Instructions to use pythainlp/thainer-corpus-v2-base-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pythainlp/thainer-corpus-v2-base-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pythainlp/thainer-corpus-v2-base-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pythainlp/thainer-corpus-v2-base-model") model = AutoModelForTokenClassification.from_pretrained("pythainlp/thainer-corpus-v2-base-model") - Inference
- Notebooks
- Google Colab
- Kaggle
Multilingual model — testing for mobile deployment
#3
by 3morixd - opened
This model covers Polish, Finnish, German, Urdu, Tamil — exactly what we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone. We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to check quality retention across languages after 4-bit quantization.
Would love to see multilingual eval at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE