Text Classification
Transformers
Safetensors
Spanish
bert
feature-extraction
text-embeddings-inference
Instructions to use dmadera/dapt-beto-skill-variations with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmadera/dapt-beto-skill-variations with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dmadera/dapt-beto-skill-variations")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dmadera/dapt-beto-skill-variations") model = AutoModel.from_pretrained("dmadera/dapt-beto-skill-variations") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This is a domain-adapted transformer-based language model for Spanish-speaking job postings from Indeed Mexico, filtered to the automotive industry.
Model Description
A Spanish BERT Model that has undergone additional domain-adaptive pre-training on 7215 job postings from Indeed Mexico, specifically targeted to the automotive industry. By incorporating domain-specific knowledge from the job market, this model may offer a deeper understanding of the nuances and terminology in job descriptions, enhancing its suitability for the job recommendation task.
- Developed by: Diana P. Madera-Espíndola: dmadera [at] Tecnologico de Monterrey
- Model type: Transformer-based language model
- Language(s) (NLP): Spanish
- Finetuned from model [optional]: Spanish Job Postings from Indeed Mexico
- Downloads last month
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Model tree for dmadera/dapt-beto-skill-variations
Base model
dccuchile/bert-base-spanish-wwm-uncased