Text Classification
Transformers
TensorBoard
Safetensors
Ukrainian
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use lapa-llm/fasttext-quality-score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lapa-llm/fasttext-quality-score with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lapa-llm/fasttext-quality-score")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lapa-llm/fasttext-quality-score") model = AutoModelForSequenceClassification.from_pretrained("lapa-llm/fasttext-quality-score") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e602064c7f907e55290ad94f58fb698a17dad3891e1f170f8035a84e44bb9f8f
- Size of remote file:
- 5.43 kB
- SHA256:
- 6b5e2bfc8b75891d42590a6d116729e76c70b5f789d6c5b534635316675756e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.