Text Classification
Transformers
ONNX
Safetensors
English
Portuguese
modernbert
feature-extraction
typestrict
decision-model
typed-decisions
encoder
decision-index
cpu
text-embeddings-inference
Instructions to use typestrict/TS-moBERTo-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use typestrict/TS-moBERTo-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="typestrict/TS-moBERTo-150M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("typestrict/TS-moBERTo-150M") model = AutoModel.from_pretrained("typestrict/TS-moBERTo-150M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Card: every TS model trains on typestrict.ai
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2 days ago
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Card: the quickstart shows what this model answers
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Card: quickstart, history (Dinah-1), the TS-Ettin models are published
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TS-moBERTo-150M: the typestrict base model (published before as Dinah-1)
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