.0 | 1668 | 0.2853 | 0.7934 | 0.8150 | 0.8041 | 0.9193 | |
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| 0.1801 | 5.0 | 2085 | 0.2935 | 0.8004 | 0.8111 | 0.8057 | 0.9194 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
