RuPERTa-base-finetuned-pos Open-source Model - Free for Efficient Spanish Part-of-Speech Tagging

Ruperta Base Finetuned Pos

Developed by mrm8488

A Spanish POS tagging model fine-tuned on the CONLL corpus based on RuPERTa-base

Sequence Labeling Spanish#Spanish POS tagging #High-precision POS #RoBERTa fine-tuning

Downloads 19

Release Time : 3/2/2022

Model Overview

This model is specifically designed for Spanish part-of-speech tagging tasks, capable of identifying and labeling various POS categories such as nouns, verbs, adjectives, etc.

Model Features

High-precision POS tagging

Fine-tuned on the CONLL corpus with an F1 score of 97.39, capable of accurately identifying multiple POS categories.

Spanish language optimization

Based on the RuPERTa-base model, specifically optimized for Spanish text.

Comprehensive label coverage

Supports 16 different POS tags, including verbs, nouns, adjectives, adverbs, and other common POS categories.

Model Capabilities

Spanish POS tagging

Text analysis

Natural language processing

Use Cases

Natural language processing

Text analysis

Perform POS tagging on Spanish text to aid in understanding text structure.

Accurately identifies various POS categories in text

Language learning tool

Can be used to develop Spanish language learning aids to help learners understand vocabulary usage.

🚀 RuPERTa-base (Spanish RoBERTa) + POS 🎃🏷

This model is a fine - tuned version of RuPERTa-base on the CONLL CORPORA for the Part - of - Speech (POS) downstream task. It aims to accurately classify the parts of speech in text, providing valuable linguistic analysis.

🚀 Quick Start

This model is a fine - tuned version of RuPERTa-base on the CONLL CORPORA for the Part - of - Speech (POS) downstream task.

✨ Features

Fine - tuned on the CONLL Corpora ES dataset for the POS task.
Covers a wide range of POS labels including ADJ, ADP, ADV, etc.
Achieves high F1, precision, and recall scores on the evaluation set.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')

id2label = {
    "0": "O",
    "1": "ADJ",
    "2": "ADP",
    "3": "ADV",
    "4": "AUX",
    "5": "CCONJ",
    "6": "DET",
    "7": "INTJ",
    "8": "NOUN",
    "9": "NUM",
    "10": "PART",
    "11": "PRON",
    "12": "PROPN",
    "13": "PUNCT",
    "14": "SCONJ",
    "15": "SYM",
    "16": "VERB"
}

text ="Mis amigos están pensando viajar a Londres este verano."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)

outputs = model(input_ids)
last_hidden_states = outputs[0]

for m in last_hidden_states:
  for index, n in enumerate(m):
    if(index > 0 and index <= len(text.split(" "))):
      print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
      
'''
Output:
--------
Mis: NUM
amigos: PRON
están: AUX
pensando: ADV
viajar: VERB
a: ADP
Londres: PROPN
este: DET
verano..: NOUN
'''

Advanced Usage

There is no advanced usage example in the original document.

📚 Documentation

Details of the downstream task (POS) - Dataset

Dataset: CONLL Corpora ES 📚

Dataset	# Examples
Train	445 K
Dev	55 K

Fine - tune on NER script provided by Huggingface
Labels covered:

ADJ
ADP
ADV
AUX
CCONJ
DET
INTJ
NOUN
NUM
PART
PRON
PROPN
PUNCT
SCONJ
SYM
VERB

Metrics on evaluation set 🧾

Metric	# score
F1	97.39
Precision	97.47
Recall	97.32

🔧 Technical Details

There is no detailed technical implementation information in the original document.

📄 License

There is no license information in the original document.

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with ♥ in Spain

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