--- language: es thumbnail: --- # Spanish BERT (BETO) + Syntax POS tagging ✍🏷 This model is a fine-tuned version of the Spanish BERT [(BETO)](https://github.com/dccuchile/beto) on Spanish **syntax** annotations in [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) dataset for **syntax POS** (Part of Speech tagging) downstream task. ## Details of the downstream task (Syntax POS) - Dataset - [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) #### [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py) #### 21 Syntax annotations (Labels) covered: - \_ - ATR - ATR.d - CAG - CC - CD - CD.Q - CI - CPRED - CPRED.CD - CPRED.SUJ - CREG - ET - IMPERS - MOD - NEG - PASS - PUNC - ROOT - SUJ - VOC ## Metrics on test set 📋 | Metric | # score | | :-------: | :-------: | | F1 | **89.27** | | Precision | **89.44** | | Recall | **89.11** | ## Model in action 🔨 Fast usage with **pipelines** 🧪 ```python from transformers import pipeline nlp_pos_syntax = pipeline( "ner", model="mrm8488/bert-spanish-cased-finetuned-pos-syntax", tokenizer="mrm8488/bert-spanish-cased-finetuned-pos-syntax" ) text = 'Mis amigos están pensando viajar a Londres este verano.' nlp_pos_syntax(text)[1:len(nlp_pos_syntax(text))-1] ``` ```json [ { "entity": "_", "score": 0.9999216794967651, "word": "Mis" }, { "entity": "SUJ", "score": 0.999882698059082, "word": "amigos" }, { "entity": "_", "score": 0.9998869299888611, "word": "están" }, { "entity": "ROOT", "score": 0.9980518221855164, "word": "pensando" }, { "entity": "_", "score": 0.9998420476913452, "word": "viajar" }, { "entity": "CD", "score": 0.999351978302002, "word": "a" }, { "entity": "_", "score": 0.999959409236908, "word": "Londres" }, { "entity": "_", "score": 0.9998968839645386, "word": "este" }, { "entity": "CC", "score": 0.99931401014328, "word": "verano" }, { "entity": "PUNC", "score": 0.9998534917831421, "word": "." } ] ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with in Spain