Text Classification
Transformers
Safetensors
Filipino
English
xlm-roberta
sentiment-analysis
code-switching
taglish
filipino-nlp
lexiliksik
thesis-model
text-embeddings-inference
Instructions to use GMCTech/LexCAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GMCTech/LexCAT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GMCTech/LexCAT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GMCTech/LexCAT") model = AutoModelForSequenceClassification.from_pretrained("GMCTech/LexCAT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5ec463144c6e0060bcb4e9e73fd47716918a43c46696ad59fcbd9c580964153
- Size of remote file:
- 17.1 MB
- SHA256:
- 3c088c06cf975b7097e469bd69630cdb0d675c6db1ce3af1042b6e19c6d01f22
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