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
- Xet hash:
- 0c4e22f3d4fe03b44ecded31558bce1c9ef82810aff6f8706080c87616850e23
- Size of remote file:
- 419 MB
- SHA256:
- 8a9471ce65ce8721497d899e4c8a0e8e54579a310084b0e80ac6d5252539f23c
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