Text Classification
sentence-transformers
Safetensors
Transformers
English
bert
natural-language-inference
nlp
model-card
text-embeddings-inference
Instructions to use agentlans/NoInstruct-small-Embedding-v0-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use agentlans/NoInstruct-small-Embedding-v0-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("agentlans/NoInstruct-small-Embedding-v0-nli") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use agentlans/NoInstruct-small-Embedding-v0-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agentlans/NoInstruct-small-Embedding-v0-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("agentlans/NoInstruct-small-Embedding-v0-nli") model = AutoModelForSequenceClassification.from_pretrained("agentlans/NoInstruct-small-Embedding-v0-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c36f2c2db6e64eadb541a74f9cdecc9455e80b8e6031193e05e87e196c3904a4
- Size of remote file:
- 5.24 kB
- SHA256:
- b0dd3a70001121e0260fa0d5930c4b6964393c8348ec5d8596611fc5bc98cf97
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