Token Classification
Transformers
PyTorch
Safetensors
English
roberta
feature extraction
mobile apps
reviews
token classification
named entity recognition
Instructions to use quim-motger/t-frex-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quim-motger/t-frex-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="quim-motger/t-frex-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("quim-motger/t-frex-roberta-base") model = AutoModelForTokenClassification.from_pretrained("quim-motger/t-frex-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17548e6bbfb7e38aade32f2c8b000710f66e7f45e6f16a93e2ab8e7e31d80a0e
- Size of remote file:
- 4.98 kB
- SHA256:
- 57817dee2c4bf33835c981bcbee2a13e690f7c763ce659b0aa87a28b502670d3
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