
# gunghio/xlm-roberta-base-finetuned-panx-ner

This model was trained starting from xlm-roberta-base on a subset of xtreme dataset.

`xtreme` datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish.

Only 75% of the whole dataset was used.

## Intended uses & limitations

Fine-tuned model can be used for Named Entity Recognition in it, en, de, fr, and es.

## Training and evaluation data

Training dataset: [xtreme](https://huggingface.co/datasets/xtreme)

### Training results

It achieves the following results on the evaluation set:

- Precision: 0.8744154472771157
- Recall: 0.8791424269015351
- F1: 0.8767725659462058
- Accuracy: 0.9432040948504613

Details:

| Label   | Precision | Recall | F1-Score | Support |
|---------|-----------|--------|----------|---------|
| PER     | 0.922     | 0.908  | 0.915    | 26639   |
| LOC     | 0.880     | 0.906  | 0.892    | 37623   |
| ORG     | 0.821     | 0.816  | 0.818    | 28045   |
| Overall | 0.874     | 0.879  | 0.877    | 92307   |

## Usage

Set aggregation stragey according to [documentation](https://huggingface.co/docs/transformers/v4.18.0/en/main_classes/pipelines#transformers.TokenClassificationPipeline).

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

tokenizer = AutoTokenizer.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner")
model = AutoModelForTokenClassification.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner")

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

ner_results = nlp(example)
print(ner_results)
```

{"language": ["it", "en", "de", "fr", "es", "multilingual"], "license": ["mit"], "datasets": ["xtreme"], "metrics": [{"precision": 0.874}, {"recall": 0.88}, {"f1": 0.877}, {"accuracy": 0.943}], "inference": {"parameters": {"aggregation_strategy": "first"}}}
transformers pytorch xlm-roberta token-classification it en de fr es multilingual dataset:xtreme license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #it #en #de #fr #es #multilingual #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us 

gunghio/xlm-roberta-base-finetuned-panx-ner
===========================================


This model was trained starting from xlm-roberta-base on a subset of xtreme dataset.


'xtreme' datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish.


Only 75% of the whole dataset was used.


Intended uses & limitations
---------------------------


Fine-tuned model can be used for Named Entity Recognition in it, en, de, fr, and es.


Training and evaluation data
----------------------------


Training dataset: xtreme


### Training results


It achieves the following results on the evaluation set:


* Precision: 0.8744154472771157
* Recall: 0.8791424269015351
* F1: 0.8767725659462058
* Accuracy: 0.9432040948504613


Details:



Usage
-----


Set aggregation stragey according to documentation.



### Training results


It achieves the following results on the evaluation set:


* Precision: 0.8744154472771157
* Recall: 0.8791424269015351
* F1: 0.8767725659462058
* Accuracy: 0.9432040948504613


Details:



Usage
-----


Set aggregation stragey according to documentation.
TAGS
#transformers #pytorch #xlm-roberta #token-classification #it #en #de #fr #es #multilingual #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training results


It achieves the following results on the evaluation set:


* Precision: 0.8744154472771157
* Recall: 0.8791424269015351
* F1: 0.8767725659462058
* Accuracy: 0.9432040948504613


Details:



Usage
-----


Set aggregation stragey according to documentation.
passage: TAGS
#transformers #pytorch #xlm-roberta #token-classification #it #en #de #fr #es #multilingual #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training results


It achieves the following results on the evaluation set:


* Precision: 0.8744154472771157
* Recall: 0.8791424269015351
* F1: 0.8767725659462058
* Accuracy: 0.9432040948504613


Details:



Usage
-----


Set aggregation stragey according to documentation.