Text Classification
Transformers
Safetensors
Chinese
qwen3
text-generation
cross-encoder
reranker
event-coreference
event-deduplication
chinese
news-clustering
knowledge-distillation
calibrated-probabilities
text-embeddings-inference
Instructions to use MaYiding/EventTwin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaYiding/EventTwin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaYiding/EventTwin")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaYiding/EventTwin") model = AutoModelForCausalLM.from_pretrained("MaYiding/EventTwin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from MaYiding/EventTwin: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/MaYiding/EventTwin/resolve/main/tokenizer.json
- Command line
-
hf download hf://MaYiding/EventTwin/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MaYiding/EventTwin/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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