Text Classification
Transformers
Safetensors
xlm-roberta
sentiment-analysis
thai
multilingual
fine-tuned
southeast-asian
text-embeddings-inference
Instructions to use ZombitX64/MultiSent-E5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZombitX64/MultiSent-E5-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZombitX64/MultiSent-E5-Pro")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ZombitX64/MultiSent-E5-Pro") model = AutoModelForSequenceClassification.from_pretrained("ZombitX64/MultiSent-E5-Pro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from ZombitX64/MultiSent-E5-Pro: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/ZombitX64/MultiSent-E5-Pro/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://ZombitX64/MultiSent-E5-Pro/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/ZombitX64/MultiSent-E5-Pro/resolve/main/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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