
# 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 <span style="color: #e25555;">&hearts;</span> in Spain

{"language": "es"}
transformers pytorch jax bert token-classification es autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #region-us 

Spanish BERT (BETO) + Syntax POS tagging
========================================


This model is a fine-tuned version of the Spanish BERT (BETO) on Spanish syntax annotations in CONLL CORPORA dataset for syntax POS (Part of Speech tagging) downstream task.


Details of the downstream task (Syntax POS) - Dataset
-----------------------------------------------------


* Dataset: CONLL Corpora ES


#### Fine-tune script on NER dataset provided by Huggingface


#### 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
-------------------



Model in action
---------------


Fast usage with pipelines



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
> 



#### Fine-tune script on NER dataset provided by Huggingface #### 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
-------------------



Model in action
---------------


Fast usage with pipelines



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>
TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #region-us 
 #### Fine-tune script on NER dataset provided by Huggingface #### 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
-------------------



Model in action
---------------


Fast usage with pipelines



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>
passage: TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #region-us 
#### Fine-tune script on NER dataset provided by Huggingface#### 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
-------------------



Model in action
---------------


Fast usage with pipelines



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>