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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2504f1d95292407dfca54306229b9e3ba0f141938a0778ef256b111def8f4b2
- Size of remote file:
- 1.11 GB
- SHA256:
- a6812ffa59e9246090459e5a82ecd76a252c87933f002a27af2cb008fe4ad799
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